A Hybrid Framework for Joint Optimization of Resource Allocation and Load Balancing in Cloud Systems

Energy-Efficient Hybrid Metaheuristic Resource Allocation Model for Dynamic Virtual Machine Scheduling in Cloud Computing Platforms

Comprehensive Analysis of Load Balancing and Resource Provisioning Methods in Cloud Computing Environments

Cloud Computing (CC) is the cornerstone of modern information technology that provides scalable, flexible, and cost-efficient services across diverse applications. Dynamic workloads and heterogeneous infrastructure face some difficulties in effective load balancing and resource provisioning which results in resource underutilization, overload and response time increases. This paper presents a comprehensive and comparative analysis of current methods addressing these issues. It also presents active resource provisioning frameworks, namely: probabilistic load balancing models, Machine Learning (ML)-based, Deep Learning (DL)-based, workload prediction techniques, genetic algorithms, Reinforcement Learning (RL) strategies, and hybrid meta-heuristic methods. Each method is analyzed in terms of methodology, advantages, limitations, and performance metrics, therefore providing an insight of their applicability in dynamic and large-scale cloud environments. A taxonomy architecture is presented to categorize the systematic comparison and research gaps. The comparative evaluation segment demonstrates enhancement in throughput, resource utilization, and cost efficiency, while also identifying limitations such as computational overhead and scalability constraints. The survey concludes by highlighting the necessity for intelligent, adaptive, and energy-aware solutions to confirm resilient and efficient cloud infrastructures.

Masterpiece Optimization Algorithm-Based Priority-Aware Load Balancing Strategy for Cloud Data Centers

: 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.

Efficient Algorithm for Multi-Objective Dynamic Resource Allocation in Cloud Computing

Cloud computing environments depend heavily on efficient Dynamic Resource Allocation (DRA) mechanisms to ensure optimal utilization of computational resources while maintaining low operational cost, reduced energy consumption, and acceptable Quality of Service (QoS) under continuously fluctuating workloads. However, many existing resource allocation techniques in cloud systems are limited by poor adaptability, high computational overhead, inefficient virtual machine migration, and inability to simultaneously optimize multiple conflicting objectives such as throughput, Service Level Agreement (SLA) compliance, and power efficiency. These limitations create the need for a more intelligent, scalable and adaptive resource management framework capable of making real-time allocation decisions in heterogeneous cloud environments. This study therefore presents the design and development of DynamiCloud, a scalable and computationally efficient multi-objective dynamic resource allocation model for cloud computing. The research aimed at developing an efficient algorithm for multi-objective Dynamic Resource Allocation (DRA) in cloud computing. An object-oriented system design methodology was adopted in modeling the proposed framework; while a Deep Reinforcement Learning (DRL)-based optimization algorithm was implemented to enable the system learn optimal VM allocation and reallocation policies from environmental states, reward signals, and workload behavior patterns. The design was implemented using python. Comparing the results of our implementation with the existing tools shows that our objectives were met.

Scalable Load Balancing Algorithms for Cloud Infrastructures

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.

SPES: A Stochastic Predictive Energy-Aware Scheduling Approach for Efficient Multi-Region Cloud Computing

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.