The CloudSME simulation platform and its applications: A generic multi-cloud platform for developing and executing commercial cloud-based simulations

Taylor, Simon J. E.; Kiss, Tamás; Anagnostou, Anastasia; Terstyánszky, Gábor; Kacsuk, Péter; Costes, Joris; Fantini, Nicola

doi:10.1016/j.future.2018.06.006

Cited by 37 publications

(20 citation statements)

References 28 publications

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“…There exist several examples and investigation of simulation models that have been successfully deployed to public cloud computing infrastructures. 35…”

Section: Impacts On the Modeling And Simulation As A Service Domainmentioning

confidence: 99%

“…That is exactly what Taylor et al are stating. 35 However, not all simulations will benefit equally from MSaaS.…”

Section: Impacts On the Modeling And Simulation As A Service Domainmentioning

confidence: 99%

“…There exist several examples and investigation of simulation models that have been successfully deployed to public cloud computing infrastructures. 35 • A cloud-based distributed agent-based traffic simulator named Megaffic. 36…”

Section: Example Cloud Simulation Use Casesmentioning

confidence: 99%

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Towards cloud-native simulations – lessons learned from the front-line of cloud computing

Kratzke

Siegfried²

2020

Journal of Defense Modeling & Simulation

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Cloud computing can be a game-changer for computationally intensive tasks like simulations. The computational power of Amazon, Google, or Microsoft is even available to a single researcher. However, the pay-as-you-go cost model of cloud computing influences how cloud-native systems are being built. We transfer these insights to the simulation domain. The major contributions of this paper are twofold: (A) we propose a cloud-native simulation stack and (B) derive expectable software engineering trends for cloud-native simulation services. Our insights are based on systematic mapping studies on cloud-native applications, a review of cloud standards, action research activities with cloud engineering practitioners, and corresponding software prototyping activities. Two major trends have dominated cloud computing over the last 10 years. The size of deployment units has been minimized and corresponding architectural styles prefer more fine-grained service decompositions of independently deployable and horizontally scalable services. We forecast similar trends for cloud-native simulation architectures. These similar trends should make cloud-native simulation services more microservice-like, which are composable but just “simulate one thing well.” However, merely transferring existing simulation models to the cloud can result in significantly higher costs. One critical insight of our (and other) research is that cloud-native systems should follow cloud-native architecture principles to leverage the most out of the pay-as-you-go cost model.

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“…There exist several examples and investigation of simulation models that have been successfully deployed to public cloud computing infrastructures. 35…”

Section: Impacts On the Modeling And Simulation As A Service Domainmentioning

confidence: 99%

“…That is exactly what Taylor et al are stating. 35 However, not all simulations will benefit equally from MSaaS.…”

Section: Impacts On the Modeling And Simulation As A Service Domainmentioning

confidence: 99%

See 1 more Smart Citation

Towards cloud-native simulations – lessons learned from the front-line of cloud computing

Kratzke

Siegfried²

2020

Journal of Defense Modeling & Simulation

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“…To improve the timeliness and accuracy of enterprise resource scheduling, Hoyland [12] designed the reinforced enterprise business architecture (REBAR), which illustrates the basic figures and tables of general workflows; The REBAR captures the essence of the unique enterprise in a graphical application that can be queried and dynamically recombined to illustrate details of complex workplace collaborations, enabling decision-makers to make correct decisions. Using cloud computing workflow scheduling technology, Taylor et al developed an enterprise resource workflow management system to effectively analyze and optimize the use of enterprise cloud resources, and later proposed a cloud simulation platform for small and medium-sized enterprises (SMEs) (CloudSME); the CloudSME is a generic approach that combines an AppCenter with the workflow of the WS-PGRADE/gUSE science gateway framework, reducing the development cost of cloud-based simulator for business scheduling [13,14].…”

Section: Literature Reviewmentioning

confidence: 99%

An Optimal Scheduling Path Algorithm for Enterprise Resource Allocation Based on Workflow

Guo¹

2020

JESA

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In many enterprises, the work efficiency is suppressed by the irrational allocation of enterprise resources. To solve the problem, this paper puts forward the optimal scheduling path algorithm for enterprise resource workflow (ERWOSA). To measure the task efficiency of each person after project issuance, the ERWOSA models enterprise resource workflow, and quantifies each node in the model. Then, novel attributes like mean execution accuracy, mean execution time, and node weight were calculated. Based on the calculation result, the optimal scheduling path was solved, which is highly accurate and efficient. In this way, the ERWOSA manages to bring more benefits to the enterprise.

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“…The users can get this facility to fulfil its work or any application so the users can have this facility to avail the services on demand. The service providers provide these resources to their users at any time [4].…”

Section: On-demand Servicementioning

confidence: 99%

Intelligent reliability management in hyper-convergence cloud infrastructure using fuzzy inference system

Tabassum

Khan

Abbas

et al. 2018

ICST Transactions on Scalable Information Systems

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Hyper-convergence is a new innovation in data center technology, it changes the way clouds manage and maintain enterprise IT infrastructure. Hyper-convergence is more efficient and basically agile technology environment. Cloud computing is a latest technology due to provision of latest cloud services over the internet. The cloud service providers cannot promise accurate reliability of their services i.e. problem in provisioning of software or hardware failure etc. Reliability of cloud computing services depends on the ability of fault tolerance during the execution of services. There are so many factors can cause faults, such as network failure, browser crash, request time out or hacker attacks. When users are facing these types of faults, they usually resubmit their requests. However, if there is any key element involved in faults or errors, additional action may be needed to deal with system logs. If there is anomaly behavior occurred in faulted virtual machine, these VMs may need extra attention from cloud system protection and security point of view. In this paper, provision of reliability management in hyper-convergence cloud infrastructure is proposed and self-healing techniques in software as a service on the basis of failure in cloud services. Intelligent cloud service reliability framework will increase the reliability during execution of cloud service.

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The CloudSME simulation platform and its applications: A generic multi-cloud platform for developing and executing commercial cloud-based simulations

Cited by 37 publications

References 28 publications

Towards cloud-native simulations – lessons learned from the front-line of cloud computing

Towards cloud-native simulations – lessons learned from the front-line of cloud computing

An Optimal Scheduling Path Algorithm for Enterprise Resource Allocation Based on Workflow

Intelligent reliability management in hyper-convergence cloud infrastructure using fuzzy inference system

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