A Multi-Layered Scheme for Distributed Simulations on the Cloud Environment

Guan, Shichao; Grande, Robson E. De; Boukerche, Azzedine

doi:10.1109/tcc.2015.2453945

Cited by 15 publications

(3 citation statements)

References 55 publications

(42 reference statements)

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“…It appears that a single cloud instance was used (actual cloud not specified). This work was further developed into a multi-layered elastic cloud simulation scheme supporting the capability of automatic deployment, reduction of energy consumption, and local to cloud resource scaling using a deployment management scheme [30]. It argued that this approach could support multiple clouds by using an architecturally layered approach.…”

Section: Related Work: Cloud-based Distributed Simulationmentioning

confidence: 99%

“…Workflow management, job submission and software deployment services are common to all. Only WS-PGRADE [58] has a dedicated Comparing our architecture to other works in CBDS, [27] and [29] (extended in [30]) propose comprehensive architectures with services similar to ours in terms of distributed simulation experimentation management, job submission to cloud, job management on cloud and cloud resource management. However, neither use any form of workflow or indeed the ability to choose which cloud or cloud resources to use.…”

Section: A Workflow Architecture For Cloud-based Distributed Simulationmentioning

confidence: 99%

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A Workflow Architecture for Cloud-based Distributed Simulation

Chaudhry

Anagnostou

Taylor

2022

ACM Trans. Model. Comput. Simul.

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Distributed Simulation has still to be adopted significantly by the wider simulation community. Reasons for this might be that distributed simulation applications are difficult to develop and access to multiple computing resources are required. Cloud computing offers low-cost on-demand computing resources. Developing applications that can use cloud computing can be also complex, particularly those that can run on different clouds. Cloud-based Distributed Simulation (CBDS) is potentially attractive, as it may solve the computing resources issue as well as other cloud benefits, such as convenient network access. However, as possibly shown by the lack of sustainable approaches in the literature, the combination of cloud and distributed simulation may be far too complex to develop a general approach. E-Infrastructures have emerged as large-scale distributed systems that support high-performance computing in various scientific fields. Workflow Management Systems (WMS) have been created to simplify the use of these e-Infrastructures. There are many examples of where both technologies have been extended to use cloud computing. This article therefore presents our investigation into the potential of using these technologies for CBDS in the above context and the WORkflow architecture for cLoud-based Distributed Simulation (WORLDS), our contribution to CBDS. We present an implementation of WORLDS using the CloudSME Simulation Platform that combines the WS-PGRADE/gUSE WMS with the CloudBroker Platform as a Service. The approach is demonstrated with a case study using an agent-based distributed simulation of an Emergency Medical Service in REPAST and the Portico HLA RTI on the Amazon EC2 cloud.

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Section: Related Work: Cloud-based Distributed Simulationmentioning

confidence: 99%

Section: A Workflow Architecture For Cloud-based Distributed Simulationmentioning

confidence: 99%

A Workflow Architecture for Cloud-based Distributed Simulation

Chaudhry

Anagnostou

Taylor

2022

ACM Trans. Model. Comput. Simul.

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“…However, Overutilized state describes the fact that available resources are fewer than requested, leading to longer response time, and affecting the SLA. SLA violations (SLAVs) impose penalty by customers and increase costs for cloud providers 11 …”

Section: Introductionmentioning

confidence: 99%

An autonomic decision tree‐based and deadline‐constraint resource provisioning in cloud applications

Mazidi

Mahdavi

Roshanfar

2021

Concurrency and Computation

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Cloud computing provides a set of resources and services for customers on the Internet on demand and based on a pay as you go model. Cloud providers are looking to decrease costs and increase profits. Therefore, resource management and provisioning are very important for cloud providers. Automated scaling can be used to provide resources for user requests. Auto‐scaling can decrease the total operational costs for providers, although it does have its own cost and time overheads. In this paper, a new solution is presented for resource provisioning on multi‐layered cloud applications based on MAPE‐K loop. A weighted ensemble prediction model is proposed to estimate the resources utilization in each cloud layer. In addition, accuracy of the model and a regularization technique are used to regulate the weights of the models in the proposed hybrid prediction model. Furthermore, a decision tree‐based algorithm is presented to analyze status of the resources to make scaling decision. In addition, we propose a resource allocation algorithm that is based on Virtual Machine priority and request deadline in order to allocate requests on suitable resources. The experimental results indicate that the proposed algorithm has the best performance among its counterparts.

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Autonomic resource provisioning for multilayer cloud applications with K‐nearest neighbor resource scaling and priority‐based resource allocation

2020

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SummaryProviding a pool of various resources and services to customers on the Internet in exchanging money has made cloud computing as one of the most popular technologies. Management of the provided resources and services at the lowest cost and maximum profit is a crucial issue for cloud providers. Thus, cloud providers proceed to auto‐scale the computing resources according to the users' requests in order to minimize the operational costs. Therefore, the required time and costs to scale‐up and down computing resources are considered as one of the major limits of scaling which has made this issue an important challenge in cloud computing. In this paper, a new approach is proposed based on MAPE‐K loop to auto‐scale the resources for multilayered cloud applications. K‐nearest neighbor (K‐NN) algorithm is used to analyze and label virtual machines and statistical methods are used to make scaling decision. In addition, a resource allocation algorithm is proposed to allocate requests on the resources. Results of the simulation revealed that the proposed approach results in operational costs reduction, as well as improving the resource utilization, response time, and profit.

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A Multi-Layered Scheme for Distributed Simulations on the Cloud Environment

Cited by 15 publications

References 55 publications

A Workflow Architecture for Cloud-based Distributed Simulation

A Workflow Architecture for Cloud-based Distributed Simulation

An autonomic decision tree‐based and deadline‐constraint resource provisioning in cloud applications

Autonomic resource provisioning for multilayer cloud applications with K‐nearest neighbor resource scaling and priority‐based resource allocation

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