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Article: OnDisc: Online Latency-Sensitive Job Dispatching and Scheduling in Heterogeneous Edge-Clouds
Title | OnDisc: Online Latency-Sensitive Job Dispatching and Scheduling in Heterogeneous Edge-Clouds |
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Authors | |
Keywords | Edge computing approximation algorithms scheduling algorithms |
Issue Date | 2019 |
Publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=90 |
Citation | IEEE/ACM Transactions on Networking, 2019, v. 27 n. 6, p. 2472-2485 How to Cite? |
Abstract | In edge-cloud computing, a set of servers (called edge servers) are deployed near the mobile devices to allow these devices to offload their jobs to and subsequently obtain their results from the edge servers with low latency. One fundamental problem in edge-cloud systems is how to dispatch and schedule the jobs so that the job response time (defined as the interval between the release of the job and the arrival of the computation result at the device) is minimized. In this paper, we propose a general model for this problem, where the jobs are generated in arbitrary order and at arbitrary times at the mobile devices and then offloaded to servers with both upload and download delays. Our goal is to minimize the total weighted response time of all the jobs. The weight is set based on how latency-sensitive the job is. We derive the first online job dispatching and scheduling algorithm in edge-clouds, called OnDisc, which is scalable in the speed augmentation model; that is, OnDisc is (1 + ε)-speed O(1/ε)-competitive for any small constant ε > 0. Moreover, OnDisc can be easily implemented in distributed systems. We also extend OnDisc with a fairness knob to incorporate the trade-off between the average job response time and the degree of fairness among jobs. Extensive simulations based on a real-world data-trace from Google show that OnDisc can reduce the total weighted response time dramatically compared with heuristic algorithms. |
Persistent Identifier | http://hdl.handle.net/10722/294269 |
ISSN | 2023 Impact Factor: 3.0 2023 SCImago Journal Rankings: 2.034 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | HAN, Z | - |
dc.contributor.author | Tan, H | - |
dc.contributor.author | Li, X | - |
dc.contributor.author | JIANG, S | - |
dc.contributor.author | LI, Y | - |
dc.contributor.author | Lau, FCM | - |
dc.date.accessioned | 2020-11-23T08:28:56Z | - |
dc.date.available | 2020-11-23T08:28:56Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | IEEE/ACM Transactions on Networking, 2019, v. 27 n. 6, p. 2472-2485 | - |
dc.identifier.issn | 1063-6692 | - |
dc.identifier.uri | http://hdl.handle.net/10722/294269 | - |
dc.description.abstract | In edge-cloud computing, a set of servers (called edge servers) are deployed near the mobile devices to allow these devices to offload their jobs to and subsequently obtain their results from the edge servers with low latency. One fundamental problem in edge-cloud systems is how to dispatch and schedule the jobs so that the job response time (defined as the interval between the release of the job and the arrival of the computation result at the device) is minimized. In this paper, we propose a general model for this problem, where the jobs are generated in arbitrary order and at arbitrary times at the mobile devices and then offloaded to servers with both upload and download delays. Our goal is to minimize the total weighted response time of all the jobs. The weight is set based on how latency-sensitive the job is. We derive the first online job dispatching and scheduling algorithm in edge-clouds, called OnDisc, which is scalable in the speed augmentation model; that is, OnDisc is (1 + ε)-speed O(1/ε)-competitive for any small constant ε > 0. Moreover, OnDisc can be easily implemented in distributed systems. We also extend OnDisc with a fairness knob to incorporate the trade-off between the average job response time and the degree of fairness among jobs. Extensive simulations based on a real-world data-trace from Google show that OnDisc can reduce the total weighted response time dramatically compared with heuristic algorithms. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=90 | - |
dc.relation.ispartof | IEEE/ACM Transactions on Networking | - |
dc.rights | IEEE/ACM Transactions on Networking. Copyright © Institute of Electrical and Electronics Engineers. | - |
dc.rights | ©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.subject | Edge computing | - |
dc.subject | approximation algorithms | - |
dc.subject | scheduling algorithms | - |
dc.title | OnDisc: Online Latency-Sensitive Job Dispatching and Scheduling in Heterogeneous Edge-Clouds | - |
dc.type | Article | - |
dc.identifier.email | Lau, FCM: fcmlau@cs.hku.hk | - |
dc.identifier.authority | Lau, FCM=rp00221 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TNET.2019.2953806 | - |
dc.identifier.scopus | eid_2-s2.0-85077302215 | - |
dc.identifier.hkuros | 319182 | - |
dc.identifier.volume | 27 | - |
dc.identifier.issue | 6 | - |
dc.identifier.spage | 2472 | - |
dc.identifier.epage | 2485 | - |
dc.identifier.isi | WOS:000505578800022 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 1063-6692 | - |