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Performance Evaluation of Energy-Efficient Parallel I/O Systems with Write Buffer Disks
- In Proc. 38th International Conference on Parallel Processing
, 2009
"... Abstract-In the past decade, parallel disk systems have been developed to address the problem of I/O performance. A critical challenge with modern parallel I/O systems is that parallel disks consume a significant amount of energy in servers and highperformance computers. To conserve energy consumpti ..."
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Abstract-In the past decade, parallel disk systems have been developed to address the problem of I/O performance. A critical challenge with modern parallel I/O systems is that parallel disks consume a significant amount of energy in servers and highperformance computers. To conserve energy consumption in parallel I/O systems, one can immediately spin down disks when disk are idle; however, spinning down disks might not be able to produce energy savings due to penalties of spinning operations. Unlike powering up CPUs, spinning down and up disks need physical movements. Therefore, energy savings provided by spinning down operations must offset energy penalties of the disk spinning operations. To substantially reduce the penalties incurred by disk spinning operations, we developed a novel approach to conserving energy of parallel I/O systems with write buffer disks, which are used to accumulate small writes using a log file system. Data sets buffered in the log file system can be transferred to target data disks in a batch way. Thus, buffer disks aim to serve a majority of incoming write requests, attempting to reduce the large number of disk spinning operations by keeping data disks in standby for long period times. Interestingly, the write buffer disks not only can achieve high energy efficiency in parallel I/O systems, but also can shorten response times of write requests. To evaluate the performance and energy efficiency of our parallel I/O systems with buffer disks, we implemented a prototype using a cluster storage system as a testbed. Experimental results show that under light and moderate I/O load, buffer disks can be employed to significantly reduce energy dissipation in parallel I/O systems without adverse impacts on I/O performance. I.
DARAW: A New Write Buffer to Improve Parallel I/O Energy-Efficiency
"... In the past decades, parallel I/O systems have been used widely to support scientific and commercial applications. New data centers today employ huge quantities of I/O systems, which consume a large amount of energy. Most large-scale I/O systems have an array of hard disks working in parallel to mee ..."
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Cited by 8 (6 self)
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In the past decades, parallel I/O systems have been used widely to support scientific and commercial applications. New data centers today employ huge quantities of I/O systems, which consume a large amount of energy. Most large-scale I/O systems have an array of hard disks working in parallel to meet performance requirements. Traditional energy conservation techniques attempt to place disks into low-power states when possible. In this paper we propose a novel strategy, which aims to significantly conserve energy while reducing average I/O response times. This goal is achieved by making use of buffer disks in parallel I/O systems to accumulate small writes to form a log, which can be transferred to data disks in a batch way. We develop an algorithm- dynamic request allocation algorithm for writes or DARAW- to energy efficiently allocate and schedule write requests in a parallel I/O system. DARAW is able to improve parallel I/O energy efficiency by the virtue of leveraging buffer disks to serve a majority of incoming write requests, thereby keeping data disks in low-power state for longer period times. Buffered requests are then written to data disks at a predetermined time. Experimental results show that DARAW can significantly reduce energy dissipation in parallel I/O systems without adverse impacts on I/O performance. 1.
PRE-BUD: Prefetching for Energy-Efficient Parallel I/O Systems with Buffer Disks
"... A critical problem with parallel I/O systems is the fact that disks consume a significant amount of energy. To design economically attractive and environmentally friendly parallel I/O systems, we propose an energy-aware prefetching strategy (PRE-BUD) for parallel I/O systems with disk buffers. We in ..."
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Cited by 5 (3 self)
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A critical problem with parallel I/O systems is the fact that disks consume a significant amount of energy. To design economically attractive and environmentally friendly parallel I/O systems, we propose an energy-aware prefetching strategy (PRE-BUD) for parallel I/O systems with disk buffers. We introduce a new architecture that provides significant energy savings for parallel I/O systems using buffer disks while maintaining high performance. There are two buffer disk configurations: (1) adding an extra buffer disk to accommodate prefetched data, and (2) utilizing an existing disk as the buffer disk. PRE-BUD is not only able to reduce the number of power-state transitions, but also to increase the length and number of standby periods. As such, PRE-BUD conserves energy by keeping data disks in the standby state for increased periods of time. Compared with the first prefetching configuration, the second configuration lowers the capacity of the parallel disk system. However, the second configuration is more cost-effective and energy-efficient than the first one. Finally, we quantitatively compare PRE-BUD with both disk configurations against three existing strategies. Empirical results show that PRE-BUD is able to reduce energy dissipation in parallel disk systems by up to 50 percent when compared against a non-energy aware approach. Similarly, our strategy is capable of conserving up to 30 percent energy when compared to the dynamic power management technique.
EDR: An Energy-Aware Runtime Load Distribution System for Data-Intensive Applications in the Cloud
"... Abstract—Data centers account for a growing percentage of US power consumption. Energy efficiency is now a first-class design constraint for the data centers that support cloud services. Service providers must distribute their data efficiently across multiple data centers. This includes creation of ..."
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Abstract—Data centers account for a growing percentage of US power consumption. Energy efficiency is now a first-class design constraint for the data centers that support cloud services. Service providers must distribute their data efficiently across multiple data centers. This includes creation of data replicas that provide multiple copies of data for efficient access. However, selecting replicas to maximize performance while minimizing energy waste is an open problem. State of the art replica selection approaches either do not address energy, lack scalability and/or are vulnerable to crashes due to use of a centralized coordinator. Therefore, we propose, develop and evaluate a simple cost-oriented decentralized replica selection system named EDR (Energy-Aware Distributed Running system), implemented with two distributed optimization algorithms. We demonstrate experimentally the cost differences in various replica selection scenarios and show that our novel approach is as fast as the best available decentralized approach DONAR,while additionally considering dynamic energy costs. We show that an average of 12 % savings on total system energy costs can be achieved by using EDR for several data intensive applications. I.
Configuring a MapReduce Framework for Dynamic and Efficient Energy Adaptation
- In Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on
, 2012
"... Abstract—MapReduce has become a popular framework for Big Data applications. While MapReduce has received much praise for its scalability and efficiency, it has not been thoroughly evaluated for power consumption. Our goal with this paper is to explore the possibility of scheduling in a power-effici ..."
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Abstract—MapReduce has become a popular framework for Big Data applications. While MapReduce has received much praise for its scalability and efficiency, it has not been thoroughly evaluated for power consumption. Our goal with this paper is to explore the possibility of scheduling in a power-efficient manner without the need for expensive power monitors on every node. We begin by considering that no cluster is truly homogeneous with respect to energy consumption. From there we develop a MapReduce framework that can evaluate the current status of each node and dynamically react to estimated power usage. In so doing, we shift power consumption work toward more energy efficient nodes which are currently consuming less power. Our work shows that given an ideal framework configuration, certain nodes may consume only 62.3 % of the dynamic power they consumed when the same framework was configured as it would be in a traditional MapReduce implementation. I.
2009 Eighth IEEE International Symposium on Network Computing and Applications Energy-Aware Prefetching for Parallel Disk Systems Algorithms, Models, and Evaluation
"... Abstract — Parallel disk systems consume a significant amount of energy due to the large number of disks. To design economically attractive and environmentally friendly parallel disk systems, in this paper we design and evaluate an energy-aware prefetching strategy for parallel disk systems consisti ..."
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Abstract — Parallel disk systems consume a significant amount of energy due to the large number of disks. To design economically attractive and environmentally friendly parallel disk systems, in this paper we design and evaluate an energy-aware prefetching strategy for parallel disk systems consisting of a small number of buffer disks and large number of data disks. Using buffer disks to temporarily handle requests for data disks, we can keep data disks in the low-power mode as long as possible. Our prefetching algorithm aims to group many small idle periods in data disks to form large idle periods, which in turn allow data disks to remain in the standby state to save energy. To achieve this goal, we utilize buffer disks to aggressively fetch popular data from regular data disks into buffer disks, thereby putting data disks into the standby state for longer time intervals. A centrepiece in the prefetcing mechanism is an energy-saving prediction model, based on which we implement the energy-saving calculation module that is invoked in the prefetching algorithm. We quantitatively compare our energy-aware prefetching mechanism against existing solutions, including the dynamic power management strategy. Experimental results confirm that the buffer-disk-based prefetching can significantly reduce energy consumption in parallel disk systems by up to 50 percent. In addition, we systematically investigate the energy efficiency impact that varying disk power parameters has on our prefetching algorithm. Keywords-storage systems; energy-efficiency;prefetching I.
2009 IEEE International Conference on Networking, Architecture, and Storage Can We Improve Energy Efficiency of Secure Disk Systems without Modifying Security Mechanisms?
"... Abstract—Improving energy efficiency of security-aware storage systems is challenging, because security and energy efficiency are often two conflicting goals. The first step toward making the best tradeoffs between high security and energy efficiency is to profile encryption algorithms to decide if ..."
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Abstract—Improving energy efficiency of security-aware storage systems is challenging, because security and energy efficiency are often two conflicting goals. The first step toward making the best tradeoffs between high security and energy efficiency is to profile encryption algorithms to decide if storage systems would be able to produce energy savings for security mechanisms. We are focused on encryption algorithms rather than other types of security services, because encryption algorithms are usually computation-intensive. In this study, we used the XySSL libraries and profiled operations of several test problems using Conky- a lightweight system monitor that is highly configurable. Using our profiling techniques we concluded that although 3DES is much slower than AES encryption, it more likely to save energy in security-aware storage systems using 3DES than AES. The CPU is the bottleneck in 3DES, allowing us to take advantage of dynamic power management schemes to conserve energy at the disk level. After profiling several hash functions, we noticed that the CPU is not the bottleneck for any of these functions, indicating that it is difficult to leverage the dynamic power management technique to conserve energy of a single disk where hash functions are implemented for integrity checking.
A Reliability Model of Energy-Efficient Parallel Disk Systems with Data Mirroring
"... In the last decade, parallel disk systems have increasingly become popular for data-intensive applications running on high-performance computing platforms. Conservation of energy in parallel disk systems has a strong impact on the cost of cooling equipment and backup power-generation. This is becaus ..."
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In the last decade, parallel disk systems have increasingly become popular for data-intensive applications running on high-performance computing platforms. Conservation of energy in parallel disk systems has a strong impact on the cost of cooling equipment and backup power-generation. This is because a significant amount of energy is consumed by parallel disks in high-performance computing centers. Although a wide range of energy conservation techniques have been developed for disk systems, research on reliability analysis for energy-efficient parallel disk systems is still in its infancy. In this paper, we make use of a Markov process to develop a quantitative reliability model for energy-efficient parallel disk systems using data mirroring. With the new model in place, a reliability analysis tool is developed to efficiently evaluate reliability of fault-tolerant parallel disk systems with two power modes. More importantly, the reliability model makes it possible to provide a good compromise between energy efficiency and reliability in energy-efficient and fault-tolerant parallel disk systems. 1.
Energy Efficient Pre-Fetching- Models to Implementation
, 2010
"... With the rapid growth of the production and storage of large scale data sets it is important to investigate methods to drive the cost of storage systems down. We are currently in the midst of an information explosion and large scale storage centers are increasingly used to help store generated data. ..."
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With the rapid growth of the production and storage of large scale data sets it is important to investigate methods to drive the cost of storage systems down. We are currently in the midst of an information explosion and large scale storage centers are increasingly used to help store generated data. There are several methods to bring the cost of large scale storage centers down and we investigate a technique that focuses on transitioning storage disks into lower power states. To achieve this goal this dissertation introduces a model of disk systems that leverages disk access patterns to prefetch popular sets of data to produce energy saving opportunities. Using our model, we have developed a simulator that allows us to quickly change various parameters to investigate the relationship that file access patterns, disk energy parameters, and simulation parameters have on the overall energy efficiency of disk systems. To help improve the validity of our simulation results we leveraged the validated disk simulator, DiskSim, and added disk power models to DiskSim. This allowed us to test our energy efficient strategies with a validated storage system
Heat-Based Dynamic Data Caching: A Load Balancing Strategy for Energy- Efficient Parallel Storage Systems with Buffer Disks
"... Performance improvement and energy conservation are two conflicting objectives in large scale parallel storage systems. In this paper, we propose a novel solution to achieve the twin objectives of maximizing performance and minimizing energy consumption of parallel storage systems. Specifically, a b ..."
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Performance improvement and energy conservation are two conflicting objectives in large scale parallel storage systems. In this paper, we propose a novel solution to achieve the twin objectives of maximizing performance and minimizing energy consumption of parallel storage systems. Specifically, a buffer-disk based architecture (BUD for short) is designed to conserve energy. A heat-based dynamic data caching strategy is developed to improve performance. The BUD architecture strives to allocate as many requests as possible to buffer disks, thereby keeping a large number of idle data disks in low-power states. This can provide significant opportunities for energy conservation while making buffer disks a potential performance bottleneck. The heat-based data caching strategy aims to achieve good load balancing in buffer disks and alleviate overall performance degradation caused by unbalanced workload. Our experimental results have shown that the proposed BUD framework and dynamic data caching strategy are able to conserve energy by 84.4 % for small reads and 78.8% for large reads with slightly degraded response time. 1.
