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From Geographically Dispersed Data Centers towards Hierarchical Edge Computing.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	From Geographically Dispersed Data Centers towards Hierarchical Edge Computing./
Author:	Kiani, Abbas.
Description:	1 online resource (122 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Contained By:	Dissertation Abstracts International79-12B(E).
Subject:	Electrical engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9780438155565

From Geographically Dispersed Data Centers towards Hierarchical Edge Computing.
Kiani, Abbas.

From Geographically Dispersed Data Centers towards Hierarchical Edge Computing. - 1 online resource (122 pages)

Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.

Thesis (Ph.D.)--New Jersey Institute of Technology, 2018.

Includes bibliographical references

Internet scale data centers are generally dispersed in different geographical regions. While the main goal of deploying the geographically dispersed data centers is to provide redundancy, scalability and high availability, the geographic dispersity provides another opportunity for efficient employment of global resources, e.g., utilizing price-diversity in electricity markets or utilizing locational diversity in renewable power generation. In other words, an efficient approach for geographical load balancing (GLB) across geo-dispersed data centers not only can maximize the utilization of green energy but also can minimize the cost of electricity. However, due to the different costs and disparate environmental impacts of the renewable energy and brown energy, such a GLB approach should tap on the merits of the separation of green energy utilization maximization and brown energy cost minimization problems. To this end, the notion of green workload and green service rate, versus brown workload and brown service rate, respectively, to facilitate the separation of green energy utilization maximization and brown energy cost minimization problems is proposed. In particular, a new optimization framework to maximize the profit of running geographically dispersed data centers based on the accuracy of the G/D/1 queueing model, and taking into consideration of multiple classes of service with individual service level agreement deadline for each type of service is developed. A new information flow graph based model for geo-dispersed data centers is also developed, and based on the developed model, the achievable tradeoff between total and brown power consumption is characterized.

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

Mode of access: World Wide Web

ISBN: 9780438155565Subjects--Topical Terms:

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

554714
Electronic books.

From Geographically Dispersed Data Centers towards Hierarchical Edge Computing.
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500 $a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 $a Adviser: Nirwan Ansari.
502 $a Thesis (Ph.D.)--New Jersey Institute of Technology, 2018.
504 $a Includes bibliographical references
520 $a Internet scale data centers are generally dispersed in different geographical regions. While the main goal of deploying the geographically dispersed data centers is to provide redundancy, scalability and high availability, the geographic dispersity provides another opportunity for efficient employment of global resources, e.g., utilizing price-diversity in electricity markets or utilizing locational diversity in renewable power generation. In other words, an efficient approach for geographical load balancing (GLB) across geo-dispersed data centers not only can maximize the utilization of green energy but also can minimize the cost of electricity. However, due to the different costs and disparate environmental impacts of the renewable energy and brown energy, such a GLB approach should tap on the merits of the separation of green energy utilization maximization and brown energy cost minimization problems. To this end, the notion of green workload and green service rate, versus brown workload and brown service rate, respectively, to facilitate the separation of green energy utilization maximization and brown energy cost minimization problems is proposed. In particular, a new optimization framework to maximize the profit of running geographically dispersed data centers based on the accuracy of the G/D/1 queueing model, and taking into consideration of multiple classes of service with individual service level agreement deadline for each type of service is developed. A new information flow graph based model for geo-dispersed data centers is also developed, and based on the developed model, the achievable tradeoff between total and brown power consumption is characterized.
520 $a Recently, the paradigm of edge computing has been introduced to push the computing resources away from the data centers to the edge of the network, thereby reducing the communication bandwidth requirement between the sources of data and the data centers. However, it is still desirable to investigate how and where at the edge of the network the computation resources should be provisioned. To this end, a hierarchical Mobile Edge Computing (MEC) architecture in accordance with the principles of LTE Advanced backhaul network is proposed and an auction-based profit maximization approach which effectively facilitates the resource allocation to the subscribers of the MEC network is designed. A hierarchical capacity provisioning framework for MEC that optimally budgets computing capacities at different hierarchical edge computing levels is also designed. The proposed scheme.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
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710 2 $a New Jersey Institute of Technology. $b Electrical and Computer Engineering. $3 1190500
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856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10809549 $z click for full text (PQDT)