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The proposed approach includes a forecasting module to take into account not only the current situation but also the potential customer resource needs in the future. To address performance and cost issues, we propose an energy efficient resource allocation approach that integrates the Holt Winters forecasting model for optimizing energy consumption. However, VM migration and server consolidation techniques cause low throughput from the perspective of the service consumer as well as energy overheads from the perspective of the service provider. Another approach in the literature is server consolidation, which reduces the number of active physical machines through VM migration or by collocating VMs to a small set of physical machines.
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However, a static threshold may lead to machines being turned on or off unnecessarily since the resource demand in the future is not considered.
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To solve performance degradation caused by running at full capacity, the static optimal utilization threshold is defined for each resource type, including CPU, RAM, bandwidth and so on. Many existing studies have proposed running servers’ computational units at full capacity to increase energy efficiency, but this causes performance degradation. When cloud data centers are running at low usage levels of computing capacity without optimization, it causes high energy inefficiency. Improving the energy efficiency of data centers has received significant attention in recent years. However, it is not easy to address the needs of the users and the resources that will meet these requirements. For this reason, while aiming to reduce energy consumption and cost, the performance of the service offered to users should also be considered. On the other hand, users also want to have the same service with acceptable quality and less cost which are defined through Service Level Agreement (SLA). In addition, reducing the cost of the services and increasing the profit rate are other goals of providers. Service providers try to reduce the energy cost in data centers due to both laws and regulations and standards. Increases of 48% and 34% are estimated for total world energy consumption and CO2 emissions, respectively, between 20. As customers' needs for the services offered by data centers increase, the amount of energy consumed by data centers increases linearly.
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Service providers offer customers three services, Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS), through data centers. The experimental results show that the proposed algorithm leads to a consumption reduction of up to 45% to complete one workload compared with the LR-MMT.Ĭloud computing is a collection of computer system resources that are dynamically provisioned to provide services to users based on demand access. LAA is compared with the best approach provided by CloudSim based on VM migration called Local Regression-Minimum Migration Time (LR-MMT). To evaluate the proposed algorithm, experiments are conducted with real-world workload traces from Google Cluster. The energy-performance trade-off relies on periodic comparisons of the predicted and active numbers of servers. Energy efficiency and performance are inversely proportional. The proposed approach, named Look-ahead Energy Efficient VM Allocation (LAA), contains a Holt Winters-based prediction module. A novel efficient resource management algorithm for virtualized data centers that optimizes the number of servers to meet the requirements of dynamic workloads without migration is proposed in this paper. Energy efficient provisioning is addressed at the data center level in this research. In the literature, live migration of virtual machines (VMs) among servers is commonly proposed to reduce energy consumption and to optimize resource usage, although it comes with essential drawbacks, such as migration cost and performance degradation. Energy efficient resource provisioning in cloud environments is a challenging problem because of its dynamic nature and varied application workload characteristics.
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Energy efficiency is an important issue for reducing environmental dissipation.