Opportunistic scheduling and resources consolidation system based on a new economic model
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Opportunistic scheduling and resources consolidation system based on a new economic model Tarek Menouer1 · Christophe Cérin2 · Ching‑Hsien Hsu3,4,5
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract This paper presents a new opportunistic scheduling and resource consolidation system based on an economic model related to different service level agreements (SLAs) classes. The goal is to address the problem of companies that manage a private infrastructure of machines, i.e., a cloud platform and would like to optimize the scheduling of several requests submitted online by users. For the sake of simplicity of the presentation, the proposed economic model has two SLAs classes (qualitative and quantitative) with three Quality of Service for each SLA class (Premium, Advanced and Best effort). The consequence of this choice as well as the need to serve requests as they come have an impact on the algorithmic ways to consolidate an infrastructure. Indeed, our system proposes a new allocation heuristic that adapts the number of active machines in the cloud according to the global resources usage of all machines inside the infrastructure. This heuristic can be examined as a consolidation heuristic, based on the idea that the system can make reasonable choices, based on the SLAs, for the placement and the allocation of resources for each request. Experimentation with our system is conducted on Prezi (Web workload) and Google Cloud Data (HPC-oriented workload) traces, and they demonstrate the potential of our approach under different scenarios. From a methodological point of view, we propose a general framework which is limited in scope, for the sake of simplicity in reading the paper, with a small number of SLAs, but the idea can be extended to many more SLAs and performance metrics. In this way, the user or the provider operating the cloud have more latitude, thanks to our multi-criteria approach, to control the workload without a sacrifice on performance. Keywords Scheduling · Optimization · Distributed computing · Cloud computing · Service computing · Consolidation of servers
* Tarek Menouer [email protected] Extended author information available on the last page of the article
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1 Introduction The past few years have been devoted to the progress of the innovations in hardware architectures such as the multi-core parallel machines or many integrated cores (MIC). This allows researchers to propose new parallel applications that fully exploit the potential of such hardware. Currently, to execute these applications, Cloud Computing became a popular field that provides to users a lot of flexible execution models with large capacity of computing resources. In this context, requests scheduling and resources allocation present challenging issues. The general problem we address in this paper is the following. Given a queue of user requests, find an allocation of the requests on physical machines such that the user satisfaction is maximized and the number of
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