Quality of Information in Gathering Information via Video Analytics for Military Networks

Mehmeti, Fidan; Felemban, Noor; Lu, Zongqing; Wheatman, Kristina Sorensen; Cirincione, Greg; Porta, Thomas F. La

doi:10.1109/mcom.001.2000852

Cited by 3 publications

(1 citation statement)

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“…Edge computing [1] is an important new paradigm that enables mobile devices to request complex processing services. Edge clouds may be used for emergency services or tactical situations [2] where an access network with processing resources is deployed in the field, or in cases in which access service providers provide some computing resources to subscribers. Edge clouds are not as well-resourced as backbone cloud networks, so resource allocation in these edge clouds is an important problem.…”

Section: Introductionmentioning

confidence: 99%

Online Resource Allocation in Edge Computing Using Distributed Bidding Approaches

Rublein¹,

Mehmeti²,

Towers³

et al. 2021

2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS)

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Edge computing has become a very popular service that enables mobile devices to run complex tasks with the help of network-based computing resources. However, edge clouds are often resource-constrained, which makes resource allocation a challenging issue. We focus on a distributed resource allocation method in which servers operate independently and do not communicate with each other, but interact with clients (tasks) to make allocation decisions. This provides robustness and does not require service providers to share information about their configurations or workloads. We propose a two-round bidding approach of assigning tasks to edge cloud servers, while taking into account various processing requirements and server constraints. We consider cases in which all jobs have equal utility, cases where jobs have different utilities but users do not disclose these utilities to servers, and cases where users disclose the utility of their jobs to servers. We evaluate the performance using extensive realistic simulations. Results show that our approach is very close to an optimal assignment, with discrepancy not exceeding 5%.

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Section: Introductionmentioning

confidence: 99%