Heruistik Parçacık Sürü Optimisazyonu Kullanarak Bulut Sistemlerde Zaman Çizelgeleme

Öz

Bulut sistemlerinde görev çizelgeleme, görevleri mevcut kaynaklar arasında en etkili şekilde dağıtmayı amaçlayan kritik bir optimizasyon problemidir. Bu konu NP-zor problemler kategorisine girer ve kesin ve kesin çözümler üretmek yüksek hesaplama maliyetleri gerektirir. Metasezgisel yaklaşımların bu tür problemleri çözmede başarılı sonuçlar sağladığı kanıtlanmıştır. Bu algoritmalardan biri olan Parçacık Sürüsü Optimizasyonu, hızlı yakınsama, basit uygulanabilirlik ve düşük hesaplama maliyeti gibi avantajları nedeniyle literatürde yaygın olarak kullanılan bir yöntemdir. Bu çalışmada, görev çizelgeleme verimliliğini artırmak için sezgisel tabanlı bir Parçacık Sürüsü Optimizasyonu yaklaşımı önerilmiştir. Önerilen yaklaşım, Parçacık Sürüsü Optimizasyonunun rastgele popülasyon oluşturma sürecine sezgisel bir mekanizma entegre ederek çözüm kalitesini artırır. Deneysel analizler, önerilen yöntemin First Come First Server, Karınca Kolonisi Optimizasyonu ve Parçacık Sürüsü Optimizasyonu ile karşılaştırıldığında daha düşük makespan süresi ve enerji tüketimi sunduğunu göstermektedir. CloudSim simülasyon ortamında yapılan deneyler, Heuristic PSO'nun makespan süresini standart PSO ve ACO algoritmalarına kıyasla ortalama %61,42 ve %62,84 oranında azalttığını ortaya koymaktadır. Ayrıca enerji tüketimi açısından PSO'ya kıyasla %26,18 ve ACO'ya kıyasla %27,33 daha az enerji tükettiği belirlenmiştir. Bulgular, önerilen yaklaşımın bulut bilişim sistemlerinin görev çizelgeleme için daha verimli bir alternatif sağladığını göstermektedir.

Anahtar Kelimeler

• Bulut Bilişim
• Görev Çizelgeleme
• Parçacık Sürü Optimisazyonu
• Heruistik Parçacık Sürü Optimisazyonu

Hybrid Heuristic and Particle Swarm Optimization Approach to Cloud Task Scheduling

Abstract

Scheduling tasks on cloud systems is a critical optimization problem that aims to distribute tasks among available resources in the most effective way. This issue falls under the category of NP-hard problems and generating exact and deterministic solutions requires high computational costs. Metaheuristic approaches have proven to provide successful results in solving such problems. Particle Swarm Optimization (PSO), one of these algorithms, is a widely used method in the literature due to its advantages, such as fast convergence, simple applicability, and low computational cost. In this study, a hybrid heuristic-based Particle Swarm Optimization approach is proposed to improve task scheduling efficiency. The proposed approach improves the solution quality by integrating a heuristic mechanism into the random population generation process of PSO. In comparison to First Come First Serve, Ant Colony Optimization (ACO), and conventional PSO, the suggested approach delivers better makespan and reduced energy consumption, according to the simulation analysis carried out in the CloudSim simulation environment. According to simulations, Heuristic PSO outperforms traditional PSO and ACO methods in terms of makespan time, reducing it by an average of 61.42% and 62.84%, respectively. It also uses 26.18% less energy than PSO and 27.33% less than ACO, according to its energy consumption data. The results show that the suggested method offers a more effective substitute for scheduling tasks in cloud computing systems.

Keywords

• Cloud Computing
• task scheduling
• Particle Swarm Optimization
• Heuristic Particle Swarm Optimization

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