Enhanced task scheduling in cloud data centres using orthogonal opposition-based partial reinforcement optimizer

DQN-Scheduler: A Multi-Objective Optimization Framework for Scheduling Microservices in Cloud Computing

Cloud computing has emerged as an information technology solution, providing software and infrastructure solutions for companies and individuals. The pay-as-you-go approach has increased demands for the cloud. The massive range of resources, the variety of services, and flexible pricing grab attention. In addition, microservices have emerged as a new way of building software, with applications developed as loosely dependent tasks. Additionally, container technology has boosted the popularity of microservices by offering a platform for this type of architecture. Containers and microservices improve the flexibility and scalability of cloud applications. There are two primary types of microservices: batch and online services, with the majority of applications falling into the online service category. Scheduling microservices is challenging because it requires careful management of resource utilization, load balancing, network latency, reliability, and availability. In this study, we introduce the DQN-Scheduler, a novel reinforcement learning-based agent designed to optimize microservice scheduling in cloud environments. Our approach aims to optimize multiple scheduling objectives simultaneously, such as resource utilization, load balancing, latency, reliability, and availability. To our knowledge, this is the first framework to address all these objectives simultaneously. The DQN-Scheduler was tested against benchmark algorithms in the field. The experimental results demonstrate that the DQN-Scheduler outperforms benchmark algorithms.

A Hierarchical Multi-Agent Reinforcement Learning With a Heterogeneous Metaheuristic Aware Resource Allocation in Big Data-Cloud

The rapid advancement of technology like Internet of Things (IoT) and Cloud computing (CC)based heterogeneous environment required dynamic resource management system. Thecomplexity of IoT-Cloud is increasing due to abundance of dynamic data dissemination thatcreate poor performance like high energy consumption (EC), workload imbalancing, pooradaptability, and fail to handle SLA violations. The two primary contributions of the proposedwork are the Optimized Priority-Aware Hierarchical Multi-Agent Deep Q Network (OPHMDQN)and the Adaptive Multi-Objective Dung Beetle Optimization Algorithm (ADBOA). Themulti-agent strategies increase the scalability and adaptability of resource managementthrough local and global hierarchies. The approach integrates information-based decisionmakingand priority-aware allocation while accounting for SLA requirements, systemconstraints, and job complexity to optimise resource generation, utilisation, allocation, andtask scheduling. In comparison to existing optimization and Reinforcement Learning (RL)techniques, experimental results show that the proposed OPHM-DQN-ADBOA frameworkconsistently reduces EC (up to 30 % lower), execution delay, and SLA violations whileimproving resource utilisation and LB. The ADBOA enhances the proposed model throughoptimal multi-objective training, reducing EC, SLA violation, and cost while improvingresource utilization and scheduling efficiency. As a results, the model achieves high scalabilityand adaptability in heterogeneous IoT-Cloud resource management.

Task scheduling and load balancing in cloud, fog, and edge computing systems: a survey

OPTIMIZED TASK SCHEDULING IN FOG-CLOUD ENVIRONMENTS USING A COST-AWARE GENETIC ALGORITHM

This research proposes a cost-aware, genetic-based task scheduling algorithm tailored for fog-cloud environments, which seeks to improve cost efficiency for real-time applications with strict deadlines, and demonstrates that the proposed algorithm surpasses existing techniques like Round-Robin and Trade-off algorithms.

A Hybrid Deep Reinforcement Learning Framework for Efficient Cloud Resource Scheduling

A hybrid Deep Reinforcement Learning (DRL) framework that combines Deep Q-Network, Proximal Policy Optimization and Advantage Actor-Critic to enable adaptive resource scheduling in cloud environments is proposed.

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.