Towards scalable on-demand collective data access in IaaS clouds: An adaptive collaborative content exchange proposal

• Studies efficient on-demand read access to shared content in large scale IaaS clouds.
• Introduces I/O access pattern aware techniques and algorithms.
• Substantial benefits demonstrated using experimental results involving dozens of nodes.

A critical feature of IaaS cloud computing is the ability to quickly disseminate the content of a shared dataset at large scale. In this context, a common pattern is collective read, i.e., accessing the same VM image or dataset from a large number of VM instances concurrently. Several approaches deal with this pattern either by means of pre-broadcast before access or on-demand concurrent access to the repository where the image or dataset is stored. We propose a different solution using a hybrid strategy that augments on-demand access with a collaborative scheme in which the VMs leverage similarities between their access pattern in order to anticipate future read accesses and exchange chunks between themselves in order to reduce contention to the remote repository. Large scale experiments show significant improvement over conventional approaches from multiple perspectives: completion time, sustained read throughput, fairness of I/O read operations and bandwidth utilization.