Resource Request Based Energy Efficient Heuristic for Server Offloading in Cloud Computing Environment

Increasing demand for high computing power led to the establishment of large-scale data centers. A data center is a collection of millions of servers. These large-scale data centers consume a huge amount of electrical energy. Managing these servers for provisioning and de-provisioning of resources in an automatic and efficient way is a great challenge. We attempt to minimise the power consumption by reducing the number of servers and maximizing the resource utilization of the servers that are in use through virtualization of resources. We work upon Virtual Machine Consolidation for consolidating them on fewer servers for maximizing resource utilization. In this article, we propose a resource request based heuristic for offloading the overloaded servers to optimise power consumption efficiency.

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Interconnection Structures, Management and Routing Challenges in Cloud-Service Data Center Networks: A Survey

International Journal of Interactive Mobile Technologies (iJIM) ◽

10.3991/ijim.v12i1.7573 ◽

2018 ◽

Vol 12 (1) ◽

pp. 36 ◽

Cited By ~ 3

Author(s):

Ahmad Nahar Quttoum

Keyword(s):

Resource Utilization ◽

Data Center ◽

Large Scale ◽

Data Centers ◽

Virtual Machines ◽

Cloud Service ◽

Data Center Networks ◽

Large Scale Data ◽

Service Data ◽

Scale Data

Today’s data center networks employ expensive networking equipments in associated structures that were not designed to meet the increasing requirements of the current large-scale data center services. Limitations that vary between reliability, resource utilization, and high costs are challenging. The era of cloud computing represents a promise to enable large-scale data centers. Computing platforms of such cloud service data centers consist of large number of commodity low-price servers that, with a theme of virtualization on top, can meet the performance of the expensive high-level servers at only a fraction of the price. Recently, the research in data center networks started to evolve rapidly. This opened the path for addressing many of its design and management challenges, these like scalability, reliability, bandwidth capacities, virtual machines’ migration, and cost. Bandwidth resource fragmentation limits the network agility, and leads to low utilization rates, not only for the bandwidth resources, but also for the servers that run the applications. With Traffic Engineering methods, managers of such networks can adapt for rapid changes in the network traffic among their servers, this can help to provide better resource utilization and lower costs. The market is going through exciting changes, and the need to run demanding-scale services drives the work toward cloud networks. These networks that are enabled by the notation of autonomic management, and the availability of commodity low-price network equipments. This work provides the readers with a survey that presents the management challenges, design and operational constraints of the cloud-service data center networks

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