Maximizing User Service Satisfaction for Delay-Sensitive IoT Applications in Edge Computing
- Publisher:
- IEEE COMPUTER SOC
- Publication Type:
- Journal Article
- Citation:
- IEEE Transactions on Parallel and Distributed Systems, 2022, 33, (5), pp. 1199-1212
- Issue Date:
- 2022-05-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Maximizing_User_Service_Satisfaction_for_Delay-Sensitive_IoT_Applications_in_Edge_Computing.pdf | Published version | 1.22 MB |
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The Internet of Things (IoT) technology provisions unprecedented opportunities to evolve the interconnection among human beings. However, the latency brought by unstable wireless networks and computation failures caused by limited resources on IoT devices prevents users from experiencing high efficiency and seamless user experience. To address these shortcomings, the integrated Mobile Edge Computing (MEC) with remote clouds is a promising platform to enable delay-sensitive service provisioning for IoT applications, where edge-clouds (cloudlets) are co-located with wireless access points in the proximity of IoT devices. Thus, computation-intensive and sensing data from IoT devices can be offloaded to the MEC network immediately for processing, and the service response latency can be significantly reduced. In this paper, we first formulate two novel optimization problems for delay-sensitive IoT applications, i.e., the total utility maximization problems under both static and dynamic offloading task request settings, with the aim to maximize the accumulative user satisfaction on the use of the services provided by the MEC, and show the NP-hardness of the defined problems. We then devise efficient approximation and online algorithms with provable performance guarantees for the problems in a special case where the bandwidth capacity constraint is negligible. We also develop efficient heuristic algorithms for the problems with the bandwidth capacity constraint. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising in reducing service delays and enhancing user satisfaction, and the proposed algorithms outperform their counterparts by at least 10.8 percent.
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