On-Demand Resource Provisioning for BPEL Workflows Using Amazon's Elastic Compute Cloud

Dörnemann, Tim; Juhnke, Ernst; Freisleben, Bernd

doi:10.1109/ccgrid.2009.30

Cited by 89 publications

(54 citation statements)

References 10 publications

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“…4 how both the execution and the monitoring components access indistinctly to the cloud or embedded nodes, in order to gather information about each instance initiated in the distributed servers [27] [28]. As we could observe, BPM as well as many other specialties in IT, have suffered changes due to the different service models in the cloud.…”

Section: Component Communicationmentioning

confidence: 99%

Constructing and monitoring processes in BPM using hybrid architectures

Garro¹,

Bazán²

2013

SpecialIssue

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Abstract-With the entrance of BPM in the Cloud, a change in the conception and design of Business Processes has been produced. Distributed environments, in this context offer computing possibilities which are advantageous for processes, especially in a decomposition context. This last concept has been introduced in BPM allowing processes to be executed in a cloud environment as well as in an embedded one. This situation takes advantage of both approaches under criteria like sensitive data, high computing performance and system portability. An unexplored aspect in current bibliography is process monitoring over a decomposed environment. In the present article we introduce the analysis of some concepts presented in current bibliography, and we propose also the architecture for a distributed process monitoring system. In this architecture we consider different design factors like location transparency, and the data needed for instance tracking over a cloud system.

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Section: Component Communicationmentioning

confidence: 99%

Constructing and monitoring processes in BPM using hybrid architectures

Garro¹,

Bazán²

2013

SpecialIssue

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“…In an earlier work [6] they presented an on demand deployment scheme to avoid peak loads but the solution showed some shortcomings for workflows with respect to throughput and cost. To find a trade-off between task based scheduling (imprecise) and graph based scheduling (complex) of workflows, [7] proposes a critical path based scheduling.…”

Section: Related Workmentioning

confidence: 99%

Infrastructure Aware Scientific Workflows and Infrastructure Aware Workflow Managers in Science Gateways

et al. 2016

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Abstract-The workflow interoperability problem was successfully solved by the SHIWA project if the workflows to be integrated were running in the same grid infrastructure. However, in the more generic case when the workflows were running in different infrastructures the problem has not been solved yet. In the current paper we show a solution for this problem by introducing a new type of workflow called infrastructure-aware workflow. These are scientific workflows extended with new node types that enable the on-the-fly creation and destruction of the required infrastructures in the clouds. The paper shows the semantics of these new types of nodes and workflows and also how they can solve the workflow interoperability problem. The paper also describes how these new type of workflows can be implemented by a new service called Occopus, and how this service can be integrated with the existing SHIWA Simulation Platform services like the WS-PGRADE/gUSE portal to provide the required functionalities of solving the workflow interoperability problem.

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“…The notion of cost of processing is not implemented by this. In [19] a new approach is presented to handle peak-load issues in cloud computing products such as EC2 in terms of allocating resources on-demand. In [20] also discussion is made on the resource allocations in grid environment for workflows related to scientific work.…”

Section: Related Workmentioning

confidence: 99%

Parallel Data Processing for Effective Dynamic Resource Allocation in the Cloud

Jyothi¹

2013

IJCA

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Parallel data processing has become more and more reliable phenomenon due to the realization of could computing, especially using IaaS (Infrastructure as a Service) clouds. The cloud service providers such as IBM, Google, Microsoft and Oracle have made provisions for parallel data processing in their cloud services. Nevertheless, the frameworks used as of now are static and homogenous in nature in a cluster environment. The problem with these frameworks is that the resource allocation when large jobs are submitted is not efficient as they take more time for processing besides incurring more cost. In this paper we discuss the possibilities of parallel processing and its challenges. One of the IaaS products meant for parallel processing is presented in this paper. VMs are allocated to tasks dynamically for execution of jobs. With proposed framework we performed parallel job processing which involves Map Reduce, a new programming phenomenon. We also compare this with Hadoop.

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On-Demand Resource Provisioning for BPEL Workflows Using Amazon's Elastic Compute Cloud

Cited by 89 publications

References 10 publications

Constructing and monitoring processes in BPM using hybrid architectures

Constructing and monitoring processes in BPM using hybrid architectures

Infrastructure Aware Scientific Workflows and Infrastructure Aware Workflow Managers in Science Gateways

Parallel Data Processing for Effective Dynamic Resource Allocation in the Cloud

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