國立虎尾科技大學 |

Energy Management of Multi-Component Computing Platforms Under Energy Constraints.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Energy Management of Multi-Component Computing Platforms Under Energy Constraints./
作者:	Begum, Rizwana.
面頁冊數:	1 online resource (169 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-09(E), Section: B.
標題:	Computer engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9781369731439

Energy Management of Multi-Component Computing Platforms Under Energy Constraints.
Begum, Rizwana.

Energy Management of Multi-Component Computing Platforms Under Energy Constraints. - 1 online resource (169 pages)

Source: Dissertation Abstracts International, Volume: 78-09(E), Section: B.

Thesis (Ph.D.)--Drexel University, 2017.

Includes bibliographical references

Energy management is a problem of all types of computing devices. For example, short battery life is a complaint of today's mobile device users, while high operational costs of cooling and supplying power are the concerns of modern datacenters. It is inevitable that all modern multi-component devices manage energy in order to address these concerns. While, a majority of previous research efforts focus on single components such as CPU and DRAM, only a few optimize the energy consumption of multiple components---without understanding the interactions among these components.

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

Mode of access: World Wide Web

ISBN: 9781369731439Subjects--Topical Terms:

569006
Computer engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Energy Management of Multi-Component Computing Platforms Under Energy Constraints.
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245 1 0 $a Energy Management of Multi-Component Computing Platforms Under Energy Constraints.
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300 $a 1 online resource (169 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 78-09(E), Section: B.
500 $a Adviser: Mark D. Hempstead.
502 $a Thesis (Ph.D.)--Drexel University, 2017.
504 $a Includes bibliographical references
520 $a Energy management is a problem of all types of computing devices. For example, short battery life is a complaint of today's mobile device users, while high operational costs of cooling and supplying power are the concerns of modern datacenters. It is inevitable that all modern multi-component devices manage energy in order to address these concerns. While, a majority of previous research efforts focus on single components such as CPU and DRAM, only a few optimize the energy consumption of multiple components---without understanding the interactions among these components.
520 $a Core and memory systems consume a majority of total power consumption in today's computing devices. While Dynamic Voltage and Frequency Scaling (DVFS) for cores is a well established technique to make energy-performance trade-offs, Dynamic Frequency Scaling (DFS) is emerging as a promising technique for memory systems. This dissertation presents a holistic approach that optimizes performance of a system capable of core DVFS and DRAM DFS under energy constraints in a coordinated fashion. The dissertation introduces a relative energy constraint, inefficiency, that is independent of applications and agnostic to devices unlike existing absolute energy constraints. Our cross-component performance and energy models understand the interaction between core and DRAM. The dissertation introduces new relative and adaptive energy management algorithms that use the models to choose optimal frequency settings under given inefficiency budget.
520 $a In addition to the constraints, the system also needs metrics to provide insights into how well the system is tuning. This dissertation introduces the concept of Power-Agility and a set of novel metrics to measure Power-Agility---ability of the systems to select and transition to the efficient power settings dynamically during the application execution. In the end, the dissertation characterizes and compares the power-agility of the proposed energy management approach to a state-of-the-art server system managing core DVFS and DRAM DFS under performance constraints.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer engineering. $3 569006
650 4 $a Electrical engineering. $3 596380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0464
690 $a 0544
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Drexel University. $b Computer Engineering (College of Engineering). $3 1186946
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