Review on Service Composition

Hua, Huang; Guo, Zhihan

doi:10.1109/smartcloud52277.2021.00028

Cited by 4 publications

(1 citation statement)

References 114 publications

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“…Random service allocation was discussed by Souza 46 but resulted in a high service delay due to poor load balancing. Huang 47 proposed an energy‐efficient approach to the idea of co‐locating service placement, which was adequate for a small network but achieved poor service placement in a larger network. A similar approach by Wang et al 48 reduced execution costs by placing predictive modeling tasks.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement optimization for decentralized service placement policy in IoT‐centric fog environment

Sulimani

Sajjad

Alghamdi

et al. 2022

Trans Emerging Tel Tech

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A decentralized service placement policy plays a key role in distributed systems, such as fog computing, where sharing workloads fairly among active computing nodes is critical. A decentralized policy is an inherent feature of the service placement process that may improve load balancing among computers and can reduce the latency in many real‐time Internet of Things (IoT) applications. This article proposes reinforcement optimization for a decentralized service placement policy, which attempts to mitigate some of the drawbacks of existing service placement policies. Matching task size with node specifications and the allocation of less popular but time‐sensitive applications in the fog layer are the primary contributions of this study. Extensive experimental comparisons are made between the proposed algorithm and other well‐known algorithms over service latency, network usage, and computing usage using the iFogSim simulator. A microservice‐based application with varying sizes of computing requests are tested experimentally and show that the proposed algorithm effectively serves computing instances that are closer to users, reducing service latency and network usage. Compared to the existing models, the proposed modified algorithm reduces service latency by 24.1%, network usage by 4%, and computing usage by 20%, thus highlighting positive outcomes when using the proposed algorithm for fog analytics in future real‐time IoT applications.

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Section: Related Workmentioning

confidence: 99%