Resource-Aware Scheduling of Graph Partitions for Distributed Graph Computing in Heterogeneous Clusters

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

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.

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.

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

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.

Future Generation Computer Systems

Resource Allocation and Container Scaling for Microservices in Multi-Cluster Edge Computing System

With the advent of the 6G era and the evolution of distributed systems, edge computing has become a pivotal architecture for deploying latency-sensitive, resource-efficient applications. In particular, the microservice architecture, characterized by modular and loosely coupled components, has gained significant traction for building scalable and maintainable applications at the network edge. However, deploying microservice-based applications in heterogeneous and geographically distributed Multi-Cluster Edge Computing (MCEC) environments presents critical challenges, especially in achieving efficient and scalable resource management. Although existing research has explored resource allocation and container scaling for microservice-based systems, most prior works consider container efficiency in isolation or within single-cluster or cloud-centric environments, without jointly addressing container-level efficiency, inter-cluster task offloading, and resource allocation in MCEC scenarios. To address this gap, we propose RACCOON, a request-offloading cascaded resource allocation algorithm tailored for microservice-oriented deployments in MCEC settings. RACCOON aims to minimize user-perceived service latency while optimizing overall resource utilization. Complementing this, we introduce RASCAL, a reinforcement learning (RL)-based container scaling mechanism that dynamically adjusts resource provisioning at the container level to further enhance system performance. Experimental evaluation shows that our approach consistently outperforms methods that address only resource allocation, only task offloading, or only container scaling, by jointly optimizing these dimensions to reduce end-to-end user-perceived latency and computational overhead.

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

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.