Abstract
•TAGS a job allocation algorithm modelled using Markovian processing algebra, known as PEPA.•The working environment is assumed to be a heterogeneous, and the job size distribution is assumed to be two phase hyper-exponential.•The analysis of the results reveals the TAGS algorithm is sensitive to the time out value.•Concerning total energy consumption, TAGS is shown to consume more energy than the shortest queue and the weighted random in all the server combinations considered.•Energy per job could be used to identify the best time out value for TAGS, i.e. that which produces the highest possible throughput with minimal impact on energy consumption.
This paper models the task assignment based on guessing size (TAGS) job allocation algorithm using Markovian processing algebra; PEPA. It aims to analyse performance and energy consumption. The working environment is assumed to be heterogeneous, and the job size distribution is assumed to be a two phase hyper-exponential. Furthermore, the queues are bounded. A two nodes system is implemented with exponentially distributed incoming tasks. We analysed the performance metrics and energy consumption under different arrival rates. We found TAGS can perform well and improve performance, although it increases total energy consumption. Finally, we calculated the energy per job to evaluate TAGS in a heterogeneous environment, and demonstrated that TAGS reduces energy consumption per job when the system is under a heavy load.