Opportunistic scheduling and resources consolidation system based on a new economic model

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.