Virtual Machine (VM) placement has to carefully consider the aggregated resource consumption of co-located VMs in order to obey service level agreements at lower possible cost. In this paper, we focus on satisfying the traffic demands of the VMs in addition to CPU and memory requirements. This is a much more complex problem both due to its quadratic nature (being the communication between a pair of VMs) and since it involves many factors beyond the physical host, like the network topologies and the routing scheme. Moreover, traffic patterns may vary over time and predicting the resulting effect on the actual available bandwidth between hosts within the data center is extremely difficult. We address this problem by trying to allocate a placement that not only satisfies the predicted communication demand but is also resilient to demand time-variations. This gives rise to a new optimization problem that we call the Min Cut Ratio-aware VM Placement (MCRVMP). The general MCRVMP problem is NP-Hard, hence, we introduce several heuristics to solve it in reasonable time. We present extensive experimental results, associated with both placement computation and run-time performance under time-varying traffic demands, to show that our heuristics provide good results (compared to the optimal solution) for medium size data centers.

Cloud Computing in general and Virtualized Infrastructure Provisioning in particular, are significant trends with the potential to increase agility and lower costs of IT. An emerging cloud service is a virtual server shop, that allows cloud customers to order virtual appliances to be delivered virtually on the cloud. Like physical shops, customers want to customize the ordered products, e.g., have them pre-installed with their desired applications and pre-configured. Global cloud providers need to create customized virtual-server disk images and deliver them on time to meet the customer reservations and service level. This framework creates a new flavor of content distribution over the web, where large virtual server images need to be delivered to the target compute farms (either on the global cloud or on customer private clouds). In order to reduce provisioning time and meet reservation deadlines, one approach is to stage images on storage near the customer. This introduces an optimization problem of finding an optimal staging schedule, according to network bandwidth, pending reservations schedule, and customer value. This problem has some similarities to cache pre-filling and production-line scheduling. It combines scheduling, bandwidth considerations, and storage capacity constraints. In this paper we study the fundamental properties of this approach and formalize several flavors of the related optimization problem. We prove useful properties of the problem and then use those properties to provide exact efficient algorithms to solve it. We also derive efficient approximate solutions with proven error bounds.

User-transparent live migration is one of the most interesting features of Virtual Machine (VM) environments. Current live-migration technologies require that the VM retains its IP network address; therefore, are typically restricted to movement within an IP subnet. The growing number of portable computing devices has led to the development of IP mobility solutions that enable uninterrupted network connectivity while moving between different IP subnets. In this paper we study the application of current network mobility approaches to VM cross-subnet live-migration. We show that although the core problems are similar, there are significant differences between these domains, in terms of both assumptions and requirements. We present a specific solution for live migration of a VM across IP subnets, and introduce a new framework for synchronizing migration and network configuration, which allows better optimization for different scenarios of live migration.