Online Power-Aware Deployment and Load Distribution Optimization for Application Server Clusters

Abstract

The energy conservation of application server clusters is a pressing problem. In this paper, we propose an online power-aware deployment and load distribution optimization strategy for application server clusters, whose objective is to minimize the cluster's power while ensuring that the server's CPU utilization is not higher than a preset value. The strategy includes two schemes: a mixed integer linear programming (MILP)-based scheme and a mixed integer non-linear programming (MINLP)-based scheme. The former formulates the cluster optimization problem as a MILP problem and adopts a toolkit to solve it. When the cluster scale is small, it can find the global optimal solution quickly. So, the MILP-based scheme is applicable to small-scale clusters. In the MINLP-based scheme, we first formulate the cluster optimization problem as a non-linear programming problem, and then design a method to reduce the number of variables and reformulate it as an MINLP problem. We finally propose an efficient solution method based on the flower pollination algorithm. Due to the small number of variables and the high solution efficiency, the solution method can quickly obtain a high-quality solution, so the MINLP-based scheme can be applied to largescale clusters. The experimental results demonstrate the effectiveness of our strategy.

Microservice cluster load balancing method based on improved least active number algorithm

“…Through this performance quantification method, an abstract server can be quantified into parameters that can be compared. Calculate the quantitative value of server performance, as shown in (1).…”

“…Therefore, it is necessary to deploy micro-services on multiple servers. The performance of the service cluster poses a very serious challenge [1].…”

A QoS-aware workload routing and server speed scaling policy for energy-efficient data centers: a robust queueing theoretic approach

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