Robust Multi-tenant Server Consolidation in the Cloud for Data Analytics Workloads
Joseph Maté, Khuzaima Daudjee, Shahin Kamali · 2017
Server consolidation is the hosting of multiple tenantson a server machine. Given a sequence of data analyticstenant loads defined by the amount of resources that thetenants require and a service-level agreement (SLA) between thecustomer and the cloud service provider, significant cost savingscan be achieved by consolidating multiple tenants. Since servermachines can fail causing their tenants to become unavailable,service providers can place replicas of each tenant on multipleservers and reserve capacity to ensure that tenant failover willnot result in overload on any remaining server. We present theCubeFit algorithm for server consolidation that reduces costsby utilizing fewer servers than existing approaches for dataanalytics workloads. Unlike existing consolidation algorithms,CubeFit can tolerate multiple server failures while ensuring thatno server becomes overloaded. Through theoretical analysis andexperimental evaluation, we show that CubeFit is superior toexisting algorithms and produces near-optimal tenant allocationwhen the number of tenants is large. Through evaluation anddeployment on a cluster of 73 machines as well as throughsimulation studies, we experimentally demonstrate the efficacyof CubeFit.