Journal
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
Volume 16, Issue 1, Pages 420-432Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TASE.2018.2826723
Keywords
Cloud computing; energy efficiency; model predictive control (MPC); quality-aware placement; security; virtual machine placement
Categories
Funding
- research project GESTEC-Tecnologie orientate ai servizi per lo sviluppo e per l'integrazione di piattaforme ICT through the Italian Ministry of University and Research
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Modern datacenters rely on virtualization to deliver complex and scalable cloud services. To avoid inflating costs or reducing the perceived service level, suitable resource optimization techniques are needed. Placement can be used to prevent inefficient maps between virtual and physical machines. In this perspective, we propose a holistic placement framework considering conflicting performance metrics, such as the service level delivered by the cloud, the energetic footprint, hardware or software outages, and security policies. Unfortunately, computing the best placement strategies is nontrivial, as it requires the ability to trade among several goals, possibly in a real-time manner. Therefore, we approach the problem via model predictive control to devise optimal maps between virtual and physical machines. Results show the effectiveness of our technique in comparison with classical heuristics.
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