Reducing Energy Consumption for Reconfiguration in Cloud Data Centers

Transcription

1 Reducng Energy Consumpton for Reconfguraton n Cloud Data Centers Invted Paper Omar Chakroun, Soumaya Cherkaou INTERLAB Research Laboratory, Unversté de Sherbrooke, Canada {omar.chakroun, soumaya.cherkaou}@usherbrooke.ca Abstract Moble Cloud Computng (MCC) leverages moble devces and nfrastructure equpment to ncrease servces accessblty. It uses ncreased devces computng capablty to enhance servces usablty and ensure hgh avalablty. Ths growth n performances results n an ncreased nterest for platforms use to accommodate a multtude of applcatons. To support such an ncrease n demand, new desgns for resource management have to be mplemented n order to reach usage optmalty. In ths work, we propose to desgn new algorthms to optmse MCC resources management technques based on stochastc networks optmzaton. Our approach s focused on energy consumpton optmzaton on the cloud data center sde whle ensurng resources elastcty to adapt to users demands and nsure a hghly avalable platform. We elected an overclockng technque to enhance servers capabltes and Lyapunov optmsaton to ensure desgn stablty and to mnmse the energy cost. We perform extensve smulatons under dfferent charge condtons n order to prove the desgn effectveness n ensurng the servce wth lower power consumpton. Smulatons results confrm the effectveness of the proposed resources management desgn. Index Terms MCC, resource management, energy, elastcty and hgh avalablty, Lyapunov optmzaton. I. INTRODUCTION Moble cloud computng (MCC) denotes the convergence between two well nvestgated concepts: a) cloud computng, whch provdes computatonal and storage capacty for users applcatons and b) moble computng whch allows portable devces to access dstant resources wth ad hoc or nfrastructurebased wreless communcaton technologes. The motvaton behnd MCC s the need to accommodate resource-ntensve applcatons wthn moble devces wth comparatvely very lmted capacty. The ultmate goal s to enable the executon of rch moble applcatons on a set of mobles devces by leveragng dstant platforms for almost unrestrcted capacty and functonalty. To ensure hgh servce avalablty, cloud provders deploy huge and costly nfrastructures comprsng a large number of data centers. Once these nfrastructures are deployed, servers energy consumpton consttutes a sgnfcant proporton of operatng costs [1]. Reducng the energy cost can translate nto a huge gan for cloud provders. Statstc studes conducted by cloud provders conclude that coolng and processng energy consttutes up to 50% of the total energy used n a data center [2]. Energy effcency can be acheved wth more effcent hardware and ntegrated thermal management [3, 4, 5]. However, desgns for resource management and network reconfguraton polces can also acheve sgnfcant gans n energy savngs. Many researchers tred to reduce energy cost of such platforms by proposng new desgns for resource management based on optmsaton processes. These optmzaton processes range from smple optmzaton-under-constrants [6] to complex algorthms leveragng game theory and stochastc analyss [7]. Cloud resource management has to be both hghly effcent and adaptve. Adaptablty here means the capablty to manage resources effcently, when facng rapd demand changes n term of resource requests, whle supportng heterogeneous applcatons. In ths work, we use a stochastc approach for resource management n order to optmze energy consumpton n cloud data centers whle mantanng servce avalablty. We defne resource management as the process of allocatng computng, storage, and networkng capabltes n order to meet both user demand and moble cloud provder obectves. Resource management n MCC uses vrtualzaton technques to facltate resource multplexng. Vrtualzaton combned wth moblty, allows Vrtual Machne (VM) mgraton and /or consoldaton. VM Mgraton can be used to move resources closer to the end user so as to reduce response tme. VM consoldaton s usually promoted to save on energy consumpton. Movng VMs strewn over multple Physcal Machnes (PM) toward a smaller set of PM can reduce energy usage. Our approach s bult on the followng fndngs: (1) VM mgraton consumes an mportant amount of physcal server resources especally on the source PM. The resources needed for VM mgraton account for 10%-20% of the CPU and memory resources of PMs [8, 9]. Based on that observaton, PMs are only run at 90% of ther capacty n the most optmstc scenaro. The other 10% of the resources are reserved for possble VM mgratons. (2) A PM whch s started and s not handlng any requests, consumes an approxmate energy of 45% of ts maxmum energy consumpton at full charge. The above value s called the nomnal power consumpton. (3) VM consoldaton consttutes a good approach to reduce overall data center power consumpton, whch gves cloud provders the ablty to turn off unused PMs /16/$ IEEE

2 Based on the prevously mentoned remarks, we elected an overclockng technque to accommodate VM mgraton requests whle makng an effcent use of servers resources and reducng the overall cloud data center power consumpton. We show that overclockng allows usng PMs to a better capacty. We also show that at the small expense of energy cost due to overclockng, we can acheve much more sgnfcant savngs on overall servers energy consumpton. The remander of ths paper s organzed as follows; Secton II presents desgn prncples for our overclockng approach. Secton III ntroduces the system model to ensure resource allocaton optmzaton. Secton IV presents the smulaton envronment and an overvew of the results. Fnally, Secton V concludes the paper. II. OVERCLOCKING TECHNIQUE Overclockng s the fact of confgurng a computer to operate at a faster rate (clock frequency) than the one that was certfed for by the manufacturer. The man purpose from overclockng s to gan extra performance from a gven component by ncreasng ts operatng speed. Overclockng s usually appled on maor components such as man processors and graphc controllers. Most components are desgned wth a safety margn to deal wth operatng condtons outsde the manufacturers control, and overclockng s the acton of settng the devce to run n the hgher end of that margn wth the understandng that temperature and voltage must be controlled as the safety margn s reduced. Whle most modern devces are farly tolerant of overclockng, all devces have fnte lmts - generally for any gven voltage most parts wll have a maxmum "stable" speed where they stll operate correctly. A. Overclockng extent and desgn consderatons In our desgn, we make use of overclockng n servers whle stayng n the safety operatng range n order not to decrease the components lfetme, nvolve the need for extra nvestment n coolng systems, or ncrease voltage requests. Thus, we use overclockng up to a certan extent, not to exceed 15% of the processng speed, and wthout any modfcaton to the processor Vcore voltage or the coolng system. We use overclockng only f a VM mgraton s needed and on the source PM sde only. Upon completng the VM mgraton, the processor speed s retuned down to the maxmum value specfed by the manufacturer and overclockng s turned off. Authors n [10] present some results of overclocked processors performances wthout the need of extra nvestments on coolng or modfcatons on the hardware. They conclude that a gan n processor speed up to 26% s achevable wthout any changes. Of course, runnng a processor over ts maxmum speed can make the system consume more power, especally for heat dsspaton. However, studes n [11] showed that the extra power consumed when overclockng s actvated s relatvely low and ranges from 2W - 6W per processor and per 200 MHz overclockng step up to 600 MHz. Over 600 MHz overclockng related power consumpton can ncrease rapdly to reach up to 40W per 200 MHz step. Thus, we are consderng a maxmum of 15% overclockng n order to lmt the power consumpton overhead and we are actvatng the overclockng for short perods of tme correspondng to VM lve mgraton duratons n order not to degrade the servers performances and relablty. B. Reducng the mpact of reconfguraton There exsts a multtude of reconfguraton technques, for better resources management, such as VM reszng and lve mgraton. Modern hypervsors nvolve reduced overhead whch allows resources enttlements to be changed on a runnng VM. Researchers n [12] studed the mpact of VM mgraton on the cloud performances and partcularly on the source server sde. VM mgraton s usually useful for clustered applcatons and t s for hgh nterest to data centers ether to consoldate VM to reduce the number of actve servers to save power or to ensure hgher resources for resources ntensve tasks. The need of extra resources on the source server s related to the need of more actve memory usually due to cache contenton between colocated VMs. In ths perspectve, temporarly actvatng the overclockng when a VM mgraton s needed can be benefcal n reducng the mpact on coexstent VM that share the same physcal memory, and can speed up the mgraton process. We wll take such an approach on the source Data Center (DC) server sde only and we wll measure ts mpact on power consumpton whch we denote by the reconfguraton cost. Theoretcally, the gan n processor speed translates n a gan on the number of tasks processed and can mpact the memory speed. Algorthm 1 depcts the steps for computng resources allocaton and the actvaton of overclockng when VM mgraton s requested. C. Power consumpton characterzaton We focus on servers n our energy model. For servers, we adopt the model from [4] that characterzes the ndvdual server power consumpton functon of the processng speed as an affne functon as n equaton Eq.(1) where P dle, P peak and U denotes respectvely the power consumpton n dle state, power consumpton when the server s fully utlzed and the utlzaton level rangng from 0 to 1. Equaton Eq.(2) presents an extenson of the equaton Eq.(1) when the overclockng s supported. P over denotes the maxmum power consumed when overclockng s at ts maxmum tolerable value and U over denotes the rate of overclockng functon of the maxmum processor speed. P P = P + ( P P ) U (1) Cons dle peak dle * Cons _ over + ( P over = P + ( P P ) * U (2) P dle peak ) * U peak over Fgure 1 llustrates the power consumpton wth and wthout overclockng actvaton and the gan n power consumpton for a 2.8GHz processor. We are takng nto consderaton 6W extrapower consumpton for every 200MHz overclockng step up to 600 MHz and a 45W over consumpton above that threshold for every step [8]. In Fgure 1, standard approach refers to the case where no overlockng s actvated and servers are workng at a maxmum of 90% of ther capacty. Overclockng approach refers to the overclockng approach where servers are workng at a maxmum rate of 100% and usage of overclockng s lmted to the case where a VM mgraton s needed. dle

3 (c) In the DC, the set of runnng VMs are strewn over multple PMs. In our approach, we am to consoldate the maxmum number of VMs on the mnmum number of PM to reduce the global DC power consumpton. Ths wll help us reduce the number of actve PMs whch at the end reduces the overall power consumpton (see algorthm 1). Algorthm 1 VM consoldaton FIGURE I OVERCLOCKING VS STANDARD APPROACH POWER CONSUMPTION D. Power consumpton optmzaton Our desgn of a power-effcent data center s based on two deas; (1) consoldatng the maxmum number of VM nto the mnmum number of PM to ensure the lowest power consumpton and (2) make use of 100% of servers resources wthout pre-reservng resources for possble VM mgratons. The resources assocated wth VM mgraton n our desgn wll be ensured by actvatng the overclockng technque on the source server sde when needed. Thus, we descrbe our approach n three scenaros: (a) new request arrval, (b) VM mgraton request and (c) VM consoldaton to reduce the number of actve PM. (a) Assume that a new resource request s receved on the admsson controller sde of our cloud data center. The applcaton profler wll dagnose how much computng resources are needed to accommodate that request and dependng on the servers state, wll route the request to the optmal server. In case there are not enough resources on the set of actve servers handlng that applcaton, the resources handler wll actvate a new server and nstantate the VM on t before routng the request to the newly actvated server. Thus power consumpton on the new actvated server wll be P dle +(P peak - P dle )*U where U desgnates the utlzaton level needed to accommodate the demand. Otherwse, f one of the actve servers s able to handle the request, ts power consumpton wll be ncreased by a factor correspondng to the extra utlzaton needed U+uextra. It s worth notng that n the tradtonal approach, the maxmum utlzaton does not exceed 90%. In our approach, we use the server resources at a full extent (100%). (b) In case of a VM mgraton request, the tradtonal approach does not ntroduce any extra usage or resources reservaton snce VM mgraton resources account for 10% of the PM resources and these resources are always pre-reserved. On the other hand, n our approach we are makng use of 100% of PM resources for request handlng and an overclockng actvaton s needed before proceedng to the VM mgraton. Overclockng deactvaton s needed after VM mgraton completon. III. δs : workload of server δ max : maxmum workload on a server TSU: table contanng all actve servers workloads ordered ncreasngly except servers at δ max,: ndexes Begn: Whle (<=szeof(tsu)) For =+1 to szeof (TSU) f (δs + δs <= δ max ) actvate overclockng on S allocate resources on S update δs = δs + δs mgrate VM(S ) to S deactvate overclockng on S shutdown S remove S from TSU and Shft table elements to the left Szeof(TSU) Else ++ End f End for End whle PROBLEM FORMULATION AND THEORETICAL ANALYSIS A. Problem formulaton We are consderng a data center wth S servers that hosts a set of N applcatons denoted by A. each server hosts a subset of applcatons. It uses one VM per applcatons n order to ensure applcatons severablty and an applcaton can have multple nstances runnng across the data center. We defne ndcator functon e as equal 1 f applcaton s hosted on server, 0 otherwse. We assume a tme slotted system where at every tmeslot new requests arrve for applcaton wth a rate λ accordng to a random process whch s ndependent from the amount of unfnshed work and we suppose that we have no knowledge of the statstcs of these arrvals. Let W (t) denote the router buffer contanng all admtted requests after the admsson controller. R (t) the requests for applcaton that are routed to server n slot t and R (t) the newly admtted requests. Thus the router dynamc can be characterzed by (3). W ( t + 1) = W R + R Let S (t) denote the set of actve servers capable of handlng the applcaton at slot t thus the routng decson must satsfy the followng constrants (4) and (5) at every slot. (3)

4 0 R = 0 f e = 0 (4) e S ( t ) R W Let us denote the set of control acton avalable at the server level at the nstant t under any control polcy by I (t) and let P (t) the correspondng power consumpton. Then the queung dynamcs of the requests of the applcaton on server follows (6) where μ (I (t)) denotes the servce rate for the applcaton at server under the control decson I (t). U [ U ( I ),0] R ( ) (5) ( t + 1) = max μ t (6) + We assume that the expected value of the servce rate can be known snce, as dscussed n secton II, we can derve the experenced power consumpton based on the processor frequency assgnment. Thus at every slot t, the followng decsons have to be made: (1) Routng decson for the admtted requests R (t). (2) Resource allocaton decson I (t) ncludng resources dstrbuton among VMs and actvatng of the overclockng. Let us denote the average expected rate of admtted requests for n the applcaton under control polcy by r and the average n expected power under the same condtons by p whch expressons are as follows. r 1 = lm t t t 1 τ = 0 E { R ( )} { P ( τ )} τ (7) t 1 1 p = lm E (8) t t τ = 0 Thus consderng a collecton of non-negatve weghtsα, β, our obectve s to desgn a control polcy that solves the followng stochastc optmzaton problem. Maxmze: α r β p A S (9) S.T: 0 r λ Thus the obectve problem s a general weghted lnear combnaton of the sum throughput of the applcatons and the sum of the average power consumpton n the data center. B. Control Algorthm based on Lyapunov optmzaton We use Lyapunov optmzaton n order to acheve stablty of the system. Let V>=0 be a control parameter that has to be chosen by the system admnstrator to ensure the desred tradeoff between the performance and the power consumpton. Let W (t) and U (t) be the router and the server queue backlog at tmeslot t. As the backlog values evolve over tme as descrbed n (3) and (6), the algorthm adapts to the system changes and solves the problem n (9) leveragng a sequence of optmzaton problems over tme on three dstnct steps. Request routng: let denote the server that s havng the smallest queue backlog and belongng to the set of servers that are able to process the requests for applcaton. thus, a routng polcy can redrect all requests for applcaton to such a server under the condton that W (t)>u (t). Resources allocaton: at each server, choose the resource allocaton I (t) that solves the Lyapunov optmzaton process where p max denotes the maxmum power consumpton per server. Maxmze: U E{ μ ( I ) } Vp A ST: p p max (10) IV. RESULT OVERVIEW We chose to mplement our approach under Matlab snce t offers good computatonal capablty and offers a multtude of optmzaton frameworks. We mplemented three man approaches: (1) the nomnal approach whch uses servers capabltes up to 100%, (2) the standard approach whch uses servers up to 90% of ther capactes and leaves 10% of t for VM mgraton purposes and (3) our overclockng approach whch uses servers up to 100% for processng and overclocks servers CPU n case of VM mgraton up to 15%. A. Smulaton envronment We smulated a data center consttuted of 100 servers usng homogeneous processors that have a mnmum speed of 1.4GHz and a maxmum speed of 2.8GHz whle power consumpton ranges from 45 watts to 95 watts when server s actvated. The number of request arrvng to the DC s randomzed and ranges between 0 and 100 requests per server per tmeslot. Each request s charactersed by the needed processng power expressed as a rato from the server maxmum processng capacty. Servers processng capabltes range from 5 to 10 requests per server per tmeslot. The smulaton duraton s 4000 TS. We fxed the control parameter V to a value of 1 to ensure a far trade-off between power consumpton and performance n term of processed requests. Table I below summarzes the man smulaton parameters TABLE I GLOBAL SIMULATION PARAMETERS Parameter Value Number of servers 100 Number of actve servers at the 10 start Number of request arrvng to the per TS DC Servers processng frequency GHz Processng capabltes 5-10 requests per server per TS Servers power range watts Overclockng maxmum rate 15% (up to 3.2GHz) Smulaton duraton 4000 TS B. Results overvew and analyss We are manly nterested n assessng the gan n DC power

5 consumpton ensured by our desgned overclockng approach whle subectng the DC to dfferent requests admsson rates. We also make use of a VM consoldaton approach n order to reduce the number of actve PM and consequently ensure the same processng rate whle consumng less power. Fgure II shows the number of admtted requests accepted by the admsson controller dependng on the servers processng charge over the whole smulaton duraton. The number of admtted requests per server per TS range from 7 to 27 wth a mean value of 20 requests per server per TS. Fgure III shows the mean servers charge over tme. It shows that based on our model, we make use of servers capabltes to the maxmum extent tryng to use the power more effcently. The mean charge per server s around 92% of the maxmum capactes and most of the tme servers are workng at rate hgher than 90%. Ths s reflected by the left hand sde of the formula n Eq. (10), we are tryng to maxmze the porton U (t)e{μ (I (t))} whch corresponds to the server processng charge where U (t) corresponds to the number of request routed to server and μ (I (t)) s the server servce rate under the control decson I (t). Also the resulted charge shows the effectveness of proposed VM consoldaton approach to reduce the number of actve servers and consequently the total DC power consumpton. accommodate the same requests. The latter approach ensures a gan n the number of actvated servers compared to the nomnal approach of 23% and 13.5% compared to the standard approach. FIGURE III MEAN SERVERS CHARGE RATE OVER TIME FIGURE II ADMITTED REQUEST TO THE DATA CENTER OVER TIME The second step of the smulaton s to assess the performances of our approach compared to a nomnal approach where servers are used to a full extent but wthout VM mgraton support and compared to the standard approach whch uses up to 90% of the servers capabltes and leaves 10% for VM mgraton purposes. Our approach, on the other hand, uses the servers capabltes to the maxmum extent and actvates overclockng to a maxmum of 15% when a VM mgraton s needed. Fgure IV shows the performances evaluaton between the three approaches n term of actve servers to accommodate the admtted requests. We notce that the nomnal approach needs to actvate approxmatvely 43 servers per tmeslot to accommodate the admtted requests shown n Fgure II. The standard approach, uses approxmatvely 37 servers per tmeslot to accommodate the same requests and fnally the overclockng technque uses only 33 servers per tmeslot to FIGURE IV NUMBER OF ACTIVE SERVERS FOR OVERCLOCKING, NOMINAL AND STANDARD APPROACHES (SERVERS PER TS) Snce the combnaton of VM consoldaton and overclockng ensures a gan n term of actve servers compared to both other approaches,that gan should translate n gan n power consumpton. Fgure V shows the mean DC power consumpton over tme. It shows a comparson for power consumpton for the smulated DC between the standard approach and the overclockng approach. The overclockng approach consumes a mean 3638 Watts per tmeslot whle the standard approach uses approxmatvely 4042 Watts per tmeslot. Thus, the overclockng technque ensures a gan n power consumpton of 10% n every tmeslot whch shows the effectveness of the proposed scheme. Another mportant remark s, snce we are usng Lyapunov optmzaton, the system behavor and performances are stablzed, whch s reflected n Fgures IV and V. In Fgure V, the standard devaton from the mean number of servers usng the nomnal approach s 2.25%. Where, t s respectvely 2.27% and 2.35% for the overclockng and the standard approach. In Fgure V, the standard devaton for the power consumpton on

6 the DC for the standard approach s Watts per tmeslot whle t s Watts per tmeslot for the overclockng approach whch corresponds to 2.28% of the mean power consumpton for both approaches. resources, therefore, the combned complexty s lnear on the number of total hosts. V. CONCLUSION AND FUTURE WORK In ths paper, we present an analytcal and smulaton based study on the mpact of an overclockng technque on reducng data centers power consumpton. We presented a new scheme for VM consoldaton to reduce the number of actve servers on the DC. The results of the study present the mpact of the combnaton of the VM consoldaton and overclockng technque on the DC performance n term of actve servers and by consequence on the total power consumpton. Smulatons show that, even at hgh servers charge and hgh requests volume, our desgn allows stablzng system behavour whle ensurng a gan up to 23% n the number of actve servers and of 10% n total energy consumpton. REFERENCES FIGURE V DATA CENTER ENERGY CONSUMPTION FOR OVERCLOCKING AND STANDARD APPROACHES (WATTS PER TS) Fgure VI shows a comparson between the gan n term of energy and servers usage ensured by our overclockng approach (A) compared to the ADMM approach n [2] denoted by (B) and IMAPP proposed by Chen et al n [4] denoted by (C). On one hand, we notce that the overclockng approach presents relatvely low gan n term of energy compared to the other two approaches (10% compared to 35% and 20% for the ADMM and IMAPP respectvely), but on the other hand, t ensures a hgh level of servers utlzaton up to 90% compared to 80% and not a sgnfcant gan for ADMM and IMAPP respectvely. FIGURE VI APPROACHES COMPARISON IN TERM OF POWER GAIN AND SERVERS USAGE From the computatonal complexty pont of vew, our proposed approach ensure a complexty of O(M*N) f we are consderng M servers deployed on the data center and N request per TS admtted to the system for processng. Snce on every TS, the table contanng all servers states s sorted n term of computng usage, ths extra computaton wll cost up to O(M) for the worst case. Whch n total results n a worst case complexty of O(M*N)+O(M). Snce the number of VMs that can be hosted on a sngle host s lmted by the physcal [1] P. X. Gao, A. R. Curts, B. Wong, and S. Keshav, It s not easy beng green, n Proc. ACM SIGCOMM 2012 Conf. Appl., Technol., Archt, Protocols Comput. Commun., 2012, pp [2] H. Xu, C. Feng, and B. L, Temperature aware workload management n geo-dstrbuted datacenters, SIGMETRICS Perform. Eval. Rev. 41, 1 (June 2013), DOI= [3] C. Bash and G.Forman, Cool ob allocaton: measurng the power savngs of placng obs at coolng-effcent locatons n the data center, In 2007 USENIX Annual Techncal Conference on Proceedngs of the USENIX Annual Techncal Conference (ATC'07), Jeff Chase and Srnvasan Seshan (Eds.). USENIX Assocaton, Berkeley, CA, USA,, Artcle 29, 6 pages. [4] Y. Chen, D. Gmach, C. Hyser,W. Zhku, C. Bash, C. Hoover,S. Snghal, Integrated management of applcaton performance, power and coolng n data centers, n Network Operatons and Management Symposum (NOMS), 2010 IEEE, vol., no., pp , Aprl 2010 do: /NOMS [5] X. Fan, W.D. Weber, L. A. Barroso, Power provsonng for a warehouse-szed computer, SIGARCH Comput. Archt. News 35, 2 (June 2007), DOI= [6] S. Boyd, N. Parkh, E. Chu, B. Peleato, and J. Ecksten, Dstrbuted optmzaton and statstcal learnng va the alternat- ng drecton method of multplers, Found. Trends Mach. Learn., vol. 3, no. 1, pp , [7] X. Xu and H.Yu, A Game Theory Approach to Far and Effcent Resource Allocaton n Cloud Computng, Mathematcal Problems n Engneerng, vol. 2014, Artcle ID , 14 pages, do: /2014/ [8] I. Takouna, R. Roas-Cessa, K. Sachs,C. Menel, Communcaton-Aware and Energy-Effcent Schedulng for Parallel Applcatons n Vrtualzed Data Centers, n Utlty and Cloud Computng (UCC), 2013 IEEE/ACM 6th Internatonal Conference on, vol., no., pp , 9-12 Dec do: /UCC [9] Susmt Bagch, Emergng Research n Cloud Dstrbuted Computng Systems, [10] [11] [12] A. Verma, G. Kumar,R. Koller, A. Sen, CosMg: Modelng the Impact of Reconfguraton n a Cloud, n Modelng, Analyss & Smulaton of Computer and Telecommuncaton Systems (MASCOTS), 2011 IEEE 19th Internatonal Symposum on, vol., no., pp.3-11, July 2011, do: /MASCOTS