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What and Who
Title:Improving the energy efficiency of virtualized datacenters
Speaker:Vlad Nitu
coming from:Toulouse University
Speakers Bio:Vlad Nitu is a PhD student at Toulouse University and interested in operating and visualization systems with a focus on optimizing their energy consumption. Starting October, he will be post-doc at EPFL in the group of Prof. Rachid Guerraoui.
Event Type:SWS Colloquium
Visibility:D1, D2, D3, INET, D4, D5, SWS, RG1, MMCI
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Level:AG Audience
Language:English
Date, Time and Location
Date:Friday, 21 September 2018
Time:10:30
Duration:60 Minutes
Location:Kaiserslautern
Building:G26
Room:111
Abstract
Energy consumption is an important concern for cloud datacenters. Its cost represents about 80% of the total cost of ownership and it is estimated that in 2020, the US datacenters alone will spend about $13 billion on energy bills. Generally, the servers are manufactured in such a way that they achieve high energy efficiency at high utilizations. Thereby for a low cost per computation all datacenter servers should push the utilization as high as possible. In order to fight the historically low utilization, cloud computing adopted server virtualization. This technology enables a cloud provider to pack (consolidate) the entire set of virtual machines (VMs) on a small set of physical servers and thereby, reduce the number of active servers. Even so, the datacenter servers rarely reach utilizations higher than 50% which means that they operate with a set of long-term unused resources (called 'holes'). Our first contribution is a cloud management system that dynamically splits/fusions VMs such that they can better fill the holes. However the datacenter resource fragmentation has a more fundamental problem. Over time, cloud applications demand more and more memory but the physical servers provide more an more CPU. In nowadays datacenters, the two resources are strongly coupled since they are bounded to a physical sever. Our second contribution is a practical way to decouple the CPU-memory tuple that can simply be applied to a commodity server. The underutilization observed on physical servers is also true for virtual machines. It has been shown that VMs consume only a small fraction of the allocated resources because the cloud customers are not able to correctly estimate the resource amount necessary for their applications. Our third contribution is a system that estimates the memory consumption (i.e. the working set size) of a VM, with low overhead and high accuracy. Thereby, we can now consolidate the VMs on based on their working set size (not the booked memory). However, the drawback of this approach is the risk of memory starvation. If one or multiple VMs have an sharp increase in memory demand, the physical server may run out of memory. This event is undesirable because the cloud platform is unable to provide the client with the memory he paid for. Our fourth contribution is a system that allows a VM to use remote memory provided by a different rack server. Thereby, in the case of a peak memory demand, our system allows the VM to allocate memory on a remote physical server.
Contact
Name(s):Susanne Girard
Video Broadcast
Video Broadcast:NoTo Location:
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Created:
Susanne Girard/MPI-SWS, 09/17/2018 10:23 AM
Last modified:
Uwe Brahm/MPII/DE, 09/21/2018 07:01 AM
  • Susanne Girard, 09/17/2018 10:25 AM -- Created document.