Multiobjective differential evolution for workflow execution on grids

Talukder, A.K.M. Khaled Ahsan; Kirley, Michael; Buyya, Rajkumar

doi:10.1145/1376849.1376852

Cited by 14 publications

(3 citation statements)

References 17 publications

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“…For instance, [12] employs the hyper-volume metric for pruning the trade-off solutions generated in each step, at cost that is exponential with the number of objectives. On the other hand, genetic algorithms can provide better solutions [100,89]; however, it is well-known that their performance unacceptably degrades beyond three objectives [51]. Few approaches target three objectives, like the ones from [49,83].…”

Section: 12mentioning

confidence: 99%

On Processing Extreme Data

Petcu¹,

Iuhasz²,

Pop³

et al. 2016

SCPE

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Abstract. Extreme Data is an incarnation of Big Data concept distinguished by the massive amounts of data that must be queried, communicated and analyzed in near real-time by using a very large number of memory or storage elements and exascale computing systems. Immediate examples are the scientific data produced at a rate of hundreds of gigabits-per-second that must be stored, filtered and analyzed, the millions of images per day that must be analyzed in parallel, the one billion of social data posts queried in real-time on an in-memory components database. Traditional disks or commercial storage nowadays cannot handle the extreme scale of such application data.Following the need of improvement of current concepts and technologies, we focus in this paper on the needs of data intensive applications running on systems composed of up to millions of computing elements (exascale systems). We propose in this paper a methodology to advance the state-of-the-art. The starting point is the definition of new programming paradigms, APIs, runtime tools and methodologies for expressing data-intensive tasks on exascale systems. This will pave the way for the exploitation of massive parallelism over a simplified model of the system architecture, thus promoting high performance and efficiency, offering powerful operations and mechanisms for processing extreme data sources at high speed and/or real time.

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Section: 12mentioning

confidence: 99%

On Processing Extreme Data

Petcu¹,

Iuhasz²,

Pop³

et al. 2016

SCPE

View full text Add to dashboard Cite

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“…If one of these recursive calls to ScheduleParents fails, there are two different situations: either the task that causes this failure belongs to the current partial critical path, or it does not 1 (lines [17][18][19][20][21][22][23][24]. In the former case, we cancel all schedules until now and call SchedulePath to schedule the partial critical path again, but with a new constraint for the failed task.…”

Section: Definition 2 the Partial Critical Path Of Node T Is: I Emptmentioning

confidence: 99%

“…Other methods like Integer Programming [19], Mixed-Integer Non-Linear Programming [20], and Game Theory [21] are also used for this problem. In addition, some researchers use Metahuristics like Genetic Algorithm [2], Ant Colony Optimization [22], Tabu Search, Simulated Annealing and Guided Local Search [23], and Multiobjective differential evolution [24] to solve this problem.…”

Section: Related Workmentioning

confidence: 99%

Cost-Driven Scheduling of Grid Workflows Using Partial Critical Paths

Abrishami

Naghibzadeh

Epema

2012

IEEE Trans. Parallel Distrib. Syst.

203

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Abstract-Recently, utility grids have emerged as a new model of service provisioning in heterogeneous distributed systems. In this model, users negotiate with providers on their required Quality of Service and on the corresponding price to reach a Service Level Agreement. One of the most challenging problems in utility grids is workflow scheduling, i.e., the problem of satisfying users' QoS as well as minimizing the cost of workflow execution. In this paper, we propose a new QoS-based workflow scheduling algorithm based on a novel concept called Partial Critical Path. This algorithm recursively schedules the critical path ending at a recently scheduled node. The proposed algorithm tries to minimize the cost of workflow execution while meeting a user-defined deadline. The simulation results show that the performance of our algorithm is very promising.

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Multi-objective Optimal Grid Workflow Scheduling with QoS Constraints

Jin-tao

Jie-wu

et al. 2009

Advances in Soft Computing

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Multiobjective differential evolution for workflow execution on grids

Cited by 14 publications

References 17 publications

On Processing Extreme Data

On Processing Extreme Data

Cost-Driven Scheduling of Grid Workflows Using Partial Critical Paths

Multi-objective Optimal Grid Workflow Scheduling with QoS Constraints

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