MHDNNL

Qirui Li, Zhiping Peng, Delong Cui, Jianpeng Lin, Jieguang He

OAI: oai:igi-global.com:310053 • DOI: 10.4018/IJITWE.310053

Task optimization scheduling is one of the key concerns of both cloud service providers (CSPs) and cloud users. The CSPs hope to reduce the energy consumption of executing tasks to save costs, while the users are more concerned about shorter task completion time. In cloud computing, multi-queue and multi-cluster (MQMC) is a common resource configuration mode, and batch is a common task commission mode. The task scheduling (TS) in these modes is a multi-objective optimization (MOO) problem, and it is difficult to get the optimal solution. Therefore, the authors proposed a MOO scheduling algorithm for this model based on multiple heterogeneous deep neural networks learning (MHDNNL). The proposed algorithm adopts a collaborative exploration mechanism to generate the samples and use the memory replay mechanism to train. Experimental results show that the proposed algorithm outperforms the benchmark algorithms in minimizing energy consumption and task latency.