Performance Estimation for Business Workflows on Public Cloud Offerings Using Probabilistic Model Checker

Kikuchi, Shinji

doi:10.1109/ucc.2014.41

Cited by 1 publication

(1 citation statement)

References 13 publications

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“…As a consequence, workflows make possible the automation of business processes (Decker et al, 2010). Furthermore, the combined use of workflows and virtualised infrastructures such as clouds reduce the financial cost and improves the efficiency of business process executions (Saurabh and Ranjan, 2012;Robinson et al, 2012;Kikuchi, 2014;Suping et al, 2016). The elasticity, scalability and robustness, commonly found in cloud infrastructures, also play a crucial role in executing workflows.…”

Section: Introductionmentioning

confidence: 99%

An execution environment as a service for adaptive long-running workflows

Souza-Júnior

Rosa

Lins

2021

IJWIS

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Purpose This paper aims to present Long4Cloud (long-running workflows execution environment for cloud), a distributed and adaptive LRW execution environment delivered “as a service” solution. Design/methodology/approach LRWs last for hours, days or even months and their duration open the possibility of changes in business rules, service interruptions or even alterations of formal regulations of the business before the workflow completion. These events can lead to problems such as loss of intermediary results or exhaustion of computational resources used to manage the workflow execution. Existing solutions face those problems by merely allowing the replacement (at runtime) of services associated with activities of the LRW. Findings LONG4Cloud extends the previous works in two main aspects, namely, the inclusion of dynamic reconfiguration capabilities and the adoption of an “as a service” delivery mode. The reconfiguration mechanism uses quiescence principles, data and state management and provides multiple adaptive strategies. Long4Cloud also adopts a scenario-based analysis to decide the adaptation to be performed. Events such as changes in business rules or service failures trigger reconfigurations supported by the environment. These features have been put together in a solution delivered “as a service” that takes advantage of cloud elasticity and allows to better allocate cloud resources to fit into the demands of LRWs. Originality/value The original contribution of Long4Cloud is to incorporate adaptive capabilities into the LRW execution environment as an effective way to handle the specificities of this kind of workflow. Experiments using current data of a Brazilian health insurance company were carried out to evaluate Long4Cloud and show performance gains in the execution of LRWs submitted to the proposed environment.

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