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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Power-aware scheduling problem has been a recent issue in cluster systems not only for operational cost due to electricity cost, but also for system reliability. As recent commodity processors support multiple operating points under various supply voltage levels, Dynamic Voltage Scaling (DVS) scheduling algorithms can reduce power consumption by controlling appropriate voltage levels. In this paper, we provide power-aware scheduling algorithms for bag-of-tasks applications with deadline constraints on DVS-enabled cluster systems in order to minimize power consumption as well as to meet the deadlines specified by application users. A bag-of-tasks application should finish all the sub-tasks before the deadline, so that the DVS scheduling scheme should consider the deadline as well. We provide the DVS scheduling algorithms for both time-shared and space-shared resource sharing policies. The simulation results show that the proposed algorithms reduce much power consumption compared to static voltage schemes. Applying classical dynamic voltage scaling (DVS) techniques to real-time systems running on processors with discrete voltage frequency modes causes a waste of computational resources. In fact, whenever the ideal speed level computed by the DVS algorithm is not available in the system, to guarantee the feasibility of the task set, the processor speed must be set to the nearest level greater than the optimal one, thus underutilizing the system. Whenever the task set allows a certain degree of flexibility in specifying timing constraints, rate adaptation techniques can be adopted to balance performance (which is a function of task rates) versus energy consumption (which is a function of the processor speed). In this paper, we propose a new method that combines discrete DVS management with elastic scheduling to fully exploit the available computational resources. Depending on the application requirements, the algorithm can be set to improve performance or reduce energy consumption, so enhancing the flexibility of the system. A reclaiming mechanism is also used to take advantage of early completions. To make the proposed approach usable in real-world applications, the task model is enhanced to consider some of the real CPU characteristics, such as discrete voltage frequency levels, switching overhead, task execution times nonlinear with the frequency, and tasks with different power consumption. Implementation issues and experimental results for the proposed algorithm are also discussed Along with the prevailing of mobile devices, the demand for efficient power consumption has become one of the major issues in designing embedded system. Dynamic Voltage Scaling (DVS) is a technique that can reduce energy consumption by changing the processor voltage levels dynamically. Fixed Priority with Preemption Threshold (FPPT) scheduling is a scheduling policy that includes preemptive and non-preemptive aspect of scheduling policy. In this paper, an efficient universal fixed priority DVS algorithm (FPPT-DVS) will be presented. This algorithm has the advantage of both Fixed Priority Preemptive (FPP) DVS scheduling and Fixed Priority Non-Preemptive (FPNP) DVS scheduling. FPPT-DVS algorithm also combines the on-line DVS and off-line DVS together. Experimental results show that the proposed FPPT-DVS algorithm can save up to 20 energy over the existing DVS algorithms.