Distributed resource management in dew based edge to cloud computing ecosystem: A hybrid adaptive evolutionary approach,Transactions on Emerging Telecommunications Technologies

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Distributed resource management in dew based edge to cloud computing ecosystem: A hybrid adaptive evolutionary approach
Transactions on Emerging Telecommunications Technologies ( IF 2.5 ) Pub Date : 2020-07-01 , DOI: 10.1002/ett.4018
Asmita Roy ₁ , Sadip Midya ₁ , Koushik Majumder ₁ , Santanu Phadikar ₁

Affiliation

To extend the reach of cloud computing, the concept of edge computing and dew computing is introduced to execute various Internet of things (IoT) application with minimized delay in real time. The requested tasks are allocated computing resources that best suits their purpose. In this work, a novel hybrid hierarchical dew based edge to cloud architecture is developed. The objective of the study is to provide a detailed analysis and validation of real‐time scheduling of IoT application in this hybrid hierarchical ecosystem. The problem of optimally mapping requested tasks to the computing layers is mathematically formulated based on several quality of service factors and solved using the proposed hybrid adaptive metaheuristic algorithm. This is a combination of learning‐based adaptive particle swarm optimization and genetic algorithm. The exploitative and exploratory feature of the proposed algorithm helps in achieving better global optima compared with other existing metaheuristic algorithms.

中文翻译：

基于露水的边缘到云计算生态系统的分布式资源管理：一种混合自适应进化方法

为了扩展云计算的范围，引入了边缘计算和露水计算的概念，以最小的实时延迟执行各种物联网（IoT）应用程序。向请求的任务分配最适合其目的的计算资源。在这项工作中，开发了一种新颖的基于边缘层到云体系结构的混合分层露水。该研究的目的是在此混合分层生态系统中提供详细的分析和物联网应用实时调度的验证。基于几种服务质量因素，以数学方式提出了将请求的任务最佳地映射到计算层的问题，并使用提出的混合自适应元启发式算法解决了该问题。这是基于学习的自适应粒子群优化和遗传算法的结合。

更新日期：2020-07-01

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