Optimized Cloud Resource Management And Scheduling
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Optimized Cloud Resource Management and Scheduling
Optimized Cloud Resource Management and Scheduling identifies research directions and technologies that will facilitate efficient management and scheduling of computing resources in cloud data centers supporting scientific, industrial, business, and consumer applications. It serves as a valuable reference for systems architects, practitioners, developers, researchers and graduate level students. Explains how to optimally model and schedule computing resources in cloud computing Provides in depth quality analysis of different load-balance and energy-efficient scheduling algorithms for cloud data centers and Hadoop clusters Introduces real-world applications, including business, scientific and related case studies Discusses different cloud platforms with real test-bed and simulation tools
Resource Management in Cloud Computing
This book addresses fundamental concepts and practical implementations in cloud computing environments, focusing on load balancing and resource management. As cloud computing's popularity grows, expertise in infrastructure management is crucial for delivering flawless subscription-based services and hosted data solutions. The book presents novel models for cloud resource management to improve operational efficiency through better virtual machine (VM) placements. Beginning with task scheduling and resource allocation basics, the book progresses to resource management concepts. It introduces innovative models for dynamic resource allocation, heuristic approaches for optimal host selection, secure resource management frameworks, multi-objective VM allocation schemes, and data security models. A significant contribution is an effective model integrating load balancing, resource management, Quality of Service (QoS), security, and cloud performance for Infrastructure as a Service (IaaS). The book offers innovative methodologies for dynamic resource allocation and service administration in cloud datacenters. It presents traffic management techniques to reduce energy consumption, improve resource utilization, and enhance security through optimized VM placement, with experimental validation. These models improve response time, throughput, resource utilization, energy consumption, and failure node management. Security is addressed through secure VM placement strategies, making it harder for attackers to achieve co-tenancy. A multi-objective approach for secure load balancing optimizes multiple conflicting objectives simultaneously. The book includes cyber-threat countermeasures and provides recommendations for organizations and users. Suitable for senior undergraduate and graduate courses in cloud computing, resource allocation, security, and energy consumption methods, the book includes examples and tutorials using Cloudsim tools for beginners. This helps them understand virtual infrastructure and service design. The methodologies benefit both cloud service providers and customers, offering cost-effective solutions for revenue maximization. The comprehensive approach makes the book valuable for academic study and practical application in cloud computing environments.
Resource Management in Distributed Systems
This book focuses on resource management in distributed computing systems. The book presents a collection of original, unpublished, and high-quality research works, which report the latest research advances on resource discovery, allocation, scheduling, etc., in cloud, fog, and edge computing. The topics covered in the book are resource management in cloud computing/edge computing/fog computing/dew computing, resource management in Internet of things, resource allocation, scheduling, monitoring, and orchestration in distributed computing systems, resource management in 5G network and beyond, latency-aware resource management, energy-efficient resource management, interoperability and portability, security and privacy in resource management, reliable resource management, trustworthiness in resource management, fault tolerance in resource management, and simulation related to resource management.