Dynamic Resource Management For Cloud Hosted Internet Applications
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Dynamic Resource Management for Cloud-hosted Internet Applications
Author: Hangwei Qian
language: en
Publisher: LAP Lambert Academic Publishing
Release Date: 2012
Internet is evolving toward service-oriented computing platforms (e.g., cloud computing platforms, such as Amazon EC2 and Microsoft Azure). In these platforms, service providers (owners of the platforms) offer resource pools by building multiple geo-distributed data centers; application providers (owners of the applications) outsource the hosting of their applications to these platforms, and pay by the amount of resources used as utility. These multi-tenant platforms need to dynamically allocate resources to applications so as to meet their demand variation. In this thesis, we address several issues of the dynamic resource management in these platforms. On the one hand, we consider the resource provisioning problems within data centers. In order to allocate resources to applications quickly, we propose deploying ghost virtual machines (VMs) which host spare application instances across the physical machines. When an application needs more instances, we can configure the request distributer to forward requests to ghost VMs, which takes only 5-7 seconds. Also, to deal with the scalability issues in mega data center (with hundreds of thousands of servers), we introduce hierarchical resource management scheme in which servers are divided into groups (pods), each with about 5k servers, and existing techniques are employed to manage resources in each pod efficiently. Meanwhile, multiple strategies are explored to balance the load among the pods. In addition, we also propose a new data center architecture in which we can apply DNS-based mechanism to balance the load among the access links which connect data center to Internet. On the other hand, we address the resource management problems among multiple data centers. We proposed a unified approach to decide in how many/which data centers each application should be deployed, and how client requests are forwarded to the geo-distributed service replicas. We make these decisions based on a min-cost network flow model, and apply a novel demand clustering technique to overcome the scalability issue when solving the min-cost problem. Furthermore, we also introduce a new client-side DNS architecture which brings local DNS server close to clients so that DNS-based server selection can precisely choose close service replicas for clients.
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
Autonomic Computing in Cloud Resource Management in Industry 4.0
Author: Tanupriya Choudhury
language: en
Publisher: Springer Nature
Release Date: 2021-08-04
This book describes the next generation of industry—Industry 4.0—and how it holds the promise of increased flexibility in manufacturing, along with automation, better quality, and improved productivity. The authors discuss how it thus enables companies to cope with the challenges of producing increasingly individualized products with a short lead-time to market and higher quality. The authors posit that intelligent cloud services and resource sharing play an important role in Industry 4.0 anticipated Fourth Industrial Revolution. This book serves the different issues and challenges in cloud resource management CRM techniques with proper propped solution for IT organizations. The book features chapters based on the characteristics of autonomic computing with its applicability in CRM. Each chapter features the techniques and analysis of each mechanism to make better resource management in cloud.