Performance evaluation of selected job management systems

Gaj, Kris; El‐Ghazawi, Tarek; Alexandridis, Nikitas A.; Vroman, Frederic; Nguyen, Nguyen; Radzikowski, Jacek; Samipagdi, Preeyapong; Suboh, S.

doi:10.1109/ipdps.2002.1016674

Cited by 10 publications

(7 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The other performance metrics are closely related with different workloads. For parallel batch jobs or MapReduce jobs, the major concern of an SP is the throughput in terms of the number of completed jobs [3] [8]; while the main concern of end users is the average turnaround time per job, which is the average time from submitting jobs till completing them [8] [10]. For Web servers and search engines, the major concern of an SP is the throughput in terms of requests per second [5] [7], while the quality of service in terms of the average response time per request is the major concern of end users [5] [7].…”

Section: Ieee Transactions On Computersmentioning

confidence: 99%

Cost-Aware Cooperative Resource Provisioning for Heterogeneous Workloads in Data Centers

Zhan

Wang

et al. 2013

IEEE Trans. Comput.

View full text Add to dashboard Cite

Abstract-Recent cost analysis shows that the server cost still dominates the total cost of high-scale data centers or cloud systems. In this paper, we argue for a new twist on the classical resource provisioning problem: heterogeneous workloads are a fact of life in large-scale data centers, and current resource provisioning solutions do not act upon this heterogeneity. Our contributions are threefold: first, we propose a cooperative resource provisioning solution, and take advantage of differences of heterogeneous workloads so as to decrease their peak resources consumption under competitive conditions; second, for four typical heterogeneous workloads: parallel batch jobs, Web servers, search engines, and MapReduce jobs, we build an agile system PhoenixCloud that enables cooperative resource provisioning; and third, we perform a comprehensive evaluation for both real and synthetic workload traces. Our experiments show that our solution could save the server cost aggressively with respect to the non-cooperative solutions that are widely used in state-of-the-practice hosting data centers or cloud systems: e.g., EC2, which leverages the statistical multiplexing technique, or RightScale, which roughly implements the elastic resource provisioning technique proposed in related state-of-the-art work.

show abstract

Section: Ieee Transactions On Computersmentioning

confidence: 99%

Cost-Aware Cooperative Resource Provisioning for Heterogeneous Workloads in Data Centers

Zhan

Wang

et al. 2013

IEEE Trans. Comput.

View full text Add to dashboard Cite

show abstract

“…For parallel batch jobs, the metrics are as follows: we choose the well known metricsthe throughput in terms of the number of completed jobs [3] [11] to reflect the major concern of a service provider. We use the average turnaround time per job to measure the main concern of end users.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…We use the average turnaround time per job to measure the main concern of end users. The average turnaround time of jobs is the time from submitting a job till completing it, averaged over all jobs submitted [11] [20]. For Web service, the metrics are as follows: we choose the well-know metrics, the throughput in terms of requests per second to reflect the major concern of a service provider [6] [10].…”

Section: Evaluation Metricsmentioning

confidence: 99%

PhoenixCloud: Provisioning Resources for Heterogeneous Workloads in Cloud Computing

Zhan¹,

Wang²,

Shi³

et al. 2010

Preprint

View full text Add to dashboard Cite

As more and more service providers choose Cloud platforms, which is provided by third party resource providers, resource providers needs to provision resources for heterogeneous workloads in different Cloud scenarios. Taking into account the dramatic differences of heterogeneous workloads, can we coordinately provision resources for heterogeneous workloads in Cloud computing? In this paper we focus on this important issue, which is investigated by few previous work. Our contributions are threefold: (1) we respectively propose a coordinated resource provisioning solution for heterogeneous workloads in two typical Cloud scenarios: first, a large organization operates a private Cloud for two heterogeneous workloads; second, a large organization or two service providers running heterogeneous workloads revert to a public Cloud; (2) we build an agile system PhoenixCloud that enables a resource provider to create coordinated runtime environments on demand for heterogeneous workloads when they are consolidated on a Cloud site; and (3) A comprehensive evaluation has been performed in experiments. For two typical heterogeneous workload traces: parallel batch jobs and Web services, our experiments show that: a) in a private Cloud scenario, when the throughput is almost same like that of a dedicated cluster system, our solution decreases the configuration size of a cluster by about 40%; b) in a public Cloud scenario, our solution decreases not only the total resource consumption, but also the peak resource consumption maximally to 31% with respect to that of EC2 +RightScale solution.

show abstract

“…We choose the number of completed jobs in a certain period [16] to evaluate the performance metric of the HTC service providers; and we choose tasks per second [1] to reflect the performance metric of the MTC service providers. For a service provider, we choose the resource consumption in terms of node*hour to evaluate the cost.…”

Section: Evaluation Metricsmentioning

confidence: 99%

In cloud, do MTC or HTC service providers benefit from the economies of scale?

Wang

Zhan

Shi

et al. 2009

Proceedings of the 2nd Workshop on Many-Task Computing on Grids and Supercomputers

View full text Add to dashboard Cite

Cloud computing, which is advocated as an economic platform for daily computing, has become a hot topic for both industrial and academic communities in the last couple of years. The basic idea behind cloud computing is that resource providers, which own the cloud platform, offer elastic resources to end users. In this paper, we intend to answer one key question to the success of cloud computing: in cloud, do many task computing (MTC) or high throughput computing (HTC) service providers, which offer the corresponding computing service to end users, benefit from the economies of scale? To the best of our knowledge, no previous work designs and implements the enabling system to consolidate MTC and HTC workloads on the cloud platform and no one answers the above question. Our research contributions are threefold: first, we propose an innovative usage model, called dynamic service provision (DSP) model, for MTC or HTC service providers. In the DSP model, the resource provider provides the service of creating and managing runtime environments for MTC or HTC service providers, and consolidates heterogeneous MTC or HTC workloads on the cloud platform; second, based on the DSP model, we design and implement Dawningcloud, which provides automatic management for heterogeneous workloads; third, a comprehensive evaluation of Dawningcloud has been performed in an emulatation experiment. We found that for typical workloads, in comparison with the previous two cloud solutions, Dawningcloud saves the resource consumption maximally by 46.4% (HTC) and 74.9% (MTC) for the service providers, and saves the total resource consumption maximally by 29.7% for the resource provider. At the same time, comparing with the traditional solution that provides MTC or HTC services with dedicated systems, Dawningcloud is more cost-effective. To this end, we conclude that for typical MTC and HTC workloads, on the cloud platform, MTC and HTC service providers and the resource service provider can benefit from the economies of scale.

show abstract

Performance evaluation of selected job management systems

Cited by 10 publications

References 1 publication

Cost-Aware Cooperative Resource Provisioning for Heterogeneous Workloads in Data Centers

Cost-Aware Cooperative Resource Provisioning for Heterogeneous Workloads in Data Centers

PhoenixCloud: Provisioning Resources for Heterogeneous Workloads in Cloud Computing

In cloud, do MTC or HTC service providers benefit from the economies of scale?

Contact Info

Product

Resources

About