Ontology-based approach to scheduling of jobs processed by applications running in virtual environments

Abstract

This paper presents an ontology-based approach to the problem of jobs scheduling in case where jobs are processed by applications running in virtual environments and number of applications and their performance varies over time. Using ontology-based framework brings benefits when system has a varying number of components and their performing properties are also non-constant. The work is focused on ontology model needed to organize information exchange for intelligent agents embedded into virtual machines and gathering information about applications performance. In cases when jobs of one type can be processed by several applications having different performance, the existence of optimal threshold queuing policy has been proven earlier. It can reduce the average job processing time. In order to calculate thresholds we need relevant information about active applications and their current performance, the rate of jobs stream, the number of jobs in the queues, etc. The presented approach solves the problem of effective gathering of relevant information about the system state based on intelligent agents interaction where each intelligent agent uses ontology to publish only information about changes that are relevant to decision making. This reduces the system’s overhead for monitoring of ongoing parameters.