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Drowsy-DC: Data Center Power Management System Mathieu Bacou, Gregoire Todeschi, Alain Tchana, Daniel Hagimont, Baptiste Lepers, Willy Zwaenepoel

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Drowsy-DC: Data Center Power Management System Mathieu Bacou, Gregoire Todeschi, Alain Tchana, Daniel Hagimont, Baptiste Lepers, Willy Zwaenepoel Drowsy-DC: Data center power management system Mathieu Bacou, Gregoire Todeschi, Alain Tchana, Daniel Hagimont, Baptiste Lepers, Willy Zwaenepoel To cite this version: Mathieu Bacou, Gregoire Todeschi, Alain Tchana, Daniel Hagimont, Baptiste Lepers, et al.. Drowsy- DC: Data center power management system. 33rd IEEE International Parallel & Distributed Process- ing Symposium (IPDPS), May 2019, Rio de Janeiro, Brazil. pp.825-834, 10.1109/IPDPS.2019.00091. hal-02891652 HAL Id: hal-02891652 https://hal.archives-ouvertes.fr/hal-02891652 Submitted on 7 Jul 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Open Archive Toulouse Archive Ouverte OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible This is a publisher’s version published in: http://oatao.univ-toulouse.fr/26182 Official URL DOI:10.1109/IPDPS.2019.00091 To cite this version: Bacou, Mathieu a es hi r ire and Tchana, Alain-Bouzaïde and Hagimont, Daniel and Lepers, Baptiste and Zwaenepoel, Willy Drowsy-DC: Data center power management system. (2019) In: 33rd IEEE International Parallel & Distributed Processing Symposium (IPDPS), 20 May 2019 - 24 May 2019 (Rio de Janeiro, Brazil) Any correspondence concerning this service should be sent to the repository administrator: [email protected] 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Drowsy-DC: Data center power management system Mathieu Bacou†∗, Grégoire Todeschi†, Baptiste Lepers‡, Willy Zwaenepoel‡ Alain Tchana†, Daniel Hagimont† ‡Operating Systems Laboratory †IRIT, Université de Toulouse, CNRS, Toulouse, France École Polytechnique Fédérale de Lausanne {first.last}@enseeiht.fr Lausanne, Vaud, Switzerland †Atos Integration, Toulouse, France {first.last}@epfl.ch Abstract—In a modern data center (DC), a large majority of efficiency for CPUs, HDDs and NICs, state-of-the-art proposals costs arise from energy consumption. The most popular technique are still a long way from the ideal energy proportional machines. used to mitigate this issue is virtualization and more precisely Therefore, some DC-level power management techniques such virtual machine (VM) consolidation. Although consolidation may as VM consolidation have been proposed. increase server usage by about 5–10%, it is difficult to actually witness server loads greater than 50%. By analyzing the traces VM consolidation seeks to dynamically assign VMs to a from our cloud provider partner, confirmed by previous research minimum number of servers, so that empty servers — which are work, we have identified that some VMs have sporadic moments consequently inactive — can be transitioned to low power states of data computation followed by large periods of idleness. These (suspend to RAM or suspend to disk, respectively ACPI S3 and VMs often hinder the consolidation system which cannot further S4). By increasing the load of active servers, this technique also increase the energy efficiency of the DC. In this paper we propose a novel DC power management system called Drowsy-DC, which improves the energy efficiency of the DC. However, the general is able to identify the aforementioned VMs which have matching utilization boost is around 5–10% which makes it difficult to patterns of idleness. These VMs can thus be colocated on the actually witness server loads greater than 50% for even the same server so that their idle periods are exploited to put the best adapted workloads [1, 2, 3, 4]. This is explained by several server to a low power mode (suspend to RAM) until some data reasons. First, VM consolidation is a bin-packing problem computation is required. While introducing a negligible overhead, our system is able to significantly improve any VM consolidation which is NP-complete. Second, the resource partitioning system; evaluations showed improvements up to 81% and more depends on the resource type. For instance, unlike CPU which when compared to OpenStack Neat. is time-shared, memory is space-shared between the VMs and Index Terms—virtualization, consolidation, long-lived mostly is not preempted. Consequently, memory is often the limiting idle, low-power state, power consumption resource in the consolidation process [5]. Several solutions tried to address this problem by minimizing VMs memory footprints I. INTRODUCTION (e.g. page sharing [6, 7, 8], page compression [9, 10]) and Cloud platforms have proven to be able to provide very allowing memory overcommitment (ballooning [11, 12, 7]). attractive prices. The enabling element of cloud computing However, these techniques are generally not employed in is the virtualization technology. It allows a server to execute, mainstream cloud DCs for several reasons. First, despite the simultaneously and in an isolated manner, multiple operating achieved memory gains, they all have a detrimental impact on systems (OSs) called virtual machines (VMs). Reducing the performance of user applications. Second and specifically datacenters (DC) energy consumption is a major concern for for ballooning, these solutions depend on efficiently estimating cloud providers and an important center of interest for the the memory footprint which is a fairly difficult task considering research community. According to the scale at which the issue the variance of the working-set over time [13, 14]. is addressed, previous works can be organized in two categories: In this paper, we propose a new DC power management (1) those interested in minimizing the power consumption system called Drowsy-DC, based on an innovative idea which of a single server; and (2) those which act at the DC scale, combines concepts from the two previous categories: we state leveraging the virtualized nature of the workload. that using consolidation, we can create situations where a DC In the first category, the holy grail is to achieve energy server may be suspended despite not being empty (i.e. it is proportional servers [1]. Such a server consumes energy in hosting VMs), thus greatly improving its power consumption. exact proportion to its utilization. Thereby, a server with We observed patterns in VMs activity traces. From the point no load would theoretically consume no energy. In this of view of their activity patterns, VMs may be classified in respect, an essential goal is to independently control the power three categories: short-lived mostly-used VMs (noted SLMU, state of each hardware component (e.g. by leveraging the 1 e.g. MapReduce tasks), long-lived mostly-used VMs (noted ACPI ). Subsequently, this modular system is able to adapt the LLMU, e.g. popular Web services), and long-lived mostly-idle energy consumed by each component according to its current VMs (noted LLMI, e.g. seasonal Web services). In this paper solicitation. Although these solutions improve the energy we focus on LLMI VMs, as defined by Zhang et al. [15]. Their 1The Advanced Configuration and Power Interface (ACPI) is an standard activity pattern is composed of isolated operations followed by used by OSs to manage the power of hardware components. long periods of idleness. Notice that LLMI VMs do exist in a 1530-2075/19/$31.00 ©2019 IEEE 825 DOI 10.1109/IPDPS.2019.00091 Authorized licensed use limited to: UNIVERSITE PAUL SABATIER TOULOUSE 3. Downloaded on June 23,2020 at 14:48:27 UTC from IEEE Xplore. Restrictions apply. proportion significant enough in public clouds, that Amazon With these two modules in place, the existing dynamic and Microsoft recently provided special pricing for such cases, consolidation solution can be augmented with Drowsy-DC despite the implementation of Function-as-a-Service [16]. As idleness-based consolidation algorithm. The resulting system for private clouds host a majority of LLMI VMs: for instance, can make VM placement decisions based on classic criteria, at least half of Nutanix’s workload is made of enterprise such as resource requirements, as well as the new criterion of applications which are typically LLMI [17]. Based on this VMs’ idleness patterns. Drowsy-DC’s idleness-based consoli- observation, the basic idea of Drowsy-DC is to enforce as far dation algorithm is described in the next section. as possible2 the colocation of VMs exhibiting similar idleness patterns. In this way, the hosting servers can be suspended III. IDLENESS AWARE VM PLACEMENT during idle periods. Such suspended servers are called drowsy The central concept that rules placement decisions in Drowsy- servers in the paper. To the best of our knowledge, we are DC is idle periods. We want to colocate VMs which are likely the first to investigate this approach in the context of cloud to be idle during the next time interval (e.g. the next hour) so DCs. To implement it, Drowsy-DC is composed of three main that their host can remain suspended during that interval. To components: a consolidation support module, a suspension this end, Drowsy-DC continually builds each VM’s idleness subsystem and a resuming subsystem. In summary we make model (noted IM), which summarizes its past idleness. By so the following contributions: doing, each time a VM is candidate for placement, Drowsy-DC • A consolidation approach based on modeling VM behavior derives from its IM an idleness probability (noted IP), which of idleness, which enables VM colocation based on quantifies the likelihood of this VM being idle in the next matching idleness periods. time interval. We also define a server’s IP as the average of its • An algorithm allowing to efficiently detect the right time VMs’ IPs: as we add a special consolidation step to keep the for suspending a server.
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