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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
META-HEURISTIC ALGORITHMS FOR ADVANCED DISTRIBUTED SYSTEMS Discover a collection of meta-heuristic algorithms for distributed systems in different application domains Meta-heuristic techniques are increasingly gaining favor as tools for optimizing distributed systems--generally, to enhance the utility and precision of database searches. Carefully applied, they can increase system effectiveness, streamline operations, and reduce cost. Since many of these techniques are derived from nature, they offer considerable scope for research and development, with the result that this field is growing rapidly. Meta-Heuristic Algorithms for Advanced Distributed Systems offers an overview of these techniques and their applications in various distributed systems. With strategies based on both global and local searching, it covers a wide range of key topics related to meta-heuristic algorithms. Those interested in the latest developments in distributed systems will find this book indispensable. Meta-Heuristic Algorithms for Advanced Distributed Systems readers will also find: * Analysis of security issues, distributed system design, stochastic optimization techniques, and more * Detailed discussion of meta-heuristic techniques such as the genetic algorithm, particle swam optimization, and many others * Applications of optimized distribution systems in healthcare and other key??industries Meta-Heuristic Algorithms for Advanced Distributed Systems is ideal for academics and researchers studying distributed systems, their design, and their applications.
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Table of Contents
Cover
Table of Contents
Title Page
Copyright
About the Book
About the Editors
List of Contributors
Preface
1. The Future of Business Management with the Power of Distributed Systems and Computing
2. Applications of Optimized Distributed Systems in Healthcare
3. The Impact of Distributed Computing on Data Analytics and Business Insights
4. Machine Learning and Its Application in Educational Area
5. Approaches and Methodologies for Distributed Systems: Threats, Challenges, and Future Directions
6. Efficient-Driven Approaches Related to Metaheuristic Algorithms Using Machine Learning Techniques
7. Security and Privacy Issues in Distributed Healthcare Systems – A Survey
8. Implementation and Analysis of the Proposed Model in a Distributed e-Healthcare System
9. Leveraging Distributed Systems for Improved Educational Planning and Resource Allocation
10. Advances in Education Policy Through the Integration of Distributed Computing Approaches
11. Revolutionizing Data Management and Security with the Power of Blockchain and Distributed System
12. Enhancing Business Development, Ethics, and Governance with the Adoption of Distributed Systems
13. Leveraging Distribution Systems for Advanced Fraud Detection and Prevention in Finance
14. Advances in E-commerce Through the Integration of Distributed Computing Approaches
15. The Impact of Distributed Computing on Online Shopping and Consumer Experience
16. Wireless Sensor-Based IoT System with Distributed Optimization for Healthcare
17. Optimizing Financial Transactions and Processes Through the Power of Distributed Systems
18. Leveraging Distributed Systems for Improved Market Intelligence and Customer Segmentation
19. The Future of Financial Crime Prevention and Cybersecurity with Distributed Systems and Computing Approaches
20. Innovations in Distributed Computing for Enhanced Risk Management in Finance
21. Leveraging Blockchain and Distributed Systems for Improved Supply Chain Traceability and Transparency
22. Advances in Resource Management Through the Integration of Distributed Computing Approaches
1 The Future of Business Management with the Power of Distributed Systems and Computing
1.1 Introduction
1.2 Understanding Distributed Systems and Computing
1.3 Applications of Distributed Systems and Computing in Business Management
1.4 Limitations of Distributed Systems in Business Management
1.5 Future Developments and Opportunities
1.6 Conclusion
References
2 Applications of Optimized Distributed Systems in Healthcare
2.1 Introduction
2.2 Literature Survey
2.3 Real Cases
2.4 Conclusion
References
Note
3 The Impact of Distributed Computing on Data Analytics and Business Insights
3.1 Introduction
3.2 Distributed Computing and Data Analytics
3.3 Business Insights and Decision-Making
3.4 Challenges and Limitations
3.5 The Impact of Distributed Computing on Data Analytics
3.6 Conclusion
References
4 Machine Learning and Its Application in Educational Area
4.1 Introduction
4.2 Previous Work
4.3 Technique
4.4 Analysis of Data
4.5 Educational Data Mining
4.6 Hadoop Approach
4.7 Artificial Neural Network (ANN)
4.8 Decision Tree
4.9 Results/Discussion
4.10 Increasing Efficiency Using Learning Analytics
4.11 Predictive Analysis for Better Assessment Evaluation
4.12 Future Scope
4.13 Conclusion
References
5 Approaches and Methodologies for Distributed Systems: Threats, Challenges, and Future Directions
5.1 Introduction
5.2 Distributed Systems
5.3 Literature Review
5.4 Threats to Distributed Systems Security
5.5 Security Standards and Protocols
5.6 Network Security
5.7 Access Control
5.8 Authentication and Authorization
5.9 Privacy Concerns
5.10 Case Studies
5.11 Conclusion
5.12 Future Scope
References
6 Efficient-driven Approaches Related to Meta-Heuristic Algorithms using Machine Learning Techniques
6.1 Introduction
6.2 Stochastic Optimization
6.3 Heuristic Search
6.4 Meta-Heuristic
6.5 Machine Learning
References
7 Security and Privacy Issues in Distributed Healthcare Systems – A Survey
7.1 Introduction
7.2 Previous Study
7.3 Security and Privacy Needs
7.4 Security and Privacy Goals
7.5 Type of Attacks in Distributed Systems
7.6 Recommendations and Future Approaches
7.7 Conclusion
References
8 Implementation and Analysis of the Proposed Model in a Distributed e-Healthcare System
8.1 Introduction
8.2 Outmoded Systems
8.3 Distributed Systems
8.4 Previous Work
8.5 Service-Oriented Architecture of e-Healthcare
8.6 Implementation of the Proposed Model
8.7 Evaluation of the Proposed Model Performance
8.8 Conclusion and Future Work
References
9 Leveraging Distributed Systems for Improved Educational Planning and Resource Allocation
9.1 Introduction
9.2 Theoretical Framework
9.3 Distribution System in Education
9.4 Technical Aspects of Distributed Systems in Education
9.5 Challenges and Limitations
9.6 Discussion
9.7 Conclusion
References
10 Advances in Education Policy Through the Integration of Distributed Computing Approaches
10.1 Introduction
10.2 Distributed Computing Approaches
10.3 Advances in Education Policy Through Distributed Computing Approaches
10.4 Challenges: Privacy Concerns
10.5 Conclusion
References
11 Revolutionizing Data Management and Security with the Power of Blockchain and Distributed System
11.1 Introduction
11.2 Blockchain Technology
11.3 Distributed System
11.4 Revolutionizing Data Management and Security with Blockchain and Distributed Systems
11.5 Challenges of Using Blockchain and Distributed Systems
11.6 Discussion
11.7 Conclusion
References
12 Enhancing Business Development, Ethics, and Governance with the Adoption of Distributed Systems
12.1 Introduction
12.2 Applications of Distributed Systems in Business Development
12.3 The Importance of Ethics in Distributed Systems
12.4 Governance in Distributed Systems
12.5 Conclusion
References
13 Leveraging Distribution Systems for Advanced Fraud Detection and Prevention in Finance
13.1 Introduction
13.2 Benefits of Distributed Systems
13.3 Prevention Techniques
13.4 Leveraging Distributed Systems for Fraud Detection and Prevention
13.5 Future Directions
13.6 Conclusion
References
14 Advances in E-commerce Through the Integration of Distributed Computing Approaches
14.1 Introduction
14.2 Distributed Computing Approaches for E-commerce
14.3 Integration of Distributed Computing Approaches in E-commerce
14.4 Advancements in E-commerce Through the Integration of Distributed Computing Approaches
14.5 Future Trends in the Integration of Distributed Computing Approaches in E-commerce
14.6 Conclusion
References
15 The Impact of Distributed Computing on Online Shopping and Consumer Experience
15.1 Introduction
15.2 Benefits of Distributed Computing for Online Shopping
15.3 Limitations of Distributed Computing in Online Shopping
15.4 Impact of Distributed Computing on Online Shopping Trends
15.5 Ethical Implications of Distributed Computing in Online Shopping
15.6 Conclusion
References
16 Wireless Sensor-based IoT System with Distributed Optimization for Healthcare
16.1 Introduction
16.2 Literature Review
16.3 Challenges Faced by Existing Research
16.4 Proposed Research Methodology
16.5 Simulation of Research
16.6 Conclusion
16.7 Future Scope
References
17 Optimizing Financial Transactions and Processes Through the Power of Distributed Systems
17.1 Introduction
17.2 Overview of Financial Transactions and Processes
17.3 Distributed Systems in Finance
17.4 Blockchain Technology and Finance
17.5 Smart Contracts
17.6 Conclusion
References
18 Leveraging Distributed Systems for Improved Market Intelligence and Customer Segmentation
18.1 Introduction
18.2 Distributed Systems for Customer Segmentation
18.3 Distributed Systems for Market Intelligence
18.4 Distributed Systems for Customer Segmentation
18.5 Challenges in Integrating Distribution System in Market Intelligence
18.6 Conclusion
References
19 The Future of Financial Crime Prevention and Cybersecurity with Distributed Systems and Computing Approaches
19.1 Introduction
19.2 Distributed Systems and Computing Approaches for Financial Crime Prevention and Cybersecurity
19.3 Challenges and Opportunities in Implementing Distributed Systems and Computing Approaches
19.4 Benefits of Distributed Systems in Financial Crime Prevention
19.5 Limitations of Distributed Systems in Financial Crime Prevention
19.6 Conclusion
References
20 Innovations in Distributed Computing for Enhanced Risk Management in Finance
20.1 Introduction
20.2 Theoretical Framework
20.3 Comparison of Distributed Computing Approaches for Finance Risk Management
20.4 Innovations in Distributed Computing for Enhanced Risk Management in Finance
20.5 Challenges and Limitations of Distributed Computing for Finance Risk Management
20.6 Future Directions
20.7 Conclusion
References
21 Leveraging Blockchain and Distributed Systems for Improved Supply Chain Traceability and Transparency
21.1 Introduction
21.2 Overview of Blockchain and Distributed Systems
21.3 Applications of Blockchain and Distributed Systems in Supply Chain
21.4 Benefits and Limitations of Blockchain and Distributed Systems in Supply Chain
21.5 Conclusion
References
22 Advances in Resource Management Through the Integration of Distributed Computing Approaches
22.1 Introduction
22.2 Distributed Computing Approaches for Resource Management
22.3 Integration of Distributed Computing Approaches for Resource Management
22.4 Future Directions and Research Challenges
22.5 Discussion
22.6 Conclusion
References
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Sales volume of three products across two sales channels: online a...
Chapter 3
Table 3.1 Benefits of distributed computing for data analytics.
Chapter 5
Table 5.1 Comparative table of access control methods.
Chapter 6
Table 6.1 Techniques of heuristic search used in AI.
Table 6.2 Arrangement of population-based algorithms into categories.
Table 6.3 Various algorithms.
Chapter 9
Table 9.1 Comparison of different distributed systems architectures.
Chapter 10
Table 10.1 Types of distributed computing approaches.
Chapter 13
Table 13.1 Distribution of data processing across the nodes.
Table 13.2 Machine learning-based systems.
Chapter 14
Table 14.1 Comparison of the advantages and disadvantages of four different ...
Chapter 15
Table 15.1 Benefits of distributed computing.
Chapter 16
Table 16.1 Literature review.
Table 16.2 Comparison of energy efficiency.
Table 16.3 Comparison of delay.
Table 16.4 Comparison of call drop.
Table 16.5 Comparison of throughput
Chapter 17
Table 17.1 Levels of facilities to move products from manufacturers to store...
Chapter 18
Table 18.1 DS for customer segmentation.
Chapter 19
Table 19.1 Comparison of distributed systems and computing approaches with t...
Chapter 20
Table 20.1 Traditional risk management vs distributed computing.
Chapter 21
Table 21.1 Fundamental concepts of blockchain technology.
List of Illustrations
Chapter 1
Figure 1.1 Sales volumes.
Figure 1.2 Customer relationship management.
Chapter 2
Figure 2.1 Distributed system architecture.
Figure 2.2 Technologies using distributed architecture.
Figure 2.3 Advantages of optimized distributed systems.
Figure 2.4 Parameters for optimization.
Figure 2.5 Characteristics of optimized distributed systems.
Figure 2.6 Distribution of electronic health records systems.
Figure 2.7 Exchange of patient data.
Figure 2.8 Telehealth constituents.
Figure 2.9 Clinical decision support system.
Figure 2.10 Big Data in healthcare.
Chapter 3
Figure 3.1 Distributed computing and data analytics.
Figure 3.2 Challenges and limitations.
Chapter 4
Figure 4.1 Block diagram for phases of machine learning in education system....
Figure 4.2 Basic block diagram for working of supervised learning.
Figure 4.3 Basic block diagram for working of unsupervised learning.
Figure 4.4 Processes in educational data mining [11].
Figure 4.5 Basic working of Map-reduce processing technique.
Figure 4.6 Storage methodology used in Hadoop approach.
Figure 4.7 Basic block diagram of artificial neural network [16].
Figure 4.8 Diagram of decision tree for student database.
Chapter 5
Figure 5.1 Architecture of DS.
Figure 5.2 Role-based access control.
Figure 5.3 Discretionary access control (DAC).
Figure 5.4 Mandatory access control (MAC).
Chapter 6
Figure 6.1 Types of optimization approaches.
Figure 6.2 Types of stochastic optimization approaches.
Figure 6.3 Steps of genetic algorithm.
Figure 6.4 Structure of the meta-heuristic algorithms.
Figure 6.5 Types of single-based meta-heuristic algorithms.
Figure 6.6 Types of population-based meta-heuristic algorithms.
Figure 6.7 Algorithm of single-based meta-heuristic algorithms.
Figure 6.8 Algorithm of population-based meta-heuristic algorithms.
Chapter 7
Figure 7.1 Basic architecture of distributed healthcare system.
Figure 7.2 Basic architecture of traditional system.
Figure 7.3 3-Tier basic architecture of distributed system.
Figure 7.4 Healthcare architecture security and privacy in 3-tier format.
Figure 7.5 Cycle of smart path automated healthcare system.
Figure 7.6 Process cycle of IT security task force.
Chapter 8
Figure 8.1 Basic architecture of outmoded system before the distributed syst...
Figure 8.2 Basic 3-tier architecture of distributed system.
Figure 8.3 Peer-to-peer architecture of distributed system.
Figure 8.4 Client–server architecture of distributed system.
Figure 8.5 Layered diagram of SOA.
Figure 8.6 Three-layered architecture of clinical module of the system.
Figure 8.7 Graphical representation of latency of the model (ms) vs prescrip...
Chapter 9
Figure 9.1 Technical aspects of distributed systems in education.
Chapter 11
Figure 11.1 Basic elements of BT.
Figure 11.2 Challenges in blockchain and DS for data security.
Chapter 12
Figure 12.1 Governance in distributed systems.
Figure 12.2 Distributed systems: business and governance challenges.
Chapter 13
Figure 13.1 Types of DSs.
Figure 13.2 Data processing across the nodes.
Chapter 14
Figure 14.1 E-commerce and distributed computing advancements.
Chapter 15
Figure 15.1 Challenges and limitations of distributed computing in online sh...
Chapter 16
Figure 16.1 Traditional Wireless Sensor Protocols.
Figure 16.2 Wireless Sensor Technology Fit into the IoT.
Figure 16.3 Remote patient monitoring.
Figure 16.4 Process flow research methodology.
Figure 16.5 Comparison of normalized network energy.
Figure 16.6 Comparison of delay in case of different iteration.
Figure 16.7 Comparison of call drop rate in case of different iterations.
Figure 16.8 Comparison of throughput in case of different iterations.
Chapter 17
Figure 17.1 Distributed network for retail company.
Figure 17.2 BT and Finance.
Chapter 18
Figure 18.1 Market segmentation alternatives.
Chapter 19
Figure 19.1 Analysis of benefits and limitations.
Chapter 20
Figure 20.1 Traditional risk management vs distributed computing.
Chapter 21
Figure 21.1 Benefits and Limitations Blockchain and DSs in SC.
Chapter 22
Figure 22.1 Distributed Computing Approaches.
Figure 22.2 Addressing challenges in distributed resource management.
Guide
Cover
Table of Contents
Title Page
Copyright
About the Book
About the Editors
List of Contributors
Preface
Begin Reading
Index
End User License Agreement
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Meta-Heuristic Algorithms for Advanced Distributed Systems
Edited by
Rohit Anand
Department of Electronics and Communication Engineering
G.B. Pant DSEU Okhla-1 Campus
(formerly G.B.Pant Engineering College)
Government of NCT of Delhi
New Delhi, India
Abhinav Juneja
KIET Group of Institutions
Ghaziabad, India
Digvijay Pandey
Department of Technical Education,Government of Uttar Pradesh,Kanpur, India
Sapna Juneja
Department of CSE (AI)
KIET Group of Institutions
Ghaziabad, India
Nidhi Sindhwani
Amity Institute of Information Technology
Amity University
Noida, India
Copyright © 2024 by John Wiley & Sons, Inc. All rights reserved.
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About the Book
The main aim of using a distributed system is to simplify the problem of computation by sharing a common objective and distribute the complex problem into many simpler problems. The failure of each component is independent of the failure of the other components, and hence a distributed system is very reliable. Further, a distributed system may be scaled as per requirements. But a distributed system suffers from overhead more than a basic centralized system, and there is an issue of security and troubleshooting due to the distributed computing in the system.
Meta-heuristic techniques have a huge scope in optimization, and hence they may be applied to increase the efficiency of the distributed system and also to minimize cost and time. These intelligent techniques are based on global and local search and are very simple to apply. Various kinds of meta-heuristic techniques are derived from nature, and hence a lot of development is going on in this field.
This book will focus on the existing/modified/innovative meta-heuristic techniques for optimization purposes in various kinds of distributed systems.
About the Editors
Dr. Rohit Anand is currently working as an assistant professor in the Department of Electronics and Communication Engineering at G.B. Pant DSEU Okhla-1 Campus (formerly G.B.Pant Engineering College), Government of NCT of Delhi, New Delhi, India. He was awarded his PhD in the field of microwave and optimization. He has more than 19 years of teaching experience, including teaching undergraduate and graduate courses. He is a life member of the Indian Society for Technical Education (ISTE). He has published 6 book chapters, 12 papers in Scopus/SCI-indexed journals, more than 20 papers in international conferences, and 4 patents. He has chaired a session in fourteen international conferences. His research areas include electromagnetic field theory, antenna theory and design, optimization, wireless communication, image processing, optical fiber communication, IoT.
Dr. Abhinav Juneja is currently working as a professor at KIET Group of Institutions, Ghaziabad, India. He has also worked as an associate director and professor at BMIET, Sonepat. He has more than 19 years of teaching experience teaching postgraduate and undergraduate engineering students. He completed his doctorate in computer science and engineering from M.D. University, Rohtak, in 2018 and has a master’s degree in information technology from GGSIPU, Delhi. He has research interests in the fields on software reliability, IoT, machine learning, and soft computing. He has published several papers in reputed national and international journals. He has been the organizer of several national and international conferences. He has been a resource person for faculty development programs on recent issues related to cybersecurity. He is the reviewer of several international journals of repute. He has been the mentor of several postgraduate and undergraduate research-oriented projects.
Dr. Digvijay Pandey is currently working as an acting head of department in the Department of Technical Education, Kanpur, Government of Uttar Pradesh, India. Before this, he joined TCS in 2012 as an IT analyst and worked on various US/UK/Canada projects until 2016. He is also a faculty member at IERT Allahabad. He has teaching and industry experience of more than 11 years. He works as an editor for a peer-reviewed international journal. He has over 11 years of experience in the field industry as well as teaching. He has written 16 book chapters and 70 papers that have been published in Science Direct (Elsevier)/SCI/UGC/Scopus-indexed journals and also acts as an editor for a peer-reviewed international journal. He has presented several research papers at national and international conferences. He has chaired sessions at IEEE International Conference on Advance Trends in Multidisciplinary Research and Innovation (ICATMRI-2020). He has four patents that have been published in The Patent Office Journal and two that are currently being processed in the Australian Patent Office Journal. He serves as a reviewer for a number of prestigious journals, including Scientific Reports (nature Publication). Clinical and Translational Imaging (Springer), ijlter (Scopus indexed), and a slew of others. His research interests include medical image processing, image processing, text extraction, information security, and other related fields.
https://scholar.google.com/citations?user=uie7AAYAAAAJ&hl=en
https://orcid.org/
0000-0003-0353-174X.
Dr. Sapna Juneja is working as Professor in Department of CSE(AI) at KIET Group of Institutions, Ghaziabad, India. She has more than 16 years of teaching experience. She completed her doctorate and Master’s in Computer Science and Engineering from M. D. University, Rohtak in 2018 and 2010 respectively. Her topic of research is Software Reliability of Embedded System. Her areas of Interest are Software Engineering, Computer Networks, Operating Systems, Database Management Systems, Artificial Intelligence etc. She has guided several research thesis of UG and PG students in Computer Science and Engineering. She is the reviewer of several international journals of repute. She has published several patents. She has published various research papers in the renowned National and International Journals.
Dr. Nidhi Sindhwani is currently working as an assistant professor at Amity Institute of Information Technology, Amity University, Noida, India. She has her PhD (ECE) from Punjabi University, Patiala, Punjab, India. She has teaching experience of more than 17 years. She is a life member of the Indian Society for Technical Education (ISTE) and a member of IEEE. She has published 13 book chapters, 10 papers in Scopus/SCIE-indexed journals, and 4 patents. She has presented various research papers in national and international conferences. She has chaired a session at twelve international conferences. Her research areas include wireless communication, image processing, optimization, machine learning, and IoT.
List of Contributors
V. Abhinav
Koneru Lakshmaiah Education
Foundation
Vaddeswaram, Andhra Pradesh
India
Sunil Adhav
Faculty of Management (PG)
Dr. Vishwanath Karad
MIT World Peace University
Pune, Maharashtra
India
Frakruddin A. Ahmed
School of Management
Presidency University
Bangalore
Karnataka
India
Tanweer Alam
Islamic University of Madinah
Department of Computer Science
Faculty of Computer and
Information Systems
Madinah
Saudi Arabia
Shaik Altaf
Koneru Lakshmaiah Education
Foundation
Vaddeswaram, Andhra Pradesh
India
Rohit Anand
Department of ECE
G.B. Pant DSEU Okhla-1 Campus
(formerly GBPEC)
New Delhi
India
and
Department of Electronics and Communication Engineering
G.B. Pant Engineering College
(Government of NCT of Delhi)
New Delhi
India
M. Anirudh
Koneru Lakshmaiah Education Foundation
Vaddeswaram, Andhra Pradesh
India
Sri R.R. Annapureddy
Koneru Lakshmaiah Education
Foundation
Vaddeswaram, Andhra Pradesh
India
Ashima Arya
Department of Computer Science and Information Technology
KIET Group of Institutions
Ghaziabad, Uttar Pradesh
India
Rashika Bangroo
Department of Computer Science and Information Technology
KIET Group of Institutions
Ghaziabad, Uttar Pradesh
India
Manish Bhardwaj
Department of Computer Science and Information Technology
KIET Group of Institutions
Delhi-NCR
Ghaziabad, Uttar Pradesh
India
K. Bhavana Raj
Department of Management Studies
Institute of Public Enterprise
Hyderabad
India
Bhawna
KIET Group of Institutions
Ghaziabad, Uttar Pradesh
India
Luigi P.L. Cavaliere
Department of Economics
University of Foggia
Foggia
Italy
Chunduru R. Chandan
Koneru Lakshmaiah Education
Foundation
Vaddeswaram, Andhra Pradesh
India
Radha R. Chandan
School of Management Sciences (SMS)
Department of Computer Science
Varanasi, Uttar Pradesh
India
Vikas Choudhary
Department of Computer Science and Engineering (AIML)
ABES Engineering College
Ghaziabad, Uttar Pradesh
India
Aarti Dawra
Manav Rachna International Institute of Research and Studies
Faridabad, Haryana
India
M.K. Dharani
Department of Artificial Intelligence
Kongu Engineering College
Erode, Tamil Nadu
India
Satish M. Dhoke
Moreshwar Arts Science and
Commerce College
Department of Commerce
Jalna, Maharashtra
India
Venkata Harshavardhan Reddy Dornadula
Startups and IIC
Chairman Office
Sree Venkateswara College of Engineering
Nellore, Andhra Pradesh
India
Lavanya Durga
Koneru Lakshmaiah Education Foundation
KL University
Guntur, Andhra Pradesh
India
S. Durga
Koneru Lakshmaiah Education Foundation
Vaddeswaram, Andhra Pradesh
India
Syed M. Faisal
Department of Management
Jazan University
Kabul Saudi Arabia
Ernesto N.T. Figueroa
Universidad Nacional del
Altiplano de Puno
Academic Department of Computer and Statistics Engineering
Puno
Peru
Ruchi Rani Garg
Department of Applied Science
Meerut Institute of Engineering and Technology
Meerut, Uttar Pradesh
India
Umakant B. Gohatre
Department of Electronics and Telecommunications Engineering
Smt. Indira Gandhi College of Engineering
Navi Mumbai, Maharashtra
India
José L.A. Gonzáles
Department of Business
Pontifical Catholic University of Peru
Lima
Peru
Brijesh Goswami
Institute of Business Management
GLA University
Mathura, Uttar Pradesh
India
Sushmita Goswami
Institute of Business Management
GLA University
Mathura, Uttar Pradesh
India
Jitendra Gowrabhathini
K L Business School
Koneru Lakshmaiah Education Foundation
K L University
Vijayawada, Andhra Pradesh
India
Shouvik K. Guha
The West Bengal National University of Juridical Sciences
Kolkata, West Bengal
India
Amit Kumar Gupta
Department of Computer Applications
KIET Group of Institutions
Delhi-NCR
Ghaziabad, Uttar Pradesh
India
Deena N. Gupta
CDAC
Mumbai, Maharashtra
India
Priti Gupta
P.G. Department of Economics
Bhupendra Narayan Mandal
University (West Campus) P.G. Centre Saharsa
India
T.V.N.J.L Haritha
Koneru Lakshmaiah Education Foundation
Vaddeswaram, Andhra Pradesh
India
Nayana Harshitha
Koneru Lakshmaiah Education Foundation
Vaddeswaram, Andhra Pradesh
India
Yu-Chen Hu
Department of Computer Science and Information Management
Providence University
Taichung City
Taiwan R.O.C.
Julio C.L. Huanca
Academic Department of Basic Sciences
Universidad Nacional de Juliaca
Puno
Peru
Sujay M. Jayadeva
Department of Health System Management Studies
JSS Academy of Higher Education & Research
Mysuru, Karnataka
India
K. Jayalakshmamma
Government RC College of Commerce and Management
Bengaluru, Karnataka
India
Kapil Joshi
Uttaranchal Institute of Technology
Uttaranchal University
India
Abhinav Juneja
KIET Group of Institutions
Ghaziabad
India
Sapna Juneja
Department of CSE(AI), KIET Group of Institutions, Ghaziabad, India
IITM Group of Institutions
Murthal
India
Latika Kharb
Jagan Institute of Management Studies
Rohini
Sector-5
Delhi
India
Nitin Kulshrestha
Christ (Deemed to be University)
Bengaluru, Karnataka
India
Jaideep Kumar
Department of Computer Science and Engineering (IoT)
RKGIT
Ghaziabad, Uttar Pradesh
India
Cheedella A.S. Lakshmi
Koneru Lakshmaiah Education Foundation
Vaddeswaram, Andhra Pradesh
India
S. Mahabub Basha
Department of Commerce
International Institute of Business
Studies Bangalore
India
Gangu N. Mandala
Department of Business
Administration
Central Tribal University of Andhra Pradesh
Konda Karakam, Andhra Pradesh
India
Geetha Manoharan
School of Business
SR University
Hyderabad, Telangana
India
S.S.C. Mary
Loyola Institute of Business
Administration
Business Analytics
India
Haider Mehraj
Department of Electronics and Communication Engineering
Baba Ghulam Shah Badshah University
Rajouri, Jammu and Kashmir
India
Kamakshi Mehta
TAPMI School of Business
Manipal University
Jaipur, Rajasthan
India
Charles I. Mendoza-Mollocondo
Universidad Nacional del
Altiplano de Puno
Academic Department of Computer and Statistics Engineering
Puno
Peru
Kali Charan Modak
IPS Academy
Institute of Business Management and Research
Indore, Madhya Pradesh
India
Debasis Mohanty
Department of Commerce and Management
Kalinga University
Raipur, Chhattisgarh
India
Vinay K. Nassa
Rajarambapu Institute of Technology
Walwa, Maharashtra
India
Samad Noeiaghdam
Industrial Mathematics Laboratory
Baikal School of BRICS
Irkutsk National Research Technical University
Irkutsk
Russia
M.Z.M. Nomani
Faculty of Law
Aligarh Muslim University
Aligarh
India
Digvijay Pandey
Department of Technical Education
IET
Dr. A.P.J. Abdul Kalam Technical University
Lucknow, Uttar Pradesh
India
Veena Parihar
KIET Group of Institutions
Ghaziabad, Uttar Pradesh
India
T. Pavan ReddyKoneru Lakshmaiah Education Foundation
KL University
Guntur, Andhra Pradesh
India
Venkateswararao Podile
K. L. Business School
Koneru Lakshmaiah Education Foundation
Vaddeswaram, Andhra Pradesh
India
B. Rachanasree
Koneru Lakshmaiah Education Foundation
KL University
Guntur, Andhra Pradesh
India
T.S. Rajeswari
Koneru Lakshmaiah Education Foundation
Department of English
Vaddeswaram, Andhra Pradesh
India
K.K. Ramachandran
Management/Commerce/International Business
DR. G R D College of Science
Coimbatore, Tamil Nadu
India
J.V.N. Ramesh
Department of Computer Science and Engineering
Koneru Lakshmaiah Education Foundation
Vaddeswaram
Guntur, Andhra Pradesh
India
P.S. Ranjit
Department of Mechanical Engineering
Aditya Engineering College Surampalem
Kakinada, Andhra Pradesh
India
A.S.K. Reddy
Department of CS and AI
SR University
Warangal, Telangana
India
Dhyana S. Ross
Loyola Institute of Business
Administration (LIBA)
India
Kanamarlapudi P.S. Sabareesh
Koneru Lakshmaiah Education Foundation
Vaddeswaram, Andhra Pradesh
India
K.V.D. Sagar
Koneru Lakshmaiah Education Foundation
KL University
Guntur, Andhra Pradesh
India
Devati B. Sambasiva Rao
Koneru Lakshmaiah Education Foundation
Vaddeswaram, Andhra Pradesh
India
Abdullah Samdani
School of Law
University of Petroleum & Energy Studies
Dehradun
India
Saurabh
KIET Group of Institutions
Ghaziabad, Uttar Pradesh
India
Franklin J. Selvaraj
Vignana Jyothi Institute of
Management
Department of Marketing
Hyderabad
India
Asif I. Shah
Xavier Law School
St. Xavier’s University
Kolkata
India
Kunjan Shah
Unitedworld School of Computational Intelligence
Karnavati University
Gandhinagar, Gujarat
India
Arti Sharma
KIET Group of Institutions
Ghaziabad, Uttar Pradesh
India
Dilip K. Sharma
Department of Mathematics
Jaypee University of Engineering and
Technology
Guna Madhya Pradesh
India
Himanshu Sharma
United World School of Business
Karnavati University
Gandhinagar, Gujarat
India
M.K. Sharma
Department of Mathematics
Chaudhary Charan Singh University
Meerut, Uttar Pradesh
India
P. Shreya Sarojini
Koneru Lakshmaiah Education
Foundation
KL University
Guntur, Andhra Pradesh
India
Ajay Kumar Shrivastava
KIET Group of Institutions
Ghaziabad, Uttar Pradesh
India
Someshwar Siddi
St. Martin’s Engineering College
Secunderabad, Telangana
India
Veer B.P. Singh
School of CSIT
Department of Cyber Security
Symbiosis Skills and Professional University
Kiwale, Pune
India
Yashasvi Singh
KIET Group of Institutions
Ghaziabad, Uttar Pradesh
India
Swasti Singhal
Department of Computer Science and Information Technology
KIET Group of Institutions
Ghaziabad, Uttar Pradesh
India
S. Silas Sargunam
Department of Management Studies
Anna University Regional Campus
Tirunelveli, Tamilnadu
India
Nidhi Sindhwani
Amity Institute of Information
Technology (AIIT)
Amity University
Noida, Uttar Pradesh
India
and
Amity School of Engineering and Technology Delhi
Amity University
Noida
India
Pratibha Singh
Department of CSE
Guru Ghasidas Vishwavidyalaya
Bilaspur, Chhattisgarh
India
Dharini R. Sisodia
Army Institute of Management & Technology
Department of Management
Greater Noida, Uttar Pradesh
India
Katakam V. Siva Praneeth
Koneru Lakshmaiah Education
Foundation
Vaddeswaram, Andhra Pradesh
India
K. Suresh Kumar
MBA Department
Panimalar Engineering College
Chennai, Tamil Nadu
India
Ayush Thakur
Amity Institute of Information
Technology (AIIT)
Amity University
Noida, Uttar Pradesh
India
Mohit Tiwari
Department of Computer Science and Engineering
Bharati Vidyapeeth’s College of
Engineering
Delhi
India
Abhinav Tripathi
KIET Group of Institutions
Ghaziabad, Uttar Pradesh
India
Mano A. Tripathi
Motilal Nehru National Institute of
Technology
Department of Humanities and Social
Sciences
Allahabad
India
Fred Torres-Cruz
Academic Department of Statistics
and Computer Engineering
Universidad Nacional del
Altiplano de Puno
Puno
Peru
Shyamasundar Tripathy
KL Business School
Koneru Lakshmaiah Education
Foundation
Guntur, Andhra Pradesh
India
Makarand Upadhyaya
University of Bahrain
College of Business
Bahrain
Rashmi Vashisth
Amity Institute of Information
Technology (AIIT)
Amity University
Noida, Uttar Pradesh
India
Veena P. Vemuri
NKES College of Arts
Commerce
and Science
Mumbai
India
Y. Venkata Ramana
KL Business School
Koneru Lakshmaiah Education
Foundation
Guntur, Andhra Pradesh
India
Suruchi Verma
Amity Institute of Information
Technology (AIIT)
Amity University
Noida, Uttar Pradesh
India
G.H.A. Vethamanikam
Department of Business
Administration
Ayya Nadar Janaki Ammal College
Sivakasi, Tamil Nadu
India
Nadanakumar Vinayagam
Department of Automobile
Engineering
Hindustan Institute of Technology
and Science
Chennai, Tamil Nadu
India
W. Vinu
Department of Physical Education and Sports
Pondicherry University
Pondicherry
India
Elena Y. Zegarra
Academic Department of Accounting
Sciences
Universidad Nacional del
Altiplano de Puno
Puno
Peru
Preface
1. The Future of Business Management with the Power of Distributed Systems and Computing
Distributed systems and computing increase operations, decision-making, and customer experience and will shape firm management in this chapter. Distributed systems and computing improve corporate management scalability, flexibility, availability, efficiency, and affordability. Inventory, supply chain, customer relationship, finance, accounting, data analytics, decision-making, collaboration, and communication use distributed systems and computing. Edge computing, blockchain, and AI in distributed systems and computing may affect business management. We also cover distributed systems and computing research and innovation prospects, such as developing new algorithms and protocols, exploring new applications, and evaluating their social and ethical impacts. Distributed systems and computing enable digital-age firms to operate better and compete. Distributed systems and computing pose several challenges and threats. Distributed systems and computing and their difficulties can help businesses prosper in the digital age.
2. Applications of Optimized Distributed Systems in Healthcare
This chapter discusses healthcare applications of optimized distributed systems. Distributed systems, their architecture, and their uses in many industries are introduced in the chapter. Telemedicine and big data analytics were developed to address healthcare practitioners’ communication and service issues. Optimized distributed systems with unrestricted parallel data processing, fault tolerance, and higher availability were developed after these methods failed. These systems improve patient care and lower healthcare costs, as described in the chapter.
3. The Impact of Distributed Computing on Data Analytics and Business Insights
Distributed computing affects data analytics and business insights. We define distributed computing and its significance in data analytics, emphasizing its benefits for large-scale data processing. We also examine business insights and how data analytics affects business operations. We then discuss how distributed computing facilitates data analytics, highlighting the benefits of numerous popular systems. We emphasize distributed computing for large-scale data processing, real-time data analytics, and machine learning. Distributed computing for data analytics and business insights has pros and cons. Scalability, latency, integration, and maintenance might affect distributed computing for data analytics and business insights. Distributed computing provides important insights into operations and a competitive edge in their marketplaces, outweighing the hurdles.
4. Machine Learning and Its Application in Educational Area
Machine learning (ML) is utilized to develop several sectors, including education, which will profoundly revolutionize learning and teaching. Educational institutions collect a lot of student data, which can be used to narrow down the changes that will improve student success. Machine learning can help instructors enhance student retention, grading, etc. Machine learning creates new insights. This chapter addresses how machine learning can be used in education to solve student and teacher issues and inform future research.
5. Approaches and Methodologies for Distributed Systems: Threats, Challenges, and Future Directions
Distributed systems are widely used, raising security concerns. With more internet-connected devices, security breaches are a huge concern. This chapter covers distributed system security threats such as hacking, malware, and denial-of-service assaults. We will also review distributed system security standards and protocols, including industry and government proposals. We will also address distributed system security, including wireless communication and network integration. The chapter will also discuss distributed system access control mechanisms like RBAC, DAC, and MAC. Finally, we will review the main distributed system security issues and outline future study and development.
6. Efficient-Driven Approaches Related to Metaheuristic Algorithms Using Machine Learning Techniques
Recent research on using machine learning (ML) to find effective, profitable, and adaptive metaheuristics has grown. Many stochastic and metaheuristic algorithms have delivered high-quality results and are cutting-edge optimization strategies. This study lacks a comprehensive survey and classification, despite many methods. This study examines numerous machine learning-metaheuristics combinations. It applies synergy to the many ways to achieve this goal. Search component-specific taxonomies are supplied. This taxonomy covers the optimization problem, minimal metaheuristics, and raised components. We also want optimization scholars to use machine learning techniques in metaheuristics. This chapter highlights unresolved scientific questions that require further study.
7. Security and Privacy Issues in Distributed Healthcare Systems – A Survey
Recent research on using machine learning (ML) to find effective, profitable, and adaptive metaheuristics has grown. Many stochastic and metaheuristic algorithms have delivered high-quality results and are cutting-edge optimization strategies. This study lacks a comprehensive survey and classification despite many methods. This study examines numerous machine learning-metaheuristics combinations. It applies synergy to the many ways to achieve this goal. Search component-specific taxonomies are supplied. This taxonomy covers the optimization problem, minimal metaheuristics, and raised components. We also want optimization scholars to use machine learning techniques in metaheuristics. This chapter highlights unresolved scientific questions that require further study.
8. Implementation and Analysis of the Proposed Model in a Distributed e-Healthcare System
Designing complex, distributed services like e-healthcare is difficult. Service-oriented design supports modular design, application integration and interoperation, and software reuse, enabling such systems. Open standards like XML, SOAP, WSDL, and UDDI enable interoperability between services on different platforms and applications in different programming languages under a service-oriented architecture. This chapter describes designing, deploying, invoking, and managing a decentralized electronic healthcare system using the service-oriented architecture. Our e-healthcare solution helps patients, medical staff, and patient monitoring devices. Because it supports text, graphics, and speech, the technology is more client-friendly than current e-healthcare systems.
9. Leveraging Distributed Systems for Improved Educational Planning and Resource Allocation
This chapter reviews distributed systems’ educational planning and resource allocation benefits and drawbacks. Distributed systems that optimize resource consumption and provide personalized learning might help educational institutions plan and allocate resources. The research examines distributed system architectures and emphasizes infrastructure requirements for successful education implementation. Distributed system data management and analysis are also mentioned. Distributed education systems face security and privacy issues, according to the research. Distributed systems’ benefits outweigh their drawbacks, and with effective planning and administration, educational institutions may overcome them to improve student outcomes. Distributed systems can improve academic achievements, personalize and collaborate on learning, and increase access to education, according to the chapter. Educational institutions could consider distributed system adoption to improve collaboration and communication, resource allocation, and digital education access.
10. Advances in Education Policy Through the Integration of Distributed Computing Approaches
Distributed computing advances education policy, according to this chapter. The chapter introduces technology’s role in education policy and how distributed computing may solve many of the education sector’s problems. The second section defines distributed computing and gives education policy examples. Distributed computing improves access, personalized learning, and data-driven decision-making in education policy, as discussed in the third section of this chapter. Distributed computing in education policy presents privacy and technological issues, which is discussed in the fourth section. Distributed computing benefits must be realized without sacrificing student and instructor privacy and security. These technologies may improve distributed computing in education policy as they grow. Distributed computing could transform education policy and student results.
11. Revolutionizing Data Management and Security with the Power of Blockchain and Distributed System
Modern organizations must secure and manage sensitive data. However, conventional centralized systems lack security and transparency. Blockchain technology (BT) and distribution systems (DS) may solve these problems. BT and DS may improve data management and security, as explored in this chapter. It begins by highlighting the necessity of data management and security in modern organizations. Additionally, it examines blockchain technology’s scalability, interoperability, data management, and security difficulties. We examine the benefits and drawbacks of blockchain technology and distribution systems for data management and security. The chapter concludes with future research topics and the possible influence of these technologies on data management and security. Researchers, practitioners, and decision-makers interested in data management and security might use this chapter. We should expect more transparency, security, and efficiency in managing sensitive data with this new method.
12. Enhancing Business Development, Ethics, and Governance with the Adoption of Distributed Systems
Distributed systems could transform business operations, ethics, and governance. Decentralization, transparency, and security enable new business models, cut costs, and increase efficiency in distributed systems. Ethical and governance practices can help organizations use distributed systems responsibly and sustainably. This chapter summarizes distributed system concepts and applications in business development, ethics, and governance. It explains distributed systems, describes their properties, and explores their business growth benefits. The research also studies how distributed systems promote ethics in corporate development and governance. The study also defines governance in distributed systems and analyzes its importance, benefits, and possible solutions. The review study also discusses distributed systems’ constraints in commercial development, ethics, and governance. Technical complexity, scalability, interoperability, regulatory issues, and governance issues must be handled. By understanding these challenges, organizations may employ distributed systems to improve governance, ethics, and growth. Distributed systems can significantly change how firms are founded, run, and regulated while encouraging moral behavior and participant confidence.
13. Leveraging Distribution Systems for Advanced Fraud Detection and Prevention in Finance
Fraud in the financial sector is rising due to the prevalence of financial crimes worldwide. Fraud detection (FD) and prevention are crucial to financial integrity and protecting organizations and individuals from financial losses. Distributed systems’ (DSs) ability to analyze enormous amounts of data and perform real-time analysis makes them an attractive FD and prevention solution. DSs, a computer network, collaborate to complete a task. Scalability, fault tolerance, and high performance are their benefits. DSs can overcome the disadvantages of rule-based and machine learning-based FD methods. In this chapter, rule-based, machine learning-based, and hybrid FD and preventative techniques and their pros and cons are discussed. Then, it investigates how DSs can be used for FD and prevention in rule-based and mixed systems. The chapter concludes by discussing FD and preventive DS implementation issues and potential prospects. DSs for FD and prevention can increase these systems’ accuracy and efficiency, improving financial security and reducing financial losses. However, DS implementation brings various problems, including data privacy concerns, security hazards, and the requirement for specialized skills and resources. Overcoming these obstacles and improving DSs for FD and prevention will be the focus of future research.
14. Advances in E-commerce Through the Integration of Distributed Computing Approaches
Distributed computing in e-commerce has improved digital enterprises. This chapter describes distributed computing approaches, their benefits, problems, and e-commerce integration issues. The concept of Distributed Computing improves scalability, flexibility, performance, efficiency, security, privacy, cost savings, operational complexity, and customer experience. The chapter also explores cloud computing, big data, and artificial intelligence trends in e-commerce distributed computing. Distributed computing is helpful for e-commerce enterprises, but they must consider hazards and take precautions. This chapter discusses the pros, cons, and future of distributed computing in e-commerce. This chapter can help firms comprehend this integration and use its benefits to stay competitive in the digital age.
15. The Impact of Distributed Computing on Online Shopping and Consumer Experience
Distributed computing affects online purchasing and consumer experience. Distributed computing lets online shopping platforms analyze and store vast volumes of data, speed up websites, and improve security. Thus, people demand smooth, personalized online shopping experiences that fit their needs. Distributed computing technology and online purchasing platforms are introduced in the chapter. Distributed computing affects website speed, personalization, and security; can increase expenses and technology dependence, as discussed in this chapter; and has transformed online shopping expectations by making it faster and more personalized. Websites must now load quickly, offer appropriate recommendations, and protect personal data. Retailers that fail to match these standards risk losing customers to competitors with a better online buying experience. While overusing this technology may have problems, it benefits shops and consumers. Distributed computing may shape the future of internet shopping as technology advances.
16. Wireless Sensor-Based IoT System with Distributed Optimization for Healthcare
WSNs are geographically distributed, purpose-built sensors that monitor and record environmental parameters and wirelessly communicate data to a central server connected to the internet in the IoT. IoT devices can assist hospitals beyond patient monitoring. IoT sensors can track wheelchairs, defibrillators, nebulizers, oxygen pumps, and other medical devices in real time. Healthcare requires scattered optimization. Healthcare IoT is hard for WSNs. Thus, WSN-based IoT and healthcare consider traditional research methodologies.
17. Optimizing Financial Transactions and Processes Through the Power of Distributed Systems
Distributed systems (DSs) improve FT efficiency, according to this chapter. like supply chains and blockchain technology can increase FT, process, security, transparency, and cost. This chapter defines, challenges, and solves FT and processes and uses DSs for payment processing, digital identity verification, supply chain financing, and insurance. These systems have scalability, interoperability, legal compliance, security, and user acceptance challenges. This chapter discusses BT and SC definitions, properties, benefits, and finance application cases, as well as DSs’ financial potential and challenges. The chapter improves DS and processes for researchers, programmers, and practitioners. To maximize system utilization, the chapter discusses removing various barriers.
18. Leveraging Distributed Systems for Improved Market Intelligence and Customer Segmentation
DS increases market intelligence and client segmentation. Market intelligence, customer segmentation, and DS research follow. It provides advantages and disadvantages of DS for market intelligence. DS’s real-time customer behavior research and customized marketing methods boost client segmentation. This chapter examines R&D challenges and directions. It recommends market intelligence and customer segmentation research using artificial intelligence, machine learning, blockchain technology, data visualization, ethics, and governance in DS. It shows various systems’ pros and cons.
19. The Future of Financial Crime Prevention and Cybersecurity with Distributed Systems and Computing Approaches
Technology has enabled new financial crime prevention and cybersecurity methods. Distributed systems and computing can improve financial transaction security and efficiency. This chapter reviews blockchain technology and distributed ledgers for financial crime prevention and cybersecurity. It also tackles regulatory and compliance difficulties, interoperability and standardization issues, and system and infrastructure integration. These technologies improve transparency, minimize fraud, and other financial crimes, and boost financial industry innovation, yet they also have drawbacks. By tackling the issues holistically, these technologies can be used to their full potential while minimizing their drawbacks. Financial professionals and cybersecurity enthusiasts will benefit from this chapter.
20. Innovations in Distributed Computing for Enhanced Risk Management in Finance
Distributed computing may improve financial risk management. Comparing traditional risk management systems to distributed computing-based ones sets the chapter’s goals. Finance risk management and distributed computing theory are covered in this chapter. Innovative strategies and trends in financial risk management include distributed computing architectures and frameworks. Distributed computing for finance risk management has technical challenges. Finally, finance risk management and distributed computing best practices are provided. This chapter suggests adopting and implementing a distributed computing solution for finance risk management and discusses distributed computing technologies in finance risk management, including pros, cons, and best practices.
21. Leveraging Blockchain and Distributed Systems for Improved Supply Chain Traceability and Transparency
In this chapter, distributed systems (DS) and blockchain improve supply chain (SC) traceability and transparency. As systems become more complicated and global, stakeholders lose visibility and accountability. SC data is protected by blockchain and DS. Food safety, traceability, counterfeiting, efficiency, and transparency are improved by SC blockchain and DS. The chapter then explores SC blockchain and DS applications including tracking commodities and raw materials and product validity. DS, AI, IoT, blockchain, and SC smart contracts. Finally, these technologies’ effects on SC stakeholders and society, including the need for standardization, regulation, efficiency, transparency, and accountability, are examined. SC traceability and transparency may be enhanced with blockchain and DS. These technologies can improve SC for businesses and customers, despite their limitations. SC should use blockchain and DS.
22. Advances in Resource Management Through the Integration of Distributed Computing Approaches
This chapter discusses resource management for grid, cloud, and EC. These technologies lack flexibility, scalability, data analysis and processing, resource scheduling, administration, monitoring, data protection, and management. Researchers are creating distributed computing resource allocation algorithms. Distributed computing resource management changes organizational computer infrastructure. Networks and systems process data, increase computer infrastructure, and save money. These technologies are necessary for future research, despite their shortcomings.
1The Future of Business Management with the Power of Distributed Systems and Computing
Venkateswararao Podile1, Nitin Kulshrestha2, Sushmita Goswami3, Lavanya Durga4, B. Rachanasree4, T. Pavan Reddy4, and P. Shreya Sarojini4
1K. L. Business School, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India
2Christ (Deemed to be University), Bengaluru, Karnataka, India
3Institute of Business Management, GLA University, Mathura, Uttar Pradesh, India
4Koneru Lakshmaiah Education Foundation, KL University, Guntur, Andhra Pradesh, India
1.1 Introduction
Distributed systems and computing have become increasingly prevalent in the business world, transforming the way organizations manage their operations, data, and communications. The benefits of these technologies are clear, including improved efficiency, agility, and cost savings, which have led to their widespread adoption. This chapter aims to explore the potential of distributed systems and computing in shaping the future of business management. The fundamental concepts of distributed systems and computing find their applications in various areas of business management, including inventory management, customer relationship management (CRM), financial management, data analytics, and collaboration. We will also examine the challenges and risks associated with these technologies and explore real-world examples of their successful implementation. As the field of distributed systems and computing continues to evolve, there is great potential for further advancements and opportunities for innovation. Therefore, we will discuss emerging trends and technologies in this field and their potential implications for the future of business management. A comprehensive understanding of the power of distributed systems and computing in business management and its potential to transform the way organizations operate in the future [1–3].
1.1.1 Distributed Systems in Business Management
Due to their capacity to increase the effectiveness and efficiency of various business operations, distributed systems and computing have grown in significance in the field of business management. Through the use of these technologies, businesses are able to distribute their computing resources among various platforms, such as cloud platforms, edge devices, and peer-to-peer networks, processing and storing massive amounts of data in real time. The ability to optimize supply chain and inventory management processes is one of the main benefits of distributed systems and computing in business management. Due to the real-time tracking of inventory levels and shipments made possible by these technologies, businesses are better able to adapt to demand changes and better manage their resources. Customers should be treated with more personal attention, and businesses should offer them services that are more pertinent to their needs. This makes financial reporting, forecasting, and analysis more effective. Distributed systems and computing can also facilitate data analytics and decision-making by giving organizations access to real-time data and cutting-edge analytical tools. This enhances an organization’s capacity to respond to changes in the business environment by enabling quick and effective decision-making. Distributed systems and computing can facilitate easier information and resource sharing among teams, enhancing collaboration and communication both within and between organizations. As a result, the organization may experience increased productivity and innovation. The value of distributed systems and computing lies in their capacity to increase the accuracy, agility, and efficiency of various business operations, giving organizations a competitive edge in the market [4, 5].
1.2 Understanding Distributed Systems and Computing
Distributed systems and computing are a type of computing model that involves multiple computer systems working together to achieve a shared goal. In this model, tasks are divided into smaller, more manageable pieces and distributed across different systems that are connected by a network. This allows the systems to collaborate and work together more efficiently, which can improve the overall performance of computing tasks. In cloud computing, for example, a shared pool of computing resources, including servers, applications, and storage, is accessed on-demand through the Internet. Edge computing, on the other hand, involves processing data and running applications closer to the source of the data, which can reduce latency and improve efficiency. Peer-to-peer networks allow devices to share resources and computing power, which can improve the resilience and efficiency of the system. However, distributed systems and computing also present challenges and risks, such as security vulnerabilities, data privacy concerns, and interoperability issues. Therefore, businesses need to carefully evaluate and plan for the adoption of distributed systems and computing to ensure its successful implementation and long-term sustainability [6–8].
Let’s understand this with an imaginary example
Suppose a clothing retailer is experiencing stockouts and overstock issues in its supply chain, resulting in lost sales and increased inventory costs. The retailer decides to implement a distributed system to better manage its inventory and improve its supply chain. The distributed system utilizes a network of sensors placed throughout the supply chain, which collects and transmits data on inventory levels, sales trends, and production schedules. This data is then processed and analyzed in real time using advanced data analytics algorithms, allowing the retailer to make more accurate and timely decisions regarding inventory management and production scheduling.
As a result, the retailer is able to reduce its inventory costs by 20%, increase its sales by 15%, and improve its on-time delivery rate by 10%. The implementation of the distributed system also results in a more efficient and streamlined supply chain, reducing lead times and improving overall customer satisfaction. This example highlights how distributed systems and computing can be applied to improve business management by providing real-time insights and enabling more efficient and effective decision-making. The retailer was able to achieve these improvements by utilizing a distributed system that allowed for real-time monitoring of inventory levels at each store. With this information, the retailer was able to optimize its inventory levels and reduce the amount of excess inventory, which in turn reduced inventory carrying costs. In addition to better inventory management, the retailer also used the distributed system to improve its supply chain management. By monitoring the supply chain in real time, the retailer was able to identify bottlenecks and other issues that were causing delays in the delivery of products. By addressing these issues, the retailer was able to improve its on-time delivery rate by 10%. Finally, the retailer was able to increase its sales by 15% by leveraging the data provided by the distributed system.
Table 1.1 and Figure 1.1 show the sales volume of three products across two sales channels: online and in-store. For example, Product 1 had sales of 500 units online and 300 units in-store, for a total of 800 units sold. Similarly, Product 2 had sales of 750 units online and 400 units in-store, for a total of 1150 units sold. These sales figures could be used to inform decisions about how to allocate inventory, allocate marketing budgets, or make other business decisions related to product sales.
Table 1.1 Sales volume of three products across two sales channels: online and in-store.
Product
Sales channel
Sales volume
Product 1
Online
500
In-store
300
Product 2
Online
750
In-store
400
Product 3
Online
1000
In-store
800
Figure 1.1 Sales volumes.
