Abstract
Energy consumption has overtaken equipment costs given the growth of computing power, thereby becoming the dominant cost to data centers. Constrained by ensuring that peak resource requirements of VMs are met, the effective use of resource fragments is an effective means of energy conservation. We apply resource reservation to the resource-utilization-aware energy efficient server consolidation algorithm (RUAEE), and propose a server consolidation energy-saving algorithm (SCES). We adjust the allocations of VMs by computing the resource reservation and resource allocation ratio dynamically. This technique maintains the host resource utilization to within a reasonable range, reduces resource fragmentation and reserves resources to satisfy the peak resource requests simultaneously. Compared with the RUAEE algorithm, our algorithm can reduce the risk of system overload and improve the stability of the system. The experimental results based on the actual workload of the Google cluster show that the algorithm is effective at reducing resource waste, improving system stability, and achieving energy savings.
Highlights
The ‘‘pay-as-you-go’’ model provided by cloud computing provides users with convenient computing services
Since it is extremely difficult to implement algorithms on large-scale cloud computing infrastructure, this experiment uses simulation experiments to evaluate the performance of the algorithm
The algorithm proposed in this paper models the resource fragmentation problem and fully considers resource fragmentation when allocating virtual machine (VM); it can effectively reduce the additional energy consumption caused by resource fragmentation
Summary
The ‘‘pay-as-you-go’’ model provided by cloud computing provides users with convenient computing services. Many types of services provided by cloud providers, such as IaaS, PaaS, and SaaS, provide high scalability and flexibility. Users can use cloud services without being restricted by hardware and software systems. The cloud is a convenient service mode whereby users can choose to pay based on a calculated amount or by time. Power consumption is a large portion of cloud computing operating costs [1]. Researchers speculate that US data center energy consumption will exceed 100 billion kWh by 2020 [2]. If the current trend continues, the energy cost of data centers will exceed the cost of the equipment [3]. Saving energy and reducing the waste of resources are gaining attention in cloud computing
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