Energy Efficient Algorithms based on VM Consolidation for Cloud Computing: Comparisons and Evaluations

Cloud Computing paradigm has revolutionized IT industry and be able to offer computing as the fifth utility. With the pay-as-you-go model, cloud computing enables to offer the resources dynamically for customers anytime. Drawing the attention from both academia and industry, cloud computing is viewed as one of the backbones of the modern economy. However, the high energy consumption of cloud data centers contributes to high operational costs and carbon emission to the environment. Therefore, Green cloud computing is required to ensure energy efficiency and sustainability, which can be achieved via energy efficient techniques. One of the dominant approaches is to apply energy efficient algorithms to optimize resource usage and energy consumption. Currently, various virtual machine consolidation-based energy efficient algorithms have been proposed to reduce the energy of cloud computing environment. However, most of them are not compared comprehensively under the same scenario, and their performance is not evaluated with the same experimental settings. This makes users hard to select the appropriate algorithm for their objectives. To provide insights for existing energy efficient algorithms and help researchers to choose the most suitable algorithm, in this paper, we compare several state-of-the-art energy efficient algorithms in depth from multiple perspectives, including architecture, modelling and metrics. In addition, we also implement and evaluate these algorithms with the same experimental settings in CloudSim toolkit. The experimental results show the performance comparison of these algorithms with comprehensive results. Finally, detailed discussions of these algorithms are provided

Distributed Dynamic Scheduling of Composite Tasks

This paper examines the issue of dynamically scheduling applications on a wide-area network computing system. We construct a simulation model for wide-area task allocation problem and study the performance of the proposed algorithm under different conditions. The simulation results indicate that the wide-area scheduling algorithm is sensitive to several parameters including machine failure rates, the local queuing policies, and arrival rates

Magnetic Resonance Imaging (mri) Simulation

In this paper, we present the implementation of a Magnetic Resonance Imaging (MRI) simulator on a GRID computing architecture. The simulation process is based on the resolution of Bloch equation [1] in a 3D space. The computation kernel of the simulator is distributed to the grid nodes using MPICH-G2 [2]. The results presented show that simulation of 3D MRI data is achieved with a reasonable cost which gives new perspectives to MRI simulations usage