New Metrics for Scheduling Jobs on Cluster of Virtual Machines

Liu, Yanbin; Bobroff, Norman; Fong, Liana; Seelam, Seetharami; Delgado, Javier

doi:10.1109/ipdps.2011.245

Cited by 4 publications

(8 citation statements)

References 8 publications

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“…For example, the EC of V M A and V M B in Figure 2 are , respectively. The computation of PC and EC is not straightforward with different min and max values; additional details on these terms are discussed in [3]. Now the job scheduling algorithms can use EC and PC for scheduling jobs on virtual machines.…”

Section: A System Model: Resource Sharing In Virtualized Platformsmentioning

confidence: 99%

“…These approaches are based on greedy heuristics such as total free machine capacity in a non-virtualized environment. In a virtualized environment, the PC or EC metrics are shown to work well to maximize the overall system utilization and job response time when serial jobs dominate the workload with homogeneous VMs [3].…”

Section: Scheduling Algorithmsmentioning

confidence: 99%

“…Here, VMs are ranked according to the value of the placement metric, in descending order, and tasks are assigned accordingly. (Some subtleties are discussed in [3]). Some intuition about the importance and reasoning behind our choices for the resource selection algorithm is exemplified in Figure 6.…”

Section: Scheduling Algorithmsmentioning

confidence: 99%

“…Our prior work investigated and proposed new VM-centric metrics of compute capacity that are useful for making job placement decisions in a cloud-like environment [3]. A limitation of that work was that only serial workloads were evaluated.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, we observed up to 47% average under-utilization of the CPU due to ripple effect when using a greedy scheduling algorithm. By applying some simple heuristics that address the elasticity constraint and ripple effects to scheduling algorithms based on the load metrics introduced in [3], we were able to reduce this to 35%, as a result improving median job expansion factor by more than a factor of 3. However, we conclude that more sophisticated algorithms are needed to directly address this issue.…”

Section: Introductionmentioning

confidence: 99%

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Efficiency Assessment of Parallel Workloads on Virtualized Resources

Delgado

Sadjadi

Fong

et al. 2011

2011 Fourth IEEE International Conference on Utility and Cloud Computing

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Abstract-In cloud computing, virtual containers on physical resources are provisioned to requesting users. Resource providers may pack as many containers as possible onto each of their physical machines, or may pack specific types and quantities of virtual containers based on user or system QoS objectives. Such elastic provisioning schemes for resource sharing may present major challenges to scientific parallel applications that require task synchronization during execution. Such elastic schemes may also inadvertently lower utilization of computing resources. In this paper, we describe the elasticity constraint effect and ripple effect that cause a negative impact to application response time and system utilization. We quantify the impact using real workload traces through simulation. Then, we demonstrate that some resource scheduling techniques can be effective in mitigating the impacts. We find that a tradeoff is needed among the elasticity of virtual containers, the complexity of scheduling algorithms, and the response time of applications.General Terms: scheduling, virtualization, parallel application I. INTRODUCTION The current trend in resource provisioning for utility and cloud computing is to provide compute resources as virtual resource containers using virtualization technologies. The most common realization is the use of virtual machine (VM) technology, in which a virtual container manager (VCM) abstracts and controls the physical resources allotted to one or more VMs. The VM abstraction simplifies the deployment of application environments across a wide range of physical systems. The VCM provides fine-grained control of the amount of shared resources to apportion to each VM when they are competing for them. This provides resource providers flexible resource allocation control to satisfy quality of service of requests and to build cost and profit models [1]. This flexibility also allows application execution to be tuned in different ways. For example, execution of a job proceeds faster than expected when its VM borrows idle CPU cycles from collocated VMs. Moreover, parallel applications need not limit the degree of parallelism to the number of physical machines.The simplicity of application deployment and the flexibility of virtual resource acquisition enabled by the Cloud is creating interest among users of CPU-intensive parallel applications [2]. However, this flexibility can present a unique challenge when scheduling and executing them.

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Section: A System Model: Resource Sharing In Virtualized Platformsmentioning

confidence: 99%

Section: Scheduling Algorithmsmentioning

confidence: 99%

Section: Scheduling Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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