Abstract
With ongoing energy shortages and rises in greenhouse emissions worldwide, increasing academic attention is being turned towards ways to improve the efficiency and sustainability of cloud computing. In this paper, we present a performance analysis and a system optimization of a cloud computing system with an energy efficient task scheduling strategy directed towards satisfying the service level agreement of cloud users while at the same time improving the energy efficiency in cloud computing system. In this paper, we propose a novel energy-aware task scheduling strategy based on a sleep-delay timer and a waking-up threshold. To capture the stochastic behavior of tasks with the proposed strategy, we establish a synchronous vacation queueing system combining vacation-delay and N-policy. Taking into account the total number of tasks and the state of the physical machine (PM), we construct a two-dimensional continuous-time Markov chain (CTMC), and produce an infinitesimal generator. Moreover, by using the geometric-matrix solution method, we analyze the queueing model in the steady state, and then, we derive the system performance measures in terms of the average sojourn time and the energy conservation level. Furthermore, we conduct system experiments to investigate the proposed strategy and validate the system model according to performance measures. Statistical results show that there is a compromise between the different performance measures when setting strategy parameters. By combining different performance measures, we develop a cost function for the system optimization. Finally, by dynamically adjusting the crossover probability and the mutation probability, and initializing the individuals with chaotic equations, we present an improved genetic algorithm to jointly optimize the sleep parameter, the sleep-delay parameter and the waking-up threshold.
Highlights
Cloud computing is a paradigm of computing in which dynamically scalable and virtualized resources are provided as a service over the Internet [1,2,3].In a cloud environment, there are two key actors: cloud providers and cloud users [4]
We provide additional analyses for our proposed task scheduling strategy, and present the two forms of the state transition based on the relationship between the number of virtual machines (VMs) in the system and the waking-up threshold
To demonstrate the effectiveness of task scheduling strategy proposed in this paper, we present results comparisons between the conventional strategy without a sleep-delay scheme [43] and the conventional synchronous multi-sleep strategy without a waking-up threshold [44]
Summary
Cloud computing is a paradigm of computing in which dynamically scalable and virtualized resources are provided as a service over the Internet [1,2,3]. The cloud providers hold enormous computing resources in data centers [5]. They lease resources out to the cloud users on a pay-per-use basis. Some researchers have conducted analyses to minimize the construction period and increase the system utilization by scheduling several cloud tasks on different virtual machines (VMs) [9]. This scheduling method requires all servers to keep incoming tasks active, leading to dramatic levels of energy consumption and elevating carbon dioxide emissions [10,11]. One of the current challenges in cloud computing is to reduce energy consumption while guaranteeing the quality of user experience
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