Cluster load balancing algorithm based on dynamic consistent hash

In server clusters, the static scheduling algorithm has superior performance when the user visits are relatively stable. In the face of sudden increase in user traffic, the dynamic scheduling algorithm has a better load balancing effect than the static scheduling algorithm. However, in the face of complex network environments, the static scheduling algorithm cannot adjust the load according to the performance of the server in real time. The dynamic scheduling algorithm using a single weight to evaluate server performance is unreliable, and load scheduling with reference to the number of connections has uncertainty. In view of this problem, this paper proposes a cluster load balancing algorithm based on dynamic consistent hash based on the study of load balancing based on LVS clusters. By analyzing the performance and load parameters, we divide the request process into in-cycle and out-of-cycle. By setting up the LVS cluster system, the performance weights, load parameters, number of virtual nodes and cycles of the algorithm in this paper are determined experimentally. Finally, the response time and throughput of the algorithm in this paper are compared with the WRR algorithm and WLC algorithm. The results show that the time and throughput of this algorithm are better than WRR algorithm and WLC algorithm.

Static Scheduling with Load Balancing for Solving Triangular Band Linear Systems on Multicore Processors

A new approach for solving triangular band linear systems is established in this study to balance the load and obtain a high degree of parallelism. Our investigation consists to attribute both adequate start time and processor to each task and eliminate the useless dependencies which are not used in the parallel solve stage. Thereby, processors execute in parallel their related tasks taking account of the considered precedence constraints. The theoretical lower bounds for parallel execution time and the number of processors required to carry out the task graph in the shortest time are determined. Experimentations are realized on a shared-memory multicore processor. The experimental results are fitted to the values derived from the determined mathematical formulas. The comparison of results obtained by our contribution with those from triangular systems resolution routine belonging to the library PLASMA, Parallel Linear Algebra Software for Multicore Architectures, confirms the efficiency of the proposed approach.

A Constrained Static Scheduling Strategy in Edge Computing for Industrial Cloud Systems

With the development of industrial internet, attention has been paid for edge computing due to the low latency. However, some problems remain about the task scheduling and resource management. In this paper, an edge computing supported industrial cloud system is investigated. According to the system, a constrained static scheduling strategy is proposed to over the deficiency of dynamic scheduling. The strategy is divided into the following steps. Firstly, the queue theory is introduced to calculate the expectations of task completion time. Thereupon, the task scheduling and resource management problems are formulated and turned into an integer non-linear programming (INLP) problem. Then, tasks that can be scheduled statically are selected based on the expectation of task completion and constrains of various aspects of task. Finally, a multi-elites-based co-evolutionary genetic algorithm (MEB-CGA) is proposed to solve the INLP problem. Simulation result shows that the MEB-CGA significantly outperforms the scheduling quality of greedy algorithm.