Jul 2026· Journal of Intelligent Decision Making and Information Science· 0 citations· 20 references
TL;DR
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.
Abstract
The backbone of modern digital machines is cloud computing′s on-demand resources, which scale to increase throughput. However, the field is increasingly complex from the point of view of heterogeneous cloud environments: requiring paradigms for virtual machines, containers and serverless computing nodes that put further challenges into ensuring an optimal performance/cost/energy balance. In traditional resource allocation and load balancing strategies, these goals are usually treated separately. As a result, there may be inadequate system utilization by either party involved in the transaction; ends responded with slow feedback time while intermediary services performed worse than predicted; or Service Level Agreements weren't satisfied. To help meet these challenges, this paper proposes a Hybrid Framework for Joint Optimization of Resource Allocation and Load Balancing that spans two layers in heterogeneous cloud computing systems. The framework integrates Dynamic Voltage and Frequency Scaling (DVFS) with migration-aware task assignment, learning-aided metaheuristic optimization to dynamically adjust to fluctuations in load while minimizing power consumption and operational costs. The optimization engine blends predictive workload modelling implemented via machine learning for proactive decision-making and hybrid solution strategies. Among its multiple objectives, this mechanism offers the ability to jointly minimize SLA violations, response time as well power usage. Experiments using a larger CloudSim and actual workload traces (extended CloudSim) show that the proposed framework obtains 25-30% energy savings compared with ordinary methods, significantly reduces p95 latency and also achieves a relatively better Quality Of Service. These results suggest the framework may provide a sustainable schedule which responds to outside influences in real time, making it suitable for future intelligent cloud system with large-scale organization.
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
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.
Prasanna Mandala, S. Chandre· 2026 5th International Confe...· 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
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 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.
Farah Al-Farsi· International Journal of App...· 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