國立虎尾科技大學 |

Fast Demand Response with Datacenter Loads : = A Green Dimension of Big Data.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Fast Demand Response with Datacenter Loads :/
其他題名:	A Green Dimension of Big Data.
作者:	McClurg, Josiah.
面頁冊數:	1 online resource (113 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.
Contained By:	Dissertation Abstracts International79-02B(E).
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355244854

Fast Demand Response with Datacenter Loads : = A Green Dimension of Big Data.
McClurg, Josiah.

Fast Demand Response with Datacenter Loads :A Green Dimension of Big Data. - 1 online resource (113 pages)

Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Demand response is one of the critical technologies necessary for allowing large-scale penetration of intermittent renewable energy sources in the electric grid. Data centers are especially attractive candidates for providing flexible, real-time demand response services to the grid because they are capable of fast power ramp-rates, large dynamic range, and finely-controllable power consumption. This thesis makes a contribution toward implementing load shaping with server clusters through a detailed experimental investigation of three broadly-applicable datacenter workload scenarios. We experimentally demonstrate the eminent feasibility of datacenter demand response with a distributed video transcoding application and a simple distributed power controller. We also show that while some software power capping interfaces performed better than others, all the interfaces we investigated had the high dynamic range and low power variance required to achieve high quality power tracking. Our next investigation presents an empirical performance evaluation of algorithms that replace arithmetic operations with low-level bit operations for power-aware Big Data processing. Specifically, we compare two different data structures in terms of execution time and power efficiency: (a) a baseline design using arrays, and (b) a design using bit-slice indexing (BSI) and distributed BSI arithmetic. Across three different datasets and three popular queries, we show that the bit-slicing queries consistently outperform the array algorithm in both power efficiency and execution time. In the context of datacenter power shaping, this performance optimization enables additional power flexibility -- achieving the same or greater performance than the baseline approach, even under power constraints. The investigation of read-optimized index queries leads up to an experimental investigation of the tradeoffs among power constraint, query freshness, and update aggregation size in a dynamic big data environment. We compare several update strategies, presenting a bitmap update optimization that allows improved performance over both a baseline approach and an existing state-of-the-art update strategy. Performing this investigation in the context of load shaping, we show that read-only range queries can be served without performance impact under power cap, and index updates can be tuned to provide a flexible base load. This thesis concludes with a brief discussion of control implementation and summary of our findings.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355244854Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Fast Demand Response with Datacenter Loads : = A Green Dimension of Big Data.
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520 $a Demand response is one of the critical technologies necessary for allowing large-scale penetration of intermittent renewable energy sources in the electric grid. Data centers are especially attractive candidates for providing flexible, real-time demand response services to the grid because they are capable of fast power ramp-rates, large dynamic range, and finely-controllable power consumption. This thesis makes a contribution toward implementing load shaping with server clusters through a detailed experimental investigation of three broadly-applicable datacenter workload scenarios. We experimentally demonstrate the eminent feasibility of datacenter demand response with a distributed video transcoding application and a simple distributed power controller. We also show that while some software power capping interfaces performed better than others, all the interfaces we investigated had the high dynamic range and low power variance required to achieve high quality power tracking. Our next investigation presents an empirical performance evaluation of algorithms that replace arithmetic operations with low-level bit operations for power-aware Big Data processing. Specifically, we compare two different data structures in terms of execution time and power efficiency: (a) a baseline design using arrays, and (b) a design using bit-slice indexing (BSI) and distributed BSI arithmetic. Across three different datasets and three popular queries, we show that the bit-slicing queries consistently outperform the array algorithm in both power efficiency and execution time. In the context of datacenter power shaping, this performance optimization enables additional power flexibility -- achieving the same or greater performance than the baseline approach, even under power constraints. The investigation of read-optimized index queries leads up to an experimental investigation of the tradeoffs among power constraint, query freshness, and update aggregation size in a dynamic big data environment. We compare several update strategies, presenting a bitmap update optimization that allows improved performance over both a baseline approach and an existing state-of-the-art update strategy. Performing this investigation in the context of load shaping, we show that read-only range queries can be served without performance impact under power cap, and index updates can be tuned to provide a flexible base load. This thesis concludes with a brief discussion of control implementation and summary of our findings.
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