To the best of our knowledge, however, commercial IaaS providers do not employ ... to one physical server, and never change VM locations while the VMs are ...
Eliminating Datacenter Idle Power with Dynamic and Intelligent VM Relocation Takahiro Hirofuchi and Hidemoto Nakada and Hirotaka Ogawa and Satoshi Itoh and Satoshi Sekiguchi
Abstract We are developing an advanced IaaS (Infrastructure-as-a-Service) datacenter management system that dynamically minimizes running physical servers depending on resource utilization. The management system periodically monitors the loading of a datacenter, and dynamically repacks virtual machines (VMs) into optimal physical servers. Live migration of VMs and the standby mode of physical servers are automatically orchestrated by a genetic algorithm (GA) engine. A preliminary experiment showed that our first prototype system correctly worked for a proof-of-concept datacenter.
1 Introduction IaaS is an emerging cloud service that provides virtualized hardware resources for customers over the Internet. A service provider runs thousands of physical machines in a 24/7 manner; the key to success in datacenter business is to reduce running cost as much as possible, thereby achieving more competitive pricing than other providers. Live migration of VMs is considered promising to reduce idle power of IaaS datacenters. It is possible to dynamically reduce running physical servers in response to the loading of a datacenter; in off-peak hours like early mornings, most VMs are operating at lower utilization levels, which are relocated to fewer physical servers to power off unused servers and facilities. To the best of our knowledge, however, commercial IaaS providers do not employ live migration for dynamic repacking of VMs. They assign a fixed number of VMs to one physical server, and never change VM locations while the VMs are running.
Takahiro Hirofuchi et al. National Institute of Advanced Industrial Science and Technology (AIST), Central 2, Umezono 1-1-1, Tsukuba, Japan 305-8568, e-mail: t.hirofuchi at aist.go.jp
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Takahiro Hirofuchi et al.
Fig. 1 Prototype Datacenter Room (left) and Power Dataloggers (right)
Although dynamic VM packing has been discussed in research papers, there still lacks practice and experience bridging the gap between academia and industry. In our project, we present an IaaS datacenter management system that dynamically reduces/increases running physical servers depending on resource utilization. Live migration of VMs is exploited to optimize VM locations for reducing the number of running physical servers, and ACPI S3 (the standby mode) of physical hardware is utilized to eliminating idle power of unused servers. Our GA engine [2] quickly determines near-optimal VM locations, solving a multi-dimensional bin packing problem with a heuristic method; VMs, consuming different amounts of CPU, memory, and I/O resources, are packed into the smallest number of physical servers. A prototype datacenter room is built to evaluate how our management system reduces power consumption. Through this project, we aim to clarify the feasible system design and its implementation meeting reliability and scalability in production-level datacenter environments. This paper is the work-in-progress report of our ongoing project. The overall design of our system is introduced and then a preliminary experiment is reported.
2 System Overview and Preliminary Experiment We have developed a prototype datacenter room in which power consumption of physical servers is recorded with power dataloggers (Figure 1). All physical servers are capable of the ACPI S3 suspend/resume; suspended nodes are resumed by means of an out-of-band hardware management mechanism, Intel AMT (Active Management Technology), which is more reliable and powerful than Wake-On-LAN. Power consumption in the suspend mode is approximately only 8W (i.e., 10% of active server power). The overview of our system is illustrated in Figure 2. The management node monitors VM’s resource usage and periodically relocates VMs to optimal locations. In every 5 seconds, our GA engine calculates optimal VM locations that meet both criteria of VM performance and server power as much as possible. Then, the
Eliminating Datacenter Idle Power with Dynamic and Intelligent VM Relocation
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Fig. 2 System Overview
Fig. 3 Server Power (left) and VM Locations (right). The power consumption of the datacenter (shown in the upper left corner) was high, because heavily-loaded VMs were distributed to all physical servers.
management node conducts relocations; which wakes up suspended server nodes if needed, starts live migration of VMs, and sends unused server nodes to sleep. In our preliminary experiments, we launched 64 VMs on 7 physical servers. A video-on-demand streaming server was installed into each VM. When many client users started watching videos over the Internet, VMs were heavily loaded and then the management node distributed VMs to all physical servers. Figure 3 shows our power monitors and VM location viewer at this moment. All physical servers were active, consuming 80-100W of power, respectively. The total power consumption of this datacenter was approximately 500W. As shown in Figure 4, when all client users left this streaming service, all VMs were relocated into 3 physical servers. 4 physical servers in the suspend mode were consuming 8W. The total power consumption went down to approximately 250W, only half the value of the maximum loading. During this experiment, all video streamings were smoothly played without any
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Takahiro Hirofuchi et al.
Fig. 4 Server Power (left) and VM Locations (right). The power consumption of the datacenter (shown in the upper left corner) decreased, because idle VMs were aggregated into 3 physical servers. The rest of physical servers were suspended.
visible frame drops. VMs were continuously transmitting video frames while they were being migrated.
3 Conclusions In this paper, we presented an IaaS datacenter management system that eliminates idle server power by means of VM migration and server standby. Through a preliminary experiment, we confirmed our proof-of-concept datacenter correctly worked; datacenter power was saved when the loading of VMs was low. We are now developing a more lightweight live migration mechanism [1], which will be integrated into our management system to achieve higher power efficiency of datacenters. This work was partially supported by KAKENHI (20700038) and JST/CREST ULP.
References 1. Hirofuchi, T., Nakada, H., Itoh, S., Sekiguchi, S.: Enabling instantaneous relocation of virtual machines with a lightweight vmm extension. In: Proceedings of the 10th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid2010). IEEE Computer Society (2010) 2. Nakada, H., Hirofuchi, T., Ogawa, H., Itoh, S.: Toward virtual machine packing optimization based on genetic algorithm. In: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living, Lecture Notes in Computer Science, vol. 5518, pp. 651–654. Springer (2009)
