Researcher:
Ali, Ahsan

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PhD Student

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Ahsan

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Ali

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Ali, Ahsan

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20163

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Researcher Of Publications: search.filters.isAuthorOfPublication.e15d1f6b-b03d-4de5-9522-b675548f2581

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    Workload management in distributed data centers: thermal and spatial awareness
    (IEEE, 2016) N/A; Department of Computer Engineering; Ali, Ahsan; Özkasap, Öznur; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 113507
    Energy consumption of distributed data centers costs huge amounts to the cloud service providers (CSP) each year. in order to make the services affordable for the cloud service consumers (CSC) and increase the profit for the CSP, it is essential to minimize the cooling and operational costs. in this work, we address cost optimization by taking advantage of time and space varying properties of distributed data centers. We consider the state-of-the-art CRaC units which utilize outside air for cooling purposes and propose a cost model based on the outside cooling which takes into account three different temperature ranges for cooling purpose and operations of CRaC units. Based on the mathematical model, we propose three different heuristics, namely TempCP, TempCD and TempPCD to manage workload using the variation of electricity price, locational outside and within the data center temperature, where the aim is to schedule the incoming request with minimum SLa violations, cooling cost and energy consumption. Experiments conducted on CloudSim platform show that our proposed scheduling policies save between 68% to 75% in terms of cooling cost and between 22% and 39% of overall operational cost compared to the existing scheduling policies.
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    Environment friendly energy efficient distributed data centers
    (Springer International Publishing Ag, 2016) N/A; Department of Computer Engineering; Ali, Ahsan; Özkasap, Öznur; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 113507
    Geographically distributed data centers form a significant technology used by the Internet users to fulfil the demand of storage, processing and large scale computations. Most of the operational cost of such data centers is due to the electricity cost, which affect both service providers and consumers. In this paper, we addressed the problem of energy consumption of data center entities and reviewed state-of-the-art solutions proposed to reduce the electricity cost. We present the full view of the problem by providing the widely used energy consumption and/or operational cost models. We identified key characteristics of efficient techniques proposed for reduction of the electricity cost, carbon emission and financial penalties in case of SLA violations. These techniques include environment friendly cost minimization, energy efficient load migration, job scheduling and resource allocation. We also identified open challenges as guidelines for future research.
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    Price/cooling aware and delay sensitive scheduling in geographically distributed data centers
    (Institute of Electrical and Electronics Engineers (IEEE), 2016) N/A; Department of Computer Engineering; Ali, Ahsan; Özkasap, Öznur; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 113507
    Servers in data centers consume large amount of energy which increase the operational cost for cloud service providers, that spend a major portion of their revenue to pay bills due to inefficient workload assignment and wastage of resources. In order to minimize the operational cost of data centers, it is essential to optimize the scheduling of the jobs. In this paper, we address the problem of inefficient cooling system, SLA violations due to network delays and processing delays in geographically distributed data centers. We propose scheduling algorithms that aim to minimize the cooling cost by exploiting the temperature variations within the data centers and electricity cost by taking advantage of time-space-varying fluctuation of electricity prices. SLA violations are aimed to be minimized by assigning jobs considering deadlines, network delays and queuing delays. Experiments conducted on CloudSim show that price/cooling aware and delay sensitive scheduling reduces the overall cost by 22% as compared to random scheduling.