A cloud computing environment is the most popular choice for workflow execution, as it gives customers on-demand access to computing resources. However, in cloud workflow scheduling, cloud-native requirements regarding QoS requirements such as monetary cost and execution time should be taken into account. This paper proposes PCP-ACO, a list scheduling algorithm for minimizing the execution cost of a workflow, while meeting its user-defined deadline in cloud environments. In PCP-ACO, first a topological sort of the workflow tasks is computed to assign a priority to each task. Then, Ant Colony Optimization (ACO) meta-heuristic is used to assign a proper resource to each task of the workflow, in order of their priorities. The Partial Critical Path (PCP) concept is also used as a heuristic to guide ACO algorithm. Several experiments are conducted using real scientific workflows, and the cost saving is compared with PSO and IC-PCP algorithms. The experimental results show that the proposed algorithm outperforms other compared algorithms in terms of cost saving.

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Recent advancements in location-aware analytics have created novel opportunities in different domains. In the area of process mining, enriching process models with geolocation helps to gain a better understanding of how the process activities are executed in practice. In this paper, we introduce our idea of geo-enabled process modeling and report on our industrial experience. To this end, we present a real-world case study to describe the importance of considering the location in process mining. Then we discuss the shortcomings of currently available process mining tools and propose our novel approach for modeling geo-enabled processes focusing on 1) increasing process interpretability through geo-visualization, 2) incorporating location-related metadata into process analysis, and 3) using location-based measures for the assessment of process performance. Finally, we conclude the paper by future research directions.