Dynamic Service Placement for Mobile Micro-Clouds with Predicted Future Costs

Mobile micro-clouds are promising for enabling performance-critical cloud applications. However, one challenge therein is the dynamics at the network edge. In this paper, we study how to place service instances to cope with these dynamics, where multiple users and service instances coexist in the sy...

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Vydáno v:	IEEE transactions on parallel and distributed systems Ročník 28; číslo 4; s. 1002 - 1016
Hlavní autoři:	Wang, Shiqiang, Urgaonkar, Rahul, He, Ting, Chan, Kevin, Zafer, Murtaza, Leung, Kin K.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.04.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithm design and analysis Algorithms Approximation algorithms Cloud computing Computer simulation Configurations Cost function Electronic mail fog/edge computing Heuristic algorithms Mathematical analysis online approximation algorithm optimization Placement Polynomials Prediction algorithms Predictions Resource allocation Upper bounds Windows (intervals) wireless networks
ISSN:	1045-9219, 1558-2183
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Abstract	Mobile micro-clouds are promising for enabling performance-critical cloud applications. However, one challenge therein is the dynamics at the network edge. In this paper, we study how to place service instances to cope with these dynamics, where multiple users and service instances coexist in the system. Our goal is to find the optimal placement (configuration) of instances to minimize the average cost overtime, leveraging the ability of predicting future cost parameters with known accuracy. We first propose an offline algorithm that solves for the optimal configuration in a specific look-ahead time-window. Then, we propose an online approximation algorithm with polynomial time-complexity to find the placement in real-time whenever an instance arrives. We analytically show that the online algorithm is 0(1)-competitive for a broad family of cost functions. Afterwards, the impact of prediction errors is considered and a method for finding the optimal look-ahead window size is proposed, which minimizes an upper bound of the average actual cost. The effectiveness of the proposed approach is evaluated by simulations with both synthetic and real-world (San Francisco taxi) usermobility traces. The theoretical methodology used in this paper can potentially be applied to a larger class of dynamic resource allocation problems.
AbstractList	Mobile micro-clouds are promising for enabling performance-critical cloud applications. However, one challenge therein is the dynamics at the network edge. In this paper, we study how to place service instances to cope with these dynamics, where multiple users and service instances coexist in the system. Our goal is to find the optimal placement (configuration) of instances to minimize the average cost overtime, leveraging the ability of predicting future cost parameters with known accuracy. We first propose an offline algorithm that solves for the optimal configuration in a specific look-ahead time-window. Then, we propose an online approximation algorithm with polynomial time-complexity to find the placement in real-time whenever an instance arrives. We analytically show that the online algorithm is 0(1)-competitive for a broad family of cost functions. Afterwards, the impact of prediction errors is considered and a method for finding the optimal look-ahead window size is proposed, which minimizes an upper bound of the average actual cost. The effectiveness of the proposed approach is evaluated by simulations with both synthetic and real-world (San Francisco taxi) usermobility traces. The theoretical methodology used in this paper can potentially be applied to a larger class of dynamic resource allocation problems.
Author	Urgaonkar, Rahul Leung, Kin K. He, Ting Chan, Kevin Wang, Shiqiang Zafer, Murtaza
Author_xml	– sequence: 1 givenname: Shiqiang surname: Wang fullname: Wang, Shiqiang email: wangshiq@us.ibm.com organization: IBM T. J. Watson Research Center, Yorktown Heights, NY, United States – sequence: 2 givenname: Rahul surname: Urgaonkar fullname: Urgaonkar, Rahul email: rahul.urgaonkar@gmail.com organization: Amazon Inc., Seattle, WA – sequence: 3 givenname: Ting surname: He fullname: He, Ting email: tzh58@psu.edu organization: School of Electrical Engineering and Computer Science, Pennsylvania State University, University Park, PA, United States – sequence: 4 givenname: Kevin surname: Chan fullname: Chan, Kevin email: kevin.s.chan.civ@mail.mil organization: Army Research Laboratory, Adelphi, MD – sequence: 5 givenname: Murtaza surname: Zafer fullname: Zafer, Murtaza email: murtaza.zafer.us@ieee.org organization: Nyansa Inc., Palo Alto, CA – sequence: 6 givenname: Kin K. surname: Leung fullname: Leung, Kin K. email: kin.leung@imperial.ac.uk organization: Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom
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SubjectTerms	Algorithm design and analysis Algorithms Approximation algorithms Cloud computing Computer simulation Configurations Cost function Electronic mail fog/edge computing Heuristic algorithms Mathematical analysis online approximation algorithm optimization Placement Polynomials Prediction algorithms Predictions Resource allocation Upper bounds Windows (intervals) wireless networks
Title	Dynamic Service Placement for Mobile Micro-Clouds with Predicted Future Costs
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