Abstract-Although there are few efficient algorithms in the literature for scientific workflow tasks allocation and scheduling for heterogeneous resources such as those proposed in grid computing context, they usually require a bounded number of computer resources that cannot be applied in Cloud computing environment. Indeed, unlike grid, elastic computing, such as Amazon's EC2, allows users to allocate and release compute resources on-demand and pay only for what they use. Therefore, it is reasonable to assume that the number of resources is infinite. This feature of Clouds has been called "illusion of infinite resources". However, despite the proven benefits of using Cloud to run scientific workflows, users lack guidance for choosing between multiple offering while taking into account several objectives which are often conflicting.On the other side, the workflow tasks allocation and scheduling have been shown to be NP-complete problems. Thus, it is convenient to use heuristic rather than deterministic algorithm. The objective of this paper is to design an allocation strategy for Cloud computing platform. More precisely, we propose three complementary bi-criteria approaches for scheduling workflows on distributed Cloud resources, taking into account the overall execution time and the cost incurred by using a set of resources.
Business process (BP) stakeholders want to enjoy the benefits of the cloud, but they are also reluctant to expose their BP models which express the know-how of their companies. To prevent such a know-how exposure, this paper proposes a designtime approach for transforming a BP model into BP fragments so that these BP fragments externalized in a multi-cloud context do not allow a cloud resource provider to understand a critical fragment of the company. While existing contributions on this topic remain at the level of principles, we propose an algorithm supporting automatically such a BP model transformation. Index Terms-Business Process; Security Risk Management; Cloud; Privacy; Obfuscation Bank Get Loan Application (GLA) Check Customer Credit (CCC) Risk Evaluation (RE) Risk Capture (RC) Direct Loan Agreement (DLA) Loan Reject (LR) Hierarchy Validation (HV) Decision Consolidation (DC)
International audienceThe Cloud computing paradigm is adopted for its several advantages like reduction of cost incurred when using a set of resources. Despite the many proven benefits of using a Cloud infrastructure to run business processes, it is still faced with a major problem that can compromise its success: the lack of guidance for choosing between multiple offerings. To ensure this, we propose a set of algorithms for business process scheduling in Cloud computing environments. More precisely, we propose an extension of our previous approaches taking into account the fact that several instances of the same process can run simultaneously, and they may have to share the same resources. The proposed approaches take into account the Cloud elasticity feature on the one hand, and on the other hand, they consider the two most important quality of service criteria when running business process in Clouds environment, namely (i) the overall execution time and (ii) the cost incurred using a set of resources. In addition they allow to ensure fairness between the different concurrent business process instances
Abstract. As for all kind of software, customers expect to find business process execution provided as a service (BPMaaS). They expect it to be provided at the best cost with guaranteed SLA. From the BPMaaS provider point of view it can be done thanks to the provision of an elastic cloud infrastructure. The provider still have to provide the service at the lowest possible cost while meeting customers expectation. We propose a customer-centric service model that link the BPM execution requirement to cloud resources, and that optimize the deployment of customer's (or tenants) processes in the cloud to adjust constantly the provision to the needs. However, migrations between cloud configurations can be costly in terms of quality of service and a provider should reduce the number of migration. We propose a model for BPMaaS cost optimization that take into account a maximum number of migrations for each tenants. We designed a heuristic algorithm and experimented using various customer load configurations based on customer data, and on an actual estimation of the capacity of cloud resources.
Even though the proven benefits of cloud computing paradigm, it must face a serious problem that can compromise its commercial success. It concerns the lack of efficient approach for using optimally the available resources. For this, several approaches have been proposed. However, they suffer from several shortcomings. For instance, often only one objective is taken into account expressing all operations in terms of cost. Furthermore, business processes should be insured with elasticity and multitenancy mechanism while adjusting the available resources to the dynamic load distribution. The proposed approach aims to optimize two conflicting objectives, namely the number of migrated tenants and the cost incurred using a set of resources. It allows to take into account the multi-tenancy property and the Cloud computing elasticity, and is efficient as shown by an extensive experimentation based on real data from Bonita BPM customers.
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