Multi-objective Scheduling of BPEL Workflows in Geographically Distributed Clouds

Juhnke, Ernst; Dörnemann, Tim; Bock, David; Freisleben, Bernd

doi:10.1109/cloud.2011.24

Cited by 49 publications

(36 citation statements)

References 19 publications

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“…Other approaches disregard SLAs, and the optimization is either Pareto-optimal or based on weights or priorities indicating the importance of cost and turnaround time [44,45]. Recent investigations have considered SLAs in terms of deadlines for single tasks and single process instances, but only sequential processes are supported [46,47,48].…”

Section: Scheduling For Elastic Processesmentioning

confidence: 99%

See 1 more Smart Citation

Elastic Business Process Management: State of the art and open challenges for BPM in the cloud

Schulte

Janiesch

Venugopal

et al. 2015

Future Generation Computer Systems

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With the advent of cloud computing, organizations are nowadays able to react rapidly to changing demands for computational resources. Not only individual applications can be hosted on virtual cloud infrastructures, but also complete business processes. This allows the realization of so-called elastic processes, i.e., processes which are carried out using elastic cloud resources. Despite the manifold benefits of elastic processes, there is still a lack of solutions supporting them.In this paper, we identify the state of the art of elastic Business Process Management with a focus on infrastructural challenges. We conceptualize an architecture for an elastic Business Process Management System and discuss existing work on scheduling, resource allocation, monitoring, decentralized coordination, and state management for elastic processes. Furthermore, we present two representative elastic Business Process Management Systems which are intended to counter these challenges. Based on our findings, we identify open issues and outline possible research directions for the realization of elastic processes and elastic Business Process Management.

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Section: Scheduling For Elastic Processesmentioning

confidence: 99%

“…Some have focused on cost-efficient resource allocation, e.g., [58], and the enforcement of SLAs, e.g., [59,60]. There are also approaches which aim at achieving a higher resource utilization [44,61,62]. Usually, resource acquisition is done in a reactive way based on rules.…”

Section: Resource Allocationmentioning

confidence: 99%

Elastic Business Process Management: State of the art and open challenges for BPM in the cloud

Schulte

Janiesch

Venugopal

et al. 2015

Future Generation Computer Systems

View full text Add to dashboard Cite

show abstract

“…The authors focus on maximizing the profit for an IaaS broker, who leases resources and provides VMs to service consumers. Juhnke et al (2011) provide an extension to a standard BPEL workflow engine, which allows making use of Cloud resources to execute business processes. As BPEL is applied, workflows are composed from services, which mirrors our approach.…”

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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“…The introduced method is not able to address the queue length of the virtual machines. A multi objective algorithm was proposed by Jahnke et al [12] for work flow scheduling in cloud computing. The algorithm minimizes the total execution time and cost by employing the pareto model.…”

Section: Related Workmentioning

confidence: 99%

An Evolutionary Multi-Objective Approach for Resource Scheduling in Mobile Cloud Computing

Dasari¹,

Saritha²

2017

IJIES

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Abstract:Mobile cloud computing (MCC) is one of the evolving fields in recent years. The complexity of MCC made researchers to concentrate on efficient application development. In MCC, resource scheduling is treated as one of the major issues. Genetic Algorithms (GAs) are efficient search techniques to find the optimal solution for the scheduling problem. GAs has the ability to optimize the resource scheduling in both homogeneous and heterogeneous environments. This paper presents the multi objective genetic algorithm for MCC (MOGAMCC) environment. To implement the MOGAMCC, the cloudsim toolkit was extended with the MOGA and its task scheduling approach determines the optimal scheduling policy. A single point crossover model is employed for the generation of new population. Mutation process is carried by randomly changing the bit positions in the chromosomes. The experimental results show that the proposed model finds the optimal trade-off between the defined objectives and which ultimately reduces the makespan.

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Multi-objective Scheduling of BPEL Workflows in Geographically Distributed Clouds

Cited by 49 publications

References 19 publications

Elastic Business Process Management: State of the art and open challenges for BPM in the cloud

Elastic Business Process Management: State of the art and open challenges for BPM in the cloud

Cost-Driven Optimization of Cloud Resource Allocation for Elastic Processes

An Evolutionary Multi-Objective Approach for Resource Scheduling in Mobile Cloud Computing

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