Workload modeling for resource usage analysis and simulation in cloud computing

Magalhães, Deborah; Calheiros, Rodrigo N.; Buyya, Rajkumar; Gomes, Danielo G.

doi:10.1016/j.compeleceng.2015.08.016

Cited by 83 publications

(39 citation statements)

References 24 publications

(47 reference statements)

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“…VCS simulator [24] Text-based Text User-defined Yes GridSim [25,26] Modified source code Chart None Yes ClusterSim [27] Clustering-based Text User-defined Yes CloudSim [28] Text …”

Section: Setup Methods Visualization Clustering Organization Simulationmentioning

confidence: 99%

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DIaaS: Resource Management System for the Intra-Cloud with On-Premise Desktops

et al. 2017

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Infrastructure as a service with desktops (DIaaS) based on the extensible mark-up language (XML) is herein proposed to utilize surplus resources. DIaaS is a traditional surplus-resource integrated management technology. It is designed to provide fast work distribution and computing services based on user service requests as well as storage services through desktop-based distributed computing and storage resource integration. DIaaS includes a nondisruptive resource service and an auto-scalable scheme to enhance the availability and scalability of intra-cloud computing resources. A performance evaluation of the proposed scheme measured the clustering performance time for surplus resource utilization. The results showed improvement in computing and storage services in a connection of at least two computers compared to the traditional method for high-availability measurement of nondisruptive services. Furthermore, an artificial server error environment was used to create a clustering delay for computing and storage services and for nondisruptive services. It was compared to the Hadoop distributed file system (HDFS).

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“…VCS simulator [24] Text-based Text User-defined Yes GridSim [25,26] Modified source code Chart None Yes ClusterSim [27] Clustering-based Text User-defined Yes CloudSim [28] Text …”

Section: Setup Methods Visualization Clustering Organization Simulationmentioning

confidence: 99%

“…Existing studies on distributed resource clustering techniques and clustering visualization are outlined in Table 2 [23][24][25][26][27]. Table 2.…”

Section: Introductionmentioning

confidence: 99%

DIaaS: Resource Management System for the Intra-Cloud with On-Premise Desktops

et al. 2017

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“…Performance measurements provide insight in how the hardware components are stressed when processing instructions and specific performance metrics can be identified for each individual component [9]. For example, [24] monitors the overall system throughput, CPU, memory and hard disk through the 'number of instructions', 'CPU utilization', 'memory utilization', and 'disk transactions per second' performance metrics. A different approach is to assume theoretical EC figures, e.g.…”

Section: B Resource Utilizationmentioning

confidence: 99%

“…On the other hand, in large-scale infrastructures the EC of network equipment is argued not to fluctuate heavily with increased traffic [30]. With regard to the instructions to process, a workload model should be made to reflect realistic conditions [24].…”

Section: B Resource Utilizationmentioning

confidence: 99%

A Resource Utilization Score for Software Energy Consumption

Jagroep¹,

Werf²,

Broekman³

et al. 2016

Proceedings of ICT for Sustainability 2016

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Abstract-Software as the true consumer of power and its potential contribution to reach sustainability goals is increasingly being acknowledged. Studies so far have presented successful results and methods to address the energy consumption of the software, indicating that different stakeholders striving for green software have different information needs with respect to their goals. However, currently there is no uniform manner to communicate measurements to the different stakeholders such that key findings are clearly identifiable and easy to understand, which is likely to hamper green software practices. In this paper we propose a metric that expresses a score for the resource utilization, such as power consumption, of a software product. The metric is designed to be a single score and is flexible to encompass those aspects that a stakeholder considers relevant in the context of software energy consumption. The metric was applied on two applications and allowed for objective comparison of application configurations and versions. Also the behavior of these applications across different hardware configurations could be analyzed. In addition to the metric we investigate means to visualize measurements which enhances communication and helped with highlighting the key findings.

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“…eir model's accuracy is evaluated by predicting future workloads of request traces to web servers. Also, Magalhaes et al [3] developed a workload model for the CloudSim simulator using generalised extreme value/lambda distributions. ey argue that user behaviour must be considered in workload modelling.…”

Section: Related Workmentioning

confidence: 99%

Cloud Workload Prediction by Means of Simulations

Kecskeméti

Kertész

Németh

2017

Proceedings of the Computing Frontiers Conference

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Clouds hide the complexity of maintaining a physical infrastructure with a disadvantage: they also hide their internal workings. Should users need to know about these details e.g., to increase the reliability or performance of their applications, they would need to detect slight behavioural changes in the underlying system. Existing solutions for such purposes o er limited capabilities. is paper proposes a technique for predicting background workload by means of simulations that are providing knowledge of the underlying clouds to support activities like cloud orchestration or work ow enactment. We propose these predictions to select more suitable execution environments for scienti c work ows. We validate the proposed prediction approach with a biochemical application. CCS CONCEPTS•Computer systems organization →Cloud computing; ACM Reference format:

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Workload modeling for resource usage analysis and simulation in cloud computing

Cited by 83 publications

References 24 publications

DIaaS: Resource Management System for the Intra-Cloud with On-Premise Desktops

DIaaS: Resource Management System for the Intra-Cloud with On-Premise Desktops

A Resource Utilization Score for Software Energy Consumption

Cloud Workload Prediction by Means of Simulations

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