TL;DR
Experimental evaluations demonstrate that GABS reduces processing time, increases system throughput, decreases execution cost, and enhances data locality by 24% compared to Hadoop’s default schedulers, highlighting the effectiveness of GABS in enhancing scalability, performance, and resource efficiency in heterogeneous computing environments.
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.
The rapid growth of data-intensive applications in scientific computing, enterprise analytics, and cloud services has increased the demand for efficient distributed data processing systems. Traditional scheduling methods like FCFS, Round Robin, and heuristic approaches often fail to meet the dynamic and heterogeneous requirements of modern environments. This paper proposes an intelligent workflow scheduling framework that improves performance through adaptive decision-making, predictive analytics, and machine learning. The system dynamically allocates tasks based on resource availability, workflow dependencies, and historical execution data, enabling it to anticipate bottlenecks and reassign tasks proactively. It also incorporates resource heterogeneity modeling and dependency-aware scheduling to reduce idle time and optimize execution. Performance is evaluated using metrics such as makespan, throughput, resource utilization, and fault tolerance, showing significant improvements over traditional methods. The framework also addresses key challenges like load balancing, scalability, energy efficiency, and fault tolerance. Overall, the proposed approach enhances system efficiency and scalability while supporting integration with emerging technologies such as edge computing and hybrid cloud environments, paving the way for more autonomous and resilient distributed scheduling systems.
Resource allocation in the cloud-edges is extremely important to reduce the time of execution, energy usage, and service-level agreement (SLA) breaches when experiencing dynamic workloads. In this paper, the author has proposed an elite co-evolution hybrid optimization model, Hybrid Hierarchical Optimization for Cloud Allocation (H2O-Cloud), which combines Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) using periodic elite migration and adaptive multi-objective evaluation. The suggested approach is also optimal in terms of makespan, SLA violation rate, energy consumption, and resource usage. Real world Azure LLM Inference Trace datasets (both code-generation, conversational AI) were used to run experiments. The analysis took into account scalable loads of 300 to 1200 tasks on heterogeneous cloud virtual machines, edge-only and hybrid edge-cloud infrastructure. Measurements confirm that H2O-Cloud is drastically more efficient than traditional FCFS scheduling with the maximum makespan decrease of 39.7% and throughput enhancement of 65.8% and SLA violation decrease of 17.1% when using the cloud only (900-task workload). The proposed approach results in an 8.0 percent reduction in makespan and a better resource utilization than PSO. Moreover, H2O-Cloud remains competitive in terms of energy efficiency and also shows better scalability to a larger amount of tasks. The strength of the proposed approach is statistically verified with the help of paired tests. This hybrid co-evolution mechanism contributes to both global exploration and local refinement at the same time, which allows the stable convergence and best load balancing on heterogeneous resources. The framework also exhibits high flexibility in edge-cloud hybrid systems, which minimizes SLA breaches and balances the energy usage. The experimental results confirm H2O-Cloud as a strong, scalable and SLA conscious resource provision strategy to next-generation AI-driven cloud computing systems.
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.
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.
