Approximation Algorithms for Energy Minimization in Cloud Service Allocation under Reliability Constraints

We consider allocation problems that arise in the context of service allocation in Clouds. More specifically, we assume on the one part that each computing resource is associated to a capacity constraint, that can be chosen using Dynamic Voltage and Frequency Scaling (DVFS) method, and to a probability of failure. On the other hand, we assume that the service runs as a set of independent instances of identical Virtual Machines. Moreover, there exists a Service Level Agreement (SLA) between the Cloud provider and the client that can be expressed as follows: the client comes with a minimal number of service instances which must be alive at the end of the day, and the Cloud provider offers a list of pairs (price,compensation), this compensation being paid by the Cloud provider if it fails to keep alive the required number of services. On the Cloud provider side, each pair corresponds actually to a guaranteed success probability of fulfilling the constraint on the minimal number of instances. In this context, given a minimal number of instances and a probability of success, the question for the Cloud provider is to find the number of necessary resources, their clock frequency and an allocation of the instances (possibly using replication) onto machines. This solution should satisfy all types of constraints during a given time period while minimizing the energy consumption of used resources. We consider two energy consumption models based on DVFS techniques, where the clock frequency of physical resources can be changed. For each allocation problem and each energy model, we prove deterministic approximation ratios on the consumed energy for algorithms that provide guaranteed probability failures, as well as an efficient heuristic, whose energy ratio is not guaranteed

Energy cost minimization with job security guarantee in Internet data center

With the proliferation of various big data applications and resource demand from Internet data centers (IDCs), the energy cost has been skyrocketing, and it attracts a great deal of attention and brings many energy optimization management issues. However, the security problem for a wide range of applications, which has been overlooked, is another critical concern and even ranked as the greatest challenge in IDC. In this paper, we propose an energy cost minimization (ECM) algorithm with job security guarantee for IDC in deregulated electricity markets. Randomly arriving jobs are routed to a FIFO queue, and a heuristic algorithm is devised to select security levels for guaranteeing job risk probability constraint. Then, the energy optimization problem is formulated by taking the temporal diversity of electricity price into account. Finally, an online energy cost minimization algorithm is designed to solve the problem by Lyapunov optimization framework which offers provable energy cost optimization and delay guarantee. This algorithm can aggressively and adaptively seize the timing of low electricity price to process workloads and defer delay-tolerant workloads execution when the price is high. Based on the real-life electricity price, simulation results prove the feasibility and effectiveness of proposed algorithm