An Examination Method of Mergeable Memory Page Distribution for Memory Deduplication

Shiba, Masahito

doi:10.1109/gcce46687.2019.9015438

Cited by 2 publications

(1 citation statement)

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“…As a result, CPU consumption will be reduced by 44.9 percent, and memory bandwidth usage will be reduced by 31.6 percent. Shiba [25], discussed how the cost of finding mergeable pages is considerably high, and how pages are distributed in address spaces. MashitoShiba, classifies pages by the state of consecutive memory pages, measures the ratio of pages in each state, and shows the measuring distribution that can be used to evaluate the likelihood of merging.…”

Section: Literature Reviewmentioning

confidence: 99%

Homogeneous Batch Memory Deduplication Using Clustering of Virtual Machines

Jagadeeswari¹,

Raj²

2023

Computer Systems Science and Engineering

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Virtualization is the backbone of cloud computing, which is a developing and widely used paradigm. By finding and merging identical memory pages, memory deduplication improves memory efficiency in virtualized systems. Kernel Same Page Merging (KSM) is a Linux service for memory pages sharing in virtualized environments. Memory deduplication is vulnerable to a memory disclosure attack, which uses covert channel establishment to reveal the contents of other colocated virtual machines. To avoid a memory disclosure attack, sharing of identical pages within a single user's virtual machine is permitted, but sharing of contents between different users is forbidden. In our proposed approach, virtual machines with similar operating systems of active domains in a node are recognised and organised into a homogenous batch, with memory deduplication performed inside that batch, to improve the memory pages sharing efficiency. When compared to memory deduplication applied to the entire host, implementation details demonstrate a significant increase in the number of pages shared when memory deduplication applied batch-wise and CPU (Central processing unit) consumption also increased.

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Section: Literature Reviewmentioning

confidence: 99%