1. Introduction. Keywords: energy efficient, resource allocation algorithm, cloud data centre

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1 Copyright ad Referece Iformatio: This material (preprit, accepted mauscript, or other author-distributable versio) is provided to esure timely dissemiatio of scholarly work. Copyright ad all rights therei are retaied by the author(s) ad/or other copyright holders. All persos copyig this work are expected to adhere to the terms ad costraits ivoked by these copyrights. This work is for persoal use oly ad may ot be redistributed without the explicit permissio of the copyright holder. The defiite versio of this work is published as [ ] Dag M. Qua, Robert Basmadjia, Herma De Meer, Ricardo Let, Toktam Mahmoodi, Domeico Saelli, Federico Mezza ad Coreti Dupot. Eergy efficiet resource allocatio strategy for cloud data cetres. I roc. of the 26th It l Symposium o Computer ad Iformatio Scieces (ISCIS 2011), pages Spriger-Verlag, Sep See for full referece details (BibTeX, XML). Eergy efficiet resource allocatio strategy for cloud data cetres Dag Mih Qua 1, Robert Basmadjia 2, Herma De Meer 2, Ricardo Let 3, Toktam Mahmoodi 3, Domeico Saelli 4, Federico Mezza 4, Corete Dupot 1 1 CreateNet, Italy, {qua.dag,cdupot}@create-et.org 2 Uiversity of assau, Germay, {Robert.Basmadjia,herma.demeer}@ui-passau.de 3 Imperial College, UK, {r.let,t.mahmoodi}@imperial.ac.uk 4 ENI, Italy, {domeico.saelli,federico.mezza}@ei.com Abstract: Cloud computig data cetres are emergig as ew cadidates for replacig traditioal data cetres. Cloud data cetres are growig rapidly i both umber ad capacity to meet the icreasig demads for highly-resposive computig ad massive storage. Makig the data cetre more eergy efficiet is a ecessary task. I this paper, we focus o the orgaisatio s iteral Ifrastructure as a Service (IaaS) data cetre type. A iteral IaaS cloud data cetre has may distiguished features with heterogeeous hardware, sigle applicatio, stable load distributio, lived load migratio ad highly automated admiistratio. This paper will propose a way of savig eergy for IaaS cloud data cetre cosiderig all stated costraits. The basic idea is rearragig the allocatio i a way that savig eergy. The simulatio results show the efficiecy of the method. Keywords: eergy efficiet, resource allocatio algorithm, cloud data cetre 1. Itroductio A IaaS cloud data cetre is used to host computer systems ad associated compoets. Cloud computig data cetres are emergig as ew cadidates for replacig traditioal data cetres that are growig rapidly i both umber ad capacity to meet the icreasig demads for computig resources ad storages. Clearly this expasio results i a sigificat icrease i the eergy cosumptio of those data cetres. Accordig to [1], data cetres i USA cosumed about 61 BkWh ad accouted for 1.5 % of total US electricity cosumptio i The reported work i this paper is shaped based o the research collaboratio withi the Fit4Gree project o eergy efficiecy i data cetres, where the ENI's IaaS data cetre is used for the ivestigatio purposes. A iteral IaaS data cetre like the ENI's data cetre has the followig characteristics: Mixed hardware eviromet with differet techologies, Sigle applicatio with stable load, Live load migratio capability, Highly automated admiistratio. To this ed, we propose a eergy efficiet resource allocatio approach that utilises the capability of VMs live migratio to reallocate resources dyamically. The basic idea is to use a heuristic that is cosolidatig ad rearragig the allocatio i a eergy efficiet maer. This paper is orgaised as follows. Sectio 2 describes the related works. After aalysig the eergy cosumptio model of differet etities i the data cetre i Sectio 3, the eergy efficiet resource allocatio ad performace ivestigatio are discussed i Sectio 4. Fially, Sectio 5 cocludes the paper with a short summary.

2 2. Related work Curretly, resource allocatio mechaisms which are widely used i data cetres iclude load balacig, roud robi ad greedy algorithms. The load balacig module tries to distribute the workload evely over the computig odes of the system. I the roud robi algorithm, the servers are i a circular list. There is a poiter poitig to the last server that received the previous request. The system seds a ew resource allocatio to the ext ode with eough physical resources to hadle the request. Havig the same set up as the roud robi algorithm, the greedy algorithm cotiues to sed ew resource allocatio request to the same ode util it is o loger has eough physical resources, the the system goes to the ext oe. The work i [2] studies geeral eergy savig approaches. Savig eergy i ICT divides ito two mai fields, savig eergy for etwork [3,4] ad savig eergy for computig odes [5,6]. Related to savig eergy i cloud data cetre, the work i [7] adjusts the workig mode of the server accordig to load to save eergy. I [8], the authors focus o savig eergy for aas (latform as a Service) cloud data cetre. Our work is differet from previous i two mai ways. We focus o IaaS sceario ad use movig workload as the mai method. [9] is the most closely work with our work. However, i [9] the authors use the predefied MIS to preset the capacity ad the load which is quite difficult to the user. We use the measured load value istead. 3. Eergy cosumptio model Server power cosumptio rocessor Based o our observatios of a custom bechmark that puts o each core variatios of load ragig from %, we oticed that the power cosumptio of idividual core of a processor icreases liearly with its utilisatio, ad is give by: LCore Core = max *, (1) 100 where LCore is the utilizatio, the workload, of the correspodig core ad max deotes the maximum power of the processor. The, the power cosumptio of multi-core processors is: CU = Core i, (2) where is costat ad deotes the power of the processor i the state. Memory Give a ubuffered SDRAM of type DDR3, the its power cosumptio is: RAM RAM = _ δ * β, where = 1.3, = 7.347, ad RAM_ is the power cosumptio give by: RAM _ i * (3) = s p, (3) such that i deotes the umber of istalled memory modules of size s, whereas

3 f p = f ( f c f ), 1000 α where fc = 1600, f deotes the iput frequecy, ad = Hard Disk We oticed that the startup ad accessig mode power cosumptios are i average respectively 3.7 ad 1.4 times more tha that of the ( ): = a * 1.4 * b * c * 3.7 *, (4) HDD where a, b, c [0,1] deote respectively the probability of accessig, ad startup. Maiboard The power cosumptio of the maiboard is give by the followig equatio: l m Maiboard = CU RAM NIC HDD j = 1 k = 1 where t is a costat havig a value of 55. Server ower Give a server composed of several compoets, the its power cosumptio is: Server = l Maiboard such that j ad k deote respectively the umber of fas ad power supply uits whose power cosumptio models ca be foud i [10]. Network power cosumptio Give that a data cetre cosists of N statios (servers) that coected through S etwork switches, which all have the same umber of ports. Switches are itercoected formig a mesh topology, ad each computig device i ca geerate ad cosume a total traffic of λ i packets per secod. Let us defie T as the total etwork traffic (i packets) geerated or cosumed by all statios. The power cosumptio of all etwork switches, S ca be estimated from the expressio, S 1 j 1 (1 ) (10) S = Φ j α T α λ i Γ j, j = 0 i = j where is the fractio of iter-switch traffic. j represets exteral traffic demad eterig ad leavig the switch. A liear approximatio of ca be utilised basig o the maximum (omial) power cosumptio,, ad the switch badwidth, that ca be obtaied from the specificatio sheets of the device. β α Φ ( λ ) = ρ α λ (11) * Λ Data cetre power cosumptio Let E(i) be the fuctio calculatig eergy cosumptio for ode i, ad w i deotes the umber of edges at ode i. The the overall eergy cosumptio of a data cetre is give by the followig equatio: m j = 1 Fa k = 1 t, SU, (5) E tot wi = E ( c ) ij E ( i), (12) j = 0

4 4. Optimisatio algorithms roblem statemet Assume that we have a set of servers S. Each server s i S is characterised with umber of cores, amout of memory ad amout of storage (s i.r_core, s i.r_ram, s i.r_stor). Each server s i has a set of ruig virtual machie V i icludig k i virtual machies. Each virtual machie v j V i is characterised with required umber of virtual CU, amout of memory, amout of storage (v j.r_vcu, v j.r_ram, v j.r_stor) ad the average CU usage rate computed i %, amout of memory, amout of storage (v j.a_urate, v j. a_ram, v j. a_stor). Because the load i each VM is quite stable, we assume that those values do ot chage through time. Whe there is a ew resource allocatio request, the ew VM must be allocated to a server i a way that total usage of VMs does ot exceed the capacity of the server ad the added eergy usage is miimum. For the global optimisatio request, the VMs must be arraged i a way that total usage of VMs does ot exceed the capacity of the server ad the eergy usage is miimum. F4G-CS (Traditioal sigle algorithm) Whe a ew VM comes, the F4G-CS algorithm will check all computig odes to fid the suitable ode usig the least amout of eergy. Step 0: Determie all servers meet the costrait ad store i array A Step 1: If the array A is empty, stop the algorithm ad iform o solutio Step 2: Set idex i at the begiig of the array A Step 3: Calculate eergy cosumptio E i of the data cetre if the VM is deployed o the server at array idex i Step 4: Store (i, E i ) i a list L Step 5: Icrease i to the ext idex of A Step 6: Repeat from Step 3 to Step 5 util i goes out of the scope of A Step 7: Determie mi E i i the list L Step 8: Assig the VM to the server at idex i mi Step 9: If server at idex i mi is OFF, put actio tur ON to actio list It is oted that E i is calculated for servers havig workload. The server without workload will be shutdow. F4G-CG (Cloud global optimisatio) algorithm The F4G-CG algorithm has two mai phases. I the first phase, we will move the VMs from low load servers to higher load servers if possible i order to free the low load server. The free low load server ca be tured off. As the icreasig eergy by icreasig the workload is much smaller tha the eergy cosumed by the low load servers, we ca save eergy. The algorithm for phase 1 is as followig: Step0: Formig the list LS of ruig servers Step1: Fid the server havig the smallest load rate.the load rate of a server is defied as the maximum (CU load rate, Memory load rate, Storage load rate) Step2: Sort the VMs i the low load server accordig to the load level i descedig order list LW Step3: Remove the low load server out of ruig server list Step4: Use F4G-CS algorithm to fid the suitable server i LS for the first VM i the list LW

5 Step5: If F4G-CS fids out the suitable server, update the load of that server, remove the VM out of LW Step6: Repeat from Step4 to Step5 util LW is empty of F4G-CS caot fid out the suitable server Step7: If F4G-CS caot fid out the suitable server, reset the state of foud servers ad mark Stop=true Step8: Repeat from Step 1 to Step7 util Stop=true I the secod phase, we will move the VMs from the old servers to the moder servers. The free old servers ca be tured off. As oe moder server ca hadle the workload of may old servers, the eergy cosumed by the moder server is smaller tha the total eergy cosumed by those may old servers. The algorithm is: Step0: Sort free servers ito a descedig order list LFS accordig to resource level.the resource level of a server is defied as the miimum(s i.r_core/max_core, s i.r_ram/max_ram, s i.r_stor/max_stor) with max_core, max_ram, max_stor are the maximum umber of Cores, memories, storages of the server i the pool. Step1: Sort ruig servers ito a ascedig order list LRW accordig to load rate.the load rate of a server is defied as the maximum(cu load rate, Memory load rate, Storage load rate) Step2: Set m_cout=0 Step3: Remove the first server s out of LFS Step4: Remove the first server w out of LRW Step5: If we ca move workload from w to s m_cout=1 Step6: Repeat from Step4 to Step5 util we caot move workload from w to s Step7: If m_cout <= 1, reset the state of moved w,s ad mark Stop=true Step8: Repeat from Step2 to Step7 util Stop=true Simulatio result The simulatio is doe to study the savig rate of the resource allocatio mechaism i differet resource cofiguratio scearios. To do the simulatio, we use 4 server classes correlated to sigle core, duel cores, quad cores ad six cores. We geerated 3 resource scearios: moder data cetre, ormal data cetre ad old data cetre. I the moder data cetre, the percetage of may cores server is domiated. I the old data cetre, the percetage of small umber of cores server is domiated. With each resource sceario, we geerated a raw set of jobs. Those jobs radomly come to the system withi the period of 1000 time slots. We select the rutime for each job from 1 to 100 time slots. To perform simulatio for global optimisatio request, with each resource ad workload sceario, we still use liear resource allocatio algorithms to allocate the ew comig job to the resource. However, every 5 time slots, we execute the F4G-CG to perform global optimisatio. The simulatio result is i Table 1. Sceario Roud robi(mwt) Roud robi F4G-CG (MWt) Greedy (MWt) Greedy F4G-CG (MWt) Load balace (MWt) Load balace F4G-CG (MWt) F4G-CS (MWt)

6 F4G-CS F4G-CG (MWt) Table 1: F4G-CS/F4G-CG simulatio result The simulatio result shows the efficiecy of the eergy aware global optimisatio algorithm. It ca sigificatly reduce the eergy cosumptio of a data cetre. 6. Coclusio This paper has preseted a method that potetially reduces the eergy cosumptio of the iteral IaaS data cetre. To save eergy, we rearrage the resources allocatio by the workload cosolidatio ad frequecy adjustmet. I the reallocatio algorithm, we take advatage of the fact that ew geeratio computer compoets have higher performace ad cosume less eergy tha the old geeratio. Thus, we use the heuristic that move the heavy load applicatios to the ew servers with larger umber of cores while movig light load applicatios to the old servers with smaller umber of cores, ad thus switch off the old servers as may umber as possible. The ivestigatios show that our preseted algorithm ca ehace the performace further whe data cetre cosists of larger umber of old server, ad also i the case whe may old servers are workig with heavy load rate ad may moder servers are workig with light load rate. Refereces 1. U.S. Evirometal rotectio Agecy. Report to cogress o server ad data ceter eergy efficiecy. Techical report, ENERGY STAR rogram, Aug A. Berl, E. Gelebe, M. di Girolamo, G. Giuliai, H. de Meer, M.-Q. Dag, ad K. etikousis. Eergy-Efficiet Cloud Computig. The Computer Joural, 53(7), September 2010, doi: /comjl/bxp E. Gelebe ad C. Morfopoulou, A Framework for Eergy Aware Routig i acket Networks. accepted for publicatio i The Computer Joural. 4. E. Gelebe ad T. Mahmoodi, Eergy-Aware Routig i the Cogitive acket Network. I Iteratioal Coferece o Smart Grids, roceedig of Eergy 2011 coferece, T. Heath, B. Diiz, E. V. Carrera, W. M. Jr., ad R. Biachii. Eergy coservatio i heterogeeous server clusters. roc. of the teth ACM SIGLAN symposium o riciples ad practice of parallel programmig - o'05, 2005, pp. pages C. Xia, Y. H. Lu, Dyamic voltage scalig for multitaskig real-time systems with ucertai executio time, roc. of the 16 th ACM Great Lakes symposium o VLSI, T. V. Do,Compariso of Allocatio Schemes for Virtual Machies i Eergy-Aware Server Farms, The Computer Joural, J. Leverich ad C. Kozyrakis, O the Eergy (I)efciecy of Hadoop Clusters, SIGOS, A.Beloglazov ad R. Buyya, Eergy Efficiet Resource Maagemet i Virtualized Cloud Data Cetres, roceedig of the 10th IEEE/ACM Iteratioal Coferece o Cluster, Cloud ad Grid Computig, R. Basmadjia, N. Ali, F. Niedermeier, H. d. Meer ad G. Giuliai, A Methodology to redict the ower Cosumptio for Data Cetres, roceedigs of e-eergy 2011, 2011.