Achieving Energy Proportionality In Server Clusters

Transcription

1 Achevng Energy Proportonalty In Server Clusters Xnyng Zheng Ph.D Canddate /Electrcal and Computer Engneerng Mchgan Technologcal Unversty Houghton, 49931, US Yu Ca Assocate Professor /School of Technology Mchgan Technologcal Unversty Houghton, 49931, US Abstract Green computng s a hot ssue that has receved a great amount of nterests n the past few years. Energy proportonalty s a prncpal to ensure that energy consumpton s proportonal to the system workload. Energy proportonal desgn can effectvely mprove energy effcency of computng systems. In ths paper, an energy proportonal model s proposed based on queung theory and servce dfferentaton n server clusters, whch can provde controllable and predctable quanttatve control over power consumpton wth theoretcally guaranteed servce performance. Further study for the transton overhead s carred out correspondng strategy s proposed to compensate the performance degradaton caused by transton overhead. The model s evaluated va extensve smulatons and justfed by the real workload data trace. The results show that our model can acheve satsfed servce performance whle stll preservng energy effcency n the system. Keywords: green computng, energy proportonal, performance dfferentaton, transton overhead 1. INTRODUCTION Green computng s to support personal and busness computng needs n a green and sustanable manner, such as mnmzng stran and mpact on resources and envronment. Computng systems, partcularly enterprse data centers and hgh-performance cluster systems consume a sgnfcant amount of energy, thus placng an ncreasng burden on power supply and operatonal cost. For example, the power consumpton of enterprse data centers n the U.S. doubled between 2000 and 2005, and wll lkely trple agan n a few years [1]. In 2005, US data centers consumed 45 bllon kwh, whch was roughly 1.2 percent of the total amount of US electrcty consumpton, resultng n utlty blls of $2.7 bllon [2]. In 2006, the U.S. Congress passed blls to rase the IT ndustry s role n energy and envronmental polcy to the natonal level [3]. Furthermore, t s estmated that servers consume 0.5 percent of the world s total electrcty [4], whch f current demand contnues, s projected to quadruple by Some analysts predcted that IT nfrastructure power usage wll soon cost more than the hardware tself [5]. Many of the exstng works on power management n server clusters rely heavly on heurstcs or feedback control [6][7][8][9]. An mportant prncple n green computng s to ensure energy Internatonal Journal of Computer Networks (IJCN), Volume (1): Issue (2) 21

2 consumpton proportonalty, whch states that the energy consumpton should be proportonal to the system workload [10]. For example, when there s no or lttle workload, the system should consume no or lttle energy; when workload ncreases, energy consumpton should ncrease proportonally, untl the system reaches the full workload. Ths dea can effectvely mprove the energy effcency n real-lfe usage. Energy proportonalty s relatvely hard to be acheved on a standalone server because of hardware constrants. However, t s possble to acheve energy proportonalty on a server cluster, snce we can control the number of actve and nactve nodes n a server cluster. In ths paper, we propose an energy proportonal model n a server cluster and study ts performance n both sngle and multple classes scenaros. We further nvestgate the transton overhead based on ths model. The smulaton results show that the energy proportonal model can provde controllable and predctable quanttatve control over power consumpton wth theoretcally guaranteed servce performance. The rest of the paper s organzed as follows. Secton 2 revews related work. Secton 3 ntroduces the energy proportonal model. Performance metrcs and servers allocaton strategy are ntroduced n secton 4. Secton 5 evaluates the model and dscusses the transton overhead nfluence, a strategy s also proposed to compensate the transton overhead n ths secton, the model s further evaluated based on the real workload data trace, and the last secton concludes the paper. 2. RELATED WORK In lteratures, green computng s often related to terms lke green IT, sustanable computng, energy effcency, energy savng, power aware, power savng, and energy proportonal. In ths secton, we revew relevant technques commonly used on sngle server and server clusters. A. Sngle Server The green computng technques for a sngle server focus on mcroprocessors, memores and dsks. Current mcroprocessors allow power management by dynamc voltage and frequency scalng (DV/FS). DV/FS works because reducng the voltage and frequency provdes substantal savngs n power at the cost of slower program executon. Some researches te the scheduler drectly to DV/FS [11][12][13]. Most works deal exclusvely wth meetng real-tme schedulng deadlnes whle conservng energy. Tradtonally, many power management solutons rely heavly on heurstcs. Recently, feedback control theoretcal approaches for energy effcency have been proposed by a number of researchers. On a sngle server, recent works [14][15] proposed power control schemes based on feedback control theory. Femal et al. [16] developed an algorthm based on lnear programmng. In [8], a control theoretcal power management scheme on standalone servers was proposed. The feedback control theory s better than the tradtonal technques by provdng hgh accuracy and stablty. Thermal management s another ssue n power-aware computng, snce temperature s a byproduct of power dsspaton [17]. Recent research demonstrated that dynamc thermal management (DTM) can respond to thermal condtons by adaptvely adjustng a chp power consumpton profle on the accordng to feedback from temperature sensors [14] [18]. Research work on memory s often combned wth processors and dsks. In [19], the authors used open-loop control to shft power between processor and memory to mantan a server power budget. In [20], they proposed a soluton to store pages and relablty data n dle RAM nstead of usng slow dsk. A large porton of the power budget of servers goes nto the I/O subsystem, the dsk array n partcular. Many dsk systems offer multple power modes and can be swtched to a low power mode when not n use to acheve energy savng. Such technques had been proposed Internatonal Journal of Computer Networks (IJCN), Volume (1): Issue (2) 22

3 n [21][22]. Sudhanva et al. [23] presented a new approach called DRPM to modulate dsk speed dynamcally, and a practcal mplementaton was provded for ths mechansm. B. Server Clusters In recent years, power management has become one of the most mportant concerns on server clusters. Some methods proposed on a sngle server can be extended to server clusters. In [24][25], the authors presented smlar ways of applyng DV/FS and cluster reconfguraton, usng threshold values, based on the utlzaton of the system load to keep the processor frequences as low as possble, wth less actve nodes. In [9], the authors extended the feedback control scheme to clusters. Power has been used as a tool for applcaton-level performance requrements. Sharma et al. [26] proposed feedback control schemes to control applcaton-level qualty of servce requrements. Chen et al. [27] presented a feedback controller to manage the response tme n server clusters. Some researchers appled DTM on an entre data center rather than ndvdual servers or chps. In [28], the authors lad out polces for workload placement to promote unform temperature dstrbuton usng actve thermal zones. Vary-On Vary-off (VOVF) s a dynamc structure confguraton mechansm to ensure energyaware computng n server clusters, whch turns nodes on and off to adjust the number of actve servers by the workload. Other work had been carred out based on VOVF [29][30][28]. In [31], The authors proposed a method to reduce network energy consumpton va sleepng and rate adaptaton by combnng VOVF and DV/FS. Another group developed power savng technques for connecton orented servers [32]. The authors tested server provsonng and load dspatchng on the MSN nstant messagng framework, and evaluated varous load skewng technques to trade off energy savng and qualty of servce. Vrtualzaton s another key strategy to reduce power consumpton n enterprse networks. Wth vrtualzaton, multple vrtual servers can be hosted on less but more powerful physcal servers, usng less electrcty [33]. In [34], researchers developed methods to effcently manage the aggregate platform resources accordng to the guest vrtual machnes (VM) of relatve mportance (Class-of-Servce), usng both the black-box and the VM-specfc approach. Hu et al. [35] used lve mgraton of vrtual machnes to transfer load among the nodes on a multlayer rng-based overlay. In [4], researchers scheduled vrtual machnes n a computer cluster to reduce power consumpton va the technque of Dynamc Voltage Frequency Scalng (DVFS). An economy drven energy and resource management framework was presented for clusters n [36]. Each servce bds for resources as a functon of delvered performance. In [37], researchers formulated the problem as a cooperatve game, and used game theory to fnd the barganng pont. The energy-related budget has accounted for a large porton of total storage system cost of ownershp. Some studes tred multspeed dsks for servers [23][38]. Other technques were ntroduced to regulate data movement. For example, the mostly used data can be transferred to specfc dsks or memory, thus other dsks can be set to a low power mode [39]. 3. ENERGY PROPORTIONAL MODEL A. Energy Proportonal Model on a Sngle Server The energy proportonal model states that energy consumpton P should be proportonal to the workload λ, whle ensurng servce performance. P = a λ + b (1) Internatonal Journal of Computer Networks (IJCN), Volume (1): Issue (2) 23

4 Fg. 1. The energy consumpton curves of non-energy proportonal server and strct energy proportonal server. Fgure.1 conceptually llustrates the energy consumpton curve n non-energy proportonal servers and energy proportonal servers. The typcal server operatng range s between 10% - 60%. We can see that n a non-energy proportonal server, t stll consumes about half of ts full power when dong vrtually no work [10]. Energy proportonal server deally consumes no power when dle (b = 0), nearly no power when very lttle work s performed, and gradually more power as the actvty level ncreases. Energy-proportonal desgns would enable large energy savngs on servers. However, most servers nowadays are CPU, memory and hard dsk ntensve servers. The energy consumpton of CPU s almost lnear to ts utlzaton [32]. But memory and hard dsks are nonlnear energy consumpton components. As a result, energy proportonalty s not easy to be acheved on a standalone sever because of the hardware constrants. B. Energy Proportonal Model on Server Clusters It s more feasble to acheve energy proportonalty n a server cluster. Most computng systems nowadays have at least two modes of operaton: an actve mode when the system s workng and an dle mode when the system s nactve and consumes lttle energy. Some researchers proposed to have fner-graned power modes, runnng at low speed and wth lower power supply voltage. It s known that to quanttatvely control energy consumpton, one feasble way s to adaptvely and dynamcally control the number of servers runnng n actve and nactve modes accordng to system workload. For smplcty, we assume all the servers n the cluster are dentcal nodes. On typcal web servers and web clusters, system workload can be descrbed by the request arrval rate λ. Let M be the total number of servers n the cluster, and Λ be the maxmum arrval rate for the cluster. m s the total number of actve servers. The total energy consumpton of a server cluster s: ac ( ) n (2) P = m P + M m P Pac s the power consumpton of fully actve nodes; n P s the power consumpton of nactve nodes. Based on the energy proportonal model, we have: P P max λ = Λ r (3) where P max = M P ac. r s a parameter, whch adjusts the energy consumpton curve n Fgure 1. The ratonale of usng parameter r s as follows. Ideally the r s set to r =1 where energy consumpton s strctly proportonal to workload. However, we can adjust t to satsfy dfferent Internatonal Journal of Computer Networks (IJCN), Volume (1): Issue (2) 24

5 performance constrants. Wth the help of (2), we can rewrte equaton (3) as: Pac m = ( r M Pn ) / ( Pac Pn ) (4) Λ M Λ / M Here s the maxmum jobs that a sngle cluster node can handle. Ideally P n = 0, whch ndcates that a server consumes no energy when t s runnng on an nactve mode. For smplcty, we suppose P n = 0 n ths paper, ths assumpton wll not affect the performance of our model. We fnally acheve that the total number of actve servers m s determned by the system workload λ : λ m = r Λ M (5) The number of servers may not be an nteger based on (5). We wll set the nteger no less than m, whch s the mnmal number of servers to run n fully actve mode. 4. SERVERS ALLOCATION BASED ON ENERGYPROPORTIONAL MODEL An mportant task of energy aware computng s to acheve energy effcency whle ensurng performance. In ths secton, we wll descrbe how to allocate servers accordng to workload, whle ensurng qualty of servces (QoS) metrcs. A. Performance Metrcs One mportant and commonly used QoS metrc on Internet servers s slowdown, whch s defned as the dvson of watng tme by servce tme. Another commonly used performance metrc s request tme whch s the sum of watng tme and servce tme. We choose slowdown and request tme as performance metrcs n our model because they are related to both watng tme and servce tme. Our theoretcal framework s bult along the lne of the prevous servce dfferentaton models presented n [40][41][42][43]. In our network model, a heavy-taled dstrbuton of packet sze s used to descrbe web traffc. Here we assume that the servce tme s proportonal to the packet sze. The packet nter-arrval tme follows exponental dstrbuted wth a mean of 1/λ, where λ s the arrval rate of ncomng packets. A set of tasks wth sze followng a heavy-taled Bounded Pareto dstrbuton are characterzed by three parameters: α the shape parameter; k, the shortest possble job; p, the upper bound of jobs. The probablty densty functon can be defned as: 1 α α 1 f ( x) k x α 1 ( k p) α = (6) where, α, k > 0, k x p. If we defne a functon: then we have: K αk α ( α, k, p) = α 1 ( k p) (7) Internatonal Journal of Computer Networks (IJCN), Volume (1): Issue (2) 25

6 K ( α, k, p) K ( α 1, k, p) ( ) ( α ) p f α 1; E[ X ] = f ( x) dx = k ln p ln k K, k, p f α = 1. (8) Smlarly, we can derve 2 E[ X ] and E[ X ] ( α k p) ( α 2, k, p) p 2 2 K,, E[ X ] = f ( x) x dx = (9) k K ( α k p) ( α + 1, k, p) p 1 1 K,, E[ X ] = f ( x) x dx = (10) k K Accordng to Pollaczek-Khnchn formula, the average watng tme for the ncomng packets s: 2 λe[ X ] E[ W ] = 2 1 [ ] ( λe X ) We can derve a closed-form expresson of the expected slowdown n an M/G/1 queue on a sngle Internet server. E[ S] = E[ W ] E[ X ] = The expected request tme wth the ncomng job rate s: λe[ X ] E[ X ] ( λe X ) 2 1 [ ] 2 λe[ X ] E[ R] = E[ W ] + E[ X ] = + E[ X ] 2 1 [ ] ( λe X ) B. Servers Allocaton for a Sngle Class In ths secton, we assume all the ncomng requests are classfed nto just one class. We want to ensure the QoS metrcs based on dfferent workload λ. For example, the expected request tme of the ncomng jobs should stay wthn a threshold, E[ R] < β. We assume m (11) (12) (13) s the number of actve server nodes handlng the ncomng requests. When usng a round-robn dspatchng polcy, the packet arrval rate of a node s can be calculated as: λ m 2 λe[ X ] E[ R] = + E[ X ] < β 2 [ ] ( m λe X ). The expected request tme n a server cluster Based on the above energy proportonalty (5), equaton (14) can be re-wrtten as: 2 E[ X ] E[ R] = E[ X ] 2 [ ] + < β ( r M Λ E X ) It s easy to observe that request tme s not dependng on workload, we can just adjust parameter r to satsfy dfferent performance thresholds. C. Servers Allocaton on Servce Dfferentaton Now we study server allocaton schemes for servce dfferentaton. In a cluster system, the ncomng requests are often classfed nto N classes. Each class may have dfferent QoS requrements. We assume m s the number of actve server nodes n class, and λ s the arrval rate n class. The expected slowdown of class n a server cluster can be calculated as: (14) (15) Internatonal Journal of Computer Networks (IJCN), Volume (1): Issue (2) 26

7 λ E X E[ S ] = 2 [ ] 2 1 [ ] E[ X ] ( m λ E X ) Here we choose not to use request tme as a performance metrc for servce dfferentaton because of ts overly complcated mathematcal expresson. However, each class should satsfy the request tme constrant. Obvously the results presented n ths paper wll not be affected by the selecton of performance metrcs. We adopt a relatve servce dfferentaton model where the QoS factor of slowdown between dfferent classes are based on ther predefned dfferentaton parameters. E[ S ] E[ S ] Where 1, j N : We assume class 1 s the hghest class and set < 1 < 2 < L < receve better servce,.e., lower slowdown [39]. j δ δ (16) = (17) j δ δ δ 0 N, then hgher classes Based on the above energy proportonalty and servce dfferentaton model, accordng to formula (5)(17), we can derve the server allocaton scheme n a cluster system as N % M λ λ * [ ] = 1 r E X Λ m = λ E[ X ] + (18) N % Here m s the number of actve servers n class, and % λ = λ δ s the normalzed arrval rate. The frst term of formula (18) ensures that the sub-cluster n class wll not be overloaded. The second term s related to arrval rates, dfferentaton parameters, and r. We can also derve the expected slowdown of class as: 2 1 N δe[ X ] E[ X ] % λ = 1 E[ S ] = (19) N M 2 λ * [ ] = 1 r E X Λ From (19) we can observe that the slowdown of class s proportonal to the pre-specfed parameter δ, and s related to r. The slowdown rato only depends on the pre-defned dfferentaton parameters. The expected request tme for class can be calculated as: 2 N δe[ X ] % λ = 1 E[ R ] = + E[ X ] N M 2 λ * [ ] = 1 r E X Λ β s request tme constrant for class. We can learn from equaton (20), request tme n class s also ndependent of workload, but depends on both the pre-specfed parameter δ and r. The performance s controllable based on our energy proportonal model, wth acceptable performance degradaton; large amounts of energy can be saved. = 1 λ β (20) Internatonal Journal of Computer Networks (IJCN), Volume (1): Issue (2) 27

8 5. PERFORMANCE EVALUATION A. Smulaton Results We buld a smulator whch conssts of a package generator, a server dspatcher, a number of watng queues, and a number of servers. The package generator produces ncomng requests wth exponental nter-arrval tme dstrbuton and bounded Pareto packet sze dstrbuton. The GNU scentfc lbrary s used for stochastc smulaton. Smulaton parameters are set as follows. The shape parameter α of the bounded Pareto dstrbuton s set to 1.5. The lower bound k and upper bound p were set to 0.1 and 100, respectvely [44]. The number of servers n the cluster s 20. And we set the normalzed maxmum jobs one server can handle [32]. Λ M = 1. We set the power consumpton 160W for actve nodes Fg. 2. Comparson of request tme between non-energy proportonal model and energy Proportonal model. r s set dfferently accordng to dfferent requrements of performance n a sngle class scenaro. Fg. 3. Comparson of power consumpton between non-energy proportonal model and energy proportonal model n a sngle class scenaro. r s set dfferently accordng to dfferent requrements of performance. we can acheve consderable energy savng wth energy proportonal model. Internatonal Journal of Computer Networks (IJCN), Volume (1): Issue (2) 28

9 Fg. 4. Comparson of request tme n hgher prorty class between non-energy proportonal model and energy proportonal models. r s set dfferently accordng to dfferent requrements of performance n a multple classes scenaro. Fg. 5. Comparson of request tme n lower prorty class between Non-energy proportonal model and energy proportonal models. r s set dfferently accordng to dfferent requrements of performance n a multple classes scenaro. We frst evaluate the energy proportonal model for the sngle class scenaro. We set the request tme β= 0.9, β= 1.1 and β= 1.3 whch correspond to adjustment parameter r = 1.1, r = 1 and r = 0.9 respectvely. We show the smulaton results n the workload range of 10% - 80%. When the workload s above 80%, the mpact of energy proportonalty constrant s very lmted. Snce the typcal server operatng range s between 10% - 60%, the results presented here are suffcent to test the energy proportonal model. As Fgure 2 ndcates that the request tme s always around the pre-defned performance parameter under dfferent workload. The request tme ncreases as the value of r decreases. The results show that wth adjustable parameter r desrable servce performance can be acheved. Fgure 3 compares the energy consumpton of energy proportonal model and non-energy proporton model for a sngle class scenaro. We can acheve better energy effcency under low workload, whch leads to large amounts of energy savng n a server cluster. Internatonal Journal of Computer Networks (IJCN), Volume (1): Issue (2) 29

10 Next, we compare the performance metrcs n a multple classes scenaro, as shown n Fgure 4, 5, 6. The number of classes s normally two or three [45][46]. In ths paper we choose two classes of ncomng requests. We set the target slowdown rato δ2 : δ 1 = 2 : 1. The energy curve parameters are set dfferently accordng to dfferent request tme constrants. Note, n a multple classes scenaro, parameter r s determned by performance requrements of all classes, whch means t should be set to be the largest value satsfyng the requrements of all the classes. We observe that the model can acheve desrable proportonalty of slowdown dfferentaton wth request tme constrants. Fgure 7 also compares the energy consumptons for proportonal and non-proportonal models n multple classes scenaro. Fg. 6. Comparson of slowdown rato between non-energy proportonal model and energy proportonal models. r s set by dfferent requrements of performance n a multple classes scenaro. Fg. 7. Comparson of power consumpton between non-energy proportonal model and energy proportonal model n multple classes scenaro. r s set by dfferent requrements of performance. We can acheve consderable energy savng compare to the non-energy proportonal model. B. Transton Overhead Analyss The model proposed n ths paper s a contnual allocaton process, where we dynamcally change the number of actve servers. The transton tme when a server transfers from an nactve mode to an actve mode can not be gnored, ths can nfluence the performance durng the transton perod. Thus, t s necessary to estmate the cost of transton overhead. Internatonal Journal of Computer Networks (IJCN), Volume (1): Issue (2) 30

11 Generally speakng, the transton tme for dfferent servers s dfferent whch depends on the processor and other hardware constrants. Therefore, we study the nfluence on performance caused by transton overhead under dfferent tme. Fgure 8 shows how the request tme changes when consderng transton overhead as the workload gradually changed from 0%-80% based on the energy proportonal model. We only concern the stuaton when the workload ncreases, snce as the workload decreases, the number of actve servers wll declne, whch wll not cause performance degradaton. The y-axs s the request tme under dfferent transton overhead. As ndcated n the fgure, larger transton tme has more mpact on performance. The performance wll be affected greatly when large number of servers can not transfer to actve mode on tme. Fg. 8. The effect to performance of transton overhead n energy proportonal model, the transton tme s set to be 15,20,25,30 respectvely Fg. 9. Request tme after addng one spare server based on energy proportonal model n a sngle class scenaro, the transton tme s set to be 15,20,25,30 respectvely. It s mportant to make sure that the QoS s not sacrfced excessvely n favor of power and energy savngs. Spare servers are added to solve the problem of transton overhead. Fgure 9, 10 llustrate the performance after one and two spare servers are added n a sngle class scenaro. By addng one spare server, the performance can be mproved dramatcally compared to the case of no spare server. Addng two spare servers, the response tme can stay under the pre-defned threshold when the workload gradually changes from 0%-80%. However, n some specal stuatons, the workload may vary sgnfcantly wthn two control perods. One or two spare servers are not adequate to compensate the performance degradaton. More spare servers Internatonal Journal of Computer Networks (IJCN), Volume (1): Issue (2) 31

12 are requred. C. Performance Evaluaton Based on Real Workload Data Trace To evaluate the model on realstc traffc patterns, we use an hour s workload trace collected by Lawrence Berkeley Natonal Laboratory [47]. Request tme threshold s set to be β= 0.6 and r = 1. Fgure 11 llustrates the performance based on our model n a sngle class scenaro. The requests arrval rate and job sze are normalzed. We evaluate the performance n the stuatons of non-spare server and spare servers respectvely. As shown n the fgure, when the workload decreases, there s no performance degradaton, however the performance degradaton can be clearly seen as the workload ncreases n the case of no spare server s added. Wth one or two spare servers, the performance can be mproved sgnfcantly. Especally, when two spare servers are always on, request tme s always under predefned threshold. The result also ndcates that as the number of spare server ncreases, the performance does not change dramatcally. The request tme tends to stay n a level, whch demonstrates proper spare servers should be set to compensate the performance degradaton. Fg. 10. Request tme when addng two spare servers based on energy proportonal model n a sngle class scenaro, the transton tme s set to be 15,20,25,30 respectvely. Fg. 11. Request tme when addng two spare servers based on energy proportonal model n a sngle class scenaro. Internatonal Journal of Computer Networks (IJCN), Volume (1): Issue (2) 32

13 Fg. 12. Power consumpton when addng two spare servers based on energy proportonal model n a sngle class scenaro. Fgure 12 evaluates the power consumpton based on our model under real workload data trace. The system arrval rate s the same as shown n fgure 11. The power consumpton s dynamcally changed as the workload changed. Wth lttle more power consumpton, we can acheve better performance, and elmnate the effect of transton overhead. 6. CONCLUSION AND FUTURE WORK Energy management becomes a key ssue n server clusters and data centers. Ths paper ams at provdng effectve strateges to reduce power consumpton and reduce the mpact of performance degradaton. We summarze out work as follows: frst, the energy proportonal model based on queung theory can provde accurate, controllable and predctable quanttatve control over power consumpton; second, we analyze the effect of transton overhead and propose a strategy to mprove the performance effcency. Fnally we evaluate the energy proportonal model va smulaton. Smulaton results show that the energy proportonal model can acheve predctable and controllable proportonal energy consumpton and desrable performance n a server cluster. Future work would nclude studyng the effect on performance when applyng dfferent dspatchng strateges n our model. We are stll tryng to extend the server states to solve the problem of nonnteger number of nodes, whch wll further enhance the energy effcency. Eventually our goal s to apply our model to the real Internet web servers n the future. 7. REFERENCES [1] U.S. Envronmental Protecton Agency. Report to Congress on Server and Data Center Energy Effcency.August [2] J. S. Aronson, Makng t a postve force n envronmental change, IT Professonal, vol. 10, pp , Jan [3] US Congress. House bll To study and promote the use of energy efcent computer servers n the unted states. Retreved: [4] G. von Laszewsk, L. Wang, A. J. Younge, and X. He, Poweraware schedulng of vrtual machnes n dvfs enabled clusters, Cluster Computng and Workshops, CLUSTER 09. IEEE Internatonal Conference on, pp. 1 10, Jan [5] Y. Chen, A. Das, W. Qn, A. Svasubramanam, and Q. Wang, Managng server energy and operatonal costs n hostng centers, Proceedngs of the 2005 ACM SIGMETRICS nternatonal, Jan Internatonal Journal of Computer Networks (IJCN), Volume (1): Issue (2) 33

14 [6] C. Lefurgy, K. Rajaman, F. Rawson, and W. Felter, Energy management for commercal servers, Computer, Jan [7] Y. Lu and G. D. Mchel, Operatng-system drected power reducton, In proc. of nternatonal symposum on Low power electroncs and desgn, Jan [8] C. Lefurgy, X. Wang, and M. Ware, Server-level power control, Autonomc Computng, ICAC 07. Fourth Internatonal Conference on, pp. 4 4, May [9] X. Wang and M. Chen, Cluster-level feedback power control for performance optmzaton, In Proc. of Symposum on Hgh-Performance Computer Archtecture, Jan [10] L. Barroso and U. Holzle, The case for energy-proportonal computng, Computer, vol. 40, pp , Dec [11] G. Quan and X. Hu, Energy effcent fxed-prorty schedulng for real-tme systems on varable voltage processors, Desgn Automaton Conference, Jan [12] M. Elnozahy, M. Kstler, and R. Rajamony, Energy conservaton polces for web servers, Proceedngs of the 4th conference on USENIX Symposum on Internet Technologes and Systems, Jan [13] J. Pouwelse, K. Langendoen, and H. Sps, Energy prorty schedulng for varable voltage processors, Proceedngs of the 2001 nternatonal symposum on Low power, Jan [14] K. Skadron, T. Abdelzaher, and M. Stan, Control-theoretc technques and thermal-rc modelng for accurate and localzed dynamc thermal management, pp , Feb [15] Q. Wu, P. Juang, M. Martonos, L. Peh, and D. Clark, Formal control technques for powerperformance management, IEEE Mcro, Jan [16] M. Femal and V. Freeh, Boostng data center performance through non-unform power allocaton, Autonomc Computng, Jan [17] R. Graybll and R. Melhem, Power aware computng, books.google.com, Jan [18] D. Brooks and M. Martonos, Dynamc thermal management for hgh-performance mcroprocessors, Hgh-Performance Computer Archtecture, Jan [19] W. Felter, K. Rajaman, T. Keller, and C. Rusu, A performanceconservng approach for reducng peak power consumpton n server systems, Proceedngs of the 19th annual nternatonal conference on Supercomputng, Jan [20] T. Newhall, D. Amato, and A. Pshenchkn, Relable adaptable network ram, 2008 IEEE Internatonal Conference on Cluster Computng, Jan [21] A. Wessel, B. Beutel, and F. Bellosa, Cooperatve I/O a novel I/O semantcs for energyaware applcatons, usenx.org. [22] D. Helmbold, D. Long, T. Sconyers, and B. Sherrod, Adaptve dsk spndown for moble computers, Moble Networks and Applcatons, Jan [23] S. Gurumurth, A. Svasubramanam, and M. Kandemr, DRPM: dynamc speed control for power management n server class dsks, Computer Archtecture, Jan [24] M. Vasc, O. Garca, J. Olver, P. Alou, and J. Cobos, A dvs system based on the trade-off between energy savngs and executon tme, Control and Modelng for Power Electroncs, COMPEL th Workshop on, pp. 1 6, Jul [25] E. Pnhero, R. Banchn, E. Carrera, and T. Heath, Dynamc cluster reconfguraton for power and performance, Complers and operatng systems for low power, Jan [26] V. Sharma, A. Thomas, T. Abdelzaher, K. Skadron, and Z. Lu, Poweraware qos management n web servers, 24th IEEE Real-Tme Systems Symposum, Jan [27] C. Dovrols, D. Stlads, and P. Ramanathan, Proportonal dfferentated servces: Delay dfferentaton and packet schedulng, Proceedngs of the conference on Applcatons, Jan [28] R. Sharma, C. Bash, C. Patel, and R. Fredrch, Balance of power: Dynamc thermal management for nternet data centers, IEEE Internet Computng, Jan [29] X. Fan, W. Weber, and L. Barroso, Power provsonng for a warehouse-szed computer, Proceedngs of the 34th annual nternatonal conference on archtecture, Jan B [30] R. Guerra, J. Lete, and G. Fohler, Attanng soft real-tme constrant and energy-effcency n web servers, Proceedngs of the 2008 ACM symposum on Appled computng, Jan [31] S. Nedevsch, L. Popa, G. Iannaccone, and S. Ratnasamy, Reducng network energy consumpton va sleepng and rate-adaptaton, NSDI, Jan [32] G. Chen, W. He, J. Lu, S. Nath, L. Rgas, and L. Xao, Energy-aware server provsonng and load dspatchng for connecton-ntensve nternet servces, Proceedngs of the 5th USENIX Internatonal Journal of Computer Networks (IJCN), Volume (1): Issue (2) 34

15 Symposum on Networked Systems Desgn and Implementaton, Jan [33] S. Murugesan, Harnessng green t: Prncples and practces, IT Professonal, Jan [34] M. Kesavan, A. Ranadve, A. Gavrlovska, and K. Schwan, Actve coordnaton (act) toward effectvely managng vrtualzed multcore clouds, 2008 IEEE Internatonal Conference on Cluster Computng, Jan [35] L. Hu, H. Jn, X. Lao, X. Xong, and H. Lu, Magnet: A novel schedulng polcy for power reducton n cluster wth vrtual machnes, 2008 IEEE Internatonal Conference on Cluster Computng, Jan [36] J. Chase, D. Anderson, P. Thakar, and A. Vahdat, Managng energy and server resources n hostng centers, Proceedngs of the eghteenth ACM symposum on Operatng Operatng System Prncples, Jan [37] I. Ahmad, S. Ranka, and S. Khan, Usng game theory for schedulng tasks on mult-core processors for smultaneous optmzaton of performance and energy, pp. 1 6, Aprl [38] E. Carrera, E. Pnhero, and R. Banchn, Conservng dsk energy n network servers, Proceedngs of the 17th annual nternatonal conference on Supercomputng, Jan [39] M. Song, Energy-aware data prefetchng for mult-speed dsks n vdeo servers, Proceedngs of the 15th nternatonal conference on Supercomputng, Jan [40] X. Zhou, Y. Ca, C. Chow, and M. Augustejn, Two-ter resource allocaton for slowdown dfferentaton on server clusters, Parallel Processng, Jan [41] X. Zhou, Y. Ca, G. Godavar, and C. Chow, An adaptve process allocaton strategy for proportonal responsveness dfferentaton on web servers, Web Servces, Proceedngs. IEEE Internatonal Conference on, pp , Jun [42] C. Dovrols and P. Ramanathan, A case for relatve dfferentated servces and the proportonaldfferentaton model, Network, Jan [43] X. Zhou and C. Xu, Harmonc proportonal bandwdth allocaton and schedulng for servce dfferentaton on streamng servers, Parallel and Dstrbuted Systems, IEEE Transactons on, vol. 15, pp , Sep [44] M. Harchol-Balter and C. U. PITTSBURGH, Task assgnment wth unknown duraton, do.eeecomputersocety.org, Jan [45] H. Zhu, H. Tang, and T. Yang;, Demand-drven servce dfferentaton n cluster-based network servers, INFOCOM Twenteth Annual Jont Conference of the IEEE Computer and Communcatons Socetes. Proceedngs. IEEE, vol. 2, pp vol.2, Mar [46] L. Zhang, A two-bt dfferentated servces archtecture for the nternet, Request for Comments (Informatonal), Jan [47] NASA Kennedy Space Center Server Traces. Internatonal Journal of Computer Networks (IJCN), Volume (1): Issue (2) 35