EDQWS: an enhanced divide and conquer algorithm for workflow scheduling in cloud

Abstract

A workflow is an effective way for modeling complex applications and serves as a means for scientists and researchers to better understand the details of applications. Cloud computing enables the running of workflow applications on many types of computational resources which become available on-demand. As one of the most important aspects of cloud computing, workflow scheduling needs to be performed efficiently to optimize resources. Due to the existence of various resource types at different prices, workflow scheduling has evolved into an even more challenging problem on cloud computing. The present paper proposes a workflow scheduling algorithm in the cloud to minimize the execution cost of the deadline-constrained workflow. The proposed method, EDQWS, extends the current authors’ previous study (DQWS) and is a two-step scheduler based on divide and conquer. In the first step, the workflow is divided into sub-workflows by defining, scheduling, and removing a critical path from the workflow, similar to DQWS. The process continues until only chain-structured sub-workflows, called linear graphs, remain. In the second step which is linear graph scheduling, a new merging algorithm is proposed that combines the resulting linear graphs so as to reduce the number of used instances and minimize the overall execution cost. In addition, the current work introduces a scoring function to select the most efficient instances for scheduling the linear graphs. Experiments show that EDQWS outperforms its competitors, both in terms of minimizing the monetary costs of executing scheduled workflows and meeting user-defined deadlines. Furthermore, in more than 50% of the examined workflow samples, EDQWS succeeds in reducing the number of resource instances compared to the previously introduced DQWS method.

A review on workflow scheduling and resource allocation algorithms in distributed mobile clouds

“…In Reference 1, an enhanced divide and conquer algorithm for workflow scheduling in cloud called (EDQWS) is proposed. This algorithm is a two‐step scheduler based on divide and conquer.…”

“…According to the above explanations, the classification of workflow scheduling methods has been shown in Figure 4. In the first part, the classification of methods is based on the nature of scheduling algorithms, which includes super-heuristic 9 Meta Heuristic methods [5], [16], [21], [35], [36], [37], [50], [51], [52], [54], [55], [56], [57], [58], [61], [62], [63], [64], [65], [66], [67] 9 Heuristic methods [1], [2], [13], [17], [18], [19], [22], [25], [26], [28], [29], [30], [31], [32], [33], [34], [42], [46], [47], [48], [49] 9 F I G U R E 4 Classification of workflow scheduling methods. 12 methods and heuristic methods.…”

“…Cloud computing enables the running of workflow applications on many types of computational resources which become available on‐demand. As one of the most important aspects of cloud computing, workflow scheduling needs to be performed efficiently to optimize resources 1 …”

“…As one of the most important aspects of cloud computing, workflow scheduling needs to be performed efficiently to optimize resources. 1 In recent years, we have seen the increasing popularity of mobile devices through the exponential development of their technology. Along with this rapid development, the demand of users to run various complex applications (such as face recognition, image processing, and augmented reality games) on mobile devices has been increased.…”

Towards optimized scheduling and allocation of heterogeneous resource via graph-enhanced EPSO algorithm

“…Specifically, heterogeneous computing platforms host a variety of computing resources, which exhibit different performance metrics due to differences in their underlying hardware. Nevertheless, these hardwarelevel differences have been neglected by existing studies [11][12][13], which inevitably leads to low task efficiency in real-world operational environments. Besides, tasks on heterogeneous computing platforms consist of multiple interdependent subtasks that should be allocated with appropriate computing resources [14][15][16].…”