TY - GEN
T1 - A game-theoretic approach to multi-objective resource sharing and allocation in mobile edge clouds
AU - Zafari, Faheem
AU - Li, Jian
AU - Leung, Kin K.
AU - Towsley, Don
AU - Swami, Ananthram
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/10/1
Y1 - 2018/10/1
N2 - Mobile edge computing seeks to provide resources to different delay-sensitive applications. However, allocating the limited edge resources to a number of applications is a challenging problem. To alleviate the resource scarcity problem, we propose sharing of resources among multiple edge computing service providers where each service provider has a particular utility to optimize. We model the resource allocation and sharing problem as a multi-objective optimization problem and present a Cooperative Game Theory (CGT) based framework, where each edge service provider first satisfies its native applications and then shares its remaining resources (if available) with users of other providers. Furthermore, we propose an O(N) algorithm that provides allocation decisions from the core, hence the obtained allocations are Pareto optimal and the grand coalition of all the service providers is stable. Experimental results show that our proposed resource allocation and sharing framework improves the utility of all the service providers compared with the case where the service providers are working alone (no resource sharing). Our O(N) algorithm reduces the time complexity of obtaining a solution from the core by as much as 71.67% when compared with the Shapley value.
AB - Mobile edge computing seeks to provide resources to different delay-sensitive applications. However, allocating the limited edge resources to a number of applications is a challenging problem. To alleviate the resource scarcity problem, we propose sharing of resources among multiple edge computing service providers where each service provider has a particular utility to optimize. We model the resource allocation and sharing problem as a multi-objective optimization problem and present a Cooperative Game Theory (CGT) based framework, where each edge service provider first satisfies its native applications and then shares its remaining resources (if available) with users of other providers. Furthermore, we propose an O(N) algorithm that provides allocation decisions from the core, hence the obtained allocations are Pareto optimal and the grand coalition of all the service providers is stable. Experimental results show that our proposed resource allocation and sharing framework improves the utility of all the service providers compared with the case where the service providers are working alone (no resource sharing). Our O(N) algorithm reduces the time complexity of obtaining a solution from the core by as much as 71.67% when compared with the Shapley value.
UR - https://www.scopus.com/pages/publications/85061504811
U2 - 10.1145/3266276.3266277
DO - 10.1145/3266276.3266277
M3 - Conference contribution
AN - SCOPUS:85061504811
T3 - Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM
SP - 9
EP - 13
BT - EdgeTech 2018 - Proceedings of the 2018 Technologies for the Wireless Edge Workshop, Co-located with MobiCom 2018
PB - Association for Computing Machinery
T2 - 1st MobiCom Workshop on Technologies for the Edge, EdgeTech 2018 at MobiCom 2018
Y2 - 2 November 2018
ER -