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It's hard to share: joint service placement and request scheduling in edge clouds with sharable and non-sharable resources

It's hard to share: joint service placement and request scheduling in edge clouds with sharable and non-sharable resources
It's hard to share: joint service placement and request scheduling in edge clouds with sharable and non-sharable resources

Mobile edge computing is an emerging technology to offer resource-intensive yet delay-sensitive applications from the edge of mobile networks, where a major challenge is to allocate limited edge resources to competing demands. While prior works often make a simplifying assumption that resources assigned to different users are non-sharable, this assumption does not hold for storage resources, where users interested in services (e.g., data analytics) based on the same set of data/code can share storage resource. Meanwhile, serving each user request also consumes non-sharable resources (e.g., CPU cycles, bandwidth). We study the optimal provisioning of edge services with non-trivial demands of both sharable (storage) and non-sharable (communication, computation) resources via joint service placement and request scheduling. In the homogeneous case, we show that while the problem is polynomial-time solvable without storage constraints, it is NP-hard even if each edge cloud has unlimited communication or computation resources. We further show that the hardness is caused by the service placement subproblem, while the request scheduling subproblem is polynomial-time solvable via maximum-flow algorithms. In the general case, both subproblems are NP-hard. We develop a constant-factor approximation algorithm for the homogeneous case and efficient heuristics for the general case. Our trace-driven simulations show that the proposed algorithms, especially the approximation algorithm, can achieve near-optimal performance, serving 2-3 times more requests than a baseline solution that optimizes service placement and request scheduling separately.

Approximation Algorithm, Complexity Analysis, Mobile Edge Computing, Request Scheduling, Service Placement
365-375
Institute of Electrical and Electronics Engineers Inc.
He, Ting
3e347968-3aec-4015-8ddd-8cc28e7b1276
Khamfroush, Hana
aac03cea-47fc-4a57-b2a3-a78d1e31d02c
Wang, Shiqiang
d26acadf-5f63-4b4f-9f73-a2c3843758ec
La Porta, Tom
15f9f4b5-613c-43d4-8b89-8f8d5277a2c0
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b
He, Ting
3e347968-3aec-4015-8ddd-8cc28e7b1276
Khamfroush, Hana
aac03cea-47fc-4a57-b2a3-a78d1e31d02c
Wang, Shiqiang
d26acadf-5f63-4b4f-9f73-a2c3843758ec
La Porta, Tom
15f9f4b5-613c-43d4-8b89-8f8d5277a2c0
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b

He, Ting, Khamfroush, Hana, Wang, Shiqiang, La Porta, Tom and Stein, Sebastian (2018) It's hard to share: joint service placement and request scheduling in edge clouds with sharable and non-sharable resources. In Proceedings - 2018 IEEE 38th International Conference on Distributed Computing Systems, ICDCS 2018. vol. 2018-July, Institute of Electrical and Electronics Engineers Inc. pp. 365-375 . (doi:10.1109/ICDCS.2018.00044).

Record type: Conference or Workshop Item (Paper)

Abstract

Mobile edge computing is an emerging technology to offer resource-intensive yet delay-sensitive applications from the edge of mobile networks, where a major challenge is to allocate limited edge resources to competing demands. While prior works often make a simplifying assumption that resources assigned to different users are non-sharable, this assumption does not hold for storage resources, where users interested in services (e.g., data analytics) based on the same set of data/code can share storage resource. Meanwhile, serving each user request also consumes non-sharable resources (e.g., CPU cycles, bandwidth). We study the optimal provisioning of edge services with non-trivial demands of both sharable (storage) and non-sharable (communication, computation) resources via joint service placement and request scheduling. In the homogeneous case, we show that while the problem is polynomial-time solvable without storage constraints, it is NP-hard even if each edge cloud has unlimited communication or computation resources. We further show that the hardness is caused by the service placement subproblem, while the request scheduling subproblem is polynomial-time solvable via maximum-flow algorithms. In the general case, both subproblems are NP-hard. We develop a constant-factor approximation algorithm for the homogeneous case and efficient heuristics for the general case. Our trace-driven simulations show that the proposed algorithms, especially the approximation algorithm, can achieve near-optimal performance, serving 2-3 times more requests than a baseline solution that optimizes service placement and request scheduling separately.

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More information

Accepted/In Press date: 2 July 2018
Published date: 19 July 2018
Venue - Dates: 38th IEEE International Conference on Distributed Computing Systems, ICDCS 2018, Vienna, Austria, 2018-07-02 - 2018-07-05
Keywords: Approximation Algorithm, Complexity Analysis, Mobile Edge Computing, Request Scheduling, Service Placement

Identifiers

Local EPrints ID: 423238
URI: https://eprints.soton.ac.uk/id/eprint/423238
PURE UUID: 697438ae-8ceb-4253-95f1-73c515d2d0c0

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Date deposited: 19 Sep 2018 16:30
Last modified: 19 Sep 2018 16:30

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Contributors

Author: Ting He
Author: Hana Khamfroush
Author: Shiqiang Wang
Author: Tom La Porta
Author: Sebastian Stein

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