Multi-cluster schedulers can dramatically improve average job turn-around time performance by making use of fragmented node resources available throughout the grid. By carefully mapping job’s across potentially many clusters, jobs that would otherwise wait in the queue for local cluster resources can begin execution much earlier; thereby improving system utilization and reducing average queue waiting time. In this paper, we demonstrate that these multi-site scheduling techniques can be successfully integrated with fairness policies to ensure that participation in the multicluster is beneficial under extremely disparate workload intensities. Furthermore, we demonstrate that the trade-off between fairness and performance is relatively small. ...
Abstract. The distributed nature of the grid results in the problem of scheduling parallel jobs prod...
Fairness is an important aspect in queuing systems. Several fairness measures have been proposed in ...
Data analytic jobs usually require large volumes of data inputs that are available at geographically...
Parallel job schedulers have mostly been evalu-ated/compared using performance metrics. The de-ducti...
International audienceThe performance of cluster computing depends on how concurrent jobs share mult...
Job scheduling affects the fairness and performance of shared Hadoop clusters. Fairness measures how...
Abstract – Tasks in modern data-parallel clusters have highly di-verse resource requirements alongCP...
Providing quality-of-service guarantees by means of fair shar-ing has never been more challenging in...
As organizations start to use data-intensive cluster comput-ing systems like Hadoop and Dryad for mo...
This dissertation focuses on algorithm design and prototype implementation of fair sharing policies ...
In this paper, we study the problem of multi- resource fairness in systems running complex jobs that...
scheduling In this paper, we utilize a bandwidth-centric job communication model that captures the i...
Recently, MapReduce and its open-source implementation Hadoop have emerged as prevalent tools for bi...
Abstract. We claim that the current scheduling systems for high performance computing environments a...
International audienceApplications structured as parallel task graphs exhibit both data and task par...
Abstract. The distributed nature of the grid results in the problem of scheduling parallel jobs prod...
Fairness is an important aspect in queuing systems. Several fairness measures have been proposed in ...
Data analytic jobs usually require large volumes of data inputs that are available at geographically...
Parallel job schedulers have mostly been evalu-ated/compared using performance metrics. The de-ducti...
International audienceThe performance of cluster computing depends on how concurrent jobs share mult...
Job scheduling affects the fairness and performance of shared Hadoop clusters. Fairness measures how...
Abstract – Tasks in modern data-parallel clusters have highly di-verse resource requirements alongCP...
Providing quality-of-service guarantees by means of fair shar-ing has never been more challenging in...
As organizations start to use data-intensive cluster comput-ing systems like Hadoop and Dryad for mo...
This dissertation focuses on algorithm design and prototype implementation of fair sharing policies ...
In this paper, we study the problem of multi- resource fairness in systems running complex jobs that...
scheduling In this paper, we utilize a bandwidth-centric job communication model that captures the i...
Recently, MapReduce and its open-source implementation Hadoop have emerged as prevalent tools for bi...
Abstract. We claim that the current scheduling systems for high performance computing environments a...
International audienceApplications structured as parallel task graphs exhibit both data and task par...
Abstract. The distributed nature of the grid results in the problem of scheduling parallel jobs prod...
Fairness is an important aspect in queuing systems. Several fairness measures have been proposed in ...
Data analytic jobs usually require large volumes of data inputs that are available at geographically...