Controlling the correlation of cost matrices to assess scheduling algorithm performance on heterogeneous platforms

Canon, Louis-Claude; Héam, Pierre-Cyrille; Philippe, Laurent

doi:10.1002/cpe.4185

Cited by 3 publications

(6 citation statements)

References 33 publications

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“…Similarly to the lognormal distribution, such distributions represent ideal candidates to study the corresponding yield. Finally, for the third direction, the experimental section could be extended to consider instances in which expected execution times can be correlated to the CVs on the VMs, as it has been done for cost matrices [9]. x CV = 1.5…”

Section: Resultsmentioning

confidence: 99%

Scheduling independent stochastic tasks on heterogeneous cloud platforms

Gao

Canon

Robert

et al. 2019

2019 IEEE International Conference on Cluster Computing (CLUSTER)

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This work introduces scheduling strategies to maximize the expected number of independent tasks that can be executed on a cloud platform within a given budget and under a deadline constraint. The cloud platform is composed of several types of virtual machines (VMs), where each type has a unit execution cost that depends upon its characteristics. The amount of budget spent during the execution of a task on a given VM is the product of its execution length by the unit execution cost of that VM. The execution lengths of tasks follow a variety of standard probability distributions (exponential, uniform, half-normal, lognormal, gamma, inverse-gamma and Weibull) whose mean and standard deviation both depend upon the VM type. Finally, there is a global available budget and a deadline constraint, and the goal is to successfully execute as many tasks as possible before the deadline is reached or the budget is exhausted (whichever comes first). On each VM, the scheduler can decide at any instant to interrupt the execution of a (long) running task and to launch a new one, but the budget already spent for the interrupted task is lost. The main questions are which VMs to enroll, and whether and when to interrupt tasks that have been executing for some time. We assess the complexity of the problem by showing its NP-completeness and providing a 2-approximation for the asymptotic case where budget and deadline both tend to infinity. Then we introduce several heuristics and compare their performance by running an extensive set of simulations.

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Section: Resultsmentioning

confidence: 99%

Scheduling independent stochastic tasks on heterogeneous cloud platforms

Gao

Canon

Robert

et al. 2019

2019 IEEE International Conference on Cluster Computing (CLUSTER)

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“…Finally, CNB (correlation noise-based) and CB (combination-based) have been proposed to control the correlation [6]. CNB is a direct variation of CB to specify the correlation more easily.…”

Section: Related Workmentioning

confidence: 99%

“…In this case, tasks are identical and it relates to the problem Q|p i = 1|C max . These instances, for which the row correlation is high and column correlation is low, have been shown to be the easiest for EFT [6].…”

Section: Constraints Effect On Scheduling Algorithmsmentioning

confidence: 99%

“…the performance of some scheduling heuristics [5]. The correlations between the rows and columns also play an important role as it corresponds to the machines being either related or specialized, with some affinity between the tasks and the machines [6]. Among existing methods, the shuffling one [5] starts by an initial matrix in which rows are proportional to each other (leading to large row and column correlations).…”

Section: Introductionmentioning

confidence: 99%

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A Markov Chain Monte Carlo Approach to Cost Matrix Generation for Scheduling Performance Evaluation

Canon

Sayah

Héam

2018

2018 International Conference on High Performance Computing &Amp; Simulation (HPCS)

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In high performance computing, scheduling of tasks and allocation to machines is very critical especially when we are dealing with heterogeneous execution costs. Simulations can be performed with a large variety of environments and application models. However, this technique is sensitive to bias when it relies on random instances with an uncontrolled distribution. We use methods from the literature to provide formal guarantee on the distribution of the instance. In particular, it is desirable to ensure a uniform distribution among the instances with a given task and machine heterogeneity. In this article, we propose a method that generates instances (cost matrices) with a known distribution where tasks are scheduled on machines with heterogeneous execution costs.

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“…Topic 3 on Scheduling and Load Balancing is represented by the paper Controlling the correlation of cost matrices to assess scheduling algorithm performance on heterogeneous platforms . The authors Louis‐Claude Canon, Pierre‐Cyrille Héam, and Laurent Philippe consider the problem of allocating independent tasks to a collection of different machines in an optimizing manner.…”

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confidence: 99%

Special issue: Euro‐Par 2016

Lengauer

Bougé

Trystram

2017

Concurrency and Computation

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Euro-Par-the European Conference on Parallel Computing-is an annual series of international conferences dedicated to the promotion and advancement of all aspects of parallel and distributed computing. Euro-Par covers a wide spectrum of topics from algorithms and theory to software technology and hardware-related issues, with application areas ranging from scientific to mobile and cloud computing. The major part of the Euro-Par audience consists of researchers in academic institutions, government laboratories, and industrial organisations.Euro-Par 2016, the 22nd conference in the Euro-Par series, was held in Grenoble, France. It was organised by Inria, Université Grenoble-Alpes, and IUT 2 Grenoble.Twelve broad topics were defined and advertised, covering a large variety of aspects of parallel and distributed computing. The call for papers attracted a total of 176 submissions. The submitted papers were reviewed at least 3 and, in most cases, 4 or even more times (4 reviews on average).A total of 47 papers were finally accepted for publication. This makes a global acceptance rate of 26.7 %. The authors of accepted papers came from 20 countries, with the 4 main contributing countries-France, the United States, Germany, and Spain-accounting for a bit more than half of them.Based on the results of the reviews and a majority opinion of the respective topic programme committees, a number of papers were recommended for this special issue. The authors who gave a convincing talk were contacted at the conference and invited to submit revised and extended versions of their papers. These new versions were given to 3 reviewers; 2 had previously reviewed the conference version, the third had not. Eventually, five papers were accepted for publication. This year, four Euro-Par topics are represented. Topic 3 on Scheduling and Load Balancing is represented by the paper Controlling the correlation of cost matrices to assess scheduling algorithm perfor-mance on heterogeneous platforms. 1 The authors Louis-Claude Canon, Pierre-Cyrille Héam, and Laurent Philippe consider the problem of allocating independent tasks to a collection of different machines in an optimizing manner. Costs are assessed via the generation of matrices that advise on the cost of a specific task on a specific machine. The authors propose a new assessment metric comparing the uniform cost matrix and a cost matrix using task and machine correlations. Two generation methods are proposed, one of them new, the other a modification of an existing one, and their effect on performance evaluation heuristics from the literature is studied. One reviewer pointed out that, despite the numerous required mathematical proofs, the authors do an admirable job of making each one accessible. Since the code-bones have an increased flexibility, the results show improvements over APOLLO's predecessor VMAD. Topic 5 on Parallel and Distributed Data Management and Analytics is represented by the paper A flexible I/O arbitration framework for netCDF-based BigData processing workflows on hi...

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Controlling the correlation of cost matrices to assess scheduling algorithm performance on heterogeneous platforms

Cited by 3 publications

References 33 publications

Scheduling independent stochastic tasks on heterogeneous cloud platforms

Scheduling independent stochastic tasks on heterogeneous cloud platforms

A Markov Chain Monte Carlo Approach to Cost Matrix Generation for Scheduling Performance Evaluation

Special issue: Euro‐Par 2016

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