concurrent workflow scheduling algorithm works in three phases, namely rank computation, tasks selection, and resource selection. In this paper, we introduce a new ranking algorithm that computes the rank of a task, b...
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concurrent workflow scheduling algorithm works in three phases, namely rank computation, tasks selection, and resource selection. In this paper, we introduce a new ranking algorithm that computes the rank of a task, based on its successor rank and its predecessors average communication time, instead of its successors rank. The advantage of this ranking algorithm is that two dependent tasks are assigned to the same machine and as a result the scheduled length is reduced. The task selection phase selects a ready task from each workflow and creates a task pool. The resource selection phase initially assigns tasks using min-min heuristic, after initial assignment, tasks are moved from the highly loaded machines to the lightly loaded machines. Our resource selection algorithm increases the load balance among the resources due to tasks assignment heuristic and reassignment of tasks from the highly loaded machines. The simulation results show that our proposed scheduling algorithm performs better over existing approaches in terms of load balance, makespan and turnaround time.
Understanding what a software engineer (a developer, an incident responder, a production engineer, etc.) is working on is a challenging problem - especially when considering the more complex software engineering workf...
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ISBN:
(纸本)9781450379588
Understanding what a software engineer (a developer, an incident responder, a production engineer, etc.) is working on is a challenging problem - especially when considering the more complex software engineering workflows in software-intensive organizations: i) engineers rely on a multitude (perhaps hundreds) of loosely integrated tools;ii) engineers engage in concurrent and relatively long running workflows;ii) infrastructure (such as logging) is not fully aware of work items;iv) engineering processes (e.g., for incident response) are not explicitly modeled. In this paper, we explain the corresponding 'work-item prediction challenge' on the grounds of representative scenarios, report on related efforts at Facebook, discuss some lessons learned, and review related work to call to arms to leverage, advance, and combine techniques from program comprehension, mining software repositories, process mining, and machine learning.
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