A Study on Performance of Dominant Scheduling Algorithms on Standard Workflow Systems in Cloud

Kanagaraj, K.; Swamynathan, S.

doi:10.1145/2980258.2980358

Cited by 3 publications

(5 citation statements)

References 8 publications

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“…In considering the first group, we have found studies that highlight the performance evaluation at task-level in order to measure and predict requirements of task resources in workflows; [14][15][16][17] improve running processes; [18][19][20] and measure performance in workflow engines; 21,22 and in workflow systems. [23][24][25][26][27] Other approaches involve measuring workflow-level performance, cloud environment performance, [28][29][30] monitoring collected information of events in the execution of a workflow, [31][32][33][34] analytical performance modelling, 35 and simulation. 36,37 Studies described by Deelman et al, 14 Ferreira et al, 15 and Ferreira et al 16 highlighted techniques related to machine learning algorithms to model and automatically predict the use of such workflow task resources such as: execution time, storage usage, and performance.…”

Section: Measuring Performancementioning

confidence: 99%

“…The approach was proposed to identify the following key performance indicators: CPU, main memory disk workloads, and completion time for experiments. The article by Kanagaraj and Swamynathan 23 presented a detailed study on the existence of schedule algorithms in the workflow, as well as an analysis of their performance. According to the authors, an effective schedule algorithm will optimise resource usage and amount for executing tasks of the workflow.…”

Section: Measuring Performancementioning

confidence: 99%

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On the analysis of makespan and performance of the task‐based execution model for enterprise application integration platforms: An empirical study

et al. 2022

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Increasingly enterprises rely on software applications to support their business processes. Since such processes are continually evolving to keep up with market dynamism, companies strive to increase their efficiency, for example, by optimising the integration of applications supporting these processes. Integration platforms are specialised software tools that allow creating integration processes so that applications can share data and functionality. However, this integration involves several challenges, especially when large volumes of heterogeneous data should be integrated and shared. The performance of an integration process, in terms of message processing, is directly related to the run-time system of the integration platform. This article investigates the impact of the volume of messages and the number of threads used by a run-time system on makespan and performance of an integration process. The greater is the number of messages per second received by the integration process, the high is the volume of messages. The study was based on a run-time system with task-based execution model and follows a strict protocol to conduct and report our empirical study. We observed an increment of makespan when increasing the volume of messages to integration processes and different behaviours when increasing the number of threads used in their executions. Makespan reduces as the number of threads increases, but only when the volume of inbound messages is not very high. We confirmed that there is a performance gain by increasing the number of threads to execute an integration process, but observed that the continuous increment of threads leads to degradation of the performance in this model.

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Section: Measuring Performancementioning

confidence: 99%

Section: Measuring Performancementioning

confidence: 99%

On the analysis of makespan and performance of the task‐based execution model for enterprise application integration platforms: An empirical study

et al. 2022

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“…WorkflowSim, proposed by Kanagaraj and Swamynathan (2016), is a simulator built on top of CloudSim (Calheiros et al, 2011) that o↵ers support for workflow scheduling and execution, in Cloud Computing research field. This simulator allows the comparison of new heuristics with the popular scheduling algorithms, such as Critical Path Algorithm, First Come First Serve, MaxMin, and MinMin.…”

Section: Related Workmentioning

confidence: 99%

“…Integration platforms are tools that allow software engineers to design, implement, run, and monitor integration processes. The conceptual model of an integration process is a workflow composed of segments of atomic tasks connected by communication channels that desynchronise one task from another (Kanagaraj and Swamynathan, 2016). Data, wrapped in messages, are received from applications, processed by tasks in the workflows, and delivered to destination applications.…”

Section: Introductionmentioning

confidence: 99%

Integration process simulator: A tool for performance evaluation of task scheduling of integration processes

Freire

Frantz

Roos‐Frantz

et al. 2022

Journal of Simulation

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“…An iPaaS allows software engineers to design, run, and monitor integration processes. An integration process carries out a workflow made up of distinct atomic tasks, connected by communication channels that desynchronise one task from another [5]. Messages move through the workflow, encapsulating data from/to the applications being integrated by the process.…”

Section: Introductionmentioning

confidence: 99%

A Multi-queue Round Robin Algorithm for Task Scheduling in Enterprise Application Integration Platforms in the Cloud

Freire

Frantz

Basto‐Fernandes

et al. 2023

Preprint

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Contemporaneous environments involve a large number of information which is received from many interconnected devices and integrated by many platforms. The Internet of Things and Cloud computing offer services to deal with such big data, so that enterprises can play in their business core by outsourcing the development, the configuration, and the maintenance of those services and their computational resources. Many enterprises use integration Platform as a Service to maintain their applications and data working in a synchronous way. However, current task scheduling algorithms of integration platforms are facing some hardships to tackle with high volumes of data. In this article, we propose a scheduling algorithm based in the round-robin heuristic through multiple task queue, which presents better performance than the traditional First-in-first-out heuristic used by current platforms. We experimented our algorithm in the execution of an integration process, and we validated the results with statistical techniques.

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A Study on Performance of Dominant Scheduling Algorithms on Standard Workflow Systems in Cloud

Cited by 3 publications

References 8 publications

On the analysis of makespan and performance of the task‐based execution model for enterprise application integration platforms: An empirical study

On the analysis of makespan and performance of the task‐based execution model for enterprise application integration platforms: An empirical study

Integration process simulator: A tool for performance evaluation of task scheduling of integration processes

A Multi-queue Round Robin Algorithm for Task Scheduling in Enterprise Application Integration Platforms in the Cloud

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