Bi-criteria strategies for business processes scheduling in cloud environments with fairness metrics

Bessai, Kahina; Youcef, Samir; Oulamara, Ammar; Godart, Claude

doi:10.1109/rcis.2013.6577701

Cited by 13 publications

(12 citation statements)

References 20 publications

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“…Similarly, the authors in [5] extend their approach already presented [4] to execute several instances of workflows concurrently. The proposed approach allows to ensure fairness between the various workflow instances.…”

Section: ) Scheduling Multiple Workflowsmentioning

confidence: 95%

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Workflow Scheduling in Cloud Computing: A Survey

Fakhfakh

Kacem

2014

2014 IEEE 18th International Enterprise Distributed Object Computing Conference Workshops and Demonstrations

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Workflow systems have become a major vehicle for easy and efficient development of scientific applications. This type of systems can benefit from the resource provisioning technology offered by the cloud computing. In fact, the latter offers on-demand virtualized resources to its users. These virtual resources can be added and released dynamically. Also, users are charged on a pay-per-use basis. How to make appropriate decisions when allocating resources to the tasks and dispatching the computing tasks to resource pool has become the main issue in cloud computing. The amount of allocated resources affects the execution time of the applications and the cost incurred by the user. In fact, resource under-provisioning will necessarily affect the performance. In contrast, over-provisioning can result in idle instances and cause additional costs. Then, efficient scheduling algorithms are required for selection of best suitable resources for task execution. This paper focuses on some of the important workflow scheduling strategies. It brings out an exhaustive survey of such strategies in cloud computing and includes a detailed classification of them. Then, it presents a comparative analysis of the studied approaches. Finally, it stands out a critical challenge for further research.

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Section: ) Scheduling Multiple Workflowsmentioning

confidence: 95%

“…Abrishami et al [1] Bessai et al [4] Jayadivya et al [9] Chopra et al [12] Jianfang et al [ 18] Jayadivya et al [17] Singh et al [30] Caron et al [10] Jung et al [20] Bessai et al [5] Bittencourt et al [7] Jiang et al [19] Kumar et al [22] Malawski et al [23] Pietri et al [28] Varalakshmi et al [31] Single WF…”

Section: Wf: Workflowmentioning

confidence: 99%

Workflow Scheduling in Cloud Computing: A Survey

Fakhfakh

Kacem

2014

2014 IEEE 18th International Enterprise Distributed Object Computing Conference Workshops and Demonstrations

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“…Hence, this approach makes use of a similar resource model as done by the SWF approaches discussed above. The same applies to the work by Bessai et al (2013), who also assume that workflows are composed from single software services. The authors propose different methods to optimize resource allocation and scheduling, aiming at cost or time optimization or to find a pareto-optimal solution covering both cost and time.…”

Section: Business Processesmentioning

confidence: 98%

“…Because workflows are not able to concurrently share service instances, the potential for optimization of resource allocation is not completely exploited. Similar to Bessai et al (2013), Wei et al also do not implement a testbed to test their algorithms, but use simulation in their evaluation. Despite the differences between our work and the work by Wei et al, there are also some commonalities, e.g., to allow different sizes of VMs at proportional cost.…”

Section: Business Processesmentioning

confidence: 99%

Cost-Driven Optimization of Cloud Resource Allocation for Elastic Processes

Schulte¹,

Schuller²,

Hoenisch³

et al. 2013

STCC

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Today's extensive business process landscapes make it necessary to handle the execution of a large number of business processes and individual process steps. Especially if process steps require the invocation of resource-intensive applications or a large number of applications need to be executed concurrently, process owners may have to allocate extensive computational resources, leading to high fixed cost. In the work at hand, we propose an alternative to the provision of fixed resources, based on automatic leasing and releasing of Cloud-based computational resources. For this, we present an integrated approach which addresses the cost-driven optimization of Cloud-based computational resources for business processes in order to realize so-called Elastic Processes. Through an evaluation, we show the practical applicability and benefits of our contributions. Specifically, we find that our approach substantially reduces the cost compared to an ad hoc approach.

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“…Within their scheduling, the authors consider cost for VMs as well as data transfer cost. Bessai et al aim at cost or time optimization or a pareto-optimal solution covering both aspects [37]. Wei et al allow generic QoS constraints, but do not explicitly discuss deadline-constrained scheduling [38].…”

Section: Related Workmentioning

confidence: 99%

Optimization of Complex Elastic Processes

Hoenisch

Schuller

Schulte

et al. 2016

IEEE Trans. Serv. Comput.

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Business Process Management is a matter of great importance in different industries and application areas. In many cases, it involves the execution of resource-intensive tasks in terms of computing power such as CPU and RAM. Due to the emergence of Cloud computing, theoretically unlimited resources can be used for the enactment of business processes. These Cloud resources render several challenges for Business Process Management Systems to ensure a predefined Quality of Service level during Cloud-based process enactment. Therefore, new solutions for process scheduling and resource allocation are required to tackle these challenges. Within this paper, we present a novel approach to schedule business processes and optimize the used Cloud-based computational resources in a cost-efficient way, thus realizing so-called elastic processes. For that, we specify the Service Instance Placement Problem, i.e., an optimization model which defines the setting of how service instances are scheduled among resources. Through extensive evaluations we show the benefits of our contributions and compare the novel approach against a baseline which follows an ad hoc approach.

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Bi-criteria strategies for business processes scheduling in cloud environments with fairness metrics

Cited by 13 publications

References 20 publications

Workflow Scheduling in Cloud Computing: A Survey

Workflow Scheduling in Cloud Computing: A Survey

Cost-Driven Optimization of Cloud Resource Allocation for Elastic Processes

Optimization of Complex Elastic Processes

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