Self-Adaptive and Resource-Efficient SLA Enactment for Cloud Computing Infrastructures

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1 2012 IEEE Fifth Intenational Confeence on Cloud Computing Self-Adaptive and Resouce-Efficient SLA Enactment fo Cloud Computing Infastuctues Michael Maue, Ivona Bandic Distibuted Systems Goup Vienna Univesity of Technology Vienna, Austia {maue, Rizos Sakellaiou School of Compute Science Univesity of Mancheste Mancheste, U.K. Abstact Cloud povides aim at guaanteeing Sevice Level Ageements (SLAs) in a esouce-efficient way. This, amongst othes, means that esouces of vitual (VMs) and physical machines (PMs) have to be autonomically allocated esponding to extenal influences as wokload o envionmental changes. Theeby, wokload volatility (WV) is one of the cucial factos that influence the quality of suggested allocations. In this pape we devise a novel appoach fo self-adaptive and esouceefficient decision-making consideing the thee conflicting goals of minimizing the numbe of SLA violations, maximizing esouce utilization, and minimizing the numbe of necessay time- and enegy-consuming econfiguation actions. We popose self-adaptive ule-based knowledge management fo autonomic VM econfiguation consideing the apidness of changes in the wokload, i.e., WV. We intoduce a novel WV categoization and pesent cost and volatility based methods fo self-tuning. We evaluate these methods by a lage vaiety of synthetically geneated wokloads, and by eal-wold measuements gatheed fom an image endeing application and a scientific wokflow fo RNA sequencing. Evaluation shows that in most cases the self-adaptive appoach outpefoms the static appoach. Index Tems Cloud Computing, Autonomic Computing, Self-Adaptation, Sevice Level Ageement, Rule-based System, Knowledge Management, Resouce Management. I. INTRODUCTION The vision of Cloud computing is to povide computing powe as a utility like electicity, gas, o wate to a boad vaiety of customes [6]. To make Cloud computing a eliable means of computing, customes agee on so-called Sevice Level Ageements (SLAs) fo cetain non-functional QoS goals, the sevice pice, and the penalty in case the povide violates the ageement. Cloud povides can offe thei infastuctue as a sevice (IaaS), whee the custome s application uns inside a vitual machine (VM). Govening such an infastuctue should happen autonomically to foste high scalability and to be able to eact pomptly and without human inteaction to unfoeseen extenal influences as wokload changes. Moeove, as enegy costs of data centes ae aleady vey high [14], IaaS Cloud povides have a high incentive to minimize thei enegy consumption. As Cloud infastuctues ae designed to host a lage numbe of VMs, even slightly downsizing each of them without causing SLA violations might esult into a big esouce gain. This lowe amount of povided esouces can then be mitigated to educe enegy consumption, e.g., by poweing off PMs that become unused. Related wok has obseved the initial placement of VMs quite well, and some woks also deal with the impact migations have [26], [18]. Howeve, VM sizes ae nomally assumed to be static (on Amazon, e.g., thee exist only thee types of VM sizes, which ae not designed to change duing untime [1]) and changing thei configuation has only been obseved by few, as in [17]. The authos pesent a ule-based knowledge management (KM) appoach that tigges actions to avoid unde- and ove-utilization of evey esouce based on theat thesholds (TTs). Howeve, as with many appoaches pesented in elated wok, also this one depends on impotant paametes, i.e., TTs, that heavily influence pefomance, but neglects the configuation theeof. To achieve eal autonomic govenance of a Cloud infastuctue, it is cucial fo any poposed appoach to self-adapt its paametes to changing conditions in application usage, SLAs, and simila factos. In this pape ou pime focus is to investigate methods to autonomically set and adapt the TTs of the ule-based appoach. We analyze seveal diffeent methodologies. Wheeas some methods set the TTs based on past pefomance, othes ely on knowledge gatheed fom monitoing the wokload itself. To achieve the latte, we intoduce the notion of wokload volatility (WV) and detemine a way to calculate it and dynamically classify wokload into WV classes. Futhemoe, we investigate synthetically geneating wide-spead wokloads fo Cloud applications. We use these and eal-wold wokloads fom an image endeing softwae, as well as a bioinfomatics wokflow fo RNA sequencing [8], to evaluate ou appoach. With this wok we ultimately pesent a pototype fo an autonomic SLA enactment and esouce management tool fo Cloud computing infastuctues on the level of VMs, whose advantages ae manifold. Pactically no a-pioi leaning is necessay and adaptation happens on the fly duing execution. The appoach automatically takes the diffeent chaacteistics of vaious esouce types into account. Finally, it is geneal in the sense that it does not only handle specific types of wokload. It does not equie specific domain knowledge no is it specialized on only some domains like medical sevices o image endeing softwae. With its self-adaptability it is independent of any impotant paametes to be tuned. The emainde of this pape is oganized as follows: Section 2 descibes elated wok. Section 3 gives backgound infoma /12 $ IEEE DOI /CLOUD

2 tion about the autonomic loop and the ule-based appoach. Theeafte, Section 4 details the autonomic paamete adaptation methods, and Section 5 evaluates them using vaious wokloads. Finally, Section 6 concludes the pape. II. RELATED WORK We have detemined two diffeent aspects to compae ou wok with: SLA and esouce allocation management in Clouds, also elated to KM; and self-adaptive algoithms in lage-scale distibuted systems. Fistly, thee has been some consideable wok on optimizing esouce usage while keeping QoS goals. These papes, howeve, concentate on specific subsystems of Lage Scale Distibuted Systems, as [13] on the pefomance of memoy systems. Futhemoe, Petucci [20] and Bichle [4] investigate only one geneal esouce constaint. A quite simila appoach to ou concept is povided by the Sandpipe famewok [25], which offes black-box and gay-box esouce management fo VMs. Contay to ou appoach, though, it plans eactions just afte violations have occued. The VCONF model [22] also pusues simila goals as pesented in Section I, but depends on specific paametes, can only execute one action pe iteation and neglects the enegy consumption of executed actions. As to KM, Bahati et al. [3] also use ules to achieve autonomic management. They povide a system achitectue including a KB and a leaning component, and divide all possible states of the system into so called egions, which they assign a cetain benefit fo being in this egion. This is quite simila to the ulebased appoach we base ou wok upon. Howeve, thei actions ae not stuctued, but ae mixed togethe into a single ule, which makes the ules vey had to manage and to detemine a salience concept behind them. Additionally, the egions ae statically set and it is not investigated how to adapt them. Hoye et al. [10] also undetake a speculative appoach as in ou wok by ovebooking PM esouces. They assign VMs to PMs that would exceed thei maximum esouce capacities, because VMs hadly eve use all thei assigned esouces. Computing this allocation they also take into consideation wokload coelation of diffeent VMs. Bogetto et al. [5] tackle the tade-off between consolidating VMs on PMs and tuning off PMs on the one hand, and attaining SLAs fo CPU and memoy on the othe. Howeve, the authos assume a static setting and do not conside dynamically changing wokloads. Summaizing, thee has been a geat deal of wok on the diffeent escalation levels, wheeas VM configuation has not been obseved yet neithe its self-adaptation. Secondly, Duteilh et al. [7] investigate hoizontal scaling, i.e., adding and emoving VMs unning an application seve by using a load balance, using a theshold-based and a einfocement leaning technique. Howeve, the authos do not conside adapting the thesholds themselves via leaning. Moeove, the authos detemine poblems with static thesholds as well as with detemining good tuning fo the einfocement algoithms. The authos also state the impotance of undestanding the wokload vaiation, but do not pesent a method how to deal with it. Kalyvianaki et al. [12] use Kalman filtes fo CPU esouce povisioning fo vitualized seves. They self-adapt thei appoach by using vaiances and covaiances. Padala et al. [19] develop self-tuning contolles fo multi-tie applications using contol theoy. Song et al. [23] use self-adaptation in the field of Cloud fedeations. Thei algoithm selects tasks and allocates them by finding a tadeoff between SLA adheence and esouce utilization. This tade-off is epesented by a paamete, which is optimized using a simila pinciple as the bisection method. Fo the optimization the benefit of a specific theshold is estimated by simulation. This estimation is executed seveal times until an adequate value is found. [21] apply genetic algoithms fo decision making and self-econfiguation, but on the netwok topology of emote data mios. III. BACKGROUND In this section we descibe the autonomic loop togethe with the ule-based knowledge management appoach. The pesented management tool is an essential building block of the FoSII poject [2], whose goal is to autonomically goven Cloud computing infastuctues. The management of the FoSII infastuctue elies on the autonomic contol loop, which consists of the following phases: fist, it monitos (M) the managed infastuctue with the help of sensos; second, it analyses the monitoed data (A); thid, it plans actions to execute (P); fouth, it executes them (E). The full loop is known as MAPE. The MAPE loop enhanced with a knowledge base (MAPE-K) [11] is the design paadigm fo ou appoach. The monitoing infomation is gatheed by the hadly intusive and highly scalable Lom2His famewok [9] and the execution of the actions is simulated by a KMtechnique agnostic simulation engine [16]. In ode to educe the complexity of the NP-had esouce management poblem in a Cloud, we hieachically stuctue the poblem into seveal so-called escalation levels [17]. This wok is about the two lowest escalation levels, i.e., doing nothing and VM configuation. It is impotant to detemine when to do nothing, since evey eallocation action consumes time and enegy. Thus, eallocation actions should only be ecommended when necessay. Reallocation actions eset VM paametes like povided CPU powe, stoage, memoy, o incoming/outgoing bandwidth. The ule-based appoach achieves this and woks as follows: The utilization of a esouce is divided into thee egions with the help of two theat thesholds (TTs), TTlow and TT high. Region +1 (ut <TTlow ) signifies a egion, whee the esouce is ovepovisioned. In egion 1 (ut >TThigh ) the esouce is in dange of unde-povisioning, o is aleady unde-povisioned. In egion 0 (TTlow ut TThigh ) the esouce is well povisioned. The cental idea behind this design is that the ideal value called taget value tv() fo the utilization of a cetain esouce is exactly in the cente of egion 0. If the utilization value afte some measuement leaves this egion by using moe (Region 1) o less esouces (Region +1), we set the utilization back to the taget value, i.e., we incease o decease allocated esouces so that the utilization 369

3 is again at tv() = TT low + TT high %. 2 As long as the utilization value stays in egion 0, no action will be ecommended. A moe detailed desciption of the ulebased appoach can be found in [17]. All in all, we take a speculative appoach: We ty to allocate less than ageed as uppe bound, but moe than utilized without unning into an SLA violation. Setting and adapting the mentioned TTs is the main focus of the emaining pape. IV. AUTONOMIC PARAMETER ADAPTATION In this section we will explain how the autonomic adaptation and configuation of the autonomic manage woks. Since the autonomic manage as pesented in Section III is configued by theat thesholds, we will pesent thei autonomic adaptation in this section. We will descibe two basically diffeent appoaches: The fist appoach (cf. Section IV-A) is based on changes within a cost function, wheeas the second appoach (cf. Section IV-B) elies on changes in the wokload. A. Appoaches based on the cost function In this appoach the autonomic adaptation of the TTs is based on the definition of the cost function in [17]. The geneal idea is that if cost has inceased fo some time, TTs should be adapted. Then two diffeent subpoblems have to be solved: 1) Detemine the most appopiate TT(s) to adapt. 2) Detemine fo how much the chosen TT(s) should be adapted. The used cost function is defined as c(p, w, c) = p (p )+w (w )+a (a ), (1) whee, fo a cetain esouce, p (p ):[0, 100] R + defines the costs due to the penalties that have to be paid accoding to the elative numbe of SLA violations (as compaed to all possible SLA violations) p ; w (w ) : [0, 100] R + defines the costs due to unutilized esouces w ; and a (a ): [0, 100] R + the costs due to the executed numbe of actions a (as compaed to the numbe of all possible actions). Duing the Analysis phase the KB does not only obseve the cost fo one esouce, which natually is defined as c (p, w, c) = p (p )+w (w )+a (a ), but also each individual component p, w, and a fo each esouce. If the cost has inceased fo a esouce ove a cetain peiod of time (called look-back hoizon k and defined late in this section), the KB stats to investigate which of the components caused this incease. Subpoblem 1 (Selecting TTs). To solve subpoblem 1, at fist the most poblematic cost facto has to be detemined. Fom this, we can elate to a specific TT incease/decease action. To achieve this one can basically imagine two diffeent methodologies: Eithe, the maximum cost paamete of the cuent iteation, o the paamete with the maximum incease in the last k iteations is chosen. Since ou cost function c woks by elative and not total costs, the fist method would yield the following poblem: Suppose that no violation has occued fo 10 iteations. Thus, p =0at iteation 10. At iteation 11, though, a violation occus which makes p =1/11. In the following iteations, whee p = 1/12, 1/13, 1/14,... (if no futhe violations occus) p could be easily geate than w and a as violations ae usually punished moe seveely than wastage o actions. Thus, fo these iteations the algoithm would always decide to act based on violations, even though violations ae not occuing any moe in the same time. Let p,t signify the elative amount of violations at iteation t, and let w,t a,t be defined similaly. Then, since an incease in, e.g., violations p,t occus iff p,t is stongly monotonically inceasing, we choose to opt fo the second methodology. Accoding to a look-back hoizon k we calculate the diffeence between the cuent cost and the minimum cost of the last k iteations. The maximum of these diffeences then points to the cost summand (ag) that needs attention: ag max(p,t min 1 j k (p,t j ), w,t min 1 j k (w,t j ), a,t min 1 j k (a,t j )). (2) This esults into thee diffeent cases, whee eithe the p, w, o a tems yield the maximum. (We omit cases whee some aguments of the maximum function ae equal. In such a case, the ode to choose the ag max is p ove w ove a. We pioitize like this, because we assume that penalties incu highe costs than wastage, and wastage incus highe costs than econfiguation actions.) We define thee options which TT(s) to incease o decease. Option A: 1) p,t min 1 j k (p,t j ) is maximal: Decease TThigh and TTlow. 2) w,t min 1 j k (w,t j ) is maximal: Incease TTlow. 3) a,t min 1 j k (a,t j ) is maximal: Decease TTlow and incease TThigh. Option B: 1) p,t min 1 j k (p,t j ) is maximal: Decease TThigh and TTlow. 2) w,t min 1 j k (w,t j ) is maximal: Incease TThigh and TT low. 3) a,t min 1 j k (a,t j ) is maximal: Decease TT low and incease TT high. Option C: 1) p,t min 1 j k (p,t j ) is maximal: Decease TT high. 2) w,t min 1 j k (w,t j ) is maximal: Incease TTlow. 3) a,t min 1 j k (a,t j ) is maximal: Decease TTlow and incease TThigh. The diffeence between options A and B is that if the w tem causes the maximum, it will incease both low and high TTs in option B, wheeas it will only incease TT low in option A. The main featue of option C is that it only deceases TT high (instead of also deceasing TT low ). So option B and even moe 370

4 option A could be seen as moe cautious as fa penalties fo SLA violations ae concened than option C. Moeove, we pesent a fouth methodology, option D, diffeing fom the fome thee ones. This methodology does not only conside the maximum cost summand incease, but handles all cost paametes that show an incease, but only fo the ecent iteation. This may pomise that the actual situation of which paamete needs to be adapted is assessed moe pecisely. Thus, one can distinguish seven diffeent cases: 1) p inceased: Decease TThigh. 2) w inceased: Incease TTlow. 3) a inceased: Decease TTlow, incease TT high. 4) p and w inceased: Incease TTlow, decease TT high. 5) p and a inceased: Decease TTlow. 6) w and a inceased: Incease TThigh. 7) p and w and a inceased: Choose the two factos with the highest incease and act accoding to the cases 4-6. Subpoblem 2 (Adapting TTs). Afte subpoblem 1 has been solved, fo subpoblem 2 it is impotant to detemine the value by how much the espective TT(s) should be moved. Again, one could imagine seveal techniques to detemine a good value fo the TTs as Case Based Reasoning (adapting the appoach as descibed in [16]), o using fixed o andom inceasing/deceasing steps. Obseving that fo the TTs the following inequalities must hold 0% <TT low <TT high < 100%, (3) we choose to use the following appoach. If we need to decease TT low o incease TT high, we set it to a cetain faction 1/ < 1 of the distance fom TT low to 0, and fom TT high to 100, espectively, expessed as TT,t+1 low = TT,t low TT,t low TT,t+1 high = 100 TT,t TT,t high high +. (5) (Supeindex t indicates the time iteation fo which the espective TT is valid. It is omitted, if not elevant.) If we need to incease TT low o decease TT high, we shink the distance d between TT low and TT high to d( 1) by moving the TT in question towads the othe, i.e., TT,t+1 low TT,t+1 high = TT,t low + TT,t high TT,t low = TT,t high TT,t high TT,t low (4) (6). (7) This especially makes sue that Eq. (3) also holds in this situation. When both TT low and TT high ae to be inceased and deceased, espectively, simultaneously (cf. case 4 in option D), we have to set >2 in ode not to violate Eq.(3). Summaizing both subpoblems, the gaphs in Figue 1 show how the TTs behave fo the diffeent options A-C accoding to the following scenaio: All options stat with TT low = 50%,TT high = 75%. At iteation 2 we encounte a maximum in penalties, at iteation 4 a maximum in wastage and at iteation 6 a maximum in actions. B. Appoach based on wokload volatility As an altenative to the cost function dependent appoach, we investigate an appoach depending on the change in the wokload, i.e., the wokload volatility. We define wokload volatility φ as the intensity of change in the measued wokload taces of a cetain esouce. We calculate this intensity as the pecentage elating the cuent value of the wokload m,t to the pevious one m,t 1, i.e., φ,t (m,t,m,t 1 )= ( max(m,t, min ) max(m,t 1 1) 100, min ) fo t 1 and min > 0. The vaiable min stands fo the lowe bound fo a cetain esouce stated in the Sevice Level Objective (SLO). E.g., we have min =10fo the SLO 10GB stoage 1000GB. This amount will always be povided, even if an application uses less. So measuements below this value should not influence the behavio of the system, neithe the classification into a WV class. To give an example fo = stoage, let us assume that m,t =20,m,t 1 =15.We would get φ,t (m,t,m,t 1 )=33. 3%. If at the next iteation we have m,t+1 =18, then φ,t+1 (m,t+1,m,t ) = 10%. This is useful, because a poblem inheent in options A- C is that the new paamete k to be tuned is intoduced. Its elevance to WV is the following: When WV is low, a long look-back hoizon is helpful, because a shot one would tigge moe TT adaptation situations, which in eality ae just insignificant changes in wokload. On the opposite, when WV is high, changes can get vey fast vey significant, and thus a shot look-back hoizon should be favoed. Fo this methodology, we intoduce WV classes, into which we automatically categoize wokload on the fly. We define the following WV classes: LOW, MEDIUM, MEDIUM HIGH, and HIGH. Algoithm 1 dynamically decides to which WV class a specific wokload tace belongs. Dynamically means that the classification might change at evey iteation, if the wokload behavio changes significantly. Significant in this context means that the cuent value fo WV is compaed to the ecent behavio of the wokload. Only if the maximum value fo the WV fom ecent and cuent behavio falls into a diffeent categoy, the classification is alteed. Fom the second iteation on, the algoithm fist calculates φ and detemines the maximum value in φq, which is a queue of size φq maxsize (lines 2-7). The method addlast() adds the input element as last element to the queue, wheeas the method emove() emoves the fist element of the queue. Lines 9-18 classify the wokload accoding to the found maximum element of the queue. An ɛ is added to this compaison in ode to hinde small statistical outlies fom alteing the classification outcome. Table I summaizes all constants used fo the evaluation. Based on this classification the following two options E and F alte thei behavio accodingly. Option E chooses a good set of TTs fom a-pioi evaluation fo diffeent WV classes. This can be tested offline, and alteed if specified in the SLA. E.g., fo high-isk applications both TTs could be loweed, wheeas fo enegy-awae applications, the TTs could 371

5 (a) TT example fo option A (b) TT example fo option B (c) TT example fo option C Figue 1: TT examples fo options A- C Algoithm 1 On-the-fly Classifying of Wokload into its Wokload Volatility Class Input:, m,t,m,t 1,φQ Output: Wokload volatility class 1: if t 1 then 2: {Calculate φ and detemine maximum in φq } 3: φq.addlast(φ,t (m,t,m,t 1 )) 4: if φq.size() >φq maxsize then 5: φq.emove() 6: end if 7: φq max max(φq ) 8: 9: {Classify wokload volatility} 10: 11: if φq max LOW THRESHOLD + ɛ then etun LOW 12: 13: else if φq max MEDIUM THRESHOLD + ɛ then etun MEDIUM 14: 15: else if φq max MEDIUM HIGH THRESHOLD + ɛ then etun MEDIUM HIGH 16: 17: else if φq max HIGH THRESHOLD + ɛ then etun HIGH 18: end if 19: end if Paamete Value LOW THRESHOLD 10 MEDIUM THRESHOLD 50 MEDIUM HIGH THRESHOLD 75 HIGH THRESHOLD 100 φq maxsize 10 ɛ 4 Table I: Paametes used fo Algoithm 1 be inceased fo all wokloads. Fo ou case, Table II shows the TTs fo the mentioned volatility classes. Also fom a-pioi expeience, option F chooses the best option with its best k accoding to the best esult in the coesponding WV class. As will be seen in Section V, the best esults fo evey WV class can be achieved by the options captued in the ight-hand side of Table II. V. EVALUATION In this section we evaluate the pesented methods fo autonomic TT configuation. We will fist descibe the used synthetic and eal-wold wokloads, and then pesent thei indepth evaluation. Option E) Option F) WV TT low TT high Choose Option LOW 70% 90% C), k =5 MEDIUM 45% 70% A), k =20 MEDIUM HIGH 30% 60% A), k =5 HIGH 20% 50% A), k =2 Table II: A-pioi defined TTs and options based on wokload volatility classes fo options E) and F) A. Wokloads In this subsection we shotly descibe the used wokloads. We will fist pesent the synthetic wokload and then two ealwold wokloads. All of them show static behavio, as well as apid, and also continuous changes. The wokload geneato oiginally developed in [16] is intended to geneate vey geneal wokloads fo IaaS platfoms. Fo one paamete, the wokload geneation is biefly sketched as follows: Afte the initial value is dawn fom a Gaussian distibution an up- o down-tend is andomly dawn, as well as a duation of this tend, both with equal pobability. Fo evey iteation, as long as the tend lasts, the cuent measued value is inceased o deceased (depending on the tend) by a pecentage evenly dawn fom the inteval [ibegin, iend]. Afte the tend is ove, a new tend is dawn and the iteations continue as descibed befoe. Clealy, the values fo ibegin and iend detemine the difficulty fo handling the wokload. A wokload that opeates with low ibegin and iend values exhibits only vey slight changes and does, consequently, not need a lot of dynamic adaptations. Lage iend values, on the contay, need the enfocement mechanisms to be vey elastically tuned. Fo the evaluation we defined and tested LOW, MEDIUM, MEDIUM HIGH and HIGH WV scenaios with iend = 10%, 50%, 75%, and 100%, espectively. As a minimum change we set ibegin = 2% fo all scenaios. Additionally, we tested eal monitoing data gatheed using the mentioned Cloud monitoing famewok Lom2His. Fist, we measued some execution uns of the image-endeing application PovRay [9]. The wokloads fo PovRay contain 13 independent measuements fom two categoies. Due to the lack of space, we will pesent the most inteesting thee esults fom each of the categoies. Measuements fom the fist categoy stem fom endeing a fish jumping out of and 372

6 into wate. We will tag these wokloads POV F1 to POV F3. The wokloads of the second categoy stem fom endeing fames fo a zoom-up on a box with othe boxes inside, which we will call POV B1 to POV B3. The diffeent uns within a categoy just diffe in the image esolution. Second, we evaluated ou appoach with measuements gained fom the execution of a bioinfomatics scientific wokflow application. We measued utilized esouces of TopHat [24], a typical bioinfomatics wokflow application analyzing RNA-Seq data [15], fo a duation of about thee hous [8]. B. TT adaptation using synthetic wokloads In this subsection we evaluate the six options A-F pesented in Section IV using synthetic wokload. As a quality measue, we will use the cost function defined by Eq.(1) with p (p) = 100p, w (w) = 5w, and a (a) = a fo all, and fo all adaptation options we set =4as used in Eqs. (4)-(7). min SLA paamete max 1GB stoage 1000 GB 1 Mbit/s incoming bandwidth 20 Mbit/s 1 Mbit/s outgoing bandwidth 50 Mbit/s 1 MIPS CPU powe 100 MIPS 8MB memoy 512 MB Table III: SLA fo synthetic wokloads Evey simulation un consists of 100 iteations. The SLA fo the synthetic wokloads is pesented in Table III. Results of the simulation uns can be seen in Figues 2-4. In all Subfigues 2-5(a) we pesent p, 100 w, a fo evey simulation un. The specifics of each un ae explained below each goup of thee bas: At fist the adaptation option is stated, o off, if none is used. Adaptation options also show k whee applicable. All autonomic TT expeiments have been conducted with TT low = 50% and TT high = 75% initially set (we will efe to this as the standad case), unless stated othewise. This was chosen based on the evaluation in [17], as this setting bought best esults fo a LOW MEDIUM WV class with iend = 18%. Fo compact notation a TT pai is witten as [TT low,tt high ]. In all Subfigues 2-5(b) we show the cost c(p, w, c) with the paametes as defined above. The fist thee (goup of) bas in Figue 2 epesent static TT configuations evaluated in [17]. The goal of the autonomic TT management is to achieve costs that ae as low o lowe than the costs esulting fom a static TT configuation. We see that the best static esult in tems of costs can be achieved setting TTs = [70%, 90%], and the cost fo the standad case is 159. This value is beaten (o attained) by evaluated options A fo k 25, C fo k =2, 5 with the standad TT pai, C fo all evaluated k with the best (a-pioi unknown) TT pai, and options E and F. The best case is attained by options C with the best TT pai, and by option E. Fo the MEDIUM WV class we deduce fom Figue 3 that options A fo k 15, E and F beat the static TT scenaio. On the contay, option C achieves the wost esults by fa. Due to space limitations, we omit the gaphs of the MEDIUM HIGH WV class, which ae quite simila to those of the HIGH WV class. Evaluation shows that all options except option C beat the esults fom the standad case. Option E achieves the best esult. As fa as the HIGH WV class is concened (cf. Figue 4), all options beat the esults fom the standad case. Fom these, again option C still achieves the wost esults, and again option E esults into lowest costs. (a) Violations p, utilization 100 w, actions a (b) Cost c(p, w, a) Figue 4: Evaluation esults fo HIGH WV class Geneally, autonomic adaptation woks best fo wokloads with highe volatility and quite acceptable fo wokloads with lowe volatility. We also see that option C fo k =5geneally achieves wost esults except fo low WV. This is explained by the fact as stated in Section IV that option C is less cautious than othe options with espect to SLA violations. These violations, natually, have a highe impact with highe WV. Option B fo k =5achieves the wost esult fo LOW WV, and only outpefoms the standad case fo MEDIUM HIGH and HIGH WV classes. Nevetheless, options E and F always outpefom the standad case, and achieve best o vey good esults, and thee is always a k fo option A such that it also outpefoms the standad case. The best cases fo each WV class have been esembled in option F. C. TT adaption using eal-wold wokloads This section pesents the evaluation of two eal-wold wokloads categoies. One impotant point to obseve with these wokloads is that they do no longe fall into the same WV class fo all the esouces. The SLA fo the POVRay application is depicted in Table IV. As we have seen that in the pevious subsection options E and F always outpefom the standad case, we chose only these options fo futhe evaluation. As can be seen in Table V (AM descibes whethe the autonomic manage is tuned on o off), we emak that fo POV F* options E and F always outpefom the standad case with patially big cost impovements up to 48% (fo POV F9), while the bette option is not clealy the one o the othe. Fo POV B* 373

7 (a) Violations p, utilization 100 w, actions a Figue 2: Evaluation esults fo LOW wokload volatility class (b) Cost c(p, w, a) (a) Violations p, utilization 100 w, actions a Figue 3: Evaluation esults fo MEDIUM wokload volatility class (b) Cost c(p, w, a) wokloads thee is one case, whee neithe option outpefoms the standad case, wheeas in the othe cases eithe option E o option F outpefom the standad case. min SLA paamete max 1GB stoage 1000 GB 1 Kbit/s incoming bandwidth Kbit/s 1 Kbit/s outgoing bandwidth 8000 Kbit/s 1 MIPS CPU powe MIPS 8MB memoy 512 MB Table IV: PovRay SLA MIPS, and 768 MB memoy 8192 MB. Figue 5 eveals that all evaluated autonomic options outpefom the standad case with option E achieving by fa the best esult. Fo option A we have also expeimented with vaying k fo diffeent esouces and could achieve the second best esult (tied with option F) by setting k =10fo stoage, k =2fo CPU, and k =5fo memoy. p 100 w a c(p, w, c) WV AM Details POV F1 off [50%, 75%] POV F1 on E) POV F1 on F) POV F3 off [50%, 75%] POV F3 on E) POV F3 on F) POV F9 off [50%, 75%] POV F9 on E) POV F9 on F) POV B1 off [50%, 75%] POV B1 on E) POV B1 on F) POV B2 off [50%, 75%] POV B2 on E) POV B2 on F) POV B3 off [50%, 75%] POV B3 on E) POV B3 on F) Table V: Results fo PovRay wokloads The SLA of the bionfomatics wokflow is defined as follows: 1 MB stoage MB, 1 MIPS CPU Powe (a) Violations p, utilization 100 w, actions a (b) Cost c(p, w, a) Figue 5: Evaluation esults fo the bioinfomatics wokflow Concluding we find that fo 11 out of 14 eal-wold wok- 374

8 loads both options E and F of the self-adaptive appoach achieve bette esults than the static appoach fo at least 7% (wokload POV F2) and at most 48% (wokload POV F9). Fom the emaining wokloads, fo two of them (POV B1 and POV B2) only option E pefoms bette, and fo only one wokload the static appoach outpefoms both self-adaptive ones by 11% (POV B3). VI. CONCLUSION In this pape we have devised seveal methodologies fo autonomically adapting paametes of a Cloud esouce management famewok on the level of VM econfiguation. The goal of the appoach is to educe SLA violations, incease esouce utilization and achieve both by a low numbe of econfiguation actions. We have devised two goups of stategies: the fist one is based on a cost function that eflects the goal of the appoach. The othe stategy is based on classifying the wokload into wokload volatility classes. It acts accoding to this classification by eithe applying the substategy of pe-configued paametes o the substategy of applying the most appopiate stategy fom the fist goup. In most cases we have seen that stategies fom the latte goup achieve bette esults fo both substategies, and outpefom the stategies not taking wokload volatility into account. Thus, we can deduce that wokload volatility is an impotant aspect fo govening Cloud infastuctues. Coesponding eseach is still at its beginning. Fo futue wok we want to pove the benefit egading the enegy consumption of this appoach. We will be able to not only captue the impovement in costs of the self-adaption, but also the eduction in enegy consumption as compaed to a non-self-adapting appoach. Futhemoe, we plan to investigate if we can genealize the findings fo autonomically adapting appoaches fo othe levels of govening Cloud infastuctues, e.g., VM migation o PM powe management. ACKNOWLEDGMENTS We want to thank Ivan Beskovic fo his valuable comments on this wok, which has been funded by the Vienna Science and Technology Fund (WWTF) though poject ICT and by COST-Action IC0804 on Enegy Efficiency in Lage Scale Distibuted Systems. REFERENCES [1] Amazon elastic compute cloud (Amazon EC2). (2010) [2] (FOSII) - Foundations of Self-govening ICT Infastuctues. (Mach 2012) [3] Bahati, R.M., Baue, M.A.: Adapting to un-time changes in policies diving autonomic management. In: ICAS 08: Poceedings of the 4th Int. Conf. on Autonomic and Autonomous Systems. IEEE Compute Society, Washington, DC, USA (2008) [4] Bichle, M., Setze, T., Speitkamp, B.: Capacity Planning fo Vitualized Seves. Pesented at Wokshop on Infomation Technologies and Systems (WITS), Milwaukee, Wisconsin, USA, 2006 (2006) [5] Bogetto, D., Casanova, H., Costa, G.D., Pieson, J.M.: Enegy-awae sevice allocation. 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