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Author: Sadhasivam Narayanan
Publisher: Anchor Academic Publishing
ISBN: 396067192X
Category : Computers
Languages : en
Pages : 45
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Book Description
Cloud computing is a new prototype for enterprises which can effectively assist the execution of tasks. Task scheduling is a major constraint which greatly influences the performance of cloud computing environments. The cloud service providers and consumers have different objectives and requirements. For the moment, the load and availability of the resources vary dynamically with time. Therefore, in the cloud environment scheduling resources is a complicated problem. Moreover, task scheduling algorithm is a method by which tasks are allocated or matched to data center resources. All task scheduling problems in a cloud computing environment come under the class of combinatorial optimization problems which decide searching for an optimal solution in a finite set of potential solutions. For a combinatorial optimization problem in bounded time, exact algorithms always guarantee to find an optimal solution for every finite size instance. These kinds of problems are NP-Hard in nature. Moreover, for the large scale applications, an exact algorithm needs unexpected computation time which leads to an increase in computational burden. However, the absolutely perfect scheduling algorithm does not exist, because of conflicting scheduling objectives. Therefore, to overcome this constraint heuristic algorithms are proposed. In workflow scheduling problems, search space grows exponentially with the problem size. Heuristics optimization as a search method is useful in local search to find good solutions quickly in a restricted area. However, the heuristics optimization methods do not provide a suitable solution for the scheduling problem. Researchers have shown good performance of metaheuristic algorithms in a wide range of complex problems. In order to minimize the defined objective of task resource mapping, improved versions of Particle Swarm Optimization (PSO) are put in place to enhance scheduling performance with less computational burden. In recent years, PSO has been successfully applied to solve different kinds of problems. It is famous for its easy realization and fast convergence, while suffering from the possibility of early convergence to local optimums. In the proposed Improved Particle Swarm Optimization (IPSO) algorithm, whenever early convergence occurs, the original particle swarm would be considered the worst positions an individual particle and worst positions global particle the whole swarm have experienced.
Author: Sadhasivam Narayanan
Publisher: Anchor Academic Publishing
ISBN: 396067192X
Category : Computers
Languages : en
Pages : 45
Get Book Here
Book Description
Cloud computing is a new prototype for enterprises which can effectively assist the execution of tasks. Task scheduling is a major constraint which greatly influences the performance of cloud computing environments. The cloud service providers and consumers have different objectives and requirements. For the moment, the load and availability of the resources vary dynamically with time. Therefore, in the cloud environment scheduling resources is a complicated problem. Moreover, task scheduling algorithm is a method by which tasks are allocated or matched to data center resources. All task scheduling problems in a cloud computing environment come under the class of combinatorial optimization problems which decide searching for an optimal solution in a finite set of potential solutions. For a combinatorial optimization problem in bounded time, exact algorithms always guarantee to find an optimal solution for every finite size instance. These kinds of problems are NP-Hard in nature. Moreover, for the large scale applications, an exact algorithm needs unexpected computation time which leads to an increase in computational burden. However, the absolutely perfect scheduling algorithm does not exist, because of conflicting scheduling objectives. Therefore, to overcome this constraint heuristic algorithms are proposed. In workflow scheduling problems, search space grows exponentially with the problem size. Heuristics optimization as a search method is useful in local search to find good solutions quickly in a restricted area. However, the heuristics optimization methods do not provide a suitable solution for the scheduling problem. Researchers have shown good performance of metaheuristic algorithms in a wide range of complex problems. In order to minimize the defined objective of task resource mapping, improved versions of Particle Swarm Optimization (PSO) are put in place to enhance scheduling performance with less computational burden. In recent years, PSO has been successfully applied to solve different kinds of problems. It is famous for its easy realization and fast convergence, while suffering from the possibility of early convergence to local optimums. In the proposed Improved Particle Swarm Optimization (IPSO) algorithm, whenever early convergence occurs, the original particle swarm would be considered the worst positions an individual particle and worst positions global particle the whole swarm have experienced.
Author: Amir H. Gandomi
Publisher: John Wiley & Sons
ISBN: 1119573874
Category : Mathematics
Languages : en
Pages : 343
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Book Description
Presents current developments in the field of evolutionary scheduling and demonstrates the applicability of evolutionary computational techniques to solving scheduling problems This book provides insight into the use of evolutionary computations (EC) in real-world scheduling, showing readers how to choose a specific evolutionary computation and how to validate the results using metrics and statistics. It offers a spectrum of real-world optimization problems, including applications of EC in industry and service organizations such as healthcare scheduling, aircraft industry, school timetabling, manufacturing systems, and transportation scheduling in the supply chain. It also features problems with different degrees of complexity, practical requirements, user constraints, and MOEC solution approaches. Evolutionary Computation in Scheduling starts with a chapter on scientometric analysis to analyze scientific literature in evolutionary computation in scheduling. It then examines the role and impacts of ant colony optimization (ACO) in job shop scheduling problems, before presenting the application of the ACO algorithm in healthcare scheduling. Other chapters explore task scheduling in heterogeneous computing systems and truck scheduling using swarm intelligence, application of sub-population scheduling algorithm in multi-population evolutionary dynamic optimization, task scheduling in cloud environments, scheduling of robotic disassembly in remanufacturing using the bees algorithm, and more. This book: Provides a representative sampling of real-world problems currently being tackled by practitioners Examines a variety of single-, multi-, and many-objective problems that have been solved using evolutionary computations, including evolutionary algorithms and swarm intelligence Consists of four main parts: Introduction to Scheduling Problems, Computational Issues in Scheduling Problems, Evolutionary Computation, and Evolutionary Computations for Scheduling Problems Evolutionary Computation in Scheduling is ideal for engineers in industries, research scholars, advanced undergraduates and graduate students, and faculty teaching and conducting research in Operations Research and Industrial Engineering.
Author: Hari Mohan Dubey
Publisher: Springer Nature
ISBN: 981161220X
Category : Technology & Engineering
Languages : en
Pages : 483
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Book Description
This book presents the outcome of two-day 2nd International e-Conference on Sustainable and Innovative Solutions for Current Challenges in Engineering and Technology (ICSISCET 2020) held at Madhav Institute of Technology & Science (MITS), Gwalior, India, from December 18–19, 2020. The book extensively covers recent research in artificial intelligence (AI) that knit together nature-inspired algorithms, evolutionary computing, fuzzy systems, computational intelligence, machine learning, deep learning, etc., which is very useful while dealing with real problems due to their model-free structure, learning ability, and flexible approach. These techniques mimic human thinking and decision-making abilities to produce systems that are intelligent, efficient, cost-effective, and fast. The book provides a friendly and informative treatment of the topics which makes this book an ideal reference for both beginners and experienced researchers.
Author: Fadi Al-Turjman
Publisher: Springer Nature
ISBN: 3030400379
Category : Technology & Engineering
Languages : en
Pages : 245
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Book Description
This book examines research topics in IoT and Cloud and Fog computing. The contributors address major issues and challenges in IoT-based solutions proposed for the Cloud. The authors discuss Cloud smart and energy efficient services in applications such as healthcare, traffic, and farming systems. Targeted readers are from varying disciplines who are interested in designing and deploying the Cloud applications. The book can be helpful to Cloud-based IoT service providers, Cloud-based IoT service consumers, and Cloud service developers in general for getting the state-of-the-art knowledge in the emerging IoT area. The book also provides a strong foundation for researchers to advance further in this domain. Presents a variety of research related to IoT and Cloud computing; Provides the industry with new and innovative operational ideas; Pertinent to academics, researchers, and practitioners around the world.
Author: Zhihong Qian
Publisher: Springer Nature
ISBN: 9819939518
Category : Technology & Engineering
Languages : en
Pages : 849
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Book Description
This proceedings includes original, unpublished, peer-reviewed research papers from the International Conference on Wireless Communications, Networking and Applications (WCNA2022), held in Wuhan, Hubei, China, from December 16 to 18, 2022. The topics covered include but are not limited to wireless communications, networking and applications. The papers showcased here share the latest findings on methodologies, algorithms and applications in communication and network, making the book a valuable asset for professors, researchers, engineers, and university students alike.
Author: Bhabani Shankar Prasad Mishra
Publisher: Springer
ISBN: 3319736760
Category : Technology & Engineering
Languages : en
Pages : 468
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Book Description
This book discusses harnessing the real power of cloud computing in optimization problems, presenting state-of-the-art computing paradigms, advances in applications, and challenges concerning both the theories and applications of cloud computing in optimization with a focus on diverse fields like the Internet of Things, fog-assisted cloud computing, and big data. In real life, many problems – ranging from social science to engineering sciences – can be identified as complex optimization problems. Very often these are intractable, and as a result researchers from industry as well as the academic community are concentrating their efforts on developing methods of addressing them. Further, the cloud computing paradigm plays a vital role in many areas of interest, like resource allocation, scheduling, energy management, virtualization, and security, and these areas are intertwined with many optimization problems. Using illustrations and figures, this book offers students and researchers a clear overview of the concepts and practices of cloud computing and its use in numerous complex optimization problems.
Author: Krishn Kumar Mishra
Publisher: CRC Press
ISBN: 100063759X
Category : Mathematics
Languages : en
Pages : 326
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Book Description
This comprehensive reference text discusses nature inspired algorithms and their applications. It presents the methodology to write new algorithms with the help of MATLAB programs and instructions for better understanding of concepts. It covers well-known algorithms including evolutionary algorithms, genetic algorithm, particle Swarm optimization and differential evolution, and recent approached including gray wolf optimization. A separate chapter discusses test case generation using techniques such as particle swarm optimization, genetic algorithm, and differential evolution algorithm. The book- Discusses in detail various nature inspired algorithms and their applications Provides MATLAB programs for the corresponding algorithm Presents methodology to write new algorithms Examines well-known algorithms like the genetic algorithm, particle swarm optimization and differential evolution, and recent approaches like gray wolf optimization. Provides conceptual linking of algorithms with theoretical concepts The text will be useful for graduate students in the field of electrical engineering, electronics engineering, computer science and engineering. Discussing nature inspired algorithms and their applications in a single volume, this text will be useful as a reference text for graduate students in the field of electrical engineering, electronics engineering, computer science and engineering. It discusses important algorithms including deterministic algorithms, randomized algorithms, evolutionary algorithms, particle swarm optimization, big bang big crunch (BB-BC) algorithm, genetic algorithm and grey wolf optimization algorithm. "
Author: Sabu M. Thampi
Publisher: Springer
ISBN: 9811068984
Category : Computers
Languages : en
Pages : 441
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Book Description
This book constitutes the refereed proceedings of the 5th International Symposium on Security in Computing and Communications, SSCC 2017, held in Manipal, India, in September 2017. The 21 revised full papers presented together with 13 short papers were carefully reviewed and selected from 84 submissions. The papers focus on topics such as cryptosystems, algorithms, primitives; security and privacy in networked systems; system and network security; steganography, visual cryptography, image forensics; applications security.
Author: Banati, Hema
Publisher: IGI Global
ISBN: 1522558535
Category : Computers
Languages : en
Pages : 435
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Book Description
As technology continues to become more sophisticated, mimicking natural processes and phenomena becomes more of a reality. Continued research in the field of natural computing enables an understanding of the world around us, in addition to opportunities for manmade computing to mirror the natural processes and systems that have existed for centuries. Nature-Inspired Algorithms for Big Data Frameworks is a collection of innovative research on the methods and applications of extracting meaningful information from data using algorithms that are capable of handling the constraints of processing time, memory usage, and the dynamic and unstructured nature of data. Highlighting a range of topics including genetic algorithms, data classification, and wireless sensor networks, this book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the application of nature and biologically inspired algorithms for handling challenges posed by big data in diverse environments.
Author: Padmalaya Nayak
Publisher: Springer Nature
ISBN: 9811629196
Category : Technology & Engineering
Languages : en
Pages : 502
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Book Description
This book includes high-quality research papers presented at the 1st International Conference on Wireless Sensor Networks, Ubiquitous Computing and Applications (ICWSNUCA, 2021), which is held at Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, India, during 26–27 February, 2021. This volume focuses on the applications, use-cases, architectures, deployments, and recent advances of wireless sensor networks as well as ubiquious computing. Different research topics are illustrated in this book, like wireless sensor networks for the Internet of Things; IoT applications for eHealth; smart cities; architectures for WSNs and IoT, WSNs hardware and new devices; low-power wireless technologies; wireless ad hoc sensor networks; routing and data transfer in WSNs; multicast communication in WSNs; security management in WSNs and in IoT systems; and power consumption optimization in WSNs.
