Multi-resource collaborative optimization for adaptive virtual machine placement

The unbalanced resource utilization of physical machines (PMs) in cloud data centers could cause resource wasting, workload imbalance and even negatively impact quality of service (QoS). To address this problem, this paper proposes a multi-resource collaborative optimization control (MCOC) mechanism for virtual machine (VM) migration. It uses Gaussian model to adaptively estimate the probability that the running PMs are in the multi-resource utilization balance status. Given the estimated probability of the multi-resource utilization balance state, we propose effective selection algorithms for live VM migration between the source hosts and destination hosts, including adaptive Gaussian model-based VMs placement (AGM-VMP) algorithm and VMs consolidation (AGM-VMC) method. Experimental results show that the AGM-VMC method can effectively achieve load balance and significantly improve resource utilization, reduce data center energy consumption while guaranteeing QoS.

A Novel Resource Optimization Algorithm Based on Clustering and Improved Differential Evolution Strategy Under a Cloud Environment

Resource optimization algorithm based on clustering and improved differential evolution strategy, as a new global optimized algorithm, has wide applications in language translation, language processing, document understanding, cloud computing, and edge computing due to high efficiency. With the development of deep learning technology and the rise of big data, the resource optimization algorithm encounters a series of challenges, such as the workload imbalance and low resource utilization. To address the preceding problems, this study proposes a novel resource optimization algorithm based on clustering and an improved differential evolution strategy (Multi-objective Task Scheduling Strategy (MTSS)). Three indexes, namely task completion time, execution cost, and workload, of virtual machines are selected and used to build the fitness function of the MTSS algorithm. At the same time, the preprocessing state is set up to cluster according to the resource and task characteristics to reduce the magnitude of their matching scale. Moreover, to solve the workload imbalance among different resource sets, local resource tasks are reallocated using the Q-value method in the MTSS strategy to achieve workload balance of global resources and improve the resource utilization rate. Experiments are carried out to evaluate the effectiveness of the proposed algorithm. Results show that the proposed algorithm outperforms other algorithms in terms of task completion time, execution cost, and workload balancing.

Research on Space Allocation Problem of Export Yard in Container Terminals

A multi-objective model is proposed, for export yard space allocation problem in container terminals, with the objectives to minimize the total distance to transport containers and the workload imbalance between blocks. An improved linear efficacy coefficient method is proposed to deal with both objectives in order to formulate the problem as a single-objective integer programming model.

Reducing Response Time in Fork-Join Systems under Heavy Traffic Via Imbalance Control

We consider the problem of reducing the response time of fork-join systems by maintaining the workload balanced among the processing stations. The general problem of modeling and finding an optimal policy that reduces imbalance is quite difficult. In order to circumvent this difficulty, the heavy traffic approach is taken, and the system dynamics are approximated by a reflected diffusion process. This way, the problem of finding an optimal balancing policy that reduces workload imbalance is set as a stochastic optimal control problem, for which numerical methods are available. Some numerical experiments are presented, where the control problem is solved numerically and applied to a simulation. The results indicate that the response time of the controlled system is reduced significantly using the devised control.

Reducing Response Time in Fork-Join Systems under Heavy Traffic Via Imbalance Control

We consider the problem of reducing the response time of fork-join systems by maintaining the workload balanced among the processing stations. The general problem of modeling and finding an optimal policy that reduces imbalance is quite difficult. In order to circumvent this difficulty, the heavy traffic approach is taken, and the system dynamics are approximated by a reflected diffusion process. This way, the problem of finding an optimal balancing policy that reduces workload imbalance is set as a stochastic optimal control problem, for which numerical methods are available. Some numerical experiments are presented, where the control problem is solved numerically and applied to a simulation. The results indicate that the response time of the controlled system is reduced significantly using the devised control.

Modeling Multi Operator-Multi-UAV (MOMU) Operator Attention Allocation Problem

This paper addresses the operator-task attention allocation problem in multiple operators managing multiple unmanned aerial vehicles (MOMU) setting. Previous works related to UAV operator-task attention allocation are mainly focused on single operator single UAV (SOSU) or single operator multiple UAVs (SOMU) models with emphasis on reward maximization only. Present research addresses two important issues viz. multi-operator paradigm, and workload imbalance in MOMU task attention allocation problem. Assigning multiple operators to multiple UAVs can improve the flexibility of human-system decision making. This added flexibility comes at a cost of added modeling complexity. Specifically, we consider important issue of operator workload imbalance in a team setting. We model workload imbalance in MOMU setting and through computational simulations show a balance between reward maximization and operators’ workload minimization is possible.