Cost-based vectorization of instance-based integration processes

Böehm, Matthias; Habich, Dirk; Preissler, Steffen; Lehner, Wolfgang; Wloka, Uwe

doi:10.1016/j.is.2010.06.007

Cited by 14 publications

(13 citation statements)

References 35 publications

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“…The runtime system execution model defines procedures for task execution and for thread allocation, during the processing of messages in integration processes. Two execution models are found in the literature, namely: process-based and taskbased [3,9,10,20]. In the process-based model, a thread executes every task of the workflow over an inbound message, so that this message can flow throughout the process.…”

Section: Task Scheduling For Integration Processesmentioning

confidence: 99%

Queue-priority optimized algorithm: a novel task scheduling for runtime systems of application integration platforms

et al. 2021

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The need for integration of applications and services in business processes from enterprises has increased with the advancement of cloud and mobile applications. Enterprises started dealing with high volumes of data from the cloud and from mobile applications, besides their own. This is the reason why integration tools must adapt themselves to handle with high volumes of data, and to exploit the scalability of cloud computational resources without increasing enterprise operations costs. Integration platforms are tools that integrate enterprises' applications through integration processes, which are nothing but workflows composed of a set of atomic tasks connected through communication channels. Many integration platforms schedule tasks to be executed by computational resources through the First-in-first-out heuristic. This article proposes a Queue-priority algorithm that uses a novel heuristic and tackles high volumes of data in the task scheduling of integration processes. This heuristic is optimized by the Particle Swarm Optimization computational method. The results of our experiments were confirmed by statistical tests, and validated the proposal as a feasible alternative to improve integration platforms in the execution of integration processes under a high volume of data.

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Section: Task Scheduling For Integration Processesmentioning

confidence: 99%

Queue-priority optimized algorithm: a novel task scheduling for runtime systems of application integration platforms

et al. 2021

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“…2. The mapping of techniques from related domains to EAI was done by for instance taking the idea of projection push-downs [16,23,25,26,35] and deriving the early-filter or early-mapping technique in EAI. We categorized the techniques according to their impact (e.g., structural or process, data-flow) in context of the objectives for which they provide solutions.…”

Section: Identifying Optimization Strategiesmentioning

confidence: 99%

Optimization Strategies for Integration Pattern Compositions

Ritter

May

Forsberg

et al. 2018

Proceedings of the 12th ACM International Conference on Distributed and Event-Based Systems

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Enterprise Application Integration is the centerpiece of current on-premise, cloud and device integration scenarios. We describe optimization strategies that help reduce the model complexity, and improve the process execution using design time techniques. In order to achieve this, we formalize compositions of Enterprise Integration Patterns based on their characteristics, and propose a realization of optimization strategies using graph rewriting. The framework is successfully evaluated on a real-world catalog of pattern compositions, containing over 900 integration scenarios.

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“…In the literature, we found two main execution models for runtime systems: process-based and task-based [14][15][16][17]. This classification has to do with the granularity with which the runtime system deals regarding task execution [11].…”

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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Cost-based vectorization of instance-based integration processes

Cited by 14 publications

References 35 publications

Queue-priority optimized algorithm: a novel task scheduling for runtime systems of application integration platforms

Queue-priority optimized algorithm: a novel task scheduling for runtime systems of application integration platforms

Optimization Strategies for Integration Pattern Compositions

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

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