Abstract-With virtual machine (VM) technology being increasingly mature, compute resources in Cloud systems can be partitioned in fine granularity and allocated on demand. We make three contributions in this paper: (1) We formulate a deadline-driven resource allocation problem based on the Cloud environment facilitated with VM resource isolation technology, and also propose a novel solution with polynomial time, which could minimize users' payment in terms of their expected deadlines. (2) By analyzing the upper bound of task execution length based on the possibly inaccurate workload prediction, we further propose an error-tolerant method to guarantee task's completion within its deadline. (3) We validate its effectiveness over a real VM-facilitated cluster environment under different levels of competition. In our experiment, by tuning algorithmic input deadline based on our derived bound, task execution length can always be limited within its deadline in the sufficient-supply situation; the mean execution length still keeps 70% as high as user-specified deadline under the severe competition. Under the original-deadline-based solution, about 52.5% of tasks are completed within 0.95∼1.0 as high as their deadlines, which still conforms to the deadline-guaranteed requirement. Only 20% of tasks violate deadlines, yet most (17.5%) are still finished within 1.05 times of deadlines.
Abstract-In cloud systems, it is non-trivial to optimize task's execution performance under user's affordable budget, especially with possible workload prediction errors. Based on an optimal algorithm that can minimize cloud task's execution length with predicted workload and budget, we theoretically derive the upper bound of the task execution length by taking into account the possible workload prediction errors. With such a state-of-the-art bound, the worst-case performance of a task execution with a certain workload prediction errors is predictable. On the other hand, we build a close-to-practice cloud prototype over a real cluster environment deployed with 56 virtual machines, and evaluate our solution with different resource contention degrees. Experiments show that task execution lengths under our solution with estimates of worstcase performance are close to their theoretical ideal values, in both non-competitive situation with adequate resources and the competitive situation with a certain limited available resources. We also observe a fair treatment on the resource allocation among all tasks.
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