Streamline: a scheduling heuristic for streaming applications on the grid

Agarwalla, Bikash; Ahmed, Nova; Hilley, David; Ramachandran, Umakishore

doi:10.1117/12.642809

Cited by 30 publications

(37 citation statements)

References 26 publications

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“…We calculate the throughput and cost for comparison. The performance of the two proposed algorithms is further compared with Streamline [7] and LDP [6]. Fig.…”

Section: Analysis Of Resultsmentioning

confidence: 99%

“…The optimization problem of scheduling DAG-structured tasks with complex execution dependencies has been studied for years and is known to be NPcomplete [7]. Over the years, workflow scheduling problems in heterogeneous environments have attracted many research efforts, among which a significant amount of efforts have been devoted to workflow scheduling in Grid environments under different scheduling and resource constraints.…”

Section: Related Workmentioning

confidence: 99%

“…Streamline [7], a workflow scheduler for streaming data, takes dynamic nature of the Grid into account and takes application requirements, constraints, and resource availability into consideration for scheduling decisions. To achieve the reduced overall execution time, Heterogeneous Earliest Finish Time (HEFT) heuristic algorithm [13] was proposed to initially order all the tasks of a workflow in descending order of their upward rank values calculated as the sum of the execution time and the communication time of the tasks.…”

Section: Related Workmentioning

confidence: 99%

“…To conduct thorough comparison, we select different budget constraints and simulate several different sizes of workflows and networks. To set up baselines for comparison, we also developed some representative workflow scheduling algorithms for maximizing throughput (due to no existing algorithm for maximizing throughput under budget constraint) including Streamline [7] and LDP [6] (which is used to find an initial schedule in B-SWAP).…”

Section: Performance Metrics and Experimental Scenariosmentioning

confidence: 99%

“…To our best knowledge, there is currently no algorithm to maximize the throughput for streaming applications under budget constraint or minimize execution cost under throughput constraint in the Grid. The superiority of these two algorithms are demonstrated in comparison with some representative workflow scheduling algorithms to maximize the throughput including Streamline [7] and LDP [6] in a set of different scales of simulation cases. For throughput constrained objective, the T P -RATE algorithm also follows layer-based scheduling scheme and chooses the minimum partial cost whose partial throughput is larger or equal to the throughput constraint for each layer; the T P -SWAP algorithm starts with the cheapest schedule of tasks onto resources, and keeps swapping tasks that can lead to higher throughput with low cost increase.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Workflow Scheduling Under Throughput and Budget Constraints in Grid Environments

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Abstract. Grids enable sharing, selection and aggregation of geographically distributed resources among various organizations. They are emerging as promising computing paradigms for resource and computeintensive scientific workflow applications modeled as Directed Acyclic Graph (DAG) with intricate inter-task dependencies. With the growing popularity of real-time applications, streaming workflows continuously produce large quantity of experimental or simulation datasets, which need to be processed in a timely manner subject to certain performance and resource constraints. However, the heterogeneity and dynamics of Grid resources complicate the scheduling of streaming applications. In addition, the commercialization of Grids as a future trend is calling for policies to take resource cost into account while striving to satisfy the users' Quality of Service (QoS) requirements. In this paper, streaming workflow applications are modeled as DAGs. We formulate scheduling problems with two different objectives in mind, namely either maximize the throughput under a budget/cost constraint or minimize the execution cost under a minimum throughput constraint. Two different algorithms named as Budget constrained RATE (B-RATE) and Budget constrained SWAP (B-SWAP) are developed and evaluated under the first objective; Another two algorithms named as Throughput constrained RATE (T P -RATE) and Throughput constrained SWAP (T P -SWAP) are evaluated under the second objective. Experimental results based on GridSim showed that our algorithms either achieved much lower cost with similar throughput, or higher throughput with similar cost compared with other comparable existing algorithms.

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“…We calculate the throughput and cost for comparison. The performance of the two proposed algorithms is further compared with Streamline [7] and LDP [6]. Fig.…”

Section: Analysis Of Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%