Proactive edge computing in latency-constrained fog networks

By Mohammed S. Elbamby, Medhi Bennis, Walid Saad. 2017

112 citations recorded by the source corpus.

In this paper, the fundamental problem of distribution and proactive caching of computing tasks in fog networks is studied under latency and reliability constraints. In the proposed scenario, computing can be executed either locally at the user device or offloaded to an edge cloudlet. Moreover, cloudlets exploit both their computing and storage capabilities by proactively caching popular task computation results to minimize computing latency. To this end, a clustering method to group spatially proximate user devices with mutual task popularity interests and their serving cloudlets is proposed. Then, cloudlets can proactively cache the popular tasks' computations of their cluster members to minimize computing latency. Additionally, the problem of distributing tasks to cloudlets is formulated as a matching game in which a cost function of computing delay is minimized under latency and reliability constraints. Simulation results show that the proposed scheme guarantees reliable computations with bounded latency and achieves up to 91% decrease in computing latency as compared to baseline schemes.

Subfields: Computer Networks and Communications

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In Computer Networks and Communications

Citations in the corpus

Latency and Reliability-Aware Task Offloading and Resource Allocation for Mobile Edge Computing (2017, background): “Mobile edge/fog computing (MEC) is an architecture that distributes computation, communication, control, and storage at the network edge [1]–[3].”
Wireless Edge Computing With Latency and Reliability Guarantees (2019, background): “At the processing level, proactive computing provides significant latency reduction while maximizing resource efficiency by avoiding repetitive and redundant on-demand computing [17], [21], [22].”
Survey of Fog Computing: Fundamental, Network Applications, and Research Challenges (2018, background): “[27] suggested a proactive caching of popular and cacheable computing tasks considering both computing and storage resources”
A Survey on Mobile Edge Computing: The Communication Perspective (2017, background): “Moreover, the recent work [134] proposed a new”
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial (2017, background): “noting that some recent works [141] have jointly combined caching and computing.”
An Overview of Service Placement Problem in Fog and Edge Computing (2020, methodology): “For instance, in Reference [71], a matching game is employed to formulate the task placement problem in Mobile Edge Computing systems while minimizing the computation latency.”
Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks (2017, background): “Finally, it is worth noting that some recent works [191] have jointly combined caching and computing.”