Analyzing the Effects of Load Distribution Algorithms on Energy Consumption of Servers in Cloud Data Centers

Cloud computing has become an important driver for IT service provisioning in recent years. It offers additional flexibility to both customers and IT service providers, but also comes along with new challenges for providers. One of the major challenges for providers is the reduction of energy consumption since today, already more than 50% of operational costs in data centers account for energy. A possible way to reduce these costs is to efficiently distribute load within the data center. Although the effect of load distribution algorithms on energy consumption is a topic of recent research, an analysis-framework for evaluating arbitrary load distribution algorithms with regard to their effects on the energy consumption of cloud data centers is still nonexistent. Therefore, in this contribution, a concept of a simulation-based, quantitative analysis-framework for load distribution algorithms in cloud environments with respect to the energy consumption of data centers is developed and evaluated

Power Management Techniques for Data Centers: A Survey

With growing use of internet and exponential growth in amount of data to be stored and processed (known as 'big data'), the size of data centers has greatly increased. This, however, has resulted in significant increase in the power consumption of the data centers. For this reason, managing power consumption of data centers has become essential. In this paper, we highlight the need of achieving energy efficiency in data centers and survey several recent architectural techniques designed for power management of data centers. We also present a classification of these techniques based on their characteristics. This paper aims to provide insights into the techniques for improving energy efficiency of data centers and encourage the designers to invent novel solutions for managing the large power dissipation of data centers.Comment: Keywords: Data Centers, Power Management, Low-power Design, Energy Efficiency, Green Computing, DVFS, Server Consolidatio

A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing

The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure and be charged on pay-per-use basis. However, Cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements (SLAs) violations. To achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be NP-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to PMs in infrastructure Clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres

Energy efficiency in virtual machines allocation for cloud data centers with lottery algorithm

Energy usage of data centers is a challenging and complex issue because computing applications and data are growing so quickly that increasingly larger servers and disks are needed to process them fast enough within the required time period. In the past few years, many approaches to virtual machine placement have been proposed. This study proposes a new approach for virtual machine allocation to physical hosts. Either minimizes the physical hosts and avoids the SLA violation. The proposed method in comparison to the other algorithms achieves better results

Proposing Optimus Scheduler Algorithm for Virtual Machine Placement Within a Data Center

With the evolution of the Internet, we are witnessing the birth of an increasing number of applications that rely on the network; what was previously executed on the user's computers as stand-alone programs has been redesigned to be executed on servers with permanent connections to the Internet, making the information available from any device that has network access. Instead of buying a copy of a program, users can now pay to obtain access to it through the network, which is one of the models of cloud computing, Software as a Service (SaaS). The continuous growth of Internet bandwidth has also given rise to new multimedia applications, such as social networks and video over the Internet; and to complete this new paradigm, mobile platforms provide the ubiquity of information that allows people to stay connected. Service providers may own servers and data centers or, alternatively, may contract infrastructure providers that use economies of scale to offer access to servers as a service in the cloud computing model, i.e., Infrastructure as a Service (IaaS). As users become more dependent on cloud services and mobile platforms increase the ubiquity of the cloud, the quality of service becomes increasingly important. A fundamental metric that defines the quality of service is the delay of the information as it travels between the user computers and the servers, and between the servers themselves. Along with the quality of service and the costs, the energy consumption and the CO2 emissions are fundamental considerations in regard to planning cloud computing networks. In this research work, an Optimus Scheduler algorithm to be proposed for Add, Remove or Resize an application by using Tabu Search Algorithm