Memory resource management in VMware ESX server
ACM SIGOPS Operating Systems Review - OSDI '02: Proceedings of the 5th symposium on Operating systems design and implementation
Virtual machine memory access tracing with hypervisor exclusive cache
ATC'07 2007 USENIX Annual Technical Conference on Proceedings of the USENIX Annual Technical Conference
Memory buddies: exploiting page sharing for smart colocation in virtualized data centers
Proceedings of the 2009 ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
Handling OS jitter on multicore multithreaded systems
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
The case for RAMClouds: scalable high-performance storage entirely in DRAM
ACM SIGOPS Operating Systems Review
I/O Deduplication: Utilizing content similarity to improve I/O performance
ACM Transactions on Storage (TOS)
WIOV'08 Proceedings of the First conference on I/O virtualization
XHive: Efficient Cooperative Caching for Virtual Machines
IEEE Transactions on Computers
Disk-locality in datacenter computing considered irrelevant
HotOS'13 Proceedings of the 13th USENIX conference on Hot topics in operating systems
An empirical analysis of similarity in virtual machine images
Proceedings of the Middleware 2011 Industry Track Workshop
Understanding performance implications of nested file systems in a virtualized environment
FAST'12 Proceedings of the 10th USENIX conference on File and Storage Technologies
New frontiers in cloud computing research
Proceedings of the 7th international workshop on Virtualization technologies in distributed computing
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File cache management is among the most important factors affecting the performance of a cloud computing system. To achieve higher economies of scale, virtual machines are often overcommitted, which creates high memory pressure. Thus it is essential to eliminate duplicate data in the host and guest caches to boost performance. Existing cache deduplication solutions are based on complex algorithms, or incur high runtime overhead, and therefore are not widely applicable. In this paper we present a simple and lightweight mechanism based on functional partitioning. In our mechanism, the responsibility of each cache becomes smaller: the host only caches data in base images and a VM guest only caches its own "private data", which is generated after the VM has started. As a result, the overall effective cache size becomes bigger. Our method requires very small change to existing software (15 lines of new/modified code) to achieves big performance improvements - more than 40% performance gains in high memory pressure settings.