Abstract
Modern applications in mobile computing become increasingly complex and computation intensive. Task offloading from mobile devices to the cloud is more and more frequent. Edge Computing, deploying relatively small-scale edge servers close to users, is a promising cloud computing paradigm to reduce the network communication delay. Due to the limited capability, each edge server can be configured with only a small amount of functions to run corresponding tasks. Moreover, a mobile application might consist of multiple dependent tasks, which can be modeled and scheduled as Directed Acyclic Graphs (DAGs). When an application request arrives online, typically with a deadline specified, we need to configure the edge servers and assign the dependent tasks for processing. In this work, we jointly tackle on-demand function configuration on edge servers and DAG scheduling to meet as many request deadlines as possible. Based on list scheduling methodologies, we propose a novel online algorithm, named OnDoc, which is efficient and easy to deploy in practice. Extensive simulations on the data trace from Alibaba (including more than 3 million application requests) demonstrate that OnDoc outperforms state-of-the-art baselines consistently on various experiment settings.
The first two authors have equal contribution.
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Acknowledgments
This work is supported partly by the National Key R&D Program of China 2018YFB0803400, China National Funds for Distinguished Young Scientists No. 61625205, NSFC Grants 61772489, 61751211, Key Research Program of Frontier Sciences (CAS) No. QYZDY-SSW-JSC002, NSF ECCS-1247944, NSF CNS 1526638, and the Fundamental Research Funds for the Central U.
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Liu, L., Huang, H., Tan, H., Cao, W., Yang, P., Li, XY. (2019). Online DAG Scheduling with On-Demand Function Configuration in Edge Computing. In: Biagioni, E., Zheng, Y., Cheng, S. (eds) Wireless Algorithms, Systems, and Applications. WASA 2019. Lecture Notes in Computer Science(), vol 11604. Springer, Cham. https://doi.org/10.1007/978-3-030-23597-0_17
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DOI: https://doi.org/10.1007/978-3-030-23597-0_17
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