A scalable, commodity data center network architecture
Proceedings of the ACM SIGCOMM 2008 conference on Data communication
Server-storage virtualization: integration and load balancing in data centers
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Future Generation Computer Systems
What's inside the Cloud? An architectural map of the Cloud landscape
CLOUD '09 Proceedings of the 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing
Coupled placement in modern data centers
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
VL2: a scalable and flexible data center network
Proceedings of the ACM SIGCOMM 2009 conference on Data communication
BCube: a high performance, server-centric network architecture for modular data centers
Proceedings of the ACM SIGCOMM 2009 conference on Data communication
A virtual network mapping algorithm based on subgraph isomorphism detection
Proceedings of the 1st ACM workshop on Virtualized infrastructure systems and architectures
Improving the scalability of data center networks with traffic-aware virtual machine placement
INFOCOM'10 Proceedings of the 29th conference on Information communications
Sharing-aware algorithms for virtual machine colocation
Proceedings of the twenty-third annual ACM symposium on Parallelism in algorithms and architectures
VMFlow: leveraging VM mobility to reduce network power costs in data centers
NETWORKING'11 Proceedings of the 10th international IFIP TC 6 conference on Networking - Volume Part I
DARGOS: A highly adaptable and scalable monitoring architecture for multi-tenant Clouds
Future Generation Computer Systems
VM consolidation: A real case based on OpenStack Cloud
Future Generation Computer Systems
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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.