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Energy Efficiency of Server Virtualization Energy Efficiency of Server Virtualization Jukka Kommeri Tapio Niemi Olli Helin Helsinki Institute of Physics, Helsinki Institute of Physics, Helsinki Institute of Physics, Technology program, CERN, Technology program, CERN, Technology program, CERN, CH-1211 Geneva 23, Switzerland CH-1211Geneva 23, Switzerland CH-1211 Geneva 23, Switzerland [email protected] [email protected] [email protected] Abstract—The need for computing power keeps on growing. combine several services into one physical server. In this The rising energy expenses of data centers have made server way, these technologies make it possible to take better consolidation and virtualization important research areas. advantage of hardware resources. Virtualization and its performance have received a lot of attention and several studies can be found on performance. In this study, we focus on energy efficiency of different So far researches have not studied the overall performance virtualization technologies. Our aim is to help the system and energy efficiency of server consolidation. In this paper we administrator to decide how services should be consolidated study the effect of server consolidation on energy efficiency with to minimize energy consumption without violating quality an emphasis on quality of service. We have studied this with of service agreements. several synthetic benchmarks and with realistic server load by performing a large set of measurements. We found out that We studied energy consumption of virtualized servers with energy-efficiency depends on the load of the virtualized service two open source virtualization solutions; KVM and Xen. and the number of virtualized servers. Idle virtual servers do They were tested both under load and idle. Several synthetic not increase the consumption much, but on heavy load it makes tests were used to measure the overhead of virtualization on more sense to share hardware resources among fewer virtual different server components. Also a couple of realistic test machines. cases were used; an Invenio database service and CMSSW, Keywords-virtualization; energy-efficiency; server consolida- The CMS Software Framework, physics analysis software. tion; xen; kvm; invenio; cmssw The results were compared with the results of the same tests on hardware without virtualization. We also studied how I. INTRODUCTION overhead of virtualization develops by sharing resources of The need for computing power in data centers is heavily physical machines equally among different number of virtual growing due to cloud computing. Large data centers host machines and running the same test set on each virtual cloud applications on thousands of servers [?], [?]. Tradi- machine set. tionally, servers have been purchased to host one service each (e.g., a web-server, a DNS server). According to many II. RELATED WORK studies the average utilization rate of a server is around 15% Virtualization and its performance is a well-studied area of maximum but depends much on the service and it can be but these studies mainly focus on performance, isolation, even as low as 5% [?], [?]. and scheduling. Even though energy efficiency is one of This level of utilization is very low compared to any field the main reasons for server consolidation and virtualization, in industry. A common explanation for the low utilization is it has not received much attention. Many of these studies that data centers are build to manage peak loads. However, evaluate overhead differences between different virtualiza- this is not a new data center specific issue, since high peak tion solutions. loads are common in many other fields. Even with this low Regola et al. studied the use of virtualization in high level of utilization the computers are usually operational performance computing (HPC) [?]. They believed that virtu- and consuming around 60% of their peak power [?]. Low alization and the ability to run heterogeneous environments utilization level is inefficient through the increased impact on on the same hardware would make HPC more accessible infrastructure, maintenance and hardware costs. For exam- to bigger scientific community. They concluded that the I/O ple, low utilization reduces the efficiency of power supplies performance of full virtualization or para-virtualization is [?] causing over 10% losses in power distribution. Thus, not yet good enough for low latency and high throughput computers should run in near full power when they do value applications such as MPI applications. adding work, because then they operate most efficiently Nussbaum et al. [?] made another study on the suitabil- when comparing consumed energy per executed task [?]. ity of virtualization on HPC. They evaluated both KVM A solution for increasing utilization is server consolidation and Xen in a cluster of 32 servers with HPC Challenge by using virtualization technologies. This enables us to benchmarks. These studies did not find a clear winner. They concluded the performance of full virtulization is B. Used Virtualization Technologies far behind that of paravirtulization. Also running workload The operating system used in all machines, virtual or among different number of virtual machines did not seem real, was a standard installation of 64-bit Ubuntu Server to have an effect. Verma et al. [?] also studied the effect 10.04.3 LTS. The same virtual machine image was used of having the same workload on different number of virtual with both KVM and Xen guests. The image was stored in machines. They found out that virtualization and division a raw format, i.e., a plain binary image of the disc image. load between several virtual machines does not impose We used the Linux 3.0.0 kernel. It was chosen as it had significant overhead. the full Xen hypervisor support. With this kernel we were Padala et al. [?] carried out a performance study of virtu- able to compare Xen with KVM without a possible effect alization. They studied the effect of server load and virtual of different kernels on performance. machine count on multi tier application performance. They For CMSSW tests, a virtual machine with Scientific Linux found OS virtualization to perform much better than par- 5 was installed with CMSSW version 4.2.4. For these tests avirtualization. The overhead of paravirtualization explained real data files produced by the CMS experiment was used. by L2 cache misses, which in the case of paravirtualization These data files were stored on a Dell PowerEdge T710 increased more rapidly when load increased. server and shared to the virtual machines with a network Another study from Deshane et al. [?] compare scalability file system, NFSv4. and isolation of a paravirtualized Xen and a full virtualized KVM server. Results said that Xen performs significantly C. Test Applications better than KVM both in isolation and CPU performance. Our synthetic test collection consisted of Linpack [?], Surprisingly, a non-paravirtual system outperforms Xen in BurnInSSE 1, Bonnie++ [?] and Iperf [?]. Processor per- I/O performance test. formance was measured with an optimized 64-bit Linpack As we can see from previous work, the energy-efficiency test. This benchmark was run in sets of thirty consecu- has not received much attention. Our study focuses mainly tive runs and power usage was measured for whole sets. on the energy-efficiency of virtualization. In addition, processor power consumption measurements were conducted with ten minute burn-in runs with 64-bit III. TESTS AND TEST ENVIRONMENT BurnInSSE collection using one, two and four threads. Disk input and output performance was measured using Bonnie++ Our tests aimed at measuring the energy consumption 1.96. The number of files for a small file creation test was and overhead of virtualization with a diverse test set. We 400. For a large file test the file size was 4 GB. For Bonnie++ used both synthetic and real applications in our tests and tests, the amount of host operating system memory was measured how performance is affected by virtualization. We limited to 2.5 GB with a kernel parameter and the amount compared the results of the measurements, that were done of guest operating system memory was limited to 2 GB. on virtual machines, with the results of the same tests on For hardware tests, a kernel limit of 2 GB was used. The physical hardware. First we measured the idle consumption tests were carried out ten times. Network performance was of virtualized machines using different number of virtual measured using Iperf 2.0.5. Three kinds of tests were run: machines. Then we compared different virtualization tech- one where the test computer acted as a server, another where nologies and operating systems. it was the client and a third where the computer did a loopback test with itself. Testing was done using four threads A. Test Hardware and a ten minute timespan. All three types of tests were carried out five times. The tests were conducted in our dedicated test envi- As real world applications, we used two different systems. ronment. The test computer was a Dell PowerEdge R410 The first one was based on the Invenio document repository server with two Intel Xeon E5520 processors and 16 GB of [?]. We used an existing Invenio installation, which had memory. Hyper-threading was disabled for the processors been modified for the CERN library database. The Invenio and clock speed was the default 2.26 GHz for all cores. document repository software suite was v0.99.2. The doc- The Turbo Boost option of Intel was enabled. The system ument repository was run on an Apache 2.2.3 web server had a single 250 GB hard disk drive. As a client computer and a MySQL 5.0.77 database management system. All this we used a server with dual Xeon processors running at software were run on Scientific Linux CERN 5.6 inside a 2.80 GHz. Network was routed through D-Link DGS-1224T chroot environment. Another server was used to send HTTP gigabit routers.
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