1 / 1

Placement Algorithms

Exploiting Sharing for Data Center Consolidation. Timothy Wood, Jim Cipar, Gabriel Tarasuk-Levin, Peter Desnoyers, Emery Berger, Mark Corner, Prashant Shenoy University of Massachusetts, Amherst. Motivation and Challenges.

clove
Download Presentation

Placement Algorithms

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Exploiting Sharing for Data Center Consolidation Timothy Wood, Jim Cipar, Gabriel Tarasuk-Levin, Peter Desnoyers, Emery Berger, Mark Corner, Prashant Shenoy University of Massachusetts, Amherst Motivation and Challenges Using the potential for memory sharing as a guide for placing VMs can lead to substantial memory savings. Memory requirements and the potential for sharing fluctuate over time, thus the system must monitor memory utilization to prevent hotspots. Memory is an expensive resource and can be the limiting factor when consolidating virtual machines with low CPU utilization. ESX Server supports page sharing – allowing virtual machines to reduce memory consumption by sharing identical pages. Sharing Memory If two VM’s have an identical page in memory, only store a single copy until one makes a write. Matches are found by comparing hashes generated for each page in a VM’s memory. Currently, ESX only monitors sharing within a single host. A VMware experiment reports memory savings of over 30% for a group of Windows servers. Table from Memory Resource Management in VMware ESX Server, Carl A. Waldspurger, OSDI 2002 Finding Similar Virtual Machines We must efficiently calculate the sharing potential between VMs across large data centers. Brute force comparison of page hashes is costly in both computation and memory. We propose an efficient Bloom filter based “fingerprinting” technique. Bloom filters still maintain a high degree of accuracy, and allow for a tradeoff between storage requirements and prediction accuracy. Bloom Filter Accuracy Sharing Estimation Time Relative Error Time (sec) 0 25 50 75 100 125 150Number of VMs 10 25 50 75 100 Sharing Rate Placement Algorithms VM VM VM VM VM VM Using sharing potential can help optimize placement of virtual machines. Sharing reduces memory requirements, increasing consolidation possibilities. Additional memory tracing techniques can help detect and prevent memory hotspots. Host Host Host Taking advantage of memory sharing allows more virtual machines to run on a smaller number of hosts VM VM VM VM VM VM Host Host

More Related