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Volunteer Computing is a type of distributed computing in which ordinary people donate their idle computer time to science projects like and many others. In our case, 20 executions per experiment instead of 50, and only half of all experiments, which means down to 20%, i.e. With our sensitivity analysis, we show that we can reduce the inaccuracy of the naive approach with only 40% of total number of executions per experiment in the learning phase. Additionally, in order to reduce the number of experiments and determine the dependency of our model against Cloud resource and workflow parameters, the conducted comprehensive sensitivity analysis shows that the correctness of our model is independent of workflow parallel section size. We determine the minimum amount of data that needs to be collected or minimum number of test cases that needs to be repeated for each experiment in order to get less than 12% inaccuracy for our noising parameter. Since our approach requires collecting data by executing the workflow in the Cloud in order to learn its behavior, we conduct a comprehensive sensitivity analysis.

Additionally, we analyse the impact of particular workflow and Cloud parameters, which shows that the Cloud performance instability is simulated more correctly for small instance type (inaccuracy of up to 11.5%), instead of medium (inaccuracy of up to 35%), regardless of the workflow. The results show that the inaccuracy of the makespan’s mean value was reduced up to 59 times compared to naively using the normal distribution.

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A series of experiments with different workflows and Cloud resources were conducted in order to evaluate our model and methodology. In order to measure the quality of simulation by quantifying the relative difference between the simulated and measured values, we introduce the parameter inaccuracy. Instead of using a normal distribution naively by using mean value and standard deviation of workflow tasks’ runtime, we inject two noises in the tasks’ runtime: noisiness of tasks within a workflow (defined as average runtime deviation) and noisiness provoked by the environment over the whole workflow (defined as average environmental deviation). We propose several metrics to model a Cloud fluctuating behavior and then by injecting them within the simulator, it starts to behave as close as the real Cloud. In this paper, we define a model and a methodology to add a noise to the simulation in order to equalise its behavior with the Clouds’ one.

However, the ideal simulated environment produces the same output for the same workflow schedule and input parameters and thus can not reproduce the Cloud variant behavior. Estimating the makespan is necessary for IT managers in order to calculate the cost of execution, for which they can use Cloud simulators. purchase decision making.Ĭloud computing provides a cheap and elastic platform for executing large scientific workflow applications, but it rises two challenges in prediction of makespan (total execution time): performance instability of Cloud instances and variant scheduling of dynamic schedulers.

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In this work, we evaluate an analytical GPU performance model based on Little's law, that expresses the kernel execution time in terms of latency bound, throughput bound, and achieved occupancy.We then combine it with the results of several research papers, introduce equations for data transfer time estimation, and finally incorporate it into the MERPSYS framework, which is a general-purpose simulator for parallel and distributed systems.The resulting solution enables the user to express a CUDA application in a MERPSYS editor using an extended Java language and then conveniently evaluate its performance for various launch configurations using different hardware units.We also provide a systematic methodology for extracting kernel characteristics, that are used as input parameters of the model.The model was evaluated using kernels representing different traits and for a large variety of launch configurations.We found it to be very accurate for computation bound kernels and realistic workloads, whilst for memory throughput bound kernels and uncommon scenarios the results were still within acceptable limits.We have also proven its portability between two devices of the same hardware architecture but different processing power.Consequently, MERPSYS with the theoretical models embedded in it can be used for evaluationof application performance on various GPUs and used for performance prediction and e.g.

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