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.
Ching-Chi Lin et. al in @cite_9 presented an improved version of Round Robin algorithm used in Eucalyptus. According to Dynamic Round Robin algorithm, if a virtual machine has finished its execution and there are still other virtual machines running on the same physical machine, this physical machine will not accept any new virtual machine requests. Such physical machines are referred to as being in retirement' state, meaning that after the execution of the remaining virtual machines, this physical machine could be shutdown. And if a physical machine is in the retirement' state for a sufficiently long period of time, the currently running virtual machines are forced to migrate on to other physical machines and shutdown after the migration operation is finished. This waiting time threshold is denoted as retirement threshold'. So, a physical machine which is in the retirement state beyond this threshold will be forced to migrate its virtual machines and shutdown.
@cite_9
2122618394 2134659242 2950965572 1485083952
Power consumption is one of the most critical problems in data centers. One effective way to reduce power consumption is to consolidate the hosting workloads and shut down physical machines which become idle after consolidation. Server consolidation is a NP-hard problem. In this paper, a new algorithms Dynamic Round-Robin (DRR), is proposed for energy-aware virtual machine scheduling and consolidation. We compare this strategy with the GREEDY, ROUNDROBIN and POWERSAVE scheduling strategies implemented in the Eucalyptus Cloud system. Our experiment results show that the Dynamic Round-Robin algorithm reduce a significant amount of power consumption compared with the three strategies in Eucalyptus. We consider optimal load balancing in a distributed computing environment consisting of homogeneous unreliable processors. Each processor receives its own sequence of tasks from outside users, some of which can be redirected to the other processors. Processing times are independent and identically distributed with an arbitrary distribution. The arrival sequence of outside tasks to each processor may be arbitrary as long as it is independent of the state of the system. Processors may fail, with arbitrary failure and repair processes that are also independent of the state of the system. The only information available to a processor is the history of its decisions for routing work to other processors, and the arrival times of its own arrival sequence. We prove the optimality of the round-robin policy, in which each processor sends all the tasks that can be redirected to each of the other processors in turn. We show that, among all policies that balance workload, round robin stochastically minimizes the nth task completion time for all n, and minimizes response times and queue lengths in a separable increasing convex sense for the entire system. We also show that if there is a single centralized controller, round-robin is the optimal policy, and a single controller using round-robin routing is better than the optimal distributed system in which each processor routes its own arrivals. Again "optimal" and "better" are in the sense of stochastically minimizing task completion times, and minimizing response time and queue lengths in the separable increasing convex sense. Dynamic distributed storage algorithms such as DynaStore, Reconfigurable Paxos, RAMBO, and RDS, do not ensure liveness (wait-freedom) in asynchronous runs with infinitely many reconfigurations. We prove that this is inherent for asynchronous dynamic storage algorithms, including ones that use @math or @math oracles. Our result holds even if only one process may fail, provided that machines that were successfully removed from the system's configuration may be switched off by an administrator. Intuitively, the impossibility relies on the fact that a correct process can be suspected to have failed at any time, i.e., its failure is indistinguishable to other processes from slow delivery of its messages, and so the system should be able to reconfigure without waiting for this process to complete its pending operations. To circumvent this result, we define a dynamic eventually perfect failure detector, and present an algorithm that uses it to emulate wait-free dynamic atomic storage (with no restrictions on reconfiguration rate). Together, our results thus draw a sharp line between oracles like @math and @math , which allow some correct process to continue to be suspected forever, and a dynamic eventually perfect one, which does not. We consider scheduling on an unreliable machine that may experience unexpected changes in processing speed or even full breakdowns. We aim for a universal solution that performs well without adaptation for any possible machine behavior. For the objective of minimizing the total weighted completion time, we design a polynomial time deterministic algorithm that finds a universal scheduling sequence with a solution value within 4 times the value of an optimal clairvoyant algorithm that knows the disruptions in advance. A randomized version of this algorithm attains in expectation a ratio of e. We also show that both results are best possible among all universal solutions. As a direct consequence of our results, we answer affirmatively the question of whether a constant approximation algorithm exists for the offline version of the problem when machine unavailability periods are known in advance. When jobs have individual release dates, the situation changes drastically. Even if all weights are equal, there are instances for which any universal solution is a factor of Ω(logn loglogn) worse than an optimal sequence. Motivated by this hardness, we study the special case when the processing time of each job is proportional to its weight. We present a non-trivial algorithm with a small constant performance guarantee.