Improving Energy Efficiency in Data Centers and federated Cloud Environments

Transcription

1 Improving Energy Efficiency in Data Center and federated Cloud Environment Comparion of CoolEmAll and Eco2Cloud approache and metric Eugen Volk, Axel Tenchert, Michael Gienger High erformance Computing Center tuttgart - HLR tuttgart, Germany { volk, tenchert, gienger }@ hlr.de Ariel Olekiak Application Department oznań upercomputing and Networking Center oznań, oland ariel@man.poznan.pl Laura ió Thermal Energy and Building erformance Group IREC Catalonia Intitute for Energy Reearch Barcelona, pain lio@irec.cat Abtract ignificant data center energy footprint and the increae in energy price have timulated invetigation into poible metric and method to define, quantify and improve the energy efficiency of data center and federated cloud environment, from variou point of view, covering alo deign and operation phae. In thi paper we preent the two complementary energy-efficiency optimization approache covered in cope of EU project CoolEmAll - with focu on building energy efficient data center, and Eco2Cloud - with focu on energy-efficient cloud-application deployment in federated cloud-environment, and decribe metric applied in thee project to ae and optimize energy-efficiency. Both approache make ue of metric to ae energy-efficiency of data center- and cloud reource, and energy-cot of application/workload execution for variou data center granularity level and -ite. Keyword energy efficiency, metric, cloud federation, energy aware data center I. Introduction ignificant data center energy footprint and the increae in energy price have timulated invetigation into method and metric to define, quantify and optimize the energy efficiency of data center and federated cloud environment, from variou point of view, covering alo deign and operation phae. In thi paper we preent the two complementary energy-efficiency optimization approache covered in cope of EU project CoolEmAll [2] - with focu on building energy efficient data center, and Eco2Cloud [1] - with focu on energy-efficient cloud-application deployment in federated cloud-environment, and decribe metric applied in both project to ae and optimize energy-efficiency. In many data center the actual IT equipment ue only half of the total energy while mot of the remaining part i required for cooling and air movement [4], [5], reulting in poor cooling efficiency and energy efficiency, leading to ignificant CO2 emiion. For thi reaon iue related to cooling, heat tranfer, and IT infratructure arrangement are gaining more attention and are carefully tudied during planning and operation of data center. An important apect when conidering the energy efficiency of modular data center i the cooling technique. The ue of approache uch a free air cooling where external air i ued to cool ytem rather than electrical chiller can help to improve efficiency and achieve UE rating cloe to the ideal of 1.0. The cooling and heat tranfer procee are not the only important apect influencing the energy efficiency of data center. Actual power uage and effectivene of energy aving method heavily depend on the type of IT, application and workload propertie. In order to addre thi, the CoolEmAll project invetigate in a holitic approach how cooling, heat tranfer, IT infratructure, application and workload influence overall cooling and energy efficiency of modular data center. The objective of the CoolEmAll project i to enable deigner and operator of a data centre to reduce it energy impact by analyzing and combining the optimization of IT, cooling and workload management trategie. To thi end, the project will provide model of datacenter building block and integrated tool that apply thee model to imulate viualize and analyze energy efficiency of data center on variou granularity level, while taking all above mentioned apect into account. The need for novel deployment trategie become more evident when an application pan multiple cloud. Cloud provider operate under different regulatory framework and cot tructure in relation to environmental policie and energy value-chain. In addition, optimizing the key aet like

2 deployed virtual machine, application and databae i contrained by a et of requirement uch a quality, privacy and cro-platform ervice-level agreement. The Eco2Cloud project invetigate method and trategie that can enure energy-efficient application deployment and execution on the cloud infratructure in federated cloud environment, reducing the energy conumption and emiion by evaluating CO2- and energy-cot for the whole execution on each cloudite in advance, and managing eco-efficient deployment of cloud application baed on monitoring of cloud environment and application. The monitoring of cloud environment involve olution applicable to phyically ditributed ytem a well a compliant with virtualization approache to gather relevant data regarding the energy conumption of VM with varying work load hoted on ingle or ditributed node. Both project - CoolEmAll and Eco2Cloud make ue of energy-efficiency metric to decribe application profile, to ae efficiency of data center- and cloud reource, and to ae energy-cot of application and workload execution for variou data center granularity level and -ite. In thi paper we compare approache and metric ued in both project. The paper i divided into the following ection: ection II decribe approach and metric applied within the CoolEmAll project, in ection III we decribe Eco-aware Cloud Monitoring approach and metric ued within the Eco2Cloud project, ection IV provide comparion and Analyi of approache metric ued in both project, finally ection V ummarize paper and preent concluion. II. CoolEmAll Optimizing Energy Efficiency in Data Center A tated, CoolEmAll project invetigate in a holitic approach how cooling, heat tranfer, IT infratructure, and application-workload influence overall cooling and energy efficiency of data center. In thi ection we decribe CoolEmAll approach and it metric ued to ae energy efficiency in data center. A. The CoolEmAll Approach The main goal of CoolEmAll i to develop advanced imulation, viualization and deciion upport toolkit (VD Toolkit) along with blueprint of computing building block for data center, enabling data center deigner and operator to plan, analyze and run energy and reource-efficient facilitie, minimizing the energy conumption and conequently the CO2 emiion of data center. In order to achieve thi goal CoolEmAll deliver two main outcome: Deign of divere type of data centre efficiency building block (DEBB) reflecting configuration of IT equipment and data centre facilitie on different granularity level (node-, node-group-, rack- and data center/container level). Development of the VD Toolkit enabling the analyi and optimization of IT infratructure built of thee building block by mean of coupled workload- and thermal-airflow (CFD) imulation. Both building block and the toolkit take into account four apect that have a major impact on the actual energy conumption: characteritic of building block under variable load, cooling model, propertie of application and workload, and reource management and workload policie. To enable the optimized deign of data center, built of optimized building block, thee data centre efficiency building block (DEBB) are preciely decribed by a et of metric expreing relation between the energy efficiency and eential factor lited above. In addition to common tatic approache, the CoolEmAll approach alo enable tudie and aement of dynamic tate of data center baed on changing workload, management policie, cooling method, and ambient temperature. Thi facilitate optimization of data centre energy efficiency alo for low and variable load rather than jut for peak load a it i uually done today. The main concept of the project i preented in Figure II-1. Figure II-1: The CoolEmAll Approach In March 2013, CoolEmAll developed the 1t prototype of the imulation, Viualization and Deciion upport toolkit (VD Toolkit), enabling the aement and uer-driven optimization of the energy- and cooling efficiency in data center by mean of coupled workload- and thermal-airflow (CFD) imulation. CFD imulation calculate a heat-map indicating hear-flow ditribution for variou environmental condition and cooling technique within a erver, rack or a room, enabling aement of efficiency of a cooling ytem of the rack or computing room. Workload imulation enable etimation of energy or heat generation information neceary for the CFD imulation a well a etimation of performance and reource utilization. Thereby, the energy or heat generated by an application or workload, imulated in cope of the workload imulator, i calculated by evaluating the application-behavior (that pecifie reource uage at different phae) and power-profile of reource (pecifying poweruage at variou utilization level of particular reource uch a CU, memory, network, etc.). Uage of workload imulation allow alo evaluation of different reource management and workload conolidation trategie. DEBB and VD Toolkit are verified in cope of experiment, uing variou application (HC and Cloud), variable workload, and variou environmental condition imulated with VD Toolkit and executed in real and cloud baed environment uing Reource Efficient Computing & torage (REC)

3 erver [8], allowing fine grained monitoring and control of reource and upporting variou configuration. B. Monintoring and Aement Metric A noted in earlier publication [6], aement and improvement of the energy efficiency in data center ha been uually driven by the minimization of the ower Uage Effectivene (UE) metric and adjutment of data center configuration baed on experience and baic meaurement. However, thi approach face many limit a it doe not allow prediction of energy efficiency and heat tranfer for data center that do not exit yet. Additionally, exiting tool and metric uually concentrate on peak or average load without deep analyi of data center efficiency depending on a level of load, application type and management policie. CoolEmAll addree thee miing iue and facilitate a proce of the detailed analyi of data-center efficiency building block (DEBB). DEBB are deigned to model data-center building block on different granularity level, from a ingle node up to a complete data center. In thi way they can upport uer in modeling and imulating a data center. Within CoolEmAll, the following DEBB level are covered: 1) Unit reflect a ingle computing node, e.g. a ingle blade CU module. 2) Group reflect an aembled unit of building block of level 1, typically a erver coniting of everal node. 3) ComputeBox1 reflect a typical rack within an data center, coniting of the building block of level 2 ( Group), power upply unit and integrated cooling device. 4) ComputeBox2 reflect a container filled with rack or even complete compute room of a data centre, aembled of unit of level 3 (ComputeBox1) and other facility component, uch a cooling device (chiller, heatexchanger, HVAC, CRAC, CRAH, etc.), DU, U, etc. Thereby, a imulation of a DEBB on level n (i.e. the ComputeBox2 level), require DEBB of level n 1 (i.e. ComputeBox1). A tated, the focu of CoolEmAll i to imulate energy and thermal behavior of DEBB in order to ae their efficiency and optimize deign. Accordingly, metric on each DEBB-level are claified a follow [7] : Reource Uage metric: Thee metric characterize the IT reource uage of application and their environment. The energy conumption of an application (ervice) i characterized a a function of reource utilization by a given application ervice, wherea reource refer to CU, Memory, I/O, torage, Network. Their utilization can be meaured on variou level of granularity. Energy metric: It include metric addreed to the energy impact of data centre conidering all it component and ubytem, wherea are ditinguihed: o ower-baed metric: Metric defined under power term. The information o provided i ueful for deigner becaue it drive to peak power meaurement. Energy-baed metric: Metric defined under energy term where the time of the meaurement mut be choen. o Heat-aware metric: The heat-aware metric take into account temperature to characterize the energy behavior of the data centre building block. Green metric: Thee metric decribe the impact of the operation of a data centre in the natural environment. Financial metric: Thee metric decribe the financial impact of the operation of a data centre in a buine organization. The following table (Table II-2 Table II-3 Table II-4) ummarize metric invetigated within the CoolEmAll project capable to ae (tet bed and imulation baed) experiment, and building block (DEBB) on different granularity level. For detailed definition of metric, pleae refer to [7]. Metric marked by are primary metric that are ued and evaluated in cope of the project; by marked metric are currently under conideration. In addition to thee metric, CoolEmAll define alo 13 application level metric called Application erformance Factor (AF), which meaure min/max/average power- and energy-uage, a well a maximum/mean latency or LA violation during the execution of application or within certain time-period. It hould be highlighted, that the CoolEmAll approach i focuing merely on heat-aware metric of data centre in order to optimize the cooling-efficiency directly from the former ite where energy conumption i originated, the node, nodegroup, or rack [6]. In thi context, the CoolEmAll uggeted new metric, compriing the Imbalance of Temperature on node, node-group and rack level, and Rack Cooling Index adapted to node-group level, which are currently under evaluation. CU Uage erver Uage Reource- Network Uage Uage Memory Uage torage Uage I/O Device Uage ower Uage MHz/Watt ower-baed Bandwidth/Watt Capacity/Watt IO/Watt ower v. Utilzation Energy-baed roductivity Heat-aware Cooling Index Max & mean heat diipation II-1: level metric

4 -Group Rack Data Center Reource- previou level metric Uage DH-UR DH-UR CU ower-baed previou level metric Wa Energy-baed -Group roductivity -Group Cooling Index -Group Humidity Index Heat-aware Imbalance temperature of CU Imbalance of heat generation of Table II-2: -Group level metric Reource- Uage previou level metric ower-baed previou level metric U Uage Heat exchanger Uage Energy-baed Rack roductivity Rack Cooling Index Rack Humidity Index Heat-aware Imbalance of temperature of -Group Imbalance of heat generation of -Group Table II-3: Rack level metric Reource- Uage ower-baed Energy-baed Heat-aware Green metric Financial previou level metric previou level metric U Uage DCU UE (ower) UE calability DCiE DCD EER Cooling ytem repone capacity UE (Energy) pue FVER EER Data Centre roductivity Imbalance of temperature of Rack Imbalance of heat generation of Rack Air management indicator rimary Energy Balance GEC Green Energy Uage ERE CUE WUE KI EE DE Carbon emiion balance CAE OE TCO ayback Return ROI Carbon credit Table II-4: Data Center level GAME GI CoolEmAll Organization Out of the focu of CoolEmAll Facility/ite Data Centre Rack Compute -Group Application Application Table II-5: Layer: GAME and CoolEmAll III. Eco-aware Cloud Monitoring ECO 2 Cloud target a dynamical approach in order to reduce the energy conumption and with that, the carbon footprint of deployment including application in federated cloud environment. In contrat to the CoolEmAll project, only cloud computing reource are targeted. In addition, the dynamical deployment including migration i highly recommended in order to optimize the execution of a cloud application. In thi ection we decribe Eco2Cloud approach and preent metric ued within the project. A. The ECO 2 Cloud Approach Application are repreented by one or everal virtual machine which are deployed in a cloud environment. A an infratructure bae etup, three provider acro Europe are dedicating cloud reource to thi project in order to improve the energy aware cheduling. All three provider receive their energy by different energy provider that have an underlying energy mix. Regarding thee fact, the carbon footprint can be mainly optimized by chooing a different infratructure provider which receive it energy by greener energy ource in general or at the current point of time. Reducing the energy conumption in general can be obtained by ditributing the load inide the cloud provider ource or taking another provider uing greener hardware. For meauring the greenne of an application, e.g. deployment of an execution, everal metric need to be identified. Thee metric are located on different layer, (i) phyical infratructure, (ii) virtual infratructure, (iii) ervice infratructure a well a (iv) the whole datacenter. ECO 2 Cloud i targeting all four layer, although the lat one depend on the availability of information. Not all energy provider are able or allowed to produce live data, furthermore the energy mix i ometime not provided which only allow an etimation of the produce CO 2. In order to provide a well-defined bai of a deciionmaking cheduling algorithm, a monitoring olution ha to be in place that focue all the layer a well. Figure 2 give a brief overview about the component of the monitoring infratructure. It hould noted, that many metric ued within CoolEmAll are originated from the Game roject [13]. The relationhip between the Green erformance Indicator (GI) layer identified within the GAME project decribed in [12] and CoolEmAll layer i clarified in Table II-5.

5 Figure 2: ECO 2 Cloud monitoring infratructure A can be een, two Zabbix monitoring aggregator are ued in order to retrieve the whole amount of monitoring information. The infratructure aggregator capture value for the phyical infratructure including the energy metric and the infratructure domain information wherea the Zabbix virtual machine aggregator capture information about the tranient virtual machine and the ervice running on them. Uing a template and metric paing interface, the virtual machine aggregator i able to retrieve the underlying monitoring information and through that, combining thee metric to provide powerful combined one. Uing that mechanim allow the cheduler to gather information about the four different layer at once including combination between all of them. In order to clarify the identified, meaured metric, the mot important one will be preented exemplarily. On infratructure layer, two important metric are defined by the CU utilization and current energy conumption, epecially for multi core ytem. ower conumption of the phyical hot decreae in percent if more core are ued becaue of the IDLE tate of the CU. With that fact, loading a ingle hot intead of two decreae total energy conumption. For the virtual layer, thoe two metric count a well. The energy conumption of a VM can be etimated uing the formula preented by Kataro, et. al. [11]. Thi formula define the energy effectivene for each node ( ). The formula expect the CU utilization in a range between 0 and 100 independently from the number of running procee and hence i identified a the total amount of CU time to be conumed by a erver, not a ingle core. It preent the CU conumption of a given VM j on node i. UE i the general power uage effectivene, identifie the real power conumption of the given node, indicate the computing performance (operation per econd) of the node i. The energy conumption of a VM depend maively on the underlying phyical hardware different kind of CU have different kind of energy conumption. Migrating a VM to another hot my decreae power conumption heavily; the overall performance i not taken in account in that cae. The ervice metric mainly conider the performance of the application. Reducing energy conumption on the one hand, the application till ha to perform. An important metric i the application execution time, which repreent the overall performance of a deployed application. Uing thi metric in combination with the power conumption provide information about the greenne of an application. Furthermore, requirement may be changed in order to deploy the application in an optimized way. In order to meaure the carbon footprint of the deployment and the application, the ite layer need to be regarded a well. Information about the energy mix can only be obtained by the provider directly. Coupling the power conumption with the energy mix provide information about the produced CO 2 for the ued energy. With that metric, the carbon footprint of an application can be calculated. For the execution, an application profile i required which conit of execution parameter baed on the developer idea. For intance, maximum performance can be taken a well a maximal eco-efficiency of an application. Uing thoe value, the cheduler calculate the optimal deployment trategy in order to fulfill the requirement in the bet fahion and finally deploy the virtual reource. During the execution, the cheduler oberve all the monitoring parameter to review the deployment ituation and react if neceary: new virtual machine can be requeted, unued releaed, application performance may drop or the energy conumption could increae. In pecial heavily frequented cloud provider with an amount of tarted and topped reource face a lot of diturbing factor, o changing parameter are very common. If the fact change, the uboptimal deployment need to be modified in order to fulfill the requirement of the application. Migrating virtual machine in-ite, o from one hot to another or ite-wide, changing the provider in general are option that can be regarded. Furthermore, vertical and horizontal elaticity can be regarded a well to improve the overall performance and energy conumption. For poible migration, the pent overhead i alo taken into account. After the execution, a ummary i generated containing all the neceary information like total runtime, reource uage, power conumption and the carbon footprint of the deployment. Analyzing thee fact offer the poibility to overwork the application profile to improve the whole application. Furthermore, the cheduler take into account previou execution including the monitoring information to optimize the deployment the bet way. For that purpoe, old

6 monitoring data need to be proceed to keep the databae mall and an efficient performance of the cheduler. Data mining algorithm are ued to reduce the dataet and provide important information of previou execution. The preented approach how that ECO 2 Cloud i repreented a ervice located above the local cloud middleware in order to provide an energy efficient deployment trategy for cloud application. B. Monitoring Metric The defined ECO 2 Cloud metric are divided into three categorie, the phyical layer, the virtual layer and the application layer. All of the three layer are making ue of energy related data. The energy related data are meaured by uing o called ower Ditribution Unit (DU) in order to combine the energy data with the data of the three mentioned layer and calculate the energy conumption for the infratructure, the virtual machine (VM) and the application hoted in the VM. The following table (Table III-1, Table III-2 and Table III-3) preent the metric ued for calculating relevant data regarding the energy conumption of the infratructure and the deployed VM. The energy conumption of the application layer depend on the certain configuration of each application. Metric on application level are decribed in [9]. Table III-1: Infratructure Metric for the Hot Metric Definition CU utilization average utilization of the proceor inide a hot Availability probability that a requet i correctly erved by a pecific hot within a maximum expected time frame Energy energy conumed by the analyzed hot in a conumption pecific time period IO/Energy average energy conumed by each I/O conumed operation aociated with a hot Table III-2: Infratructure Metric for the ite Metric Definition ite utilization average power utilization torage percentage of ued torage utilization Availability probability that a requet i correctly erved by a ite within a maximum expected time frame Green percentage of energy conumed by the ite Efficiency Coefficient (GEC) UE determine the energy efficiency of a ite ite calculate the energy efficiency of a ite Infratructure Efficiency ite Energy productivity of a ite i meaured a the roductivity ratio between the work output and the conumed energy Carbon Uage Effectivene (CUE) ite aturation Green Energy aturation CUE i CEF * UE degree in which the ite computing reource are ued degree to which the green energy i ued Table III-3: Virtual Machine Metric Metric Monitored parameter CU uage roceor utilization torage Uage torage utilization percentage I/O Uage proce execution time in percentage Memory Uage average ize of the portion of memory ued by proce to total amount of available memory Energy energy conumed conumption IO/Energy conumed IV. average energy conumed by each I/O operation Comparion and Analyi In thi ection we compare and analyze approache a well a metric ued within the both project. The comparion of approache will involve following dimenion: Approach type: imulation/model baed v. real/ituation baed Data Center lifecycle phae [10]: planning, deign, contruction, commiion, turnover & tranition, operation Granularity level: node, node-group (erver), rack, data center, federation of data center Application type: HC, Cloud Level of detail: how many level are covered in cope of the approach (high, medium, low) cope: how broaden i the cope covered within the approach (high, medium, low) A many metric ued within the both project CoolEmAll and Eco2Cloud were initially introduced in GAME project and have the ame meaning, their comparion will be metric by metric. A. Comparion of Approache In thi ection we ummarize comparion of the two approache ued within the CoolEmAll and Eco2Cloud project, preented in Table IV-1. The CoolEmAll approach i characterized by imulation/model and ituation baed approach and addree primary: (i) the planning phae, enabling evaluating variou environmental condition and their impact on energy-

7 efficiency of data-center allowing elect the right geographical location, and (ii) deign phae, enabling evaluation of different data center configuration in advance, on variou granularity level - from a node up to a complete data-center. econdary, the CoolEmAll approach addree alo the (iii) operational phae, allowing evaluating (a) energy-impact of variou configuration induced by adding or removing new/old equipment, (b) analyze and apply different reourcemanagement and workload cheduling trategie to reduce the energy-conumption and optimize efficiency. CoolEmAll upport imulation and execution of both application type: HC and Cloud related application, involving virtual machine. Model ued within the CoolEmAll approach provide very high level of detail and broad cope, compriing all component of particular (DEBB) level, and, allowing analyi of dynamic load. The Eco2Cloud approach i characterized by ituation baed (i.e. evaluation of monitored UE) approach and addree only the operational phae, enabling deployment of particular cloud application on the mot efficient node or cloud ite, according to predefined cheduling objective and preference tated in application-profile. The prediction of the efficiency of application-deployment i baed on productivity calculation, determined by the computing performance and power-conumption of the node / erver, CU-conumption of VM on particular node/erver, and monitored and teadily updated UE value of the cloud ite. The Eco2Cloud approach affect following granularity level: (i) node, (ii) erver level (node-group), (iii) ite or data-center level and (iv) federation of data-center. The focu of Eco2Cloud i virtual environment (VM), hence cloud application and ervice are primary of interet for Eco2Cloud. Comparion kind CoolEmAll Approach type model/imulation baed roject Eco2Cloud real/ituation baed (real/ituation baed to learn and validate model) primary: planning, deign Lifecycle phae econdary: operation operation node, erver, rack, node, erver, datacenter, federation Granularity level data-center application type HC, Cloud Cloud Level of detail very high low/medium cope broad limited Table IV-1: Comparion of approache B. Compariion of Metric A noted, both project CoolEmAll and Eco2Cloud - ue many metric that have been initially introduced in cope of the GAME project. Thi implifie their definition and comparion. The correpondence between the layer ued in cope of GAME and CoolEmAll project were preented in Table II-5. The layer (and many metric) ued in cope of Eco2Cloud project are equal to thoe introduced by GAME project, and were extended by virtualization layer. Hence, correpondence between the layer in CoolEmAll and Eco2Cloud project can be ummarized according to Table IV-2: Table IV-2: Comparion of layer GAME GI Eco2Cloud CoolEmAll Organization Organization Out of the focu of CoolEmAll Facility Cloud ite Data Centre Rack Compute Compute -Group Virtualiation Addreed in cope of Application Application, ervice (cloud) application Application (HC, Cloud) The additional layer (Rack and -Group) introduced in CoolEmAll are ued to model and ae efficiency on particular level. The following table (Table IV-3, Table IV-4, Table IV-5, Table IV-7) ummarize and compare the metric ued within the both project ( marked metric are currently under conideration and will not be compared in thi paper). In next ection we provide analyi of compared metric. Table IV-3: level comparion Level Type Metric Name CoolEmAll Eco2Cloud CU Uage erver Uage Reource- Network Uage Uage Memory Uage torage Uage I/O Device Uage (IO) ower Uage MHz/Watt ower- Bandwidth/Watt Baed Capacity/Watt IO/Watt ower v. Utilzation Energy-baed roductivity Heataware Cooling Index Max & mean heat diipation Availability Availability Table IV-4: -Group level comparion -Group previou level metric Reource- Deployment Hardware Uage Utiliation Ratio (DH-UR) Deployment Hardware Utiliation Ratio for CU (DH- ower- previou level metric Baed pace Watt erformance (Wa) Energybaed -Group roductivity -Group Cooling Index -Group Humidity Index Heataware Imbalance temperature of Imbalance of heat generation of Availability Availability

8 Table IV-5: Rack level comparion Rack Reource- Uage previou level metric ower- previou level metric Baed U Uage Heat exchanger Uage Energy-baedRack roductivity Rack Cooling Index Rack Humidity Index Heataware -Group Imbalance of temperature of Imbalance of heat generation of -Group Table IV-6: Data Center/ite level metric Data Center / ite Reource- Uage ower- Baed Green metric Financial previou level metric previou level metric U Uage Data Centre Utiliation (DCU), equivalent to ite utilization ower Uage Effectivene UE calability Data Centre Infratructure Efficiency (DCiE) Data Centre Denity (DCD) Energy Efficiency Ratio (EER) Cooling ytem repone UE (Energy) partual UE (pue) Fixed to Variable Energy Ratio (FVER) eaonal Energy Efficiency Ratio (EER) Data Centre roductivity Imbalance of temperature of Rack Imbalance of heat generation of Rack Air management indicator rimary Energy Balance Green Energy Coefficient Green Energy Uage Energy Reue Effectivene (ERE) Carbon Uage Effectivene (CUE) [gco2e/kwh] Water Uage Effectivene (WUE) KI EE Datacentre erformance er Energy (DE) Carbon emiion balance CAE OE TCO ayback Return ROI Carbon credit Availabilty Availabilty Table IV-7: Virtualization level comparion Virtualization layer metric Virtual Reource- Uage Energybaed Heataware ower- Baed Energybaed Green metric CU Uage Network Uage Memory Uage torage Uage I/O Device Uage (IO) VM ower Uage VM-UE Energy Conumption of VM VM-E (VM Energy roductivity) VM-GE (VM Green Efficiency) C. Analyi of metric A noted, CoolEmAll and Eco2Cloud make ue of energy-efficiency metric (i) to decribe application and hardware profile, (ii) to ae efficiency of data center- and cloud reource/ite, and (iii) to evaluate predictably energycot and carbon uage of application and workload execution for variou data center granularity level and ite. The analyi of the metric ued in both project will be done according to thee conideration. Application profile (i) in both approache are decribed uing reource-uage metric. Thu, reource-uage metric on node and node-group level in both project are motly identical. However, CoolEmAll ha more detailed application profile (focuing on reource cauing high power-uage), pecifying in addition to CU-Uage alo Memory (and Network Uage). In contrat, Eco2Cloud pecifie uage of torage and IO Device. Virtual reource uage metric on virtualization level are aeed imilarly, however Eco2Cloud meaure here in addition alo memory-uage. To ae energy-efficiency (ii) of data center- and cloud reource, power and energy conumed by reource need to be monitored. On node and node-group level, power and energy-uage (productivity a a relationhip between uefulwork/capacity and energy ued) metric in both project are almot the ame (Wa i exception). On Rack and Data Center/ite level, there are ome difference (only UE are ued equally in both project). A the focu of CoolEmAll i to enable tudy of dynamic load, metric uch a FVER and UE calability are conidered here more detailed. To ae cooling-efficiency, CoolEmAll approach ue heat-aware metric (decribed on all granularity level). Thi apect i not addreed in Eco2Cloud. Green metric are ued to ae ecological impact (iii) of energy-conumption of application-execution (carbon uage of application). Mot of the Green metric (GEC, CUE) are applied within the Eco2Cloud project, a the focu of thi project i to evaluate ecological impact of application executed on variou (cloud) ite. The apect addreed in CoolEmAll affect mainly cooling-efficiency, metered by WUE metric. The Availability or reource i ued only within the Eco2Cloud project. However, thi metric might be alo of interet for the CoolEmAll project, a operating data-center are higher temperature affect availability of reource. V. Concluion In thi paper we compared and analyzed approache and metric ued within the project CoolEmAll with focu on building and aeing energy efficient data center and (ii) Eco2Cloud with focu on ecological-aware application deployment. It turned out, that ome metric ued within the both project are very imilar, a they originate from the Game project. The difference between the other metric i a reult of different project-focue, life-cycle-phae, approache covered in cope of the project, depth, pectrum and application-type.

9 The two approache can be combined in everal way, i.e., applied according their life-cycle phae: (1) ue CoolEmAll approach to deign energy-efficient data-center; (2) ue Eco2Cloud approach to deploy application ecologically aware, electing the mot efficient and ecological data-center or cloud-ite. The overlap between the both project will be dicued at the EuroEcoDC 2013 workhop to facilitate exchange of knowledge and experience between the two project in the domain of Green-IT. Acknowledgment Thi work ha been upported by the CoolEmAll ( and ECO2Cloud ( project and ha been partly funded by the European Commiion IT activity of the 7th Framework rogramme under contract number and Thi paper expree the opinion of the author and not necearily thoe of the European Commiion. The European Commiion i not liable for any ue that may be made of the information contained in thi paper. Reference [1] ECO2Cloud roject Webite: Lat viited: July 5th, [2] CoolEmAll roject Webite: Lat viited: July 5th, [3] Hintemann, R., fah,. (2008) Energy Efficiency in the Data Center - A Guide to the lanning, Modernization and Operation of Data Center. vol 3, BITKOM, [4] (2010) tudy conducted by Bordertep Intitute and Fraunhofer IZM (Materialbetand der Rechenzentren in Deutchland, Eine Betandaufnahme zur Ermittlung von Reourcen- und Energieeinatz, UBA) [5] Koomey, Jonathan Worldwide electricity ued indata center. Environmental Reearch Letter. vol. 3, no eptember 23 [6] M. vor dem Berge, G. Da Cota, A. Olekiak, E. Volk, L. ió: Modeling and Analyi of Data Center Energy-efficiency with CoolEmAll Tool, 2013, to appear. [7] io, L., Forno, R., Napolitano, A., alom, J., Da Cota, G., Volk, E., Donoghue, A.: D5.1 White paper on Energy- and Heat-aware metric for computing module, CoolEmAll deliverable, [8] Hoyer M, vor dem Berge M, Volk E, Gallizo G, Buchholz J, Fornó R, ió L, iatek W (2012) D3.2 Firt definition of the modular compute box with integrated cooling CoolEmAll Deliverable, [9] Cinzia Cappiello, umit Datre, Maria Grazia Fugini, aco Melia, Barbara ernici, ierluigi lebani, Michael Gienger, Axel Tenchert: Monitoring and aeing energy conumption and CO2 emiion in cloud-baed ytem, in proceeding of the 2012 IEEE International Conference on ytem, [10] Jame Bartone, Larry Beer, Kevin Dalton, Jutin Grau, Chritopher Kelley, Bob Wooley: An integrated appraoch to operational efficiency and reliability, the Green Grid, [11] Kataro, G., ubirat, J., Fitó, J. O., Guitart, J., Gilet,., Epling, D. A ervice framework for energy-aware monitoring and VM management in Cloud, Future Generation Computer ytem, Available online 20 December 2012, IN , /j.future [12] A. Kipp, T. Jiang, M. Fugini, and I. alomie, Layered green performance indicator, Future Generation Computer ytem, vol. 28, no. 2, pp , [13] Game project web-ite, acce on 5. July 2013