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Energy-aware cost prediction and pricing of virtual machines in cloud computing environments محمد بن مبارك الدوسري

With the increasing cost of electricity, Cloud providers consider energy con- sumption as one of the major cost factors to be maintained within their infrastructure. Consequently, various proactive and reactive management mechanisms are used to efficiently manage the cloud resources and reduce the energy consumption and cost. These mechanisms support energy-awareness at the level of Physical Machines (PM) as well as Virtual Machines (VM) to make corrective decisions. This paper introduces a novel Cloud system architecture that facilitates an energy aware and efficient cloud operation methodology and presents a cost prediction framework to estimate the to- tal cost of VMs based on their resource usage and power consumption. The evaluation on a Cloud testbed show that the proposed energy-aware cost pre- diction framework is capable of predicting the workload, power consumption and estimating total cost of the VMs with good prediction accuracy for vari- ous Cloud application workload patterns. Furthermore, a set of energy-based pricing schemes are defined, intending to provide the necessary incentives to create an energy-efficient and economically sustainable ecosystem. Further evaluation results show that the adoption of energy-based pricing by cloud and application providers creates additional economic value to both under different market conditions.

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Energy-based Cost Model of Virtual Machines in a Cloud Environment محمد بن مبارك الدوسري

The cost 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 cost mechanism for Cloud services that can be adjusted to the actual energy costs has attracted the attention of many researchers. This paper introduces an Energy- based Cost Model that considers energy consumption as a key parameter with respect to the actual resource usage and the total cost of the Virtual Machines (VMs). A series of experiments conducted on a Cloud testbed show that this model is capable of estimating the actual cost for heterogeneous VMs based on their resource usage with consideration of their energy consumption.

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Performance and Energy-based Cost Prediction of Virtual Machines Auto-Scaling in Clouds محمد بن مبارك الدوسري

Virtual Machines (VMs) auto-scaling is an important technique to provision additional resource capacity in a Cloud environment. It allows the VMs to dynamically increase or decrease the amount of resources as needed in order to meet Quality of Service (QoS) requirements. However, the auto-scaling mechanism can be time-consuming to initiate (e.g. in the order of a minute), which is unacceptable for VMs that need to scale up/out during the computation, besides additional costs due to the increase of the energy overhead. This paper introduces a Performance and Energy-based Cost Prediction Framework to estimate the total cost of VMs auto-scaling by considering the resource usage and power consumption, while maintaining the expected level of performance. A series of experiments conducted on a Cloud testbed show that this framework is capable of predicting the auto-scaling workload, power consumption and total cost for heterogeneous VMs, with a cost-saving of up to 25% for the predicted total cost of VM self-configuration as compared to the current approaches in literature.

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Performance and Energy-based Cost Prediction of Virtual Machines Live Migration in Clouds محمد بن مبارك الدوسري

Virtual Machines (VMs) live migration is one of the important approaches to improve resource utilisation and support energy efficiency in Clouds. However, VMs live migration leads to performance loss and additional costs due to increased migration time and energy overhead. This paper introduces a Performance and Energy- based Cost Prediction Framework to estimate the total cost of VMs live migration by considering the resource usage and power consumption, while maintaining the expected level of performance. A series of experiments conducted on a Cloud testbed show that this framework is capable of predicting the workload, power consumption and total cost for heterogeneous VMs before and after live migration, with the possibility of recovering the migration cost e.g. 28.48% for the predicted cost recovery of the VM.

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Towards Virtual Machine Energy-Aware Cost Prediction in Clouds محمد بن مبارك الدوسري

Pricing mechanisms employed by different service providers signifi- cantly 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. Con- sequently, modelling a new pricing mechanism that allow Cloud providers to de- termine 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 work- load, 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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Energy Consumption-based Pricing Model for Cloud Computing محمد بن مبارك الدوسري

Pricing mechanisms that are employed by different service providers significantly influence the role of cloud computing within the IT industry. The purpose of this paper is to investigate how different pricing models influence the energy consumption, performance and cost of cloud services. Therefore, we propose a novel Energy-Aware Pricing Model that considers energy consumption as a key parameter with respect to performance and cost. Experimental results show that the implementation of the Energy- Aware Pricing Model achieves up to 63.3% reduction of the total cost as compared to current pricing models like those advertised by Rackspace.

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Research publications نيسار كوتاكاران سوبي
RESEARCH PUBLICATIONS:

Year 2020 (ISI/SCI/SCIE indexed journal papers)

N. Valliammal, C. Ravichandran, Kottakkaran Sooppy Nisar, Solutions to fractional neutral delay differential nonlocal systems, Chaos, Solitons & Fractals, 138, 2020, 109912, https://doi.org/10.1016/j.chaos.2020.109912. Impact factor 3.064 Alphonse Houwe, Souleymanou Abbagari, Mustafa Inc, Gambo Betchewe, Serge Y. Doka, Kofane T. Crépin, K.S. Nisar, Chirped solitons in discrete electrical transmission line, Results in Physics,18, 2020, 103188, https://doi.org/10.1016/j.rinp.2020.103188, Impact factor 3.042 Saima Akram, Allah Nawaz, Nusrat Yasmin, Dumitru Baleanu, Nisar K S, Periodic Solution Of Some Classes Of One Dimensional Non-autonomous System, Frontiers in Physics, 2020, Impact factor 2.83 B. Ghanbari, K.S. Nisar, Some Effective Numerical Techniques for Chaotic Systems Involving Fractal-Fractional Derivatives With Different Laws, Frontiers in Physics, 2020 , Impact factor 2.83 Mustafa, G., Ejaz, S.T., Baleanu, D. Ghaffar, A and Nisar. K. S, A subdivision-based approach for singularly perturbed boundary value problem. Adv Differ Equ 2020, 282 (2020). https://doi.org/10.1186/s13662-020-02732-8, Impact factor 1.51 Rahman, G., Nisar, K.S., Rashid, S. et al. Certain Grüss-type inequalities via tempered fractional integrals concerning another function. J Inequal Appl 2020, 147 (2020). https://doi.org/10.1186/s13660-020-02420-x, Impact factor 1.136 Hassan Waqas, Muhammad Imran, Sajjad Hussain, Farooq Ahmad, Ilyas Khan, Kottakkaran Soopy Nisar, A. Othman Almatroud, Numerical...
An Online Lab Examination Management System (OLEMS) to Avoid Malpractice زكريا محمد مختار غرس الدين
Manjur Kolhar, Abdalla Alameen, Z. M. Gharsseldien (2018)  Science and Engineering Ethics Vol. 24 No.4 pp: 1367-1369

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Predictive Reservation for Handover Optimization in Two-Tier Heterogeneous Cellular Networks زكريا محمد مختار غرس الدين
El-atty, Saied M. Abd, Z. M. Gharsseldien, and K. A. Lizos.(2018) Wireless Personal Communications, Vol. 98 pp: 1637–1661   ...
Engineering Molecular Communications Integrated with Carbon Nanotubes in Neural Sensor Nanonetworks زكريا محمد مختار غرس الدين
SA El-atty, K Lizos, Z Gharsseldien, A Tolba, ALM Zafer  (2018) IET Nanobiotechnol., Vol. 12 Iss. 2, pp. 201-210

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