Abstract
There is a tendency, during the last years, to migrate from the traditional homogeneous clouds and centralized provisioning of resources to heterogeneous clouds with specialized hardware governed in a distributed and autonomous manner. The CloudLightning architecture proposed recently introduced a dynamic way to provision heterogeneous cloud resources, by shifting the selection of underlying resources from the end-user to the system in an efficient way. In this work, an optimized Suitability Index and assessment function are proposed, along with their theoretical analysis, for improving the computational efficiency, energy consumption, service delivery and scalability of the distributed orchestration. The effectiveness of the proposed scheme is being evaluated with the use of simulation, by comparing the optimized methods with the original approach and the traditional centralized resource management, on real and synthetic High Performance Computing applications. Finally, numerical results are presented and discussed regarding the improvements over the defined evaluation criteria.
Highlights
Cloud Computing has evolved into the main computing paradigm nowadays, usually coupled with edge and fog layers formulating the Edge to Cloud Continuum (E2C) [1]
Deep Reinforcement Learning (DRL) has been proposed for developing DERP [22], a resource provisioning system that combines the capability of Deep Learning (DL) to learn multi-dimensional representations of the states of the cloud computing system, with the dynamic adaptation capability of RL to workload demand changes, with the aim of finding the optimal resource management policy
In order to compute the change in the Suitability Index when a SI = f 1 NvCores acc task passes from a logical component of the hierarchy such as a pRouter, a pSwitch or a Virtual Rack Managers (vRMs) the multi-variate Taylor expansion is required, since the assessment functions are always evaluated at the vRM level
Summary
Cloud Computing has evolved into the main computing paradigm nowadays, usually coupled with edge and fog layers formulating the Edge to Cloud Continuum (E2C) [1]. Through the CloudLightning (CL) EU project, a Self-Organization and SelfManagement (SOSM) resource allocation scheme has been proposed, targeting improved service delivery, computational efficiency, power consumption, scalability and management of heterogeneous cloud resources [13]. The evaluation framework is being presented, and numerical results, using real-world application traces and synthetic data, indicate that the improved SOSM allocation scheme can provide better energy efficiency by retaining the same levels of computational efficiency, service delivery and scalability. This includes numerical results from real-world applications along with synthetic ones at a large scale.
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