Statistical measures for quantifying task and machine heterogeneities

Al-Qawasmeh, Abdulla M.; Maciejewski, Anthony A.; Wang, Haonan; Smith, Jay; Siegel, Howard Jay; Potter, Jerry L.

doi:10.1007/s11227-011-0572-x

Cited by 16 publications

(12 citation statements)

References 28 publications

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“…Similarly let AP C be a T × M matrix where APC ij is the average power consumption for a task of type i on a machine of type j. These matrices are frequently used in scheduling algorithms [1], [5], [6], [12]. ET C and AP C are generally determined empirically based on prior task execution times.…”

Section: B Lower Boundmentioning

confidence: 99%

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Energy-Aware Profit Maximizing Scheduling Algorithm for Heterogeneous Computing Systems

Tarplee

Maciejewski

Siegel

2014

2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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Abstract-With the advent of energy-aware scheduling algorithms, it is now possible to find solutions that trade-off performance for decreased energy usage. There are now efficient algorithms to find high quality Pareto fronts that can be used to select the desired balance between makespan and energy consumption. One drawback of this approach is that it still requires a system administrator to select the desired operating point. In this paper, a market-oriented technique for scheduling is presented where the high performance computing system administrator is trying to maximize the return on investment. A model is developed where the users pay a given price to have a bag-of-tasks processed. The cost to the system administrator for processing this bag-of-tasks is strongly related to the energy consumption for executing these tasks. A novel algorithm is designed that efficiently finds the maximum profit resource allocation and tightly bounds the optimal solution. In addition, this algorithm has very desirable runtime and solution quality properties as the number of tasks and machines become large.

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Section: B Lower Boundmentioning

confidence: 99%

“…Let cost j and λ j be the purchase price and mean time to failure of machine type j respectively. To model this one can subtract j M j cost j λ j (12) from the profit per unit time.…”

Section: Model Extensionsmentioning

confidence: 99%

Energy-Aware Profit Maximizing Scheduling Algorithm for Heterogeneous Computing Systems

Tarplee

Maciejewski

Siegel

2014

2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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“…But, P 1 's HECR of 0.075 time units per work unit is better than P 2 's HECR of 0.08 time units per work unit. 4 In this case, the threshold Tρ is 0.8 time units per work unit. In addition, P 1 has the maximum variance in speed among all profiles with the mean speedρ 1 , because any change of the ρ-values in P 1 only decreases its variance in speed if P 1 still keeps the same mean speedρ 1 ; P 2 has the minimum variance in speed among all profiles with the mean speedρ 2 .…”

Section: ) Mean Speed As a Predictor Of Performancementioning

confidence: 99%

“…See Section II-A. 4 Recall that HECR is a measure of work production, not an encoding of mean speed. a higher variance is more productive, then the percentage of failed predictions is 0% for two computers per cluster, which has been shown in Theorem 3.…”

Section: ) Mean Speed As a Predictor Of Performancementioning

confidence: 99%

“…A heterogeneous multicomputer platform comprises computers that may differ in computing power and that are capable of communicating with one another [3], [4], [13]. Heterogeneity pervades almost all modern computing systems such as: Grid computing [10], [15], [16], global computing [12], volunteer computing [17], cloud computing [11], and clusters [5], [19].…”

Section: Introductionmentioning

confidence: 99%

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Statistical predictors of computing power in heterogeneous clusters

Chiang

Maciejewski

Rosenberg

et al. 2010

2010 IEEE International Symposium on Parallel &Amp; Distributed Processing, Workshops and PHD Forum (IPDPSW)

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Abstract-If cluster C1 consists of computers with a faster mean speed than the computers in cluster C2, does this imply that cluster C1 is more productive than cluster C2? What if the computers in cluster C1 have the same mean speed as the computers in cluster C2: is the one with computers that have a higher variance in speed more productive? Simulation experiments are performed to explore the above questions within a formal framework for measuring the performance of a cluster. Simulation results show that both mean speed and variance in speed (when mean speeds are equal) are typically correlated with the performance of a cluster, but not always; these statements are quantified statistically for our simulation environments. In addition, simulation results also show that: (1) If the mean speed of computers in cluster C1 is faster by at least a threshold amount than the mean speed of computers in cluster C2, then C1 is more productive than C2. (2) If the computers in clusters C1 and C2 have the same mean speed, then C1 is more productive than C2 when the variance in speed of computers in cluster C1 is higher by at least a threshold amount than the variance in speed of computers in cluster C2.

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

Canon

Héam

Philippe

2017

Concurrency and Computation

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Summary Bias in the performance evaluation of scheduling heuristics has been shown to undermine the scope of existing studies. Improving the assessment step leads to stronger scientific claims when validating new optimization strategies. This article considers the problem of allocating independent tasks to unrelated machines such as to minimize the maximum completion time. Testing heuristics for this problem requires the generation of cost matrices that specify the execution time of each task on each machine. Numerous studies showed that the task and machine heterogeneities belong to the properties impacting heuristics performance the most. This study focuses on orthogonal properties, the average correlations between each pair of rows and each pair of columns, which measure the proximity with uniform instances. Cost matrices generated with 2 distinct novel generation methods show the effect of these correlations on the performance of several heuristics from the literature. In particular, EFT performance depends on whether the tasks are more correlated than the machines and HLPT performs the best when both correlations are close to one.

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Statistical measures for quantifying task and machine heterogeneities

Cited by 16 publications

References 28 publications

Energy-Aware Profit Maximizing Scheduling Algorithm for Heterogeneous Computing Systems

Energy-Aware Profit Maximizing Scheduling Algorithm for Heterogeneous Computing Systems

Statistical predictors of computing power in heterogeneous clusters

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

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