Schlouder: A broker for IaaS clouds

Abstract

In the field of cloud computing, Infrastructure as a Service (IaaS) provides virtualized on-demand computing resources on a pay-per-use model. IaaS Cloud differ from traditional mutualized infrastructures in that the resources can be dynamically claimed and released, and the real hardware infrastructure is unknown to its users. These properties drastically changes the way resource provisioning and job scheduling can be addressed by the user because i) the large number of jobs and resources to handle becomes rapidly overwhelming for human operators, and ii) the real performances of the platform should be inferred from observations to make robust scheduling decisions. In order to optimize the resources usage by the client, we advocate the need for brokers on the client-side. This article presents our work based on Schlouder, a broker of IaaS cloud resources able to provision and schedule independent jobs or static workflows according to strategies chosen by the client. Further, we advocate that simulation can be a precious auxiliary to help the user to choose between provisioning strategies. Schlouder brings a unique feature which is to predict through simulation the makespan and cost of executions under various strategies. The contribution of this work is twofold. First, it presents the broker, available as an open source project, in which new provisioning strategies can be plugged in by third parties. The effectiveness of the tool is demonstrated through experiments involving actual applications and platforms. Second, we show that simulation produces accurate predictions making this feature a helpful means for the user to choose the appropriate strategy.

Cloud Brokerage

“…Meeting Requirements [Chichin et al 2014] [Dastjerdi et al 2010] [Patiniotakis et al 2014] [Quarati et al 2016] [Yangui et al 2014] [Amato et al 2013b] [Anastasi et al 2017] [den Bossche et al 2013] [Javed et al 2016] [Michon et al 2017] [Pawluk et al 2012] [Weerasiri et al 2015] [Munteanu et al 2014] [Elkhatib et al 2013] [Aleksiev et al 2013] [Ferrer et al 2012] [Yang et al 2012] [Apduhan et al 2015] [Calheiros et al 2012] [Itani et al 2012] [Javadi et al 2012] [Jrad et al 2013] [Kertesz et al 2014] [Kim et al 2015] [Lucas-Simarro et al 2011] [Nair et al 2010] [Sharma et al 2015] [Desair et al 2013] [Paraiso et al 2014 [A a z a m a n d H u h 2 0 1 7 In the following, each category of motivation is described in detail. Dimensionality.…”

“…In view of this, the current process of manually selecting the optimal service can overwhelm a human decision maker. This challenge motivates the development of systems to support decision making [Aazam and Huh 2017;Amato et al 2013;Anastasi et al 2017;Chichin et al 2014;Dastjerdi et al 2010;den Bossche et al 2013;Javed et al 2016;Michon et al 2017;Patiniotakis et al 2014;Pawluk et al 2012;Quarati et al 2016;Weerasiri et al 2015;Yangui et al 2014].…”

Improving Cloud Simulation Using the Monte-Carlo Method

“…Real Execution Setup Schlouder [21] is a client-side cloud broker for IaaS capable of executing the user's batch jobs, sets of independent tasks and workflows alike. The broker's main role is to schedule the tasks onto a set of cloud resources, which the broker can scale up or down.…”

An Overview of Cloud Simulation Enhancement Using the Monte-Carlo Method

“…Using the Schlouder [3] cloud batch-scheduler, we carried out multiple executions of a scientific application, OMSSA [4] on a private cloud. The application was scheduled 200 times favoring alternatively low runtimes (ASAP, as soon as possible) or low cost (AFAP, as full as possible).…”