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

Reducing the operational cost of cloud data centers through renewable energy

The success of cloud computing services has led to big computing infrastructures that are complex to manage and very costly to operate. In particular, power supply dominates the operational costs of big infrastructures, and several solutions have to be put in place to alleviate these operational costs and make the whole infrastructure more sustainable. In this paper, we investigate the case of a complex infrastructure composed of data centers (DCs) located in different geographical areas in which renewable energy generators are installed, co-located with the data centers, to reduce the amount of energy that must be purchased by the power grid. Since renewable energy generators are intermittent, the load management strategies of the infrastructure have to be adapted to the intermittent nature of the sources. In particular, we consider EcoMultiCloud, a load management strategy already proposed in the literature for multi-objective load management strategies, and we adapt it to the presence of renewable energy sources. Hence, cost reduction is achieved in the load allocation process, when virtual machines (VMs) are assigned to a data center of the considered infrastructure, by considering both energy cost variations and the presence of renewable energy production. Performance is analyzed for a specific infrastructure composed of four data centers. Results show that, despite being intermittent and highly variable, renewable energy can be effectively exploited in geographical data centers when a smart load allocation strategy is implemented. In addition, the results confirm that EcoMultiCloud is very flexible and is suited to the considered scenario

SERCON-BASED TIMESTAMPED VIRTUAL MACHINE MIGRATION SCHEME FOR CLOUD

With the advent of cloud computing, the need for deploying multiple virtual machines (VMs) on multiple hosts to address the ever-increasing user demands for services has raised concerns regarding energy consumption. Considerable energy is consumed while keeping the data centers with a large number of servers active. However, in data centers, there are cases where these servers may not get utilized efficiently. There can be servers that consume sufficient energy while running resources for a small task (demanding fewer resources), but there can also be servers that receive user requests so frequently that resources may be exhausted, and the server becomes unable to fulfill requests. In such a scenario, there is an urgent need to conserve energy and resources which is addressed by performing server consolidation. Server consolidation aims to reduce the total number of active servers in the cloud such that performance does not get compromised as well as energy is conserved in an attempt to make each server run to its maximum. This is done by reducing the number of active servers in a data center by transferring the workload of one or more VM(s) from one server to another, referred to as VM Migration (VMM). During VMM, time is supposed as a major constraint for effective and user-transparent migration. Thus, this paper proposes a novel VM migration strategy considering time sensitivity as a primary constraint. The aim of the proposed Time Sensitive Virtual Machine Migration (TS-VMM) is to reduce the number of migrations to a minimum with effective cost optimization and maximum server utilization