Comparison of scheduling heuristics for grid resource broker

Zhuk, Sergey; Chernykh, A.; Avestiyan, A.; Gaissaryan, Serguei; Kuzjurin, N. N.; Pospelov, A. I.; Грушин, Д. А.

doi:10.1109/enc.2004.1342632

Cited by 17 publications

(6 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…-Min-Completion-Time (MCT). In contrast to MLB, the earliest possible completion time is determined based on a partial schedule of already assigned jobs [9,7]. For instance, Moab [3] can estimate the completion time of all jobs in the local queue because jobs and reservations possess a start time and a wallclock limit.…”

Section: Two Level Hierarchy Schedulingmentioning

confidence: 99%

Two Level Job-Scheduling Strategies for a Computational Grid

Tchernykh

Ramírez

Avetisyan

et al. 2006

Parallel Processing and Applied Mathematics

View full text Add to dashboard Cite

Abstract. We address parallel jobs scheduling problem for computational GRID systems. We concentrate on two-level hierarchy scheduling: at the first level broker allocates computational jobs to parallel computers. At the second level each computer generates schedules of the parallel jobs assigned to it by its own local scheduler. Selection, allocation strategies, and efficiency of proposed hierarchical scheduling algorithms are discussed.

show abstract

Section: Two Level Hierarchy Schedulingmentioning

confidence: 99%

Two Level Job-Scheduling Strategies for a Computational Grid

Tchernykh

Ramírez

Avetisyan

et al. 2006

Parallel Processing and Applied Mathematics

View full text Add to dashboard Cite

show abstract

“…A more general case of the non-clairvoyant problem considering a collection of k parallel machines of different sizes and jobs that do not require more resources than available on the biggest machine (size j ≤ m m ) was considered in [18,19]. The authors address the performance of various 2-stage algorithms with respect to the makespan objective.…”

Section: Theoretical Resultsmentioning

confidence: 99%

“…Most academic studies either propose a completely distributed resource management system, see, for instance, [9], or suggest a central scheduler, see [10,11], while real installations favour a combination of decentralized and centralized structures, see [12]. Such combined systems have also been proposed using a hierarchical multilayer resource management, see [13][14][15][16][17][18][19][20]. The highest layer is often called a Grid-layer scheduler that typically has a general view of job requests while specific details on the state of the resources remain hidden from it.…”

Section: Introductionmentioning

confidence: 99%

Job Allocation Strategies with User Run Time Estimates for Online Scheduling in Hierarchical Grids

et al. 2011

View full text Add to dashboard Cite

We address non-preemptive nonclairvoyant online scheduling of parallel jobs on a Grid. We consider a Grid scheduling model with two stages. At the first stage, jobs are allocated to a suitable Grid site, while at the second stage, local scheduling is independently applied to each site. We analyze allocation strategies depending on the type and amount of information they require. We conduct a comprehensive performance evaluation study using simulation and demonstrate that our strategies perform well with respect to several metrics that reflect both user-and system-centric goals. Unfortunately, user run time estimates and information on local schedules does not help to significantly improve the outcome of the allocation strategies. When examining the overall Grid performance based on real data, we determined that an appropriate distribution of job processor requirements over the Grid has a higher performance than an allocation of jobs based on user run time estimates and information on local schedules. In general, our experiments showed that rather simple schedulers with minimal information requirements can provide a good performance.

show abstract

“…with precedence online 34 parallel, parallel, homogeneous moldable, minimize C max 3 + independent online 34 parallel, parallel, 34 parallel, parallel, homogeneous moldable, minimize C max 8 independent online 34 parallel, parallel, minimize C max homogeneous moldable and ∑ C j 2 C max and 2 ∑ C j independent off-line 35 identical clusters parallel, minimize C max 5 rigid off-line 36 data center with parallel (execution reduce average heterog. servers in the same server) response time online 37 grids with parallel, minimize C max 3 parallel and independent homog. machines online, grids with parallel, non-clairvoyant 38 A scheduling strategy commonly used in cloud computing is overbooking.…”

Section: Scheduling Strategiesmentioning

confidence: 99%

Reducing the number of response time service level objective violations by a cloud‐HPC convergence scheduler

Kraemer

Maziero

Richard

et al. 2017

Concurrency and Computation

View full text Add to dashboard Cite

Summary Job scheduling is an old topic in High‐Performance Computing (HPC), and it is more and more studied in data centers. Large data centers are often split into separate partitions for cloud computing and HPC; each partition normally has its specific scheduler. The possibility of migrating jobs from the HPC partition to the cloud one is a topic widely discussed in the literature. However, job migration from cloud to HPC is a much less explored topic. Nevertheless, such migration may be useful in many situations, in particular when the HPC platform has a low resource usage level, and the cloud usage level is high. A large number of jobs that could migrate from the cloud to the HPC partition may be observed in Google data center workloads. Job scheduling using overbooking strategy is seen as the main reason for the high resource usage level in clouds. However, overbooking can lead to a high rate of rescheduling and job dumping, which potentially causes response time violations. This work shows that HPC platforms can host and execute some cloud jobs with low interference in HPC jobs and a low number of response time violations. We introduce the definition of a cloud‐HPC convergence area and propose a job scheduling strategy for it, aiming at reducing the number of response time violations of cloud jobs without interfering with HPC jobs execution. Our proposal is formally defined and then evaluated in different execution scenarios, using the SimGrid simulation framework, with workload data from production HPC grid. The experimental results show that often, there is a large number of empty areas in the scheduling plan of HPC platforms, which makes it possible to allocate cloud jobs by backfilling. This is due to the sparse HPC job submission pattern and the low resource usage level in some HPC platforms. One performed simulation scenario considered a set of 11K parallel HPC jobs running on a 2560‐processor platform having an average resource usage level of 38.0%. The proposed convergence scheduler succeeded to inject around 267K cloud jobs in the HPC platform, with a response time violation rate under 0.00094% for such jobs, considering 80 processors in the convergence area and no effects on the HPC workload. This experiment considered cloud jobs based on job features of Google public cloud workloads, with a processing time slack factor of 1.25 (which is considered as high priority in the Google cloud SLA—Service Level Agreement). Usually, most cloud jobs show a slack factor higher than 1.25 (most cloud jobs are medium or low priority). The same simulation, repeated with a higher slack factor (4), showed no response time violations.

show abstract

Comparison of scheduling heuristics for grid resource broker

Cited by 17 publications

References 15 publications

Two Level Job-Scheduling Strategies for a Computational Grid

Two Level Job-Scheduling Strategies for a Computational Grid

Job Allocation Strategies with User Run Time Estimates for Online Scheduling in Hierarchical Grids

Reducing the number of response time service level objective violations by a cloud‐HPC convergence scheduler

Contact Info

Product

Resources

About