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Access Type
WSU Access
Date of Award
January 2025
Degree Type
Dissertation
Degree Name
Ph.D.
Department
Computer Science
First Advisor
Daniel Grosu
Abstract
Edge computing allows end-user devices to offload heavy computation to nearby edge servers for reduced latency, maximized profit, and/or minimized energy consumption. Data dependent tasks that analyze locally acquired sensing data are one of the most common candidates for task offloading in edge computing. Thus, the total latency and network loadare affected by the total amount of data transferred from end-user devices to the selected edge servers. Most existing solutions for task allocation in edge computing do not consider that some user tasks may operate on the same data items. Making the task allocation algorithm aware of the existing data sharing characteristics of tasks can help reduce the network load at a negligible profit loss by allocating more tasks sharing data on the same server.
In this dissertation, we formulate the data sharing-aware task allocation problem that makes decisions on task allocation for maximized profit and minimized network load by considering the data-sharing characteristics of tasks. In addition, because the problem is NP-hard, we design an offline algorithm called DSTA, which finds a close to optimal solution to the problem in polynomial time. We analyze the performance of our algorithm against a state-of-the-art baseline that only maximizes profit. Our analysis shows that DSTA leads to about eight times lower data load on the network while being within 1.03 times of the total profit on average compared to the baseline. In addition, we introduce the Online Data Sharing-aware Task Allocation (ODSTA) problem and design online algorithms for task allocation in edge computing that take into account the sharing of data among the tasks offloaded to the same server. We perform an extensive performance analysis by comparing our proposed data sharing-aware online algorithms with several baseline online sharing-oblivious algorithms. The results show that our algorithms are able to reduce the amount of data transferred in the network by 30.2% to 92.8% and the number of utilized servers by 1% to 82.8% compared to the sharing-oblivious baseline algorithms.
We also augment these online algorithms with a local search phase that iteratively attempts to improve the solutions obtained by our data sharing-aware algorithms by exploring the neighborhood of the current solution and making minor modifications. Our extensive experimental performance analysis shows that the algorithms augmented with local searchreduce the number of utilized servers by 9.1% to 66.7% compared to the data sharing-aware online algorithms at the expense of a small increase in the amount of data transferred in the network and a small increase in execution time. We provide a summary of the findings that can be used as a guideline for choosing a specific algorithm for a given practical scenario characterized by the tasks’ CPU demand and their data sharing characteristics.
Recommended Citation
Rabinia Haratbar, Sanaz, "Data Sharing-Aware Task Allocation Algorithms In Edge Computing Systems" (2025). Wayne State University Dissertations. 4289.
https://digitalcommons.wayne.edu/oa_dissertations/4289