Error-Tolerant Resource Allocation and Payment Minimization for Cloud System

Abstract

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.

Minimization of cloud task execution length with workload prediction errors

“…A few works (not many) also analyzed this issue for their approaches in the context of Cloud platforms, but differ a lot from this paper. Mao's auto-scaling method [27] and Di's approach [28] took into account load prediction issue in Cloud systems, whereas they both handled a different objective that aims to minimize user payment with guaranteed task deadlines. Thus, the problem formulation is fairly distinct, so is the following solution.…”

“…By combining Equation (23), Equation (26) and Inequality (27), we could further get the Inequality (28).…”

An Approach to Steadfast and Reliable Resource Scheduling in Cloud Environment

“…The work in [4], [5] focuses on identifying the properties for establishing trust in the cloud. Sheng Di and Cho-Li Wang [7] proposed a novel resource allocation algorithm for cloud system that supports VM-multiplexing technology, aiming to minimize user"s payment on his/her task and also endeavour to guarantee its execution deadline. It can be proved that the output of algorithm is optimal based on the KarushKuhn-Tucker (KKT) condition, which means any other solutions would definitely cause larger payment cost.…”

Opposition Learning-Based Grey Wolf Optimizer Algorithm for Parallel Machine Scheduling in Cloud Environment

“…Similarly, to overcome the deadline-driven resource allocation issue S. Di and C. L. Wang [19] have clarified the Error-Tolerant Resource Allocation and Payment Minimization for Cloud Scheme. J. T. Tsai et al [20] have elucidated the optimize task scheduling and resource allocation by an enhanced differential evolution algorithm (IDEA) on the basis of the cost and time models on cloud computing atmosphere.…”