Energy and performance evaluation of lossless file data compression on server systems

Kothiyal, Rachita; Tarasov, Vasily; Sehgal, Priya; Zadok, Erez

doi:10.1145/1534530.1534536

Cited by 29 publications

(15 citation statements)

References 57 publications

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“…Some of our own past studies show significant energy savings possible in commodity Linux servers running common workloads such as Web, email, database, compression, etc. [23,33]. Generally, optimal use of energy-saving techniques requires accurate models of system energy consumption with respect to appropriate parameters; the work described in this paper is a step towards the development of such models.…”

Section: Energy Efficiencymentioning

confidence: 99%

“…Our prior work, conducted by Kothiyal et al, evaluated energy consumption and performance of data compression on servers [23] and demonstrated that compression reduces energy consumption in some situations but not all. A careful application of compression can save energy in some cases by a factor of 10×, but a careless application of compression can easily waste energy and slow performance by 200×.…”

Section: Energy Consumption Of Data Compressionmentioning

confidence: 99%

“…In prior work, we analyzed the energy and performance profiles of server workloads, such as Web servers, email servers, database servers, and file compression [23,33]. We discovered large deviations for both performance and energy consumption-as much as 10 times-suggesting that there are significant opportunities to save energy and improve performance.…”

Section: Introductionmentioning

confidence: 99%

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On the energy consumption and performance of systems software

Grosu

Sehgal

et al. 2011

Proceedings of the 4th Annual International Conference on Systems and Storage

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Models of energy consumption and performance are necessary to understand and identify system behavior, prior to designing advanced controls that can balance out performance and energy use. This paper considers the energy consumption and performance of servers running a relatively simple file-compression workload. We found that standard techniques for system identification do not produce acceptable models of energy consumption and performance, due to the intricate interplay between the discrete nature of software and the continuous nature of energy and performance. This motivated us to perform a detailed empirical study of the energy consumption and performance of this system with varying compression algorithms and compression levels, file types, persistent storage media, CPU DVFS levels, and disk I/O schedulers. Our results identify and illustrate factors that complicate the system's energy consumption and performance, including nonlinearity, instability, and multi-dimensionality. Our results provide a basis for future work on modeling energy consumption and performance to support principled design of controllable energy-aware systems.

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Section: Energy Efficiencymentioning

confidence: 99%

Section: Energy Consumption Of Data Compressionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the energy consumption and performance of systems software

Grosu

Sehgal

et al. 2011

Proceedings of the 4th Annual International Conference on Systems and Storage

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“…GreenFS is a stackable file system that combines a remote network disk and a local flash-based memory buffer to keep the local disk idling for as long as possible [20]. Kothiyal et al examined file compression to improve power and performance [23].…”

Section: File System Studiesmentioning

confidence: 99%

Optimizing energy and performance for server-class file system workloads

2010

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of the ThesisOptimizing Energy and Performance for Server-Class File System Workloads by Priya Sehgal Master of Science in Computer ScienceStony Brook University 2010Recently, power has emerged as a critical factor in designing components of storage systems, especially for power-hungry data centers. While there is some research into power-aware storage stack components, there are no systematic studies evaluating each component's impact separately. Various factors like workloads, hardware configurations, and software configurations impact the performance and energy efficiency of the system. This thesis evaluates the file system's impact on energy consumption and performance. We studied several popular Linux file systems, with various mount and format options, using the FileBench workload generator to emulate four server workloads: Web, database, mail, and file server, on two different hardware configurations. The file system design, implementation, and available features have a significant effect on CPU/disk utilization, and hence on performance and power. We discovered that default file system options are often suboptimal, and even poor. In this thesis we show that a careful matching of expected workloads and hardware configuration to a single software configuration-the file system-can improve power-performance efficiency by a factor ranging from 1.05 to 9.4 times.iii To my parents and my brother and sister.

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“…This is because affordable storage continues to be orders of magnitude slower than any other component, such as the CPU, RAM, or even a fast local network. Any software that uses storage heavily, such as major scientific applications, transactional systems [40,64], databases [45], file systems [31,57,58,61,65], and more, are designed around the large difference in performance between RAM and storage devices. Using a flexible cluster architecture that can scale well with an increasing number of nodes is an excellent way to prorate these storage and computing costs [14].…”

Section: Introductionmentioning

confidence: 99%

An efficient multi-tier tablet server storage architecture

Spillane

Shetty

Zadok

et al. 2011

Proceedings of the 2nd ACM Symposium on Cloud Computing

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Distributed, structured data stores such as Big Table, HBase, and Cassandra use a cluster of machines, each running a database-like software system called the Tablet Server Storage Layer or TSSL. A TSSL's performance on each node directly impacts the performance of the entire cluster. In this paper we introduce an efficient, scalable, multi-tier storage architecture for tablet servers. Our system can use any layered mix of storage devices such as Flash SSDs and magnetic disks. Our experiments show that by using a mix of technologies, performance for certain workloads can be improved beyond configurations using strictly two-tier approaches with one type of storage technology. We utilized, adapted, and integrated cache-oblivious algorithms and data structures, as well as Bloom filters, to improve scalability significantly. We also support versatile, efficient transactional semantics. We analyzed and evaluated our system against the storage layers of Cassandra and Hadoop HBase. We used wide range of workloads and configurations from read-to write-optimized, as well as different input sizes. We found that our system is 3-10× faster than existing systems; that using proper data structures, algorithms, and techniques is critical for scalability, especially on modern Flash SSDs; and that one can fully support versatile transactions without sacrificing performance.

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Energy and performance evaluation of lossless file data compression on server systems

Cited by 29 publications

References 57 publications

On the energy consumption and performance of systems software

On the energy consumption and performance of systems software

Optimizing energy and performance for server-class file system workloads

An efficient multi-tier tablet server storage architecture

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