Data layout for power efficient archival storage systems

Reddy, R. B.; Kathpal, Atish; Basak, Jayanta Kumar

doi:10.1145/2818613.2818742

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…Experiments from the California Department of Water Resources show that a 30% hit rate can result in at least 12% power savings. Similarly, R. Reddy et al designed a data layout for power efficient archival storage system, in which an access-aware intelligent data layout mechanism is provided [94]. A twotier architecture that consist of online and offline disks is designed to store the archival data in the spin-down disks.…”

Section: Energy Aware Data Layout Policiesmentioning

confidence: 99%

“…From the macro perspectives, the energy efficiency techniques include energy-aware software techniques and energy efficient hardware techniques. Energy-aware software techniques include workload consolidation [4], [5], task or job scheduling [6]- [8], data concentration [9], data placement [10], data replication [11], [12], VM consolidation [13], [14], VM migration [15], [16] and VM scheduling [17], [18]. Energy efficient hardware techniques usually employ the energy efficient computing components [19], [20], such as, multispeed disks, SSD disks, DVFS enabled CPU and Flash memory etc.…”

Section: Introductionmentioning

confidence: 99%

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A Survey and Taxonomy on Energy-Aware Data Management Strategies in Cloud Environment

You

Zhen

et al. 2020

IEEE Access

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During the past ten years, the energy consumption problem in cloud-related environments has attracted substantial attention in research and industrial communities. Researchers have conducted many surveys on energy efficiency issues from different perspectives. All of the surveys can be classified into five categories: surveys on the energy efficiency of the whole cloud related system, surveys on the energy efficiency of a certain level or component of the cloud, surveys on all of the energy efficient strategies, surveys on a certain energy efficiency techniques, and other energy efficiency related surveys. However, to the best of our knowledge, surveys on energy-aware data management strategies in cloud-related environment are absent. In this paper, we conduct a comprehensive survey on energy saving-aware data management strategies in cloud-related environments, such as data classification, data placement and data replication strategies. Compared to current existing reviews on energy efficiency in cloud-related environments, we firstly conduct the survey on the energy consumption problem from the data management perspective. Furthermore, we classify the energy-aware data management strategies from different perspectives. This survey and the taxonomy of the energy-aware data management strategies demonstrate the potential for reducing the energy consumption at the data management level of a cloud storage system, which will compress more space for energy reduction and finally achieve energy proportionality. Moreover, this survey and taxonomy on the energy efficiency issue from the data management perspective is an important supplement to current existing surveys on energy efficiency in cloud-related environments.

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Section: Energy Aware Data Layout Policiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Survey and Taxonomy on Energy-Aware Data Management Strategies in Cloud Environment

You

Zhen

et al. 2020

IEEE Access

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“…Moreover, R. Yadav et al published the related article for minimizing energy consumption and SLA violation in cloud computing 19‐22 . Other data placement or data layout strategies for energy efficiency have been published in recent years, 15,19‐33 but the period access characteristics also have not been extracted for data clustering storing.…”

Section: Related Workmentioning

confidence: 99%

K‐ear: Extracting data access periodic characteristics for energy‐aware data clustering and storing in cloud storage systems

You

Sun

et al. 2020

Concurrency and Computation

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Rapid increase in energy consumption is a serious problem in cloud storage systems. Data accessed in large‐scale storage systems usually exhibit temporal and spatial characteristics, which make it possible to reduce energy consumption by clustering data with similar access characteristics for storage in the same zone of cloud storage systems. Existing works usually only focus on the frequency of data access. However, widely existing phenomena show data access with seasonal and tidal characteristics in cloud storage systems. The seasonal and tidal characteristics of data access are extracted thoroughly in this paper. According to the extracted data access characteristics, energy‐aware data clustering through a machine learning algorithm (K‐ear) is proposed. K‐ear classifies data into five seasonal categories according to their seasonal access characteristics and then classifies every seasonal category into three tidal categories according to its tidal access characteristics. The 15 classified categories are stored in different storage zones with different energy and performance modes. Simulation experiments using CloudSimDisk with the constructed mathematic models demonstrate that the proposed K‐ear algorithm is more energy‐efficient than the default data clustering algorithms in Hadoop and the classical data clustering storage strategy according to the data access frequency (Striping‐Based Energy‐Aware Strategy).

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“…It combines adaptive query processing techniques with customized I/O scheduling and page caching to execute queries on PostgreSQL database on CSD [5]. Further, a data layout [6] for cold storage hardware (similar to Pelican but with a switching latency of 30s) has been proposed. Based on the access patterns, groups of objects that are accessed together are stored on same disk group.…”

Section: B Data Processing On Cold Storagementioning

confidence: 99%

Towards Batch-Processing on Cold Storage Devices

Hadian

Heinis

2018

2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW)

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Large amounts of data in storage systems is cold, i.e., Written Once and Read Occasionally (WORO). The rapid growth of massive-scale archival and historical data increases the demand for petabyte-scale cheap storage for such cold data. A Cold Storage Device (CSD) is a disk-based storage system which is designed to trade off performance for cost and power efficiency. Inevitably, the design restrictions used in CSD's results in performance limitations. These limitations are not a concern for WORO workloads, however, the very low price/performance characteristics of CSDs makes them interesting for other applications, e.g., batch processes, too. Applications, however, can be very slow on CSD's if they do not take their characteristics into account. In this paper we design two strategies for data partitioning in CSDs-a crucial operation in many batch analytics tasks like hash-join, near-duplicate detection, and data localization. We show that our strategies can efficiently use CSDs for batch processing of terabyte-scale data by accelerating data partitioning by 3.5x in our experiments.

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Data layout for power efficient archival storage systems

Cited by 6 publications

References 10 publications

A Survey and Taxonomy on Energy-Aware Data Management Strategies in Cloud Environment

A Survey and Taxonomy on Energy-Aware Data Management Strategies in Cloud Environment

K‐ear: Extracting data access periodic characteristics for energy‐aware data clustering and storing in cloud storage systems

Towards Batch-Processing on Cold Storage Devices

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