Altuntaş, Tolga Buğra

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https://gcris.yasar.edu.tr/handle/123456789/12748
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Job Title
Araş.Gör.
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Main Affiliation
01.01.09.07. Yazılım Mühendisliği Bölümü
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Former Staff
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0000-0002-2931-7509
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G351aVUAAAAJ
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JHA-0317-2023

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    Master Thesis
    Sanal makine yerleştirme problemi için çok amaçlı optimizasyon çözümü
    (2024) Altuntaş, Tolga Buğra; Öz, Dindar
    Delivering different services over the Internet requires cloud computing. These services are managed by Cloud Service Providers using Virtual Machines that simulate physical machines in order to provide the required computing resources. Efficiently managing and allocating these resources is crucial for achieving optimal performance and cost-effectiveness. Nevertheless, the rapid expansion of cloud computing increased the complexity and scale of cloud environments. A key element of cloud computing is virtual machine placement (VMP), which makes sure that virtual machines are distributed among physical servers as efficiently as possible. Effective VMP strategies optimize data center performance and energy management, affecting operational cost and customer satisfaction. This work focuses on resource utilization and energy management on the multi-objective VMP problem. Extended Adapted Large Neighborhood Search (EALNS) algorithm is utilized to solve the problem. The EALNS algorithm uses a weight value to improve the spread of non-dominated solutions and create a better Pareto front. Five problem-specific destroy and repair operators are employed to adapt the EALNS algorithm to the VMP problem. To the best of our knowledge, this is the first work that uses the ALNS algorithm to solve a multi-objective VMP problem. The comparison experiments are done against three state-of-the-art multi-objective algorithms. The results show that the EALNS algorithm has great scalability and creates higher-quality Pareto fronts than its competitors.