Authors: Ágnes Salánki; Gergo Kincses; László Gönczy; Imre Kocsis
Addresses: Department of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary ' Quaopt Ltd., Bocskai Str 77-79, Budapest, Hungary ' Department of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary and Quanopt Ltd., Budapest, Hungary ' Department of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary
Abstract: Virtual computing labs dramatically changed education methodology with transforming traditional classroom-and lab-based learning models to self paced asynchronous ones. The Apache Virtual Computing Lab (VCL) platform allows students to reserve and use virtual machines (VMs) with a predefined configuration and software setup. In essence, it offers an educational cloud that provides preconfigured lab environments in 'desktop as a service' style. At our university, four courses of a specialisation branch are available in this form. While maintaining VCL, we faced the challenges of short-and long-term capacity planning. We analysed high-level reservation and platform-level monitoring data of five semesters and built mathematical models of workload and resource utilisation based on our observations. The main contribution of this study are data-driven approaches for: 1) predicting reservation patterns of students as course deadlines approach; 2) a regression-based estimate of typical resource utilisation of VMs; 3) elaboration of an optimised schedule of deadlines to avoid rejected reservation queries or a burst out to a public cloud. Applying these methods, fine-tuning of VM configurations and scheduling of upcoming semesters became possible, even in case of methodical/technical educational changes (e.g., modified course schedules, increasing number of attendees).
Keywords: Virtual Computing Lab; VCL; educational cloud; capacity planning; workload shaping; linear regression; user behaviour prediction; data analysis; load shaping; resource utilisation prediction; linear programming; cloud computing.
International Journal of Cloud Computing, 2017 Vol.6 No.4, pp.370 - 383
Accepted: 07 Sep 2017
Published online: 05 Mar 2018 *