Conceptual Modeling for Traditional and Spatio-Temporal Applications: The MADS Approach
Conceptual Modeling for Traditional and Spatio-Temporal Applications: The MADS Approach
Communications of the ACM
HotCloud'09 Proceedings of the 2009 conference on Hot topics in cloud computing
Runtime measurements in the cloud: observing, analyzing, and reducing variance
Proceedings of the VLDB Endowment
Black-box and gray-box strategies for virtual machine migration
NSDI'07 Proceedings of the 4th USENIX conference on Networked systems design & implementation
Profiling Applications for Virtual Machine Placement in Clouds
CLOUD '11 Proceedings of the 2011 IEEE 4th International Conference on Cloud Computing
Virtual Machine Provisioning Based on Analytical Performance and QoS in Cloud Computing Environments
ICPP '11 Proceedings of the 2011 International Conference on Parallel Processing
SLA-based resource provisioning for heterogeneous workloads in a virtualized cloud datacenter
ICA3PP'11 Proceedings of the 11th international conference on Algorithms and architectures for parallel processing - Volume Part I
Understanding scheduling implications for scientific applications in clouds
Proceedings of the 9th International Workshop on Middleware for Grids, Clouds and e-Science
FairCPU: Architecture for Allocation of Virtual Machines Using Processing Features
UCC '11 Proceedings of the 2011 Fourth IEEE International Conference on Utility and Cloud Computing
Performance evaluation of Amazon EC2 for NASA HPC applications
Proceedings of the 3rd workshop on Scientific Cloud Computing Date
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In Cloud Computing, the utilization of virtual machines (VMs) as a computational resource unit is a fundamental concept. In a Cloud environment, VMs are deployed on physical resources with a set computational characteristics, such as processing power. VMs have computational properties as well, which are drawn from their respective hosts, and may be migrated from one host to another. In this context, the perceived performance of a parallel application is determined by how the respective VMs are allocated to the physical resources. For instance, two VMs deployed in the same host may have their performance altered in complex ways, due to resource contention. Also, the efficiency of a application that spans many VMs is dependent on the distribution of the correspond ant processes, due to available communication bandwidth between VMs, but also due to the possibility of leveraging local communication between processes or threads in the shared memory of a single host. As an attempt to describe the complex conditions that influence the performance of applications running on the same Cloud environment, we propose a more detailed representation of VM allocation, with features that can be used in the future for building more accurate performance models.