Market-based autonomous resource and application management in private clouds
KAUST DepartmentSupercomputing, Computational Scientists
Online Publication Date2016-10-12
Print Publication Date2017-02
Permanent link to this recordhttp://hdl.handle.net/10754/623160
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AbstractHigh Performance Computing (HPC) clouds need to be efficiently shared between selfish tenants having applications with different resource requirements and Service Level Objectives (SLOs). The main difficulty relies on providing concurrent resource access to such tenants while maximizing the resource utilization. To overcome this challenge, we propose Merkat, a market-based SLO-driven cloud platform. Merkat relies on a market-based model specifically designed for on-demand fine-grain resource allocation to maximize resource utilization and it uses a combination of currency distribution and dynamic resource pricing to ensure proper resource distribution among tenants. To meet the tenant’s SLO, Merkat uses autonomous controllers, which apply adaptation policies that: (i) dynamically tune the application’s provisioned CPU and memory per virtual machine in contention periods, or (ii) dynamically change the number of virtual machines. Our evaluation with simulation and on the Grid’5000 testbed shows that Merkat provides flexible support for different application types and SLOs and good tenant satisfaction compared to existing centralized systems, while the infrastructure resource utilization is improved.
CitationCostache S, Kortas S, Morin C, Parlavantzas N (2017) Market-based autonomous resource and application management in private clouds. Journal of Parallel and Distributed Computing 100: 85–102. Available: http://dx.doi.org/10.1016/j.jpdc.2016.10.003.
SponsorsThis work was done while the first author was a Ph.D. student at INRIA and EDF R&D and was supported by ANRT through the CIFRE sponsorship No. 0332/2010. Experiments presented in this paper were carried out using the Grid’5000 experimental testbed, being developed under the INRIA ALADDIN development action with support from CNRS, RENATER and several Universities as well as other funding bodies (see https://www.grid5000.fr).