A contribution to scheduling jobs submitted by finite-sources in computational clusters

Abstract

Data science and data processing are very popular topics nowadays. Un- like a few years ago, everything is connected to data now and we have to handle these kinds of large data well. Therefore the distributed heterogeneous resources of networks e.g. the computational grid, have attracted great interest. It has become a challenge to schedule jobs in order to utilize the available resources effectively. The allocation of arriving jobs has a great impact on the efficiency and the energy consumption of the system. A generalized finite source model is presented in this paper. Our main goal is to build up models for the performance evaluation of scheduling computeintensive jobs with unknown service times in a computational cluster that consists of servers of different types. For this purpose we determine various performance measures for all combinations of three scheduling policies (two of them are the novelty of this paper: the MRT and the MRTHP policies) which can be used for assigning jobs to servers with three schemes for buffering arriving jobs. Furthermore, we investigate the effect of switching off idle servers on the energy consumption of the system under these combinations of scheduling policies and buffering schemes. Computational results obtained by simulation show that the choice of the scheduling policy and the buffering scheme plays an important role in ensuring the quality of service parameters such as the waiting time and the response time experienced in the case of arriving jobs. However, the energy consumption is only affected by the scheduling policy and the energy saving mode, while the buffering scheme does not have a significant impact.