Abstract

Abstract: The management of IaaS cloud systems is a challenging task, where a huge number of Virtual Machines (VMs) must be placed over a physical infrastructure with multiple nodes. Economical reasons and the need to reduce the ever-growing carbon footprint of modern data centers require an efficient VMs placement that minimizes the number of physical required nodes. As each VM is considered as a black box with independent characteristics, the placement process presents scalability issues due to the amount of involved data and to the resulting number of constraints in the underlying optimization problem. For large data centers, this excludes the possibility to reach an optimal allocation. Existing solutions typically exploit heuristics or simplified formulations to solve the allocation problem, at the price of possibly sub-optimal solutions. We introduce a novel placement technique, namely Class-Based, that exploits available solutions to automatically group VMs showing similar behavior. The Class-Based technique solves a placement problem that considers only some representatives for each class, and that can be replicated as a building block to solve the global VMs placement problem. Our experiments demonstrate that the proposed technique is a viable solution that can significantly improve the scalability of the VMs placement in IaaS Cloud systems with respect to existing alternatives. 1