An investigation into the impacts of task-level behavioural heterogeneity upon energy efficiency in Cloud datacentres

Cloud datacentre resources and the arriving jobs are addressed to be exhibiting increased level of heterogeneity. A single Cloud job may encompass one to several number of tasks, such tasks usually exhibit increased level of behavioural heterogeneity though they belong to the same job. Such behavioural heterogeneity are usually evident among the level of resource consumption, resource intensiveness, task duration etc. These task behavioural heterogeneity within jobs impose various complications in achieving an effective energy efficient management of the Cloud jobs whilst processing them in the server resources. To this end, this paper investigates the impacts of the task level behavioural heterogeneity upon energy efficiency whilst the tasks within given jobs are executed in Cloud datacentres. Real-life Cloud trace logs have been investigated to exhibit the impacts of task heterogeneity from three different perspectives including the task execution trend and task termination pattern, the presence of few proportions of resource intensive and long running tasks within jobs. Furthermore, the energy implications of such straggling tasks within jobs have been empirically exhibited. Analysis conducted in this study demonstrates that Cloud jobs are extremely heterogeneous and tasks behave distinctly under different execution instances, and the presence of energy-aware long tail stragglers within jobs can significantly incur extravagant level of energy expenditures

A prescriptive analytics approach for energy efficiency in datacentres.

Given the evolution of Cloud Computing in recent years, users and clients adopting Cloud Computing for both personal and business needs have increased at an unprecedented scale. This has naturally led to the increased deployments and implementations of Cloud datacentres across the globe. As a consequence of this increasing adoption of Cloud Computing, Cloud datacentres are witnessed to be massive energy consumers and environmental polluters. Whilst the energy implications of Cloud datacentres are being addressed from various research perspectives, predicting the future trend and behaviours of workloads at the datacentres thereby reducing the active server resources is one particular dimension of green computing gaining the interests of researchers and Cloud providers. However, this includes various practical and analytical challenges imposed by the increased dynamism of Cloud systems. The behavioural characteristics of Cloud workloads and users are still not perfectly clear which restrains the reliability of the prediction accuracy of existing research works in this context. To this end, this thesis presents a comprehensive descriptive analytics of Cloud workload and user behaviours, uncovering the cause and energy related implications of Cloud Computing. Furthermore, the characteristics of Cloud workloads and users including latency levels, job heterogeneity, user dynamicity, straggling task behaviours, energy implications of stragglers, job execution and termination patterns and the inherent periodicity among Cloud workload and user behaviours have been empirically presented. Driven by descriptive analytics, a novel user behaviour forecasting framework has been developed, aimed at a tri-fold forecast of user behaviours including the session duration of users, anticipated number of submissions and the arrival trend of the incoming workloads. Furthermore, a novel resource optimisation framework has been proposed to avail the most optimum level of resources for executing jobs with reduced server energy expenditures and job terminations. This optimisation framework encompasses a resource estimation module to predict the anticipated resource consumption level for the arrived jobs and a classification module to classify tasks based on their resource intensiveness. Both the proposed frameworks have been verified theoretically and tested experimentally based on Google Cloud trace logs. Experimental analysis demonstrates the effectiveness of the proposed framework in terms of the achieved reliability of the forecast results and in reducing the server energy expenditures spent towards executing jobs at the datacentres.N/