Project: Application Aware Placement and Scheduling for Multi-tenant Clouds

Team: Dr. Marco Canini, Lalith Suresh

Research institution: Technische Universität Berlin

Abstract: The widespread availability of hardware virtualization technologies have accelerated a trend towards Cloud computing, which continues to transform industries and our society. In an Infrastructure-as-a-Service (IaaS) environment, it is paramount to perform intelligent allocation of shared resources. Placement is the problem of choosing which virtual machine (VM) should run on which physical machine (PM), whereas Scheduling is the problem of sharing resources between multiple co-located VMs. An efficient placement and scheduling is one, that in addition to satisfying all constraints, increases the overall utilization of physical resources such as CPU, storage, or network. Determining an efficient placement and scheduling is a very challenging problem, especially in face of conflicting goals and partially available information about workloads.

Our overarching goal is to develop a framework to reason about placement. To this end, we first want to tackle the problem of performance interference that may affect co-located VMs --- when there is more demand by multiple VMs for a resource than is available at a given instant of time. In this project, we aim to quantify the effects of performance interference across several applications sharing the data-center. This will be done through a systematic characterization and profiling of popular cloud applications today. Once we understand how performance interference affects different applications, we will use this information as input to design an application-aware placement and scheduling orchestration layer for cloud data-centers.

Last modified 6 years ago Last modified on May 28, 2013 1:09:19 PM

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