Abstract
The prosperous growth of the Internet-of-Things industry attracts numerous interests in employing edge clouds (a.k.a. cloudlets) to enhance the performance of mobile services and applications. Most existing research has been focused on offloading computational tasks from mobile devices to a single cloudlet or a central location, yet overlooked the issue of jointly coordinating the offloaded tasks in a system of multiple cloudlets. In this paper, we fill this gap by investigating the assignment and the scheduling of mobile computational tasks over multiple cloudlets, while optimizing the overall cost efficiency by leveraging the heterogeneity of cloudlets. We model both data transfer and computation in terms of monetary and time costs, with task deadlines guaranteed. We formulate the problem as a mixed integer program and prove its NP-hardness. By introducing admission control for the cloudlet provider to shape the system workload, we transform our problem into maximizing the task admission rate over the two coupled phases: data transfer and computation. We propose an efficient two-phase scheduling algorithm, and demonstrate that, compared with the conventional approach of always selecting the closest cloudlet, our approach achieves significantly higher admission rate with up to 20% reduction in the average cost of all offloaded tasks.
Original language | English |
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Title of host publication | 2016 IEEE 24th International Conference on Network Protocols, ICNP 2016 |
Publisher | IEEE Computer Society |
ISBN (Electronic) | 9781509032815 |
DOIs | |
Publication status | Published - 14 Dec 2016 |
Externally published | Yes |
Event | 24th IEEE International Conference on Network Protocols, ICNP 2016 - Singapore, Singapore Duration: 8 Nov 2016 → 11 Nov 2016 |
Publication series
Name | Proceedings - International Conference on Network Protocols, ICNP |
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Volume | 2016-December |
ISSN (Print) | 1092-1648 |
Conference
Conference | 24th IEEE International Conference on Network Protocols, ICNP 2016 |
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Country/Territory | Singapore |
City | Singapore |
Period | 8/11/16 → 11/11/16 |
Funding
This research was partially supported by the German Research Foundation (DFG) within the Collaborative Research Center (CRC) 1053 MAKI and by the National Science Foundation of China (NSFC) Key Program 61520106005.