Video processing of complex activity detection in resource-constrained networks

Felemban, Noor; Lu, Zongqing; Porta, Thomas La; Chan, Kevin

doi:10.1109/globalsip.2016.7906060

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…The statistics for the jobs and servers in this scenario are shown in Table II (video statistics based on [8], [9]). Eight servers and 250 jobs were created based on these normal distributions.…”

Section: Utility Achieved In Batch Workloadmentioning

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: Utility Achieved In Batch Workloadmentioning

confidence: 99%