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AuthorEl Khatib, Rawan F.
AuthorElsayed, Sara A.
AuthorZorba, Nizar
AuthorHassanein, Hossam S.
Available date2024-07-14T07:57:22Z
Publication Date2022
Publication NameIEEE International Conference on Communications
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/ICC45855.2022.9838897
ISSN15503607
URIhttp://hdl.handle.net/10576/56609
AbstractEdge Computing (EC) has emerged as a key enabling paradigm for latency-critical and/or data-intensive applications. Recently, recycling abundant yet underutilized computational resources of the Extreme Edge Devices (EEDs), such as smartphones, laptops, connected vehicles, etc, has been explored. This is since EEDs can bring the computation service much closer to the edge, which can drastically reduce the delay. However, resource allocation in such environments typically follows a reactive approach, which can lead to increased delay and wasted resources. In this paper, we introduce the Optimal Proactive Resource Allocation (OPRA) benchmark to quantify the potential gains of proactive resource allocation in EC environments. OPRA exploits the predictability of request patterns to proactively perform resource allocation and create compute clusters that take future task and resource dynamics into consideration. Specifically, OPRA formulates the resource allocation problem as a Binary Integer Linear Program (BILP) problem, where it aims to minimize the total delay under full task assignment and computation capacity constraints. The optimal solution acquired under perfect knowledge acts as the upper bound on the achievable potential of predictive proactive resource allocation schemes. The effect of erroneous predictions on the performance of OPRA is also investigated. Extensive simulation results show that OPRA outperforms a reactive baseline by yielding a 50% decrease in the subtask dropping rate and 97% decrease in the service capacity.
SponsorACKNOWLEDGEMENT This research is supported by a grant from the Natural Sciences and Engineering Research Council of Canada (NSERC) under grant number ALLRP 549919-20. This research is also supported by Qatar University QUHI-CENG-21-22-1 project.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectInteger programming
Computation service
Computational resources
Computing environments
Critical data
Data-intensive application
Edge computing
Resource allocation problem
Resource dynamics
Resources allocation
Smart phones
Resource allocation
TitleOptimal Proactive Resource Allocation at the Extreme Edge
TypeConference Paper
Pagination5657-5662
Volume Number2022-May
dc.accessType Abstract Only


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