2023· International Journal of Applied Data Science & Modern Computing· 0 citations
TL;DR
The findings show that adaptive algorithm and hybrid algorithm is better in scalability, robustness and the overall performance of the system compared to the traditional centralized algorithms.
Abstract
Cloud computing has become a paradigm of providing dynamically scalable and on-demand computing in the Internet. Effective load balancing is one of the most significant issues in cloud environment that guarantees optimal resource use, low response time, high availability, and quality of service (QoS). Traditional methods of load balancing are inadequate as cloud infrastructures increase due to scale and complexity because of the dynamism and heterogeneity in their workloads. As a result, there is a need to have scalable and flexible load balancing strategies that would efficiently distribute workloads in large-scale cloud data centres. The paper provides a detailed research of the scalable load-balancing algorithms to the cloud infrastructures in terms of their architectural principles, performance indicators, scaling attributes, and fault-tolerance. Some of the classical and state of the artload balancing algorithms, such as, are statical, dynamic, heuristic, and nature inspired strategies that are reviewed in the paper. It is also suggested that a new approach to scaling hybrid load balancing methodology should be offered because it combines distributed decision-making with predictive estimation of workload. The suggested solution is intended to increase the throughput of the system, reduce the response time, and optimize the use of resources in the presence of highly changing workloads. A long analysis assessment and comparative report is carried out to reveal the efficiency of the scalable load balancing strategies at the large-scale cloud environment. The findings show that adaptive algorithm and hybrid algorithm is better in scalability, robustness and the overall performance of the system compared to the traditional centralized algorithms. The results of this paper can be helpful to researchers and practitioners during the development of the next-generation cloud load balancing mechanisms.
Experiments show that the proposed Hybrid Framework for Joint Optimization of Resource Allocation and Load Balancing that spans two layers in heterogeneous cloud computing systems obtains 25-30% energy savings compared with ordinary methods, significantly reduces p95 latency and also achieves a relatively better Quality Of Service.
Eram Fatma, Nidhi Mishra, Mohammed Abdul Bari· Journal of Intelligent Decis...· 0 citations
: Cloud Computing (CC) is one of the widely used technologies due to its advanced features such as pay-per-use, scalability, and flexibility. The primary objective of CC is to allow users to access and purchase cloud services that are on demand through internet-based applications. Efficient load-balancing in the cloud faces challenges of high-dimensional state spaces and scalability with increasing tasks. To solve this problem, the Masterpiece Optimization Algorithm (MOA) with a priority constraint is employed for load-balancing according to the tasks efficiently. The MOA is integrated with a priority-based cost function to enhance the task scheduling process by introducing a multi-dimensional approach for load balancing. The priority-based framework helps the scheduler to dynamically recalibrate workloads. The experimental results achieve a total energy consumption of 39.8 W and an average CPU resource utilization of 99.54%, which is better than the existing algorithms, such as the hybrid Particle Swarm Grey Wolf Optimization (PSGWO) algorithm.
S. Vijaykumar, S. Chandre· Journal of Computer Science· 0 citations
Cloud computing has transformed the delivery of modern applications and services by providing scalable, flexible, and cost-effective access to computing resources. One of the most critical challenges in cloud environments is the efficient distribution of dynamic workloads across heterogeneous resources, commonly addressed through load balancing and task scheduling techniques. Efficient scheduling plays a vital role in maximizing resource utilization, minimizing response time, and maintaining acceptable Quality of Service (QoS), particularly under dynamic and large-scale workloads. Despite the progress achieved by traditional heuristics such as Min-Min and metaheuristic approaches like the Improved Sparrow Search Algorithm (ISSA), challenges related to scalability, adaptability, and computational overhead remain. Metaheuristic-based approaches often involve iterative optimization processes that may limit their efficiency in real-time scheduling scenarios. In this paper, we propose a lightweight Stochastic Predictive Energy-Aware Scheduling (SPES) algorithm that integrates predictive execution estimation, multi-resource awareness, and stochastic decision-making. Unlike deterministic scheduling strategies, SPES employs a Top K candidate selection mechanism combined with probabilistic weighting and epsilon-greedy exploration to enhance adaptability and avoid suboptimal resource allocation. The proposed method considers CPU, memory, and I/O demands to achieve balanced utilization across heterogeneous hosts while implicitly addressing energy efficiency through utilization-based modeling. The proposed algorithm is implemented and evaluated using the CloudSim 5.0 simulation framework under heterogeneous multi-region cloud environments with varying workload sizes. Experimental results demonstrate that SPES consistently outperforms ISSA and achieves makespan reductions of up to 23.8% while improving scalability, resource utilization, and scheduling efficiency under dynamic cloud workloads. These results indicate that SPES provides an effective lightweight scheduling solution for large-scale and energy-aware cloud computing environments and supports green computing objectives through improved resource efficiency.
M. Yacoub, Ahmed E. Abdel Raouf, Walaa K. Gad et al.· Electronics· 0 citations
Cloud computing appears to be an ecosystem with flexible and scalable IT resources. The finding is that cloud services are increasing considerably and resource management is a real problem. This is the case with the static allocation of virtual machines (VMs) that fail to effectively manage multiple workloads in real time. This leads to inefficiencies and high costs. This article proposes a system for dynamic optimization of virtual machines in a cloud to satisfy the multiple and varied requests of users. The main objective is to design a system that dynamically adjusts the number and configurations of VMs according to cloudlet requests, while optimizing performance and costs. This solution allows to learn, scale and remove virtual machines taking into account the variation in demand. It therefore ensures the optimal use of data centre resources.
Aziz Saibou, Onyonkiton Theophile Aballo, Arsene Narcisse Dagba et al.· EPJ Web of Conferences· 0 citations
The design and development of DynamiCloud is presented, a scalable and computationally efficient multi-objective dynamic resource allocation model for cloud computing that can simultaneously optimize multiple conflicting objectives such as throughput, Service Level Agreement compliance, and power efficiency.
Onwuegbuchulem Gift., Bennett, E.O., Matthias D. et al.· Journal of Artificial Intell...· 0 citations
The findings suggest that HORAM is far better at using resources; fewer tasks are completed, and the total power consumed is lower than with traditional scheduling algorithms, suggesting the suggested architecture is a viable solution to sustainable cloud infrastructure management.
S. Balakrishnan, K. Aravind, ·. T. Veeramani et al.· SN Computer Science· 0 citations