Anti-Load Balancing to Reduce Energy Consumption

Transcription

1 Anti-Load Balancing to Reduce Energy Consumption Cheikhou Thiam, Georges Da Costa IRIT (UMR CNRS) Université de Toulouse Toulouse, France Presented at 2011 International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering Abstract A problem usually studied in distributed systems is how to evenly distribute workload. But when the goal is to reduce energy consumption, this type of algorithms can lead to have computers largely under-loaded and therefore consuming energy unnecessarily. Our fields of study will be the management of virtual machines in the cluster or grid-type systems. Here we will therefore look at the opposite problem: concentrate the load on a minimum number of machines. The goal is to turn off the released computers and therefore minimize the energy consumption of the system. We use an algorithm of global decision: a server selects a node, distribute its load to other nodes and then switch it off. To study this problem we have proceeded in three phases: (i) Model the problem (ii) Simulation (iii) Experimentation based on virtual machines (Xen, KVM,...). This work is expanded by refining the algorithm by taking into account other objectives than energy consumption: impact on performance, ability to change processors frequency. We present here several experiments on antiload balancing to reduce energy consumption significantly. Keywords: Energy, heuristic, Virtual Machines, Grid. 1 Introduction Like managing load distribution, energy consumption is a fundamental problem for large scale systems. Choice of location of running jobs has a large impact on the behavior of such systems. A minimal number of computers can be selected in order to run requested job at a particular time, even taking into account user requirements. Users can even add more criteria to obtain an optimal power consumption. Difficulties arise as dynamism is introduced and as jobs arrive, leave

2 and change over time. Several solutions exist to reduce power consumption due to computational servers and data centers. Alas, mixing energy consumption and performance (or QoS) objectives rise the difficulty level needed to find a good placement for tasks. However, to achieve energy savings while maintaining system performance, we need: i) a good description of the characteristics of applications and user feedback, ii) a complete and concise description of the system. A problem usually studied in distributed systems is to evenly distribute the load. But when the goal is to reduce power consumption, this type of algorithms can lead to have computers under-loaded and therefore consume energy unnecessarily. We will consider the opposite problem of load balancing: to concentrate the load on a minimum number of machines. The goal is to turn off the computers with no load and thus minimize the energy consumption of the whole system. This paper presents an centralized algorithm of decision: choose a server which node will transfer its load and will be shut off. We present here a series of experiments on the anti-load balancing for significantly reducing energy consumption. For this experiment, we try to avoid having some processor that over-heat which would cost more energy for cooling. So even while doing consolidation, it is important to distribute workload over time on several computers. To study this problem in section II we present the problem of energy management in distributed systems. We propose a tool for resource management that takes into account the energy dimension in Section III. A validation of this tool is made in Section IV. Section V concludes and presents perspectives. 2 Energy management in distributed systems According to IDC (International Data Corporation), 2012 for $ 1 invested in hardware, $ 1 will be spent for energy. In 2006, U.S. data centers used about 61 billion kwh, 1.5% from 4 trillion kwh consumed in total. This is the amount of energy used by 5.8 million U.S. households on average (5% of all households). It is easy to imagine the potential savings. Hardware performance are increasing and at the same time overall energy consumption of IT increases too. Energy management of computers driven by software is now an essential tool in the sustainable development of companies. Return on investment for such a solution can be very fast. Given the ever-increasing power of hardware and power consumption of data centers, operators of data centers start to realize that the practice of over provisioning with a large number of servers significantly underutilized is no more acceptable, and are eager for solutions to save energy. 2.1 Virtualization It is common for a company to have a couple of servers running at 15% capacity, the latter being there to deal with any time to punctual peak loads. A server loaded at 15% do not consume much less power than a server that is loaded at 90%, and to consolidate workload of several servers on only one can be profitable

3 if their peak loads do not always coincide. It would even stay true if virtualization had a load of 30 % on the computer. Virtualization allows to run multiple virtual machines on one physical machine, with each virtual machine sharing the resources of the physical computer across multiple environments. Different virtual machines can run different operating systems and multiple applications on the same physical computer. Virtualization can improve the effectiveness and availability of IT resources and applications. It reduces costs by reducing data center physical infrastructure and energy consumption. A virtual machine is a tightly isolated software container that can manage their own operating systems and applications as if it were a physical computer. It contains its own virtual processor, RAM, hard drive and network card. A virtual machine must satisfy the following conditions: (i) insulation: two machines can share physical resources of a single computer, they remain completely isolated from each other as if they were separate physical machines, (ii) compatibility: you can use a virtual machine to run any software you run on an real computer, (iv) encapsulation: encapsulates a complete set of virtual hardware resources and software within a software package, (v) independence material: totally independent of their underlying hardware. The term virtual machine is also used for some time in a very different sense to describe an environment created by an emulator. Such software emulates an operating system for the end user (examples: KVM, Oracle VM, XEN, Virtual PC...). 2.2 State of the art Technical options are already available to improve energy efficiency of servers such as energy-efficient hardware components, energy management systems or software basic elements such as virtualization. Some solutions, algorithms, include numerous approaches, through the network protocols [1], modeling of consumption [2], scheduling of tasks by predicting idle times [3]. This migration of virtual machines from server to server in a data center creates a challenge as when the load of a VM migrates, it is the same heat load. A host server can quickly become a hot spot. To evaluate improvements we choose to use a grid simulator. Table 4.1 presents a summary of various simulators commonly used in grids. Table 1 summarizes the literature about various existing simulators. A remark that might raise here is the lack of a simulator that can be considered as standard. Despite efforts in this direction and compared to low level network simulation, grid simulation does not appear to have reached the stage aging in order to qualify for having a referent simulator recognized by all. CloudSim is a simulation software developed by a team from the University of Melbourne, Australia, for modeling and simulation infrastructure and cloud services, which manages energy. Energy aware solutions are mainly based on virtualization. Few simulation software taking into account energy exist. Current evaluated solutions are usually based on centralized systems which is a limit when addressing large scale distributed systems.

4 Simulator Advantage Disavantage PeerSim [4] Scaling: 10 6 nodes in a cyclic No model of physical network, mode Light documentation GridSim [5] Network, grid, jobs, resources Energy not taken into account modeled, good Documentation CloudSim [6] Energy taken into account, Inherits GridSim, manages VM Manages only the energy as a money cost PlanetSim [7] Scaling: 10 5 nodes No statistics and no model of physical network SimGid [8] Scalability, network model, Energy management not taken statistics supported into account, light Documentation Table 1: Characteristics of existing simulators. Taking into account some deficiencies noted in these solutions, we will try in the following section to propose a solution of anti-load balancing leading to energy savings in distributed systems. 3 Energy aware simulation tool (ENRDC) The goal is to turn off unloaded computers and thus reduce the energy consumption of the system. We will first use three algorithms of centralized decision: a server decides which computer move away its load and switch off. 3.1 Parallelisation Scheme Our approach will facilitate resource sharing (multiple jobs) but also minimizes energy consumption. We chose initially a static approach. This will require to have information about the tasks before the execution such as the number of task and their execution time. The goal of our heuristics is to minimize energy consumption by distributing wisely jobs. Several solutions can be envisaged for the distribution of tasks between nodes: Centralized decision-making, it is usually a strategy of client-server / master-slave. A server node is responsible for responsible for allocation Distributed decision-making, at the initiative of any of the nodes. This will apply the anti-load balancing, either local or global Some nodes are responsible for dis- Semi-distributed decision-making. tributing to a group of nodes.

5 For the centralized algorithm (client-server) there are several way to manage the job queue (i) a central queue, (ii) each node has a queue of tasks. In the second approach, each node initializes its queue with its list of tasks. In the case when a node P i is lightly loaded, the server can unloaded it (migrate it to another node P j ) and put it to sleep. This centralized approaches has several advantages such as a simple implementation and policy update information, and good efficiency (low overhead since not too much communication occurs). However we can note some drawbacks related to bottlenecks when the number of node increases strongly. 3.2 Architecture Figure 1 shows the architecture of the simulator EnerSim. Figure 1: EnerSim architecture 4 Experimental validation of Enersim To evaluate our approach, we performed various simulations, watching the evolution of the energy system based on the distribution of jobs. The energy system is the sum of the energy consumed by each switched on computer 4.1 Methodology Energy model: CA = min(1, α i ) CD = α i

6 CE = α effective i = i α i min(1,cd) P i = P min αeffective i +α effective α effective i (P max P min ) power required to execute task i E = ( H j P i) t energy consumed T = execution time of jobs To highlight the influence of the number of hosts on the energy system in the first series of experiments we first run two jobs on two machines, then on two different machines. In the second experiment we vary the load of resources by performing a task migration. VMs on hosts that are at minimum load (in our case load less than 50 %) are chose as potential node to migrate their load. Finally, in the last experiment we use the algorithm below: For i=1 to 128 For j=1 to 100 For k=0 to 1 if k==0 migration=false Si k==1 migration=true EndFor if no migration: migration== false Randomly retrieves 16 jobs Gets i jobs and i VMs randomly algorithm without migration si migration : migration==true Algorithm with migration: retrieve settings from previous machines, jobs and vms EndFor EndFor 4.2 Parameters and experimental conditions Entities that we handle are the datacenter that encompass all entities, hosts, Vms, jobs. For the experimental conditions: 1. - experiments 1 and 2 Hosts : 100MIPS VMS : 50MIPS Jobs : 1000MIP 2. - experiments 3 and 4 Number of machines: 16 randomly taken between 400 and 450 MIPS Number of jobs: 128 randomly taken between 1000 and Mips Number of VMs: 128 randomly taken between 10 and 70 MIPS

7 4.3 Results To analyze the results of our simulations, we focus on the energy of the system. Indeed, the energy of the system will direct a guide to assess the impact of our method on the reduction of energy. We first present the evolution of the energy of a system to observe how it behaves when we vary the number of guests. Figure 2 was generated in the case of two jobs on two hosts. Figure 2: Evolution of energy depending on the distribution of jobs to hosts. As can be seen, the execution of two jobs on two distinct hosts causes a power consumption of 6000W, so that the same jobs run on a host cause a decrease in energy consumed, or 4000W. Using our formula to calculate the energy we get, for the execution of two jobs on two hosts, energy consumption on each host ( ) * 0.5 is 150W per second. The execution time of jobs being 20s ( ), the total energy consumption is 6000W. Using the same formula we get, in the case of the execution of two jobs on the host [100 + ( ) * 1] * 20 is 4000W. These theoretical results confirm those found using simulation. Figure 3 shows that the migration of jobs leads to a decrease in the energy system. As we can see, the higher the number of hosts N approaches k = 1, the more energy decreases. Figure 4 shows the evolution of energy in the execution of jobs with no migration and with migration. We can observe the evolution of the gap as the number of jobs increases. As might be expected, where the number of jobs is high, around 103, energy is the same for the two version. This is due to saturation of the data center, when migration can no longer be done. For the experiment of Figure 5, we try to avoid having a heating point. With a low load, energy consumption rise as the number of job increases. However, energy consuption decreases if nodes are not overloaded, whatever the load is. Figure 6 shows a more aggressive mode. A node x compares its load that a node y and decides to transfer or not. This shows three different aggressive modes:

8 Figure 3: Energy consumed during the migration of jobs. Figure 4: Energy in function of the job number, without and with migration. Figure 5: Hotspots Succ curve: the least loaded host transfer its load to the least loaded node that comes just after him if it has enough spare computing power

9 Figure 6: Architecture. Dec curve: We compare the least load node to other nodes starting with the most loaded node. Jobs are migrated to the first node that can receive them. Rand curve: the load of the node is migrated to a randomly chosen node It is clear that the random choice of the node that receives the load increases the electric consumption. 5 Conclusion Usually one studies equitable distribution of the load in distributed systems. Here we became interested in the opposite issue, concentrating the load on a minimum number of machines. The use of anti-load balancing provides a power gain in managing data centers. We have proposed some algorithms to optimize management. Our solution, based on a centralized architecture. The results of our simulations show that our solution fits better when the grid size increases. A logical extension of this work, to better optimize our approach is to integrate quality of service (QoS) but also to address the distributed solution. References [1] Barry Rountree and David K. Lowenthal and Shelby Funk and Vincent W. Freeh and Bronis R. de Supinski and Martin Schulz. - Bounding energy consumption in large-scale MPI programs Proceedings of the ACM/IEEE Conference on High Performance Networking and Computing, SC 2007, November 10-16, 2007, Reno, Nevada, USA.

10 [2] Hlavacs, Helmut and Da Costa, Georges and Pierson, Jean-Marc. Energy Consumption of Residential and Professional Switches Rapport de recherche, IRIT, Universit Paul Sabatier, Toulouse, [3] Da-Costa, Georges and Gelas, Jean-Patrick and Georgiou, Yiannis and Lefvre, Laurent and Orgerie, Anne- Ccile and Pierson, Jean-Marc and Richard, Olivier and Sharma, Kamal. The GREEN-NET Framework : Energy Efficiency in Large Scale Distributed Systems HPPAC 2009 : High Performance Power Aware ComputingWorkshop in conjunction with IPDPS [4] PeerSim: A Peer-to-Peer Simulator. [5] Rajkumar Buyya, Manzur Murshed, A Grid Simulation Toolkit for Resource Modelling and Application Scheduling for Parallel and Distributed Computing. In Concurrency and Computation: Practice and Experience, Wiley, May 2002 [6] Rodrigo N. Calheiros, Rajiv Ranjan, Anton Beloglazov, Cesar A. F. De Rose, and Rajkumar Buyya, CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing environments and Evaluation of Resource Provisioning Algorithms, Software: Practice and Experience (in press, accepted on June 14, 2010). [7] Jordi Pujol Ahull, Pedro Garca Lpez, Marc Snchez Artigas, Marcel Arrufat Arias Gerard Pars Aixal, Max Bruchmann, PlanetSim : An extensible framework for overlay network and services simulations. In Proceedings of 9th International Conference on Peer-to-Peer Computing 2009 (IEEE P2P 09). Seattle, Washington, USA, 2009, Pages [8] H. Casanova, A. Legrand, M. Quinson, SimGrid: a Generic Framework for Large-Scale Distributed Experimentations, in Proceedings of Tenth International Conference on Computer Modeling and Simulation (UKSim), Cambridge, U.K., April 2008.