Improving Energy Effeciency and Reliability of Disk Storage Systems

Numerous energy saving techniques have been developed to aggressively reduce energy dissipation in parallel disks. However, many existing energy conservation schemes have substantial adverse impacts on disk reliability. To remedy this deficiency, in this paper we address the problem of making tradeoffs between energy efficiency and reliability in parallel disk systems. Among several factors affecting disk reliability, the two most important factors - disk utilization and ages - are the focus of this study. We built a mathematical reliability model to quantify the impacts of disk age and utilization on failure probabilities of mirrored disk systems. In light of the reliability model, we proposed a novel concept of safe utilization zone, within which energy dissipation in disks can be reduced without degrading reliability. We developed two approaches to improving both reliability and energy efficiency of disk systems through disk mirroring and utilization control, enforcing disk drives to be operated in safe utilization zones. Our utilization-based control schemes seamlessly integrate reliability with energy saving techniques in the context of fault-tolerant systems. Experimental results show that our approaches can significantly improve reliable while achieving high-energy efficiency for disk systems under a wide range of workload situations

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

PowerPack: Energy Profiling and Analysis of High-Performance Systems and Applications

Energy efficiency is a major concern in modern high-performance computing system design. In the past few years, there has been mounting evidence that power usage limits system scale and computing density, and thus, ultimately system performance. However, despite the impact of power and energy on the computer systems community, few studies provide insight to where and how power is consumed on high-performance systems and applications. In previous work, we designed a framework called PowerPack that was the first tool to isolate the power consumption of devices including disks, memory, NICs, and processors in a high-performance cluster and correlate these measurements to application functions. In this work, we extend our framework to support systems with multicore, multiprocessor-based nodes, and then provide in-depth analyses of the energy consumption of parallel applications on clusters of these systems. These analyses include the impacts of chip multiprocessing on power and energy efficiency, and its interaction with application executions. In addition, we use PowerPack to study the power dynamics and energy efficiencies of dynamic voltage and frequency scaling (DVFS) techniques on clusters. Our experiments reveal conclusively how intelligent DVFS scheduling can enhance system energy efficiency while maintaining performance

Modeling Power Consumption of Applications Software Running on Servers

Reducing power consumption in computational processes is important to software devel- opers. Ideally, a tremendous amount of software design efforts goes into considerations that are critical to power efficiencies of computer systems. Sometimes, software is designed by a high-level developer not aware of underlying physical components of the system architecture, which can be exploited. Furthermore, even if a developer is aware, they design software geared towards mass end-user adoption and thus go for cross-compatibility. The challenge for the soft- ware designer is to utilize dynamic hardware adaptations. Dynamic hardware adaptations make it possible to reduce power consumption and overall chip temperature by reducing the amount of available performance. However these adaptations generally rely on input from temperature sensors, and due to thermal inertia in microprocessor packaging, the detection of temperature changes significantly lag the power events that caused them. This work provides energy performance evaluation and power consumption estimation of ap- plications running on a server using performance counters. Counter data of various performance indicators are collected using the CollectD tool. Simultaneously, during the test, a Power Meter (TED5000) is used to monitor the actual power drawn by the computer server. Furthermore, stress tests are performed to examine power fluctuations in response to the performance counts of four hardware subsystems: CPU, memory, disk, and network interface. A neural network model (NNM) and a linear polynomial model (LPM) have been developed based on process count information gathered by CollectD. These two models have been validated by four different scenarios running on three different platforms (three real servers.) Our experimental results show that system power consumption can be estimated with an average mean absolute error (MAE) between 11% to 15% on new system servers. While on old system servers, the average MAE is between 1% to 4%. Also, we find that NNM has better estimation results than the LPM, resulting in 1.5% reduction in MAE of energy estimation when compared to the LPM. The detailed contributions of the thesis are as follows: (i) develop a non-exclusive test bench to measure the power consumption of an application running on a server; (ii) provide a practical approach to extracting system performance counters and simplifying them to get the model pa- rameters; (iii) a modeling procedure is proposed and implemented for predicting the power cost of application software using performance counters. All of our contributions and the proposed procedure have been validated with numerous measurements on a real test bench. The results of this work can be used by application developers to make implementation-level decisions that affect the energy efficiency of software applications

Model and simulation of power consumption and power saving potential of energy efficient cluster hardware

In the last years the power consumption of high performance computing clusters has become a growing problem because number and size of cluster installations raised and still is raising. The high power consumption of the clusters results from the main goal of these clusters: High performance. With a low utilization the cluster hardware consumes nearly as much energy as when it is fully utilized. In these low utilization phases the cluster hardware can theoretically turned off or switched to an lower power consuming mode. In this thesis a model is designed to estimate the power consumption of the hardware with and without energy saving mechanism. With the resulting software it is possible to estimate the cluster power consumption for different configurations of a parallel program. Further energy aware hardware can be simulated to determine an upper bound for energy savings without performance leakage. The results show that is a great energy saving potential for energy aware hardware even in high performance computing. This potential should motivate research in mechanism to control the energy aware hardware in high performance clusters

Cloud computing: survey on energy efficiency

International audienceCloud computing is today’s most emphasized Information and Communications Technology (ICT) paradigm that is directly or indirectly used by almost every online user. However, such great significance comes with the support of a great infrastructure that includes large data centers comprising thousands of server units and other supporting equipment. Their share in power consumption generates between 1.1% and 1.5% of the total electricity use worldwide and is projected to rise even more. Such alarming numbers demand rethinking the energy efficiency of such infrastructures. However, before making any changes to infrastructure, an analysis of the current status is required. In this article, we perform a comprehensive analysis of an infrastructure supporting the cloud computing paradigm with regards to energy efficiency. First, we define a systematic approach for analyzing the energy efficiency of most important data center domains, including server and network equipment, as well as cloud management systems and appliances consisting of a software utilized by end users. Second, we utilize this approach for analyzing available scientific and industrial literature on state-of-the-art practices in data centers and their equipment. Finally, we extract existing challenges and highlight future research directions

Managing server energy and reducing operational cost for online service providers

The past decade has seen the energy consumption in servers and Internet Data Centers (IDCs) skyrocket. A recent survey estimated that the worldwide spending on servers and cooling have risen to above $30 billion and is likely to exceed spending on the new server hardware . The rapid rise in energy consumption has posted a serious threat to both energy resources and the environment, which makes green computing not only worthwhile but also necessary. This dissertation intends to tackle the challenges of both reducing the energy consumption of server systems and by reducing the cost for Online Service Providers (OSPs). Two distinct subsystems account for most of IDC’s power: the server system, which accounts for 56% of the total power consumption of an IDC, and the cooling and humidifcation systems, which accounts for about 30% of the total power consumption. The server system dominates the energy consumption of an IDC, and its power draw can vary drastically with data center utilization. In this dissertation, we propose three models to achieve energy effciency in web server clusters: an energy proportional model, an optimal server allocation and frequency adjustment strategy, and a constrained Markov model. The proposed models have combined Dynamic Voltage/Frequency Scaling (DV/FS) and Vary-On, Vary-off (VOVF) mechanisms that work together for more energy savings. Meanwhile, corresponding strategies are proposed to deal with the transition overheads. We further extend server energy management to the IDC’s costs management, helping the OSPs to conserve, manage their own electricity cost, and lower the carbon emissions. We have developed an optimal energy-aware load dispatching strategy that periodically maps more requests to the locations with lower electricity prices. A carbon emission limit is placed, and the volatility of the carbon offset market is also considered. Two energy effcient strategies are applied to the server system and the cooling system respectively. With the rapid development of cloud services, we also carry out research to reduce the server energy in cloud computing environments. In this work, we propose a new live virtual machine (VM) placement scheme that can effectively map VMs to Physical Machines (PMs) with substantial energy savings in a heterogeneous server cluster. A VM/PM mapping probability matrix is constructed, in which each VM request is assigned with a probability running on PMs. The VM/PM mapping probability matrix takes into account resource limitations, VM operation overheads, server reliability as well as energy effciency. The evolution of Internet Data Centers and the increasing demands of web services raise great challenges to improve the energy effciency of IDCs. We also express several potential areas for future research in each chapter