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

Design of Simulator for Energy Efficient Clustering in Mobile Ad Hoc Networks

The research on various issues in Mobile ad hoc networks are getting popularity because of its challenging nature and all time connectivity to communicate. MANET (Mobile Ad-hoc Networks) is a random deployable network where devices are mobile with dynamic topology. In the network topology, each device is termed as a node and the virtual connectivity among each node is termed as the link .Nodes in a network are dynamically organized into virtual partitions called clusters. Network simulators provide the platform to analyse and imitate the working of computer networks along with the typical devices, traffic and other entities. Cluster heads being the communication hotspots tend to drain its battery power rapidly while serving its member nodes. Further, energy consumption is a key factor that hinders the deploy ability of a real ad hoc and sensor network. It is due to the limited life time of the battery powered devices that motivates intense research into energy efficient design of operating systems, protocols and hardware devices. Clustering is a proven solution to preserve the battery power of certain nodes. In the mechanism of clustering, there exists a cluster head in every cluster that works similar to a base station in the cellular architecture. Cluster heads being the communication hotspots tend to drain its battery power rapidly while serving its member nodes. Further, energy consumption is a key factor that hinders the deploy ability of a real ad hoc and sensor network. It is due to the limited life time of the battery powered devices that motivates intense research into energy efficient design of operating systems, protocols and hardware devices. The mobile ad hoc network can be modelled as a unidirectional graph G = (V, L) where V is the set of mobile nodes and L is the set of links that exist between the nodes. We assume that there exists a bidirectional link L between the nodes and when the distance between the nodes < (transmission range) of the nodes. In the dynamic network the cardinality of the nodes remains constant, but the cardinality of links changes due to the mobility of the nodes. Network simulators are used by researchers, developers and engineers to design various kinds of networks, simulate and then analyze the effect of various parameters on the network performance. A typical network simulator encompasses a wide range of networking technologies and can help the users to build complex networks from basic building blocks such as a variety of nodes and links. The objective of our work is to design a simulator for energy efficient clustering so that the data flow as well as the control flow could be easily handled and maintained. The proposed energy efficient clustering algorithm is a distributed algorithm that takes into account the consumed battery power of a node and its average transmission power for serving the neighbour nodes as the parameters to decide its suitability to act as a cluster head. These two parameters are added with different weight factors to find the weights of the individual nodes. After the clusters are formed, gateway nodes are selected in the network that help for the inter cluster communication. The graph for the number of cluster heads selected for different number of nodes are also drawn to study the functionality of the simulator

Toward Energy Efficient Systems Design For Data Centers

Surge growth of numerous cloud services, Internet of Things, and edge computing promotes continuous increasing demand for data centers worldwide. Significant electricity consumption of data centers has tremendous implications on both operating and capital expense. The power infrastructure, along with the cooling system cost a multi-million or even billion dollar project to add new data center capacities. Given the high cost of large-scale data centers, it is important to fully utilize the capacity of data centers to reduce the Total Cost of Ownership. The data center is designed with a space budget and power budget. With the adoption of high-density rack designs, the capacity of a modern data center is usually limited by the power budget. So the core of the challenge is scaling up power infrastructure capacity. However, resizing the initial power capacity for an existing data center can be a task as difficult as building a new data center because of a non-scalable centralized power provisioning scheme. Thus, how to maximize the power utilization and optimize the performance per power budget is critical for data centers to deliver enough computation ability. To explore and attack the challenges of improving the power utilization, we have planned to work on different levels of data center, including server level, row level, and data center level. For server level, we take advantage of modern hardware to maximize power efficiency of each server. For rack level, we propose Pelican, a new power scheduling system for large-scale data centers with heterogeneous workloads. For row level, we present Ampere, a new approach to improve throughput per watt by provisioning extra servers. By combining these studies on different levels, we will provide comprehensive energy efficient system designs for data center

SimGrid VM: Virtual Machine Support for a Simulation Framework of Distributed Systems

International audienceAs real systems become larger and more complex, the use of simulator frameworks grows in our research community. By leveraging them, users can focus on the major aspects of their algorithm, run in-siclo experiments (i.e., simulations), and thoroughly analyze results, even for a large-scale environment without facing the complexity of conducting in-vivo studies (i.e., on real testbeds). Since nowadays the virtual machine (VM) technology has become a fundamental building block of distributed computing environments, in particular in cloud infrastructures, our community needs a full-fledged simulation framework that enables us to investigate large-scale virtualized environments through accurate simulations. To be adopted, such a framework should provide easy-to-use APIs as well as accurate simulation results. In this paper, we present a highly-scalable and versatile simulation framework supporting VM environments. By leveraging SimGrid, a widely-used open-source simulation toolkit, our simulation framework allows users to launch hundreds of thousands of VMs on their simulation programs and control VMs in the same manner as in the real world (e.g., suspend/resume and migrate). Users can execute computation and communication tasks on physical machines (PMs) and VMs through the same SimGrid API, which will provide a seamless migration path to IaaS simulations for hundreds of SimGrid users. Moreover, SimGrid VM includes a live migration model implementing the precopy migration algorithm. This model correctly calculates the migration time as well as the migration traffic, taking account of resource contention caused by other computations and data exchanges within the whole system. This allows user to obtain accurate results of dynamic virtualized systems. We confirmed accuracy of both the VM and the live migration models by conducting several micro-benchmarks under various conditions. Finally, we conclude the article by presenting a first use-case of one consolidation algorithm dealing with a significant number of VMs/PMs. In addition to confirming the accuracy and scalability of our framework, this first scenario illustrates the main interest of SimGrid VM: investigating through in-siclo experiments pros/cons of new algorithms in order to limit expensive in-vivo experiments only to the most promising ones

Enabling Distributed Applications Optimization in Cloud Environment

The past few years have seen dramatic growth in the popularity of public clouds, such as Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Container-as-a-Service (CaaS). In both commercial and scientific fields, quick environment setup and application deployment become a mandatory requirement. As a result, more and more organizations choose cloud environments instead of setting up the environment by themselves from scratch. The cloud computing resources such as server engines, orchestration, and the underlying server resources are served to the users as a service from a cloud provider. Most of the applications that run in public clouds are the distributed applications, also called multi-tier applications, which require a set of servers, a service ensemble, that cooperate and communicate to jointly provide a certain service or accomplish a task. Moreover, a few research efforts are conducting in providing an overall solution for distributed applications optimization in the public cloud. In this dissertation, we present three systems that enable distributed applications optimization: (1) the first part introduces DocMan, a toolset for detecting containerized application’s dependencies in CaaS clouds, (2) the second part introduces a system to deal with hot/cold blocks in distributed applications, (3) the third part introduces a system named FP4S, a novel fragment-based parallel state recovery mechanism that can handle many simultaneous failures for a large number of concurrently running stream applications