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This book illustrates various components of Distributed Computing Environment and the importance of distributed scheduling using Dynamic Load Balancing. It describes load balancing algorithms for better resource utilization, increasing throughput and improving user’s response time. Various theoretical concepts, experiments, and examples enable students to understand the process of load balancing in computing cluster and server cluster. The book is suitable for students of Advance Operating Systems, High Performance Computing, Distributed Computing in B.E., M.C.A., M. Tech. and Ph.D courses.
This book illustrates various components of Distributed Computing Environment and the importance of distributed scheduling using Dynamic Load Balancing. It describes load balancing algorithms for better resource utilization, increasing throughput and improving user’s response time. Various theoretical concepts, experiments, and examples enable students to understand the process of load balancing in computing cluster and server cluster. The book is suitable for students of Advance Operating Systems, High Performance Computing, Distributed Computing in B.E., M.C.A., M. Tech. and Ph.D courses.
A distributed system consists of many heterogeneous processors with different processing power and all processors are interconnected with a communication channel. In such a system, if some processors are less loaded or idle and others are heavily loaded, the system performance will be reduced drastically. System performance can be improved by using proper load balancing [1, 4]. The aim of load balancing is to improve the performance measures and reduce the overall completion time and cost
Distributed systems are often characterized by uneven loads on hosts and other resources. In this thesis, the problems concerning dynamic load balancing in loosely-coupled distributed systems are studied using trace-driven simulation, implementation, and measurement. Information about job CPU and I/O demands is collected from three production systems and used as input to a simulator that includes a representative CPU scheduling policy and considers the message exchange and job transfer costs explicitly. A prototype load balancer is implemented in the Berkeley UNIX and Sun/UNIX environments, and the results of a large number of measurement experiments performed on six workstations are presented.
This book focuses on the future directions of the static scheduling and dynamic load balancing methods in parallel and distributed systems. It provides an overview and a detailed discussion of a wide range of topics from theoretical background to practical, state-of-the-art scheduling and load balancing techniques.
The book covers current developments in the field of expert applications and security, which employ advances of next-generation communication and computational technology to shape real-world applications. It gathers selected research papers presented at the ICETEAS 2018 conference, which was held at Jaipur Engineering College and Research Centre, Jaipur, India, on February 17–18, 2018. Key topics covered include expert applications and artificial intelligence; information and application security; advanced computing; multimedia applications in forensics, security and intelligence; and advances in web technologies: implementation and security issues.
This book features a collection of high-quality research papers presented at the International Conference on Intelligent and Cloud Computing (ICICC 2019), held at Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, India, on December 20, 2019. Including contributions on system and network design that can support existing and future applications and services, it covers topics such as cloud computing system and network design, optimization for cloud computing, networking, and applications, green cloud system design, cloud storage design and networking, storage security, cloud system models, big data storage, intra-cloud computing, mobile cloud system design, real-time resource reporting and monitoring for cloud management, machine learning, data mining for cloud computing, data-driven methodology and architecture, and networking for machine learning systems.
Grid computing is a modern generation of distributed computing that aims to integrate heterogeneous resources which are allocated in different geographical locations to act as a one huge powered resource. Grid computing got its importance from the growth of the current scientific applications which contain a lot of complex calculations. Heterogeneous resources collaboration faces many problems like security management, tasks scheduling and load balancing. This book addresses job scheduling and load balancing current approaches, then presents the author researches in new grid structure model and job scheduling and load balancing algorithms. These new algorithms take into consideration new criteria of scheduling, and a new model of decision making. Enhancements provided by the proposed solution, would make the overall grid environment performance more efficient than the current grid computing situation due to faster and intelligent model. This book is useful for Master and PHD student who would like to read Master thesis, and all professional and interested in Grid computing field and job scheduling algorithms.
This book solicits the innovative research ideas and solutions for almost all the intelligent data intensive theories and application domains. The proliferation of various mobile and wireless communication networks has paved way to foster a high demand for intelligent data processing and communication technologies. The potential of data in wireless mobile networks is enormous, and it constitutes to improve the communication capabilities profoundly. As the networking and communication applications are becoming more intensive, the management of data resources and its flow between various storage and computing resources are posing significant research challenges to both ICT and data science community. The general scope of this book covers the design, architecture, modeling, software, infrastructure and applications of intelligent communication architectures and systems for big data or data-intensive applications. In particular, this book reports the novel and recent research works on big data, mobile and wireless networks, artificial intelligence, machine learning, social network mining, intelligent computing technologies, image analysis, robotics and autonomous systems, data security and privacy.