With the advancement of Cloud Computing over the past few years, there has been a massive shift from traditional data centers to cloud enabled data centers. The enterprises with cloud data centers are focusing their attention on energy savings through effective utilization of resources. In this work, we propose algorithms which try to minimize the energy consumption in the data center duly maintaining the SLA guarantees. The algorithms try to utilize least number of physical machines in the data center by dynamically rebalancing the physical machines based on their resource utilization. The algorithms also perform an optimal consolidation of virtual machines on a physical machine, minimizing SLA violations. In extensive simulation, our algorithms achieve savings of about 21 in terms of energy consumption and in terms of maintaining the SLAs, it performs 60 better than Single Threshold algorithm.
Dynamic Voltage Scaling (DVS) is a power management technique where under-volting (decreasing the voltage) is done to conserve power and over-volting (increasing the voltage) is done to increase computing performance. This technique of DVS has been employed in @cite_11 @cite_12 to design power-aware scheduling algorithms that minimize the power consumption. @cite_4 apply a variation of DVS called Dynamic Voltage Frequency Scaling (DVFS) by operating servers at various CPU voltage and frequency levels to reduce overall power consumption.
@cite_4 @cite_12 @cite_11
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Dynamic voltage and frequency scaling (DVFS) is a commonly-used power-management technique where the clock frequency of a processor is decreased to allow a corresponding reduction in the supply voltage. This reduces power consumption, which can lead to significant reduction in the energy required for a computation, particularly for memory-bound workloads. However, recent developments in processor and memory technology have resulted in the saturation of processor clock frequencies, larger static power consumption, smaller dynamic power range and better idle sleep modes. Each of these developments limit the potential energy savings resulting from DVFS. We analyse this trend by examining the potential of DVFS across three platforms with recent generations of AMD processors. We find that while DVFS is effective on the older platforms, it actually increases energy usage on the most recent platform, even for highly memory-bound workloads. When peak performance is unnecessary, Dynamic Voltage Scaling (DVS) can be used to reduce the dynamic power consumption of embedded multiprocessors. In future technologies, however, static power consumption due to leakage current is expected to increase significantly. Then it will be more effective to limit the number of processors employed (i.e., turn some of them off), or to use a combination of DVS and processor shutdown. In this paper, leakage-aware scheduling heuristics are presented that determine the best trade-off between these three techniques: DVS, processor shutdown, and finding the optimal number of processors. Experimental results obtained using a public benchmark set of task graphs and real parallel applications show that our approach reduces the total energy consumption by up to 46 for tight deadlines (1.5× the critical path length) and by up to 73 for loose deadlines (8× the critical path length) compared to an approach that only employs DVS. We also compare the energy consumed by our scheduling algorithms to two absolute lower bounds, one for the case where all processors continuously run at the same frequency, and one for the case where the processors can run at different frequencies and these frequencies may change over time. The results show that the energy reduction achieved by our best approach is close to these theoretical limits. Dynamic voltage and frequency scaling (DVFS) is a well-known technique for gaining energy savings on desktop and laptop computers. However, its use in server settings requires careful consideration of any potential impacts on end-to-end service performance of hosted applications. In this paper, we develop a simple metric called the gradient" that allows prediction of the impact of changes in processor frequency on the end-to-end transaction response times of multitier applications. We show how frequency gradients can be measured on a running system in a push-button manner without any prior knowledge of application semantics, structure, or configuration settings. Using experimental results, we demonstrate that the frequency gradients provide accurate predictions, and enable end-to-end performance-aware DVFS for mulitier applications. Dynamic voltage and frequency scaling (DVFS) has been widely used for energy reduction in the modern processors. How to select the optimal frequency that minimizes energy dissipation for the given performance constraint at runtime is a nontrivial problem. The problem becomes more complicated if temperature needs to be constrained (or minimized) simultaneously. The temperature, performance and energy have different nonlinear relationships with frequency voltage scaling ratio and this relationship is closely related to the characteristics of hardware and applications. In this paper, we design a reinforcement learning algorithm to tackle the problem of simultaneous temperature, performance and energy management. The proposed approach allows continuous tradeoff among these three quality measurement of a computer system. It also enables us to set two of the measurements as constraints and optimize the third one. The proposed approach is validated on an Intel Core 2 processor running Linux system.