The University of Southampton
University of Southampton Institutional Repository

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 developa 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.
IEEE
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 38th IEEE International Conference on Distributed Computing Systems (ICDCS'18). IEEE.. (In Press)

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 developa 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.

Text hard_to_share - Accepted Manuscript
Restricted to Repository staff only
Request a copy

More information

Accepted/In Press date: 28 March 2018
Venue - Dates: 38th IEEE International Conference on Distributed Computing Systems, Vienna, Austria, 2018-07-02 - 2018-07-05

Identifiers

Local EPrints ID: 421317
URI: https://eprints.soton.ac.uk/id/eprint/421317
PURE UUID: 1f01d734-383c-4738-bb50-7243a978e415

Catalogue record

Date deposited: 01 Jun 2018 16:30
Last modified: 01 Jun 2018 16:30

Export record

Contributors

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

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×