TL;DR
The experimental results demonstrate that DBS consistently outperforms state-of-the-art algorithms in terms of multiple performance metrics and confirm that the proposed scheduler effectively enhances deadline compliance, workload balance, scalability, and overall cloud system performance under heterogeneous and high-load conditions.
Cloud computing has become a dominant paradigm for delivering scalable and flexible on-demand resources; however, efficiently executing high performance computing (HPC) workloads remains challenging, particularly in heterogeneous environments. Conventional static scheduling methods often lead to poor resource utilization and increased makespan, while dynamic approaches improve load distribution but introduce significant overhead due to continuous monitoring and real-time decision-making. To address these challenges, this paper proposes an SLA-aware Dynamic Enhanced Resource-Aware Load Balancing Algorithm (SLADE- RALBA). The algorithm minimizes load imbalance by considering the computational capacities of virtual machines and ensures Service Level Agreement (SLA) compliance through a three-tier priority-based workflow. The proposed approach is evaluated using CloudSim Plus on two benchmark datasets: Heterogeneous Computing Scheduling Problem (HCSP) instances and the Google Cloud Jobs dataset. Results demonstrate that SLA-DE-RALBA consistently outperforms baseline algorithms, including RALBA, DRALBA, DE-RALBA, SLA-RALBA, Dynamic Max- Min, PSSLB, and PSSELB, across key metrics such as makespan, resource utilization, job rejection, throughput, execution time, and cost. Notably, it achieves zero job rejection, reduces energy consumption by up to 85%, improves resource utilization by 11.9%, lowers makespan by 41-45%, and decreases execution time by up to 57%, making it a robust and efficient solution for HPC workload scheduling in cloud environments.
A dynamic resource allocation and task scheduling approach based on end-edge-cloud cooperation is established in order to enhance task completion, resource utilization, satisfaction of service level agreements (SLA) and reduce delay and energy consumption.
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.
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.
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