Prioritized Job Scheduling for SLA-Aware IoT Data Analytics

Abstract

The number of IoT data analysis services is increasing over years In order to handle the technical debts of the data analysis system some companies such as Uber have proposed an end- to-end data analysis framework to deal with these issues In this type of frameworks it generally uses the First Come First Served method to execute jobs However in the field of online IoT data analysis the data flow will be affected by the stability of the network environment This causes that data required for the analytic jobs may not be available In addition to meet different service-level agreements (SLA) different jobs have different deadlines execution time and computing resources Thus the importance of the jobs is different Moreover in the online environment it is difficult to know the arrival time of all jobs to schedule in advance In order to meet these needs this thesis proposes a Multilevel Feedback Queue strategy to deal with work priority issues We also design a dynamic programming algorithm which can take into account the problem of data unavailability Finally we also examine the performance of the proposed method via various job importance level distributions and demonstrate that it outperforms the FCFS and greedy-based methods

Date of Award	2020
Original language	English
Supervisor	Kun-Ta Chuang (Supervisor)