Task scheduling in parallel and distributed systems
Task scheduling in parallel and distributed systems
A taxonomy of scheduling in general-purpose distributed computing systems
IEEE Transactions on Software Engineering
Chord: a scalable peer-to-peer lookup protocol for internet applications
IEEE/ACM Transactions on Networking (TON)
VMPlants: Providing and Managing Virtual Machine Execution Environments for Grid Computing
Proceedings of the 2004 ACM/IEEE conference on Supercomputing
A distributed and cooperative load balancing mechanism for large-scale P2P systems
SAINT-W '06 Proceedings of the International Symposium on Applications on Internet Workshops
Load Redistribution in Heterogeneous Systems
ICAS '07 Proceedings of the Third International Conference on Autonomic and Autonomous Systems
Multi-Tiered On-Demand Resource Scheduling for VM-Based Data Center
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Sandpiper: Black-box and gray-box resource management for virtual machines
Computer Networks: The International Journal of Computer and Telecommunications Networking
Cooperative dynamic scheduling of virtual machines in distributed systems
Euro-Par'11 Proceedings of the 2011 international conference on Parallel Processing - Volume 2
Managing volunteer resources in the cloud
International Journal of Computational Science and Engineering
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With the number of services using virtualization and clouds growing faster and faster, it is common to mutualize thousands of virtual machines within one distributed system. Consequently, the virtualized services, softwares, hardwares and infrastructures share the same physical resources, thus the performance of one depends of the resources usage of others. We propose a solution for vm load balancing (and rebalancing) based on the observation of the resources quota and the dynamic usage that leads to better balancing of resources. As it is not possible to have a single scheduler for the whole cloud and to avoid a single point of failure, our scheduler uses distributed and collaborative scheduling agents. We present scenarios simulating various cloud resources and vm usage experimented on our testbed p2p architecture.