A Framework to Achieve Guaranteed QoS for Applications and High System Performance in Multi-Institutional Grid Computing

Farooq, Umar; Majumdar, Shikharesh; Parsons, Eric W.

doi:10.1109/icpp.2006.8

Cited by 7 publications

(11 citation statements)

References 10 publications

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“…The flexible resource co-allocation (FlexCo) model proposed here is inspired by existing work on flexible advance reservations [12,16,20,21,24,25]. A request, or a multisite job, following this model can have relaxed start and completion times, and the flexibility to define the number of processors used in each machine.…”

Section: Flexible Resource Co-allocation Modelmentioning

confidence: 99%

“…The local schedulers use Algorithm 1 to reschedule jobs whenever a job completes before its estimated time. The rescheduling is based on the compressing method described by Weil and Feitelson [31], which consists in bringing the jobs to the current time according to their estimated start times, not their arrival times (Lines 3-5, [11][12][13][14]. This avoids the violation of the completion time of jobs given by the original schedule.…”

Section: Reschedulingmentioning

confidence: 99%

“…To overcome this problem, many researchers are working with flexible (or elastic) advance reservations, i.e. requests that have relaxed time intervals [12,16,20,24,25]. Nevertheless, the use of these flexible advance reservations for resource co-allocation has been barely explored [15].…”

Section: Introductionmentioning

confidence: 99%

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Rescheduling co-allocation requests based on flexible advance reservations and processor remapping

Netto

Buyya

2008

2008 9th IEEE/ACM International Conference on Grid Computing

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Large-scale computing environments, such as TeraGrid, Distributed ASCI Supercomputer (DAS), and Grid'5000, have been using resource co-allocation to execute applications on multiple sites. Their schedulers work with requests that contain imprecise estimations provided by users. This lack of accuracy generates fragments inside the scheduling queues that can be filled by rescheduling both local and multi-site requests. Current resource co-allocation solutions rely on advance reservations to ensure that users can access all the resources at the same time. These coallocation requests cannot be rescheduled if they are based on rigid advance reservations. In this work, we investigate the impact of rescheduling co-allocation requests based on flexible advance reservations and processor remapping. The metascheduler can modify the start time of each job component and remap the number of processors they use in each site. The experimental results show that local jobs may not fill all the fragments in the scheduling queues and hence rescheduling co-allocation requests reduces response time of both local and multi-site jobs. Moreover, we have observed in some scenarios that processor remapping increases the chances of placing the tasks of multi-site jobs into a single cluster, thus eliminating the inter-cluster network overhead.

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Section: Flexible Resource Co-allocation Modelmentioning

confidence: 99%

Section: Reschedulingmentioning

confidence: 99%

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Rescheduling co-allocation requests based on flexible advance reservations and processor remapping

Netto

Buyya

2008

2008 9th IEEE/ACM International Conference on Grid Computing

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“…It can be calculated as the ratio of the sum of the work of all the rejected ARs and the sum of the total work of all ARs [14] Utilization (U): resource utilization is the fraction of the total time the resource is consumed by executing jobs or accepted ARs. It is calculated as equation (3).…”

Section: Percentage Of Work Rejected (Wr)mentioning

confidence: 99%

“…Advance Reservation with flexible time interval is used in some works for the purpose of enhancing utilization [10,12,13,14]. In flexible AR, in contrast to rigid, start time is not fixed and could be varied in a time interval.…”

Section: Introductionmentioning

confidence: 99%

Flexible Advance Reservation Impact on Backfilling Scheduling Strategies

Moaddeli

Dastghaibyfard

Moosavi

2008

2008 Seventh International Conference on Grid and Cooperative Computing

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Advance Reservation mechanism was introduced in Grid environments to provide time Quality of Service requirements for time critical applications. Also there are applications that need resource coordination namely coallocation and workflow, which benefits from this capability.Contemporary trend in Grid computing is toward flexibility, because of its advantages in Advance Reservation such as increasing accept rate. This mechanism should be supported by local scheduler which is responsible for normal jobs. Using Backfilling methods with FCFS priority for scheduling normal jobs is the dominant approach. Therefore well investigation of performance impact of flexible Advance Reservation on backfilling scheduling is essential.In this paper incorporation of flexible Advance Reservation in major backfilling policies is investigated, regarding workload parameters impact on well known performance metrics. Here also the impact of increasing inaccuracy of user estimated job runtime is studied.Our experimental results indicate that aggressive backfilling algorithm is biased toward AR related performance metrics but conservative method has better job performance. Although more flexibility in advance reservation would improve AR performance but its impact on degrade of job performance metrics is noteworthy. Both parameter types' rates are less in conservative method. Moreover, usually increasing inaccuracy interestingly has opposing effect on backfilling methods.

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