In cloud datacenters, multiple Virtual Machines (VMs) are co-located in a Physical Machine (PM) to serve different applications. Prior VM consolidation methods for cloud datacenters schedule VMs mainly based on resource (CPU and memory) constraints in PMs but neglect serious shared Last Level cache contention between VMs. This may cause severe VM performance degradation due to cache thrashing and starvation for VMs. Current cache contention aware VM consolidation strategies either estimate cache contention by coarse VM classification for each individual VM without considering co-location and (or) require the aid of hardware to monitor the online miss rate of each VM. Therefore, these strategies are insufficiently accurate and (or) difficult to adopt for VM consolidation in clouds. In this paper, we formalize the problem of cache contention aware VM placement and migration in cloud datacenters using integer linear programming. We then propose a cache contention aware VM placement and migration algorithm (CacheVM). It estimates the total cache contention degree of co-locating a given VM with a group of VMs in a PM based on the cache stack distance profiles of the VMs. Then, it places the VM to the PM with the minimum cache contention degree and chooses the VM from a PM that generates the maximum cache contention degree to migrate out. We implemented CacheVM and its comparison methods on a supercomputing cluster. Trace-driven simulation and real-testbed experiments show that CacheVM outperforms other methods in terms of the number of cache misses, execution time and throughput.

Recognition and simulation of actions performable on rigidly-jointed actors such as human bodies have been the subject of our research for some time. One part of an ongoing effort towards a total human movement simulator is to develop a system to perform the actions of American Sign Language (ASL). However, one of the “channels” of ASL communication, the face, presents problems which are not well handled by a rigid model.