Task scheduling in cloud data centres is an optimisation problem that aims to minimise power consumption and task makespan as well as ensures the quality of service delivered to cloud consumers. Although there are several existing task scheduling approaches, these methods mainly focus on optimising makespans of tasks while ignoring critical issues. This paper presents a comprehensive multi-objective task scheduling model based on an improved Ant Colony Optimisation (ACO) algorithm, referred to as MOTS-ACO. In order to promote the diversity of the Pareto set and accelerate the convergence speed, adaptive distribution probability is incorporated into the proposed algorithm, specifically in the process of updating the global rule. The performance of MOTS-ACO is compared with several existing multi-objectives task scheduling algorithms based on the makespan time, turnaround time, power efficiency and load balancing parameters. The results show the superiority of MOTS-ACO in terms of the makespan time, turnaround time, power efficiency and load balancing. Moreover, the proposed MOTS-ACO algorithm introduces more diversity in the search and accelerates the convergence speed towards the Pareto optimal solution. K E Y W O R D S ACO, cloud computing, load balancing, makespan, power efficiency, task scheduling, turnaround time This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Recently, the problem of Virtual Machine Placement (VMP) has received enormous attention from the research community due to its direct effect on the energy efficiency, resource utilization, and performance of the cloud data center. VMP is considered as a multidimensional bin packing problem, which is a type of NP-hard problem. The challenge in VMP is how to optimally place multiple independent virtual machines into a few physical servers to maximize a cloud provider’s revenue while meeting the Service Level Agreements (SLAs). In this paper, an effective multiobjective algorithm based on Particle Swarm Optimization (PSO) technique for the VMP problem, referred to as VMPMOPSO, is proposed. The proposed VMPMOPSO utilizes the crowding entropy method to optimize the VMP and to improve the diversity among the obtained solutions as well as accelerate the convergence speed toward the optimal solution. VMPMOPSO was compared with a simple single-objective algorithm, called First-Fit-Decreasing (FFD), and two multiobjective ant colony and genetic algorithms. Two simulation experiments were conducted to verify the effectiveness and efficiency of the proposed VMPMOPSO. The first experiment shows that the proposed algorithm has better performance than the algorithms we compared it to in terms of power consumption, SLA violation, and resource wastage. The second indicates that the Pareto optimal solutions obtained by applying VMPMOPSO have a good distribution and a better convergence than the comparative algorithms.

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With the integration of unmanned aerial vehicles (UAVs) into fifth generation (5G) networks, UAVs are used in many applications since they enhance coverage and capacity. To increase wireless communication resources, it is crucial to study the trajectory of UAV-assisted relay. In this paper, an energy-efficient UAV trajectory for uplink communication is studied, where a UAV serves as a mobile relay to maintain the communication between ground user equipment (UE) and a macro base station. This paper proposes a UAV Trajectory Optimization (UAV-TO) scheme for load balancing based on Reinforcement Learning (RL). The proposed scheme utilizes load balancing to maximize energy efficiency for multiple UEs in order to increase network resource utilization. To deal with nonconvex optimization, the RL framework is used to optimize the trajectory UAV. Both model-based and model-free approaches of RL are utilized to solve the optimization problem, considering line of sight and non-line of sight channel models. In addition, the network load distribution is calculated. The simulation results demonstrate the effectiveness of the proposed scheme under different path losses and different flight durations. The results show a significant improvement in performance compared to the existing methods.

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