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SCIENTIA SINICA Informationis, Volume 50, Issue 1: 1-24(2020) https://doi.org/10.1360/N112018-00293

Research progress and development trend of cross-layer energy efficiency optimization in data centers

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  • ReceivedNov 1, 2018
  • AcceptedMar 24, 2019
  • PublishedJan 8, 2020

Abstract

Energy consumption has become a main obstacle hindering the further development of data centers. To solve this problem, many data center energy efficiency optimization studies have emerged in academia and industry in recent years. This review introduces current global research on the data center energy saving problem from the perspective of optimizing cross-layer energy efficiency, including the refrigeration and power supply system energy efficiency optimization based on IT load dispatching as well as unified energy efficiency optimization of IT and refrigeration systems, and then projects trends in these areas.


Funded by

国家重点研发计划(2017YFB1010001)

国家自然科学基金(61520206005,61761136014)


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  • Table 1   Classification of cross-layer energy efficiency optimization in data center
    System
    Classification IT Cooling Power distribution
    Cooling optimization via IT scheduling $\surd$
    Power distribution optimization via IT scheduling $\surd$
    Joint optimization of IT and cooling $\surd$ $\surd$
    Joint optimization of IT and power distribution $\surd$ $\surd$
  • Table 2   Comparison of energy efficiency optimization methods of refrigeration system based on IT load scheduling
    Classification Source Objective Constraint Method Platform Result
    12*Heat balance [18] Minimize max cabinet inlet temperature Servercapacity Temperature aware, heuristic Simulation Saving 25% cooling cost
    [20] Minimize max cabinet inlet temperature Server capacity Temperature aware, genetic algorithm, sequential quadratic programming Small data centers Reduce max cabinet inlet temperature by 2$^{\circ}$C$\sim$5$^{\circ}$C, saving20%$\sim$30% cooling cost
    [19] Minimize max cabinet inlet temperature Server capacity,delay Temperature aware, heuristic Simulation Delay increase 11%, reduce average and max temperature by 14.6 F and 4.9 F
    [25] Minimize max cabinet inlet temperature Server capacity Cooling efficiency aware, reinforcement learning Simulation Reduce max cabinet inlet temperature by 2$^{\circ}$C$\sim$3$^{\circ}$C
    [26] Minimize max cabinet inlet temperature Servercapacity Heat aware, heuristic Simulation Reduce max cabinet inlet temperature and cooling cost by 2.5$^{\circ}$C and 15%
    Reduce cooling [32] Minimize chiller plant cost Server capacity, SLA Air supply temperature Simulation Reduce cooling costby 15%, increase throughput perenergy by 6.89%
    cost [24] Minimize thecost of chillerplant and fans Servercapacity Air supply temperature, fan speed Simulation Reduce cooling cost by 13%$\sim$25%
    Peak heat shaving [45] Minimize the peak heat Server capacity, QoS PCM-TTF Single server simulation Reduce peak heat by 12%
    [46] Minimize the peak heat Server capacity, QoS PCM-VMT Simulation Reduce peak heat by 12.8% even when TTF fails
    Use free cooling [55] Minimize cooling cost Server capacity, SLA Use free cool- ing, rolling timedomain control CloudSim simulation [65] Reduce energy cost by 25.7%
    [61] Minimize internal temperature variance Server capacity Internal temperature variance aware Simulation Reduce cooling cost and internal temperature variance
  • Table 3   Comparison of energy efficiency optimization methods of power supply system based on IT load scheduling
    Classification Source Objective Constraint Method Platform Result
    Power capping [73] Reduce power budget Server capacity, PDU capacity, QoS Percentage cut Enterprise data center Reduce power budget by 20%, negligible QoS loss
    [66] Reduce power budget Server capacity, PDU capacity, QoS Percentage cut Google data center Reduce power consumption 23%
    [75] Data center power management atscale Server capacity, PDU capacity Percentage cut Facebook data center Increase QoS and power resource utilization by 13%$\sim$40% and 8%
    [74] Increase power resource utilization Server capacity, PDU capacity Reset power distribution topology Real data center Reduce power budget and data center construction cost by 47% and 32%
    [76] Rapid power management Sever capacity, PDU capacity Distributed optimization Small cluster Reduce setup delayby 72%$\sim$86%, increasethroughput by 16%
    [77] Power manage- ment of multi- tenant data center PDU capacity Supply function bidding Simulation Win-win between tenant and owner
    UPS supply [79] Reduce grid power supply Battery capacity, lifecycle, availability Centralized UPS supply Simulation Reduce power budget by 15%$\sim$45% without user experience degradation
    [81] Reduce QoS degradation Battery capa- city, server capacity Distributed UPS supply Testbed Can solve performance degradation issue in most sases
    [82] Reduce QoS loss, extent battery life Battery capa- city, servercapacity Distributed UPS supply Simulation Shaving peak power by 19.4%, increase power resource utilization by 24%
    [83] Increase power resource utilization Battery capa- city, server capacity Scheduling of load andUPS Simulation Reduce electricity cost by 20%
    [80] Increase power resource utilization Battery capacity, fairness Power allocation betweenapplications Simulation Increase system utilization and application QoS by 50% and 12%$\sim$28%
    UPS scheduling [85] Reduce the loss of UPS transfer Battery capa- city, server capacity Loss awarescheduling Small cluster Reduce power by 5.2%
    [86] Reduce the loss of UPS transfer Battery capa- city, server capacity Loss aware scheduling,UPS sleeping Simulation Reduce loss of UPS transfer and amount by 20%$\sim$40% and 20%
  • Table 4   Comparison of cross layer energy efficiency optimization methods
    Classification Source Objective Constraint Method Platform Result
    Analytic approach [12] Minimize server and cooling energy Server capacity DVFS, server sleeping, airsupply MATLAB Save 13% more energy than [20]
    [13] Minimize server and cooling energy Server capacity Server sleeping, air supply Air-PAK[102] Save 30% energy with delay less than 1.7%
    [14] Maximize server QoS, Minimize server and cooling energy Server capacity P-states, server sleeping, airsupply Simulation Average 17% performanceincrease, 9% energy reduction
    [15] Minimize server and cooling energy Server capacity, QoS DVFS, server sleeping, fanspeed, air supply Simulation Reduce energy by 4%
    [90] Minimize server, network and cooling energy Server capa- city, network bandwidth Server and network sleeping, air supply Hardware testbet Reduce energyby 44.3%, more8.8%$\sim$14.6% reduction with network
    [91] Minimize server and cooling energy Server capacity DVFS, server sleeping, fanspeed, air supply Simulation Reduce energy by 4%
    [16] Minimize server and cooling energy Server capacity, max temperature Server sleeping, fan speed, airsupply Hardware testbed Reduce energy by 5%$\sim$18%
    Prediction approach [96] Minimize PUE Resource capacity 19 types of data center features Google data center Reduce signifi- cant energy

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