In this paper, we study energy conservation techniques for disk array-based network servers. First, we introduce a new conservation technique, called Popular Data Concentration (PDC), that migrates frequently accessed data to a subset of the disks. The goal is to skew the load towards a few of the disks, so that others can be transitioned to low-power modes. Next, we introduce a user-level file server that takes advantage of PDC. In the context of this server, we compare PDC to the Massive Array of Idle Disks (MAID). Using a validated simulator, we evaluate these techniques for conventional and two-speed disks and a wide range of parameters. Our results for conventional disks show that PDC and MAID can only conserve energy when the load on the server is extremely low. When two-speed disks are used, both PDC and MAID can conserve significant energy with only a small fraction of delayed requests. Overall, we find that PDC achieves more consistent and robust energy savings than MAID.

Reducing power consumption for server computers is important, since increased energy usage causes increased heat dissipation, greater cooling requirements, reduced computational density, and higher operating costs. For a typical data center, storage accounts for 27 % of energy consumption. Conventional server-class RAIDs cannot easily reduce power because loads are balanced to use all disks even for light loads. We have built the Power-Aware RAID (PARAID), which reduces energy use of commodity server-class disks without specialized hardware. PARAID uses a skewed striping pattern to adapt to the system load by varying the number of powered disks. By spinning disks down during light loads, PARAID can reduce power consumption, while still meeting performance demands, by matching the number of powered disks to the system load. Reliability is achieved by limiting disk power cycles and using different RAID encoding schemes. Based on our five-disk prototype, PARAID uses up to 34 % less power than conventional RAIDs, while achieving similar performance and reliability. 1

Sequentiality of requested blocks on disks, or their spatial locality, is critical to the performance of disks, where the throughput of accesses to sequentially placed disk blocks can be an order of magnitude higher than that of accesses to randomly placed blocks. Unfortunately, spatial locality of cached blocks is largely ignored and only temporal locality is considered in system buffer cache management. Thus, disk performance for workloads without dominant sequential accesses can be seriously degraded. To address this problem, we propose a scheme called DULO (DUal LOcality), which exploits both temporal and spatial locality in buffer cache management. Leveraging the filtering effect of the buffer cache, DULO can influence the I/O request stream by making the requests passed to disk more sequential, significantly increasing the effectiveness of I/O scheduling and prefetching for disk performance improvements. DULO has been extensively evaluated by both tracedriven simulations and a prototype implementation in Linux 2.6.11. In the simulations and system measurements, various application workloads have been tested, including Web Server, TPC benchmarks, and scientific programs. Our experiments show that DULO can significantly increase system throughput and reduce program execution times. 1

This paper describes the first infrastructure for integrated studies of the performance and thermal behavior of storage systems. Using microbenchmarks running on this infrastructure, we first gain insight into how I/O characteristics can affect the temperature of disk drives. We use this analysis to identify the most promising, yet simple, “knobs ” for temperature optimization of high speed disks, which can be implemented on existing disks. We then analyze the thermal profiles of real workloads that use such disk drives in their storage systems, pointing out which knobs are most useful for dynamic thermal management when pushing the performance envelope.

Reducing energy consumption is an important issue for data centers. Among the various components of a data center, storage is one of the biggest energy consumers. Previous studies have shown that the average idle period for a server disk in a data center is very small compared to the time taken to spin down and spin up. This significantly limits the effectiveness of disk power management schemes. This article proposes several power-aware storage cache management algorithms that provide more opportunities for the underlying disk power management schemes to save energy. More specifically, we present an off-line energy-optimal cache replacement algorithm using dynamic programming which minimizes the disk energy consumption. We also present an off-line power-aware greedy algorithm that is more energy-efficient than Belady’s off-line algorithm (which minimizes cache misses only). We also propose two online power-aware algorithms, PA-LRU and PB-LRU. Simulation results with both real system and synthetic workloads show that, compared to LRU, our online algorithms can save up to 22% more disk energy and provide up to 64 % better average response time. We have also investigated the effects of four storage cache write policies on disk energy consumption.

Abstract—Reducing energy consumption has become one of the major challenges in designing future computing systems. This paper proposes a novel idea of using program counters to predict I/O activities in the operating system. It presents a complete design of Program-Counter Access Predictor (PCAP) that dynamically learns the access patterns of applications and predicts when an I/O device can be shut down to save energy. PCAP uses path-based correlation to observe a particular sequence of program counters leading to each idle period and predicts future occurrences of that idle period. PCAP differs from previously proposed shutdown predictors in its ability to: 1) correlate I/O operations to particular behavior of the applications and users, 2) carry prediction information across multiple executions of the applications, and 3) attain higher energy savings while incurring lower mispredictions. We perform an extensive evaluation study of PCAP using a detailed trace-driven simulation and an actual Linux implementation. Our results show that PCAP achieves lower average mispredictions and higher energy savings than the simple timeout scheme and the state-of-the-art Learning Tree scheme. Index Terms—Energy-aware systems, hardware/software interfaces, storage management.

On-disk sequentiality of requested blocks, or their spatial locality, is critical to real disk performance where the throughput of access to sequentially-placed disk blocks can be an order of magnitude higher than that of access to randomly-placed blocks. Unfortunately, spatial locality of cached blocks is largely ignored, and only temporal locality is considered in current system buffer cache managements. Thus, disk performance for workloads without dominant sequential accesses can be seriously degraded. To address this problem, we propose a scheme called DULO (DUal LOcality) which exploits both temporal and spatial localities in the buffer cache management. Leveraging the filtering effect of the buffer cache, DULO can influence the I/O request stream by making the requests passed to the disk more sequential, thus significantly increasing the effectiveness of I/O scheduling and prefetching for disk performance improvements. We have implemented a prototype of DULO in Linux 2.6.11. The implementation shows that DULO can significantly increases disk I/O throughput for real-world applications such as a Web server, TPC benchmark, file system benchmark, and scientific programs. It reduces their execution times by as much as 53%.

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 energyunaware 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.

The multi-level storage architecture has been widely adopted in servers and data centers. However, while prefetching has been shown as a crucial technique to exploit the sequentiality in accesses common for such systems and hide the increasing relative cost of disk I/O, existing multi-level storage studies have focused mostly on cache replacement strategies. In this paper, we show that prefetching algorithms designed for single-level systems may have their limitations magnified when applied to multi-level systems. Overly conservative prefetching will not be able to effectively use the lower-level cache space, while overly aggressive prefetching will be compounded across levels and generate large amounts of wasted prefetch. We take an innovative approach to this problem: rather than designing

Abstract—Many applications, especially those that run on servers, are I/O intensive and therefore require high-performance storage systems. These high-end storage systems consume a large amount of power, the bulk of which is due to the disk drives. Optimizing disk architectures is a design-time, as well as a run-time, issue, and requires performance and power trade-offs. A hard disk designer needs to balance between the disk rotational speed (rotations per minute, RPM), platter sizes, and the number of platters. The RPM and platter sizes affect performance, and all three have an impact on power. A data center manager might have specific energy budgets within which she has to extract as much performance as possible. Applications themselves may have specific optimization requirements. Therefore, there are different figures of merit, such as performance and energy, and a large space of design and runtime “knobs ” that can be used to optimize disk drive behavior. Given such a large space, it is desirable to have a systematic methodology to optimally set these knobs to satisfy the figures of merit as efficiently as possible. In this paper, we present the Sensitivity-based Optimization methodology for Disk Architectures (SODA), which leverages results previously obtained in digital circuit design optimization scenarios. Using detailed models of the electromechanical behavior of disk drives, and a suite of realistic workloads, we show how SODA can aid in design and runtime optimization of disk drive architectures.