Cost model based service placement in federated hybrid clouds

Altmann, Jörn; Kashef, Mohammad Mahdi

doi:10.1016/j.future.2014.08.014

Cited by 88 publications

(43 citation statements)

References 22 publications

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“…Previous work has looked into the area of calculating the cost of running services on Cloud infrastructure. Altmann and Kashef [13] presented the service placement optimisation based on the cost model in federated clouds to guarantee the cost minimisation for Cloud customers. This approach depends on a brute-force algorithm to evaluate the cost of each possible service placement.…”

Section: Related Workmentioning

confidence: 99%

Towards Virtual Machine Energy-Aware Cost Prediction in Clouds

Aldossary

Alzamil

Djemame

2017

Lecture Notes in Computer Science

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Abstract. Pricing mechanisms employed by different service providers significantly influence the role of cloud computing within the IT industry. With the increasing cost of electricity, Cloud providers consider power consumption as one of the major cost factors to be maintained within their infrastructures. Consequently, modelling a new pricing mechanism that allow Cloud providers to determine the potential cost of resource usage and power consumption has attracted the attention of many researchers. Furthermore, predicting the future cost of Cloud services can help the service providers to offer the suitable services to the customers that meet their requirements. This paper introduces an Energy-Aware Cost Prediction Framework to estimate the total cost of Virtual Machines (VMs) by considering the resource usage and power consumption. The VMs' workload is firstly predicted based on an Autoregressive Integrated Moving Average (ARIMA) model. The power consumption is then predicted using regression models. The comparison between the predicted and actual results obtained in a real Cloud testbed shows that this framework is capable of predicting the workload, power consumption and total cost for different VMs with good prediction accuracy, e.g. with 0.06 absolute percentage error for the predicted total cost of the VM.

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Section: Related Workmentioning

confidence: 99%

Towards Virtual Machine Energy-Aware Cost Prediction in Clouds

Aldossary

Alzamil

Djemame

2017

Lecture Notes in Computer Science

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show abstract

“…The cloud adoption must also be supported by open markets, enabling negotiation processes for handling ambiguous selection criteria (Pittl et al 2017;Slawik et al 2016;Filiopoulou et al 2017). From the provider point of view, a pay-per-use model of the cloud implies solving the problem of proposing pricing schemas based on all involved cost such as the energy consumption of virtual machines (Kostopoulos et al 2016;Kavanagh et al 2016;Altmann and Kashef 2014), or it can require new ways for maintaining cloud infrastructures such as community network clouds (Khan et al 2016).…”

Section: Sustainability and Feasibility Of Cloud Infrastructuresmentioning

confidence: 99%

Economics behind ICT infrastructure management

Bañares

Altmann

2018

Electron Markets

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“…In [34], authors present a cost model for federated clouds that considers all the cost factors, such as, electricity, hardware, software, labor, business premises, and service. Moreover, factors also include traffic and deployment related costs, such as, service placement vector, data traffic matrix, traffic cost rate matrix, and number of deployments.…”

Section: Network Load Aware Rmmentioning

confidence: 99%

“…However, authors are not considering metrics discussed in above discussed sections. On the other hand, Altmann and Kashef [34], and PANDA [35] are handling RM metrics such as, SLA violations, pricing, and revenue. None of aforementioned techniques provides a multi-criteria optimization.…”

Section: Network Load Aware Rmmentioning

confidence: 99%