QoS in the Cloud Computing: A Load Balancing Approach Using Simulated Annealing Algorithm

Hanine, Mohamed; Benlahmar, El Habib

doi:10.1007/978-3-319-96292-4_4

Cited by 5 publications

(5 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Hanine [150] addressed load balancing as one of the most difficult challenges facing VMs in a server. It requires controlling the burden of each VM in order to improve service quality (QoS).…”

Section: Metaheuristic Based Dynamic Load Balancing Algorithms For Cl...mentioning

confidence: 99%

Systematic Review: Load Balancing in Cloud Computing by Using Metaheuristic Based Dynamic Algorithms

Syed,

Muhammad,

Rizvi

2024

IASC

View full text Add to dashboard Cite

Cloud Computing has the ability to provide on-demand access to a shared resource pool. It has completely changed the way businesses are managed, implement applications, and provide services. The rise in popularity has led to a significant increase in the user demand for services. However, in cloud environments efficient load balancing is essential to ensure optimal performance and resource utilization. This systematic review targets a detailed description of load balancing techniques including static and dynamic load balancing algorithms. Specifically, metaheuristic-based dynamic load balancing algorithms are identified as the optimal solution in case of increased traffic. In a cloud-based context, this paper describes load balancing measurements, including the benefits and drawbacks associated with the selected load balancing techniques. It also summarizes the algorithms based on implementation, time complexity, adaptability, associated issue(s), and targeted QoS parameters. Additionally, the analysis evaluates the tools and instruments utilized in each investigated study. Moreover, comparative analysis among static, traditional dynamic and metaheuristic algorithms based on response time by using the CloudSim simulation tool is also performed. Finally, the key open problems and potential directions for the state-of-the-art metaheuristic-based approaches are also addressed.

show abstract

Section: Metaheuristic Based Dynamic Load Balancing Algorithms For Cl...mentioning

confidence: 99%

Systematic Review: Load Balancing in Cloud Computing by Using Metaheuristic Based Dynamic Algorithms

Syed,

Muhammad,

Rizvi

2024

IASC

View full text Add to dashboard Cite

show abstract

“…As a result, local minima are prevented and the probability of finding the ideal solution is raised (43). An acceptance probability P is defined so that SA does not get stuck in local solutions (44):…”

Section: Simulated Annealing Algorithmmentioning

confidence: 99%

Energy-Efficient Train Operation Optimization in Istanbul Metro Network Considering Variable Passenger Numbers

Yildiz,

Arikan,

Keskin

2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

This manuscript presents a speed trajectory optimization to minimize driving energy consumption using four different nature-inspired algorithms. For this purpose, a model was developed that can simulate motion of a single train. To test the train motion model and speed trajectories, Istanbul M3 subway line was modeled with its actual parameters such as gradient, curve, and speed limit. Concerning computational time and energy usage, a straightforward speed profile and proposed speed profiles by the algorithms have been compared. Afterwards, variable passenger numbers at each station in a certain hour were taken as dynamic mass input. With the marine predator algorithm (MPA) that finds the best result among the four, speed profile optimization was performed again under the effect of unstable mass. Finally, a train was operated by a driver according to the optimal speed profile. As a result, energy saving is 18.75% in simulation environment and 21.27% in real-life test.

show abstract

“…BTA, çözümde rastgele değişiklik yapabildiğinden yerel olarak optimal bir çözüme düşme olasılığı az olduğundan [22] geleneksel optimizasyon algoritmalarından daha güvenilir [23] ve problem için daha iyi sonuçlar verir [24]. BTA'nın yerel çözümlerde takılı kalmaması için bir P kabul olasılığı tanımlanır [25]:…”

Section: Benzetimli Tavlama Algoritmasıunclassified

Benzetimli Tavlama Algoritması İle Eksik Veri Tamamlama

Metin¹

2021

Fırat Üniversitesi Mühendislik Bilimleri Dergisi

View full text Add to dashboard Cite

Öz: İstatiksel birçok yöntem eksik değerlere sahip veri setleri üzerinde çalışma kapasitesine sahip değildir. Bu nedenle, girdi olarak yalnızca tam veriyi kabul eden modellerin tahmin performansı önemli ölçüde düşmektedir. Eksik verilerin tamamlanması bunun için veri analizlerinde önemli bir yere sahiptir. Bu çalışmada kullanılan veri seti üzerinde eksik olan verilerin tamamlanma probleminin çözümünde sezgisel optimizasyon yöntemi olan Benzetimli Tavlama Algoritması(BTA) kullanılmıştır. Modern sezgisel teknikler, bir problem çözümünde, kendi yerel arama sistemleri ile en iyi sonuca ulaşmayı amaçlamaktadırlar. BTA performansını etkileyen en önemli değer başlangıç sıcaklık değeri (T0) olduğundan üç farklı sıcaklık değeri ile sonuçlar alınmıştır. To=100.000 değeri için %68, To=10.000 için %51 ve To=1.000 için %46'lik bir başarı elde edilmiştir.

show abstract

QoS in the Cloud Computing: A Load Balancing Approach Using Simulated Annealing Algorithm

Cited by 5 publications

References 21 publications

Systematic Review: Load Balancing in Cloud Computing by Using Metaheuristic Based Dynamic Algorithms

Systematic Review: Load Balancing in Cloud Computing by Using Metaheuristic Based Dynamic Algorithms

Energy-Efficient Train Operation Optimization in Istanbul Metro Network Considering Variable Passenger Numbers

Benzetimli Tavlama Algoritması İle Eksik Veri Tamamlama

Contact Info

Product

Resources

About