Bag-of-Tasks Scheduling under Budget Constraints

Oprescu, Ana-Maria; Kielmann, Thilo

doi:10.1109/cloudcom.2010.32

Cited by 114 publications

(87 citation statements)

References 15 publications

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“…Oprescu et al [20] propose a budget constraintbased resource selection approach for Cloud applications. In this work they present a budgetconstrained scheduler called BaTS, which can schedule bags of tasks onto multiple clouds with different CPU performance and cost, minimizing completion time with maximized budget.…”

Section: Related Workmentioning

confidence: 99%

On Efficiency of Multi-job Grid Allocation Based on Statistical Trace Data

et al. 2013

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The ever growing number of computationintensive applications calls for utilizing large-scale, potentially interoperable distributed infrastructures. Nowadays, such distributed systems enable the management of heterogeneous scientific workflows of considerable sizes, where job scheduling and resource management is a crucial issue. In this paper we focus on the challenges of scheduling parameter sweep applications, a specific and commonly used type of workflows where ordering of job executions is irrelevant. A parameter sweep has a large set of independent job instances, called a multi-job, submitted for execution in a single step. In order to cope with the high uncertainty and unpredictable load of resources, and the si- multaneous submissions of multi-job instances, we propose a statistics-based brokering approach for allocating jobs to resources so that the makespan is minimised. Earlier studies claim that users' predictions on job runtime are inaccurate and unusable for scheduling. Our aim is to examine, whether statistical trace data for the same purpose is efficient compared to randomized allocation.

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Section: Related Workmentioning

confidence: 99%

On Efficiency of Multi-job Grid Allocation Based on Statistical Trace Data

et al. 2013

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“…For Bag-of-Tasks (BoT) applications, Alexandru Iosup [12] defined workload which consists of the BoT jobs submitted by different users, the users are ranked by the number of their submitted jobs. The tasks in a bag usually are assumed independent of each other [13] or a set of sequential tasks (possibly only one) [12]. For MapReduce applications, Yanpei Chen [14,15] offered a general MapReduce application definition, in which the execution of each MapReduce job is divided into three stages: map(input)/shuffle/reduce(output), a job is specified by input data-size, input/shuffle/output data ratio and data format.…”

Section: Related Workmentioning

confidence: 99%

Resource Demand Forecasting Model Based on Dynamic Cloud Workload

C¹,

Zhou²

2017

Preprint

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Abstract:The primary attraction of IaaS is providing elastic resources on demand. It becomes imperative that IaaS-users have an effective methodology for learning what resources they require, how many resources and for how long they need. However, the heterogeneity of resources, the diversity resource demands of different cloud applications and the variation of application-user behaviors pose IaaS-users big challenge. In this paper, we purpose a unified resource demand forecasting model suiting for different applications, various resources and diverse time-varying workload patterns. With the model, taking input from parameterized applications, resources and workload scenarios, the corresponding resources demands during any time interval can be deduced as output. The experiments configure concrete functions and parameters to help understanding the above model.

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“…Cloud computing: The following extensions can be interpreted clearly within the context of problems in cloud computing (see, e.g., [9], [22], [25]) as presented in Section I.…”

Section: Summary Extensions and Future Workmentioning

confidence: 99%

“…Another application of the studied problems comes from cloud computing (see, e.g., [22], [25]). Commercial cloud computing provides computing resources with specified computing units.…”

Section: Introductionmentioning

confidence: 99%

Optimizing busy time on parallel machines

Mertzios

Shalom

Voloshin

et al. 2015

Theoretical Computer Science

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Abstract-We consider the following fundamental scheduling problem in which the input consists of n jobs to be scheduled on a set of identical machines of bounded capacity g (which is the maximal number of jobs that can be processed simultaneously by a single machine). Each job is associated with a start time and a completion time; it is supposed to be processed from the start time to the completion time (and in one of our extensions it has to be scheduled also in a continuous number of days; this corresponds to a two-dimensional version of the problem). We consider two versions of the problem. In the scheduling minimization version the goal is to minimize the total busy time of machines used to schedule all jobs. In the resource allocation maximization version the goal is to maximize the number of jobs that are scheduled for processing under a budget constraint given in terms of busy time. This is the first study of the maximization version of the problem. The minimization problem is known to be NP-Hard, thus the maximization problem is also NP-Hard. We consider various special cases, identify cases where an optimal solution can be computed in polynomial time, and mainly provide constant factor approximation algorithms for both minimization and maximization problems. Some of our results improve upon the best known results for this job scheduling problem. Our study has applications in power consumption, cloud computing and optimizing switching cost of optical networks.

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Bag-of-Tasks Scheduling under Budget Constraints

Cited by 114 publications

References 15 publications

On Efficiency of Multi-job Grid Allocation Based on Statistical Trace Data

On Efficiency of Multi-job Grid Allocation Based on Statistical Trace Data

Resource Demand Forecasting Model Based on Dynamic Cloud Workload

Optimizing busy time on parallel machines

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