High energy consumption has become a critical challenge in all kinds of computer systems. Hardware-supported Dynamic Power Management (DPM) provides a mechanism to save disk energy by transitioning an idle disk to a low-power mode. However, the achievable disk energy saving is mainly dependent on the pattern of I/O requests received at the disk. In particular, for a given number of requests, a bursty disk access pattern serves as a foundation for energy optimization. Aggressive prefetching has been used to increase disk access burstiness and extend disk idle intervals, while caching, a critical component in buffer cache management, has not been paid a specific attention. In the absence of cooperation from caching, the attempt to create bursty disk accesses would often be disturbed due to improper replacement decision made by energy-unaware caching policies. In this paper, we present the design of a set of comprehensive energy-aware caching schemes, called C-Burst, and its implementation in Linux kernel 2.6.21. Our caching schemes leverage the ‘filtering ’ effect of buffer cache to effectively reshape the disk access stream to a bursty pattern for energy saving. The experiments under various scenarios show that C-Burst schemes can achieve up to 35 % disk energy saving with minimal performance loss.

Under a replacement policy, existing operating systems identify and maintain most frequently used storage data in buffer caches located in main memory, aiming at low-latency I/O data accesses. However, replacement policies can also strongly affect energy consumptions of various connected storage devices, which has not been a consideration in the design and implementation of buffer cache management. In this paper, we present a system framework for an energy-aware buffer cache replacement, called PS-BC (power-saving buffer cache). By considering several critical factors affecting system energy consumption, PS-BC can effectively improve system energy efficiency, while it is able to flexibly incorporate conventional performance-oriented buffer cache replacement policies for different performance objectives. Our experimental studies based on a trace-driven simulation show that the PS-BC framework embedded with the CLOCK replacement policy can achieve an energy saving rate of up to 32.5% with a minimal overhead for various workloads.

Power consumption of disk based storage systems is becoming an increasingly pressing issue for both commercial and scientific application domains. Prior work proposed several hardware based approaches to reducing disk power consumption by making use of techniques such as spinning down idle disks and rotating them at lower speeds than the maximum speed possible. While such techniques are certainly very important, it is also critical to consider the influence the software can exercise in shaping the power consumption behavior of disk-intensive application programs. Motivated by this, the main goal of this work is to study whether an optimizing compiler can be used for increasing the power benefits that could be obtained from multi-speed disks. Specifically, we propose and experimentally evaluate a compiler-directed energyaware data prefetching scheme for scientific applications that process disk-resident data sets. This scheme automatically determines the prefetch distance for all disk access instructions, the disk speeds to be employed, and the associated disk layouts (striping parameters) in a unified setting. We implemented the proposed approach within an optimizing compiler framework and conducted experiments with several disk-intensive applications. Our experimental evaluation shows that the proposed approach brings significant reductions in disk energy consumption over a state-of-the-art softwarebased I/O prefetching mechanism that does not take into account energy consumption explicitly. Our results also show that the energyaware prefetching scheme does not bring any extra performance penalties and the energy reductions achieved are consistent across a wide spectrum of values of the simulation parameters.

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.

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

Abstract—Energy consumption is becoming a growing concern in data centers. Many energy-conservation techniques have been proposed to address this problem. However, an integrated method is still needed to evaluate energy efficiency of storage systems and various power conservation techniques. Extensive measurements of different workloads on storage systems are often very timeconsuming and require expensive equipments. We have analyzed changing characteristics such as power and performance of stand-alone disks and RAID arrays, and then defined MIND as a black box power model for RAID arrays. MIND is devised to quantitatively measure the power consumption of redundant disk arrays running different workloads in a variety of execution modes. In MIND, we define five modes (idle, standby, and several types of access) and four actions, to precisely characterize power states and changes of RAID arrays. In addition, we develop corresponding metrics for each mode and action, and then integrate the model and a measurement algorithm into a popular trace tool – blktrace. With these features, we are able to run different IO traces on large-scale storage systems with power conservation techniques. Accurate energy consumption and performance statistics are then collected to evaluate energy efficiency of storage system designs and power conservation techniques. Our experiments running both synthetic and realworld workloads on enterprise RAID arrays show that MIND can estimate power consumptions of disk arrays with an error rate less than 2%. Index Terms—Energy Consumption, Disk Arrays, Black-Box I.