On reducing energy management delays in disks

Highlights

• SARD proposes to reduce spin-up delays in disk energy management schemes.

• We survey various disk energy management techniques and their delay management.

• The implementation and trace-driven simulation shows 71% reduction in delays.

• The model also achieves an additional 5.1% average reduction in energy consumption.

Abstract

Enterprise computing systems consume a large amount of energy, the cost of which contributes significantly to the operating budget. Consequently, dynamic energy management techniques are prevalent. Unfortunately, dynamic energy management for disks impose delays associated with powering up the disks from a low-power state. Systems designers face a critical trade-off: saving energy reduces operating costs but may increase delays; conversely, reduced access latency makes the systems more responsive but may preclude energy management. In this paper, we propose a System-wide Alternative Retrieval of Data (SARD) scheme. SARD exploits the similarity in software deployment and configuration in enterprise computers to retrieve binaries transparently from other nodes, thus avoiding access delays when the local disk is in a low-power state. SARD uses a software-based approach to reduce spin-up delays while eliminating custom buffering, shared memory infrastructure, or the need for major changes in the operating system. SARD achieves over 71% reduction in delays on trace-driven simulations and in an actual implementation. This will encourage users to utilize energy management techniques more frequently. SARD also achieves an additional 5.1% average reduction in energy consumption for typical desktop applications compared to the widely-used timeout-based disk energy management.

Introduction

The positive financial and environmental implications of energy conservation for stand-alone servers and workstations is significant  [5], [9], [17], [38]. Consequently, organizations are resorting to overnight shut down of workstations, unneeded servers, and cooling systems to reduce energy consumption and the associated costs  [44]. However, it is not always possible to power down systems where users frequently work remotely and at all hours, e.g., in academic institutions and global businesses. In these environments, dynamic management techniques are employed to allow energy optimization during periods of active system use.

Dynamic energy management relies on the fact that even when the system is actively used, there are periods of inactivity that can be exploited to power down devices. It is critical to be able to predict such idle periods accurately  [19], [26] to improve energy efficiency. However, even accurate idle period detection and device shutdowns still expose power-on delays (such as disk wake-up delay); this exposure can affect system performance, irritate users, and reduce energy savings because systems now have to operate for longer durations to satisfy user requests. In the worst case, irate users may choose to disable energy management rather than deal with excessive delays. Therefore, the challenge lies in realizing energy savings by keeping the system powered down for as long as possible, yet reducing the performance impact associated with energy management delays, e.g., the response latency on powering the device up when needed.

The majority of existing approaches rely on powering-on the device to satisfy an arriving request. If the system waits until a request arrives, the delays are fully exposed. Alternatively, an accurate prediction of upcoming I/O requests and speculative powering-on of devices  [11] can reduce power-on delays exposed to the execution path. However, those approaches still require powering on of the device and incur the resultant energy consumption cost. To eliminate any energy overhead and delays associated with powering on of the device, we can utilize available surrogate resources to satisfy the incoming request  [36].

Disks can be significant energy consumers  [51], [45], [42] and require a long time to spin up from a low-power mode. Furthermore, enterprise environments have hundreds of workstations, each containing a hard disk that is spinning needlessly during the periods of inactivity. Therefore, disk energy management practices, e.g., shutting down idle disks  [19], are almost ubiquitous in some form or the other. In addition to energy management being prevalent, enterprise environments usually consist of similarly configured workstations to simplify deployment and maintenance. As a result, we focus on alleviating disk energy management delays by utilizing existing alternative sources, which are common in enterprise environments, to retrieve data to satisfy I/O requests destined for a disk in low-power mode. Finally, the largest benefit is achieved if the alternative source of data retrieval is the main memory in another system. Even a medium-size enterprise with as few as a hundred machines will have an application simultaneously running on at least two machines more than 99% of the time  [46]. Therefore, obtaining data from alternative sources such as remote memories is easily achievable. We must note that there are security risks posed by sharing a binary across machines. Techniques such as self-certifying binaries  [34] or secure binary sharing  [23] can address these concerns.

Hard disks use a significant amount, 13%  [25], of the system power, making them a prime candidate for energy conservation. However, turning off or operating disks in low power mode, poses delays and causes performance degradation. Fortunately, SARD is able to alleviate delays by utilizing other systems in p2p environment and improve energy efficiency. SARD can also co-exist with other energy management schemes. Merging SARD with self-learning energy management schemes  [20], [41], [49], [24] may not be necessary as SARD already prevents system delays, but higher energy efficiency provided by those predictors can improve energy efficiency while SARD will maintain the performance.

Traditional energy management schemes like powering-off devices may lead to long delays and ultimately displeasing the end users, particularly in enterprise computing systems. Explicit delay management is critical in a successful energy management scheme, as reduction in delay has two benefits. First, the user experience is improved since the user will see fewer delays due to disk spinning up. As a result, the user is more likely to use energy management techniques as opposed to turning energy management off to prevent the irritating delays. Second, the shorter delays will allow the user to accomplish the task quicker, which increases the efficiency of the system.

Hiding latency is crucial in enabling a higher rate of adoption of existing power-saving techniques and leads to higher energy savings. As an additional bonus, servicing I/Os from alternate sources provides opportunities for keeping the local disks in low-power mode longer, and may reduce energy consumption. We avoid spinning up a disk when a user request arrives, thus hiding spin-up delays from the user, by exploiting the arrangement of local disks/file servers in enterprise environments. We present System-wide Alternative Retrieval of Data (SARD), a scheme to retrieve application binaries from loosely connected group of workstations. Rather than accessing the local disk or a central file server, SARD serves user requests by accessing the data from a peer-to-peer (p2p) network of workstations. SARD is enabled by the common practice of uniform configuration of enterprise computers, which is done to simplify system deployment and maintenance. Consequently, the application binaries are identical across multiple systems allowing SARD to satisfy a user request from a peer node.

Our approach shares some similarity to cooperative caching[13], [22]; however, SARD is distinct in its goal of reducing energy management delays. It uses a group of loosely-connected peers and eschews any additional hardware or significant software overhaul. SARD is designed to be minimally invasive to existing systems and: (1) does not require any custom buffering or shared memory infrastructure; (2) does not interfere with energy management of other systems; (3) does not require additional hardware resources; (4) requires few kernel modifications; and (5) allows participants to be loosely coupled and free to leave and join the system. Diskless workstations can offers similar features but are not scalable given the I/O-intensive nature of applications today. Furthermore, SARD exploits standard virtual memory mechanism and does not require any custom buffering that may increase memory pressure, neither does it require any shared virtual memory; hence, there is no extra overhead on memory systems. Using a p2p based solution eliminates the requirement of fixed configurations and no peer membership constraints are imposed. System management is simple in SARD because individual machines can join and leave the peer group freely. In addition, p2p infrastructure allows us to select only those peers that will not be adversely affected by serving SARD requests, something the alternatives such as LAN multicast  [15], etc., cannot support. Thus, we achieve a low-overhead design that effectively helps minimize energy consumption in enterprise environments.

We evaluate SARD using a trace-driven simulator and an actual implementation in a real system. We observe 71% average reduction in energy management delays. Compared to the widely-used timeout-based disk energy management scheme, SARD achieves an additional, 5.1% average reduction in energy consumption as well.

We note here that SARD is a practical approach to reduce energy management delays in scenarios where hard disks, with their noticeable spin up delays, are the primary storage devices. Newer types of disks such as flash based solid state drives (SSDs) and other non-mechanical storage can potentially reduce or even eliminate spin-up delays. In addition, SSDs consume much lesser energy compared to hard disks. In such cases, disk energy management schemes may be rendered redundant. However, for now, the $/GB cost of SSDs remains high enough that we envision mechanical disks to continue as the main storage medium. Thus, techniques such as SARD will be useful and relevant in the foreseeable future.