Abstract
One of the major challenges in cloud computing and data centers is the energy conservation and emission reduction. Accurate prediction algorithms are essential for building energy efficient storage systems in cloud computing. In this paper, we first propose a Three-State Disk Model (3SDM), which can describe the service quality and energy consumption states of a storage system accurately. Based on this model, we develop a method for achieving energy conservation without losing quality by skewing the workload among the disks to transmit the disk states of a storage system. The efficiency of this method is highly dependent on the accuracy of the information predicting the blocks to be accessed and the blocks not be accessed in the near future. We develop a priori information and sliding window based prediction (PISWP) algorithm by taking advantage of the priori information about human behavior and selecting suitable size of sliding window. The PISWP method targets at streaming media applications, but we also check its efficiency on other two applications, news in webpage and new tool released. Disksim, an established storage system simulator, is applied in our experiments to verify the effect of our method for various users’ traces. The results show that this prediction method can bring a high degree energy saving for storage systems in cloud computing environment.
Highlights
A group of leading computer scientists from UC Berkeley’s RAD Lab published a white paper that lays out a broad road map for cloud computing [1] in 2009
The 3SDM dynamically divides disks into overload disk group (ODG), normal disk group (NDG) and standby disk group (SDG) according to system load, and performs data exchanging among disks in real time
The theoretical algorithm of system energy saving is designed to report a greatest lower bound of energy saving and a least upper bound of energy consumption for reference
Summary
A group of leading computer scientists from UC Berkeley’s RAD Lab published a white paper that lays out a broad road map for cloud computing [1] in 2009. Energy conservation has been extensively studied in green computing and in the context of data centers, because the power related costs in storage systems are growing in importance over time. Priori information and sliding window based prediction algorithm for energy-efficient storage systems in cloud, Simulat. A typical data center consumes more than 30 times the energy that it uses to perform computation. There is a need to create an energy-efficient cloud computing system to utilize the strengths of the data center and reduces the amounts of carbon emissions. We focus on designing a new prediction algorithm based on priori information from user’s behavior With this method, we could forecast which data will be visited in the closed future exactly.
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