Online cost-efficient scheduling of deadline-constrained workloads on hybrid clouds

Bossche, Ruben Van den; Vanmechelen, Kurt; Broeckhove, J.

doi:10.1016/j.future.2012.12.012

Cited by 156 publications

(87 citation statements)

References 23 publications

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“…So far, no approach for elastic processes takes into account hybrid clouds, i.e., a combination of computational resources from private and public clouds [13], or interclouds, i.e., the usage of multiple clouds from different providers [59]. However, outsourcing of particular tasks from an internal data center to a public cloud is deemed to be a major application area for future cloud computing [51], and usage of multiple clouds in general is a common means to spread risk and circumvent the vendor lock-in issue. Hence, future work on elastic processes should be able to take into account the distinctive features of hybrid clouds or interclouds.…”

Section: Challenges 1 and 2: Scheduling And 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

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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: Challenges 1 and 2: Scheduling And 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 details of each method has been shown in form of table [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] Page 18666…”

Section: A Glance Of Existing Algorithmsmentioning

confidence: 99%

Efficient Cloud Server Job Scheduling using NN and ABC in cloud computing

Kaur¹

2016

IJECS

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High work load not only translates to high operational cost, which reduces the marginal profit of Cloudproviders, which also direct towards low throughput of system. Henceforth, efficient job execution solutions are required to minimize the impact of Cloud computing on the environment. So, to generate such solutions, deep analysis of Cloud is obligatory with regard to their job-efficiency. Otherwise, Cloud computing with progressively persistent front-end clientdevices interrelating with back-end data centers will cause an enormous load of jobs. To address this problem, data centre servers need to be managed in an efficient manner to drive computing. In proposed work, hybrid algorithm which is based on neural network with artificial bee colony and some concepts of scheduling has been introduced in work. On the basis of them, tasks execution will be enhanced and load will be reduced. The results evaluation will be done on the basis of total jobs completed in the MATLAB 2010a environment.

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“…Van den Bossche, Vanmechelen [31] have proposed a workload model which is considers non-preemptible and non-migratable workloads with a hard deadline that are characterized by CPU, memory and data transmission requirements. Also, Wang, Wang [32] have proposed a genetic algorithm for Cloud resource optimization scheduling model that promised the user needs while optimizing resource allocation.…”

Section:  2013mentioning

confidence: 99%

Multi-Objective Task Scheduling in the Cloud Computing based on the Patrice Swarm Optimization

Milani¹,

Navin²

2015

IJITCS

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Abstract-Cloud computing is the latest emerging trend in distributed computing, where shared resources are provided to end-users in an on demand fashion that brings many advantages, including data ubiquity, flexibility of access, high availability of resources, and flexibility. In this type of systems many challenges are existed that the task scheduling problem is one of them. The task scheduling problem in Cloud computing is an NP-hard problem. Therefore, many heuristics have been proposed, from low level execution of tasks in multiple processors to high level execution of tasks. In this paper, we propose a new algorithm based on PSO to schedule the tasks in the Cloud. The results demonstrated that the proposed algorithm has a better operation in terms of task execution time, waiting time and missed tasks in comparison of First Come First Served (FCFS), Shortest Process Next (SPN) and Highest Response Ratio Next (HRRN).

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Online cost-efficient scheduling of deadline-constrained workloads on hybrid clouds

Cited by 156 publications

References 23 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

Efficient Cloud Server Job Scheduling using NN and ABC in cloud computing

Multi-Objective Task Scheduling in the Cloud Computing based on the Patrice Swarm Optimization

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