Deadline-aware heuristics for reliability optimization in ubiquitous mobile edge computing

Zaman, Sardar Khaliq Uz; Maqsood, Tahir; Ramzan, Azra; Rehman, Faisal; Mustafa, Saad; Shuja, Junaid

doi:10.1007/s41060-023-00473-x

Cited by 1 publication

(1 citation statement)

References 30 publications

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“…In Nadeem et al (2023) , the authors presented a cloud setting, employing dynamic resource provisioning. Zaman et al (2023) introduced the Deadline-aware Heuristic Algorithm (DHA) for task offloading, considering latency and computing capacity. The DHA significantly reduces total latency (12.67 ms) and offloading failure probability (0.095), outperforming state-of-the-art techniques (19 ms and 0.38 probability).…”

Section: Related Workmentioning

confidence: 99%

Design of load-aware resource allocation for heterogeneous fog computing systems

Hassan,

Rehman,

Alsharabi

et al. 2024

PeerJ Computer Science

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The execution of delay-aware applications can be effectively handled by various computing paradigms, including the fog computing, edge computing, and cloudlets. Cloud computing offers services in a centralized way through a cloud server. On the contrary, the fog computing paradigm offers services in a dispersed manner providing services and computational facilities near the end devices. Due to the distributed provision of resources by the fog paradigm, this architecture is suitable for large-scale implementation of applications. Furthermore, fog computing offers a reduction in delay and network load as compared to cloud architecture. Resource distribution and load balancing are always important tasks in deploying efficient systems. In this research, we have proposed heuristic-based approach that achieves a reduction in network consumption and delays by efficiently utilizing fog resources according to the load generated by the clusters of edge nodes. The proposed algorithm considers the magnitude of data produced at the edge clusters while allocating the fog resources. The results of the evaluations performed on different scales confirm the efficacy of the proposed approach in achieving optimal performance.

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Section: Related Workmentioning

confidence: 99%