An Approach to Optimized Resource Allocation for Cloud Simulation Platform

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1 An Appoach to Optimized Resouce Allocation fo Cloud Simulation Platfom Haitao Yuan 1, Jing Bi 2, Bo Hu Li 1,3, Xudong Chai 3 1 School of Automation Science and Electical Engineeing, Beihang Univesity, Beijing, China 2 Depatment of Automation, Tsinghua Univesity, Beijing, China 3 Beijing Simulation Cente, Beijing, China Abstact. Resouce allocation fo simulation applications in cloud simulation envionment bings new challenges to infastuctue sevice povides. In ode to meet the constaint of SLA and to allocate the available vitualized esouces optimally, this pape fist pesents autonomic esouce management achitectue, and then poposes a esouce allocation algoithm fo infastuctue sevice povides who want to minimize infastuctue cost and SLA violations. Ou poposed algoithm can maximize the oveall pofit of infastuctue sevice povides when SLA guaantees ae satisfied o violated in a dynamic esouce shaing cloud simulation platfom. The expeimental evaluation with a ealistic wokload in cloud simulation platfom, and the compaison with the existing algoithm demonstate the feasibility of the algoithm and allow a cost-effective usage of esouces in cloud simulation platfom. Keywods: Cloud Simulation; Sevice Level Ageement (SLA); Resouce Allocation; Vitualization. 1 Intoduction Cloud computing can be classified as a new paadigm fo esouce allocation of computing sevices suppoted by state-of-the-at data centes that usually employ vitual machine (VM) technologies fo consolidation and envionment isolation puposes [1]. An inceasing numbe of oganizations (e.g., eseach centes, entepises) benefit fom cloud computing to host thei applications [2,3]. Diffeent fom cloud computing, cloud simulation is a new netwok-based and sevice-oiented simulation model [4,5]. The cloud simulation vitualizes diffeent types of simulation esouces and futhe constitutes the esouce pools. Hence consumes can acquie sevices of the simulation esouces at anytime and anywhee using the netwoked cloud simulation platfom. To fully ealize the potential of cloud simulation, infastuctue sevice povides (ISPs) of cloud simulation platfom have to ensue that they can be flexible in thei sevice delivey to meet vaious simulation equiements, while keeping the simulation consumes (SCs) isolated fom the undelying infastuctue. In such a

2 dynamic envionment whee SCs can join and leave the cloud simulation envionment at any time, ISPs should be able to povide thei SCs with the equied sevices accoding to a given sevice level ageement (SLA). ISPs should ensue those QoS equiements using a minimal amount of computational esouces. Consequently, an efficient and dynamic esouce allocation stategy is mandatoy in the infastuctue laye. The cuent woks in cloud computing [6,7,8] focused mostly on the poblem of esouce allocation management and maximizing the pofit of ISPs in cloud simulation envionments. Many woks do not conside the SCs-diven management, whee esouces have to be dynamically eaanged based on SCs equiements. Fu Y. et al [9] poposed an SLA-based dynamic scheduling algoithm of distibuted esouces fo steaming. Moeove, Yamolenko V. et al [10] evaluated vaious SLAbased scheduling heuistics on paallel computing esouces using esouce (numbe of CPU nodes) utilization and income as evaluation metics. Nevetheless, ou wok focuses on scheduling multi-tie simulation applications based on VMs in cloud simulation envionments, whee the heteogeneity and inaccuacy of job equests may esult in esouce unde-utilization. Lee et al [11] investigated the pofit diven sevice equest scheduling fo wokflow. In contast, ou wok focuses on SLA diven QoS paametes fom both the SCs and the ISPs point of view, and solves the challenge of dynamic changing SCs equests to gain pofit. In ode to meet the constaint of SLA and allocate the existing vitualized esouces optimally to minimize SLA violations, this pape pesents the autonomic esouce management famewok based on vitualization techniques and povides an effective vitualized esouce allocation stategy fo existing cloud simulation infastuctue envionment and allocates VMs to seve SCs equests in the esouce management middlewae of cloud simulation platfom. The poposed VMs allocation algoithm which allows a cost effective usage of esouces in cloud simulation platfom is poposed and compaed with the existing algoithm. Results show that the poposed algoithm can ensue to maximize the oveall pofit of ISPs when SLA guaantees ae satisfied o violated. The est of this pape is oganized as follows. Section 2 descibes the autonomic esouce management and pesent poposed system pofit model. Section 3 pesents the VM allocation stategy and algoithm. Section 4 demonstates the esults of pototype expeiments. Concluding emaks and discussion about futue wok ae given in Section 5. 2 System Oveview This section fist povides an oveview of ou autonomic computing appoach fo the esouce allocation poblem in multitie vitualized envionments of cloud simulation platfom. Then, we pesent the pefomance and pofit models of the system.

3 2.1 Autonomic Resouce Management This section fist povides an oveview of ou autonomic computing appoach fo the esouce allocation poblem in multitie vitualized envionments of cloud simulation platfom. Then, we pesent the pefomance and pofit models of the system. Due to such the lage vaiation in the simulation tasks, it is difficult to estimate wokload equiements in advance, and planning the capacity fo the wost-case is eithe infeasible o extemely inefficient. In ode to dynamically allocate esouces fo multitie vitualized simulation application execution envionments, the most common appoaches ae based on Monito, Analyze, Plan, and Execute (MAPE) contol loops [12, 13]. Autonomic systems maintain and adjust thei opeations in the face of changing components, demands o extenal conditions and dynamically allocate esouces to applications of diffeent customes on the base of shot-tem demand estimates. The goal is to meet the application equiements while adapting IT achitectue to wokload vaiations. High Stoage1 High Stoage 2 High Stoage N Middle&Low 1 Middle&Low 2 Middle&Low N High Stoage Pool Middle&Low Stoage Pool Fig. 1. Autonomic computing achitectue fo cloud simulation platfom.

4 The achitectue of a high-level autonomic computing oveview is shown in Fig.1, includes a set of heteogeneous physical seves which un a Vitual Machine Monito (VMM), shaed by multiple independent application envionments, hosting simulation applications (SAs) fom diffeent pofessions o depatments. Moden SAs ae usually designed using multiple ties, which ae often distibuted acoss diffeent seves. Seve physical esouces ae patitioned among multiple VMs, each one hosting a single SA. Among many seves esouces, we choose CPU as the key esouce in the allocation poblem. The esouce allocato exploits the VMM API to dynamically patition CPU capacity among multiple VMs and thei hosted SAs. Autonomic computing achitectue povides mechanisms to automate the configuing of VMs and to tune the vitualized simulation multi-tie application, theefoe the esponse time equiements of the diffeent SCs can be guaanteed. It detemines the un-time allocation fo cloud simulation platfom. It mainly includes fou components descibed as follows: SA Monito: collects the wokload and the pefomance metic of all unning SAs, such as the equest aival ate, the aveage sevice time, the CPU utilization, etc. SA Analyze: eceives and analyzes the measuements fom the monito to estimate the futue wokload. It also eceives the esponse times of diffeent SCs. SA Allocato: sets up allocation stategy fo each tie of the SAs, and uses optimization algoithms to detemine esouce allocation. SA Executo: assigns the VM configuation, and then uns the SAs to satisfy the esouce equiements of the diffeent SCs accoding to the optimized decision. 2.2 System Pefomance and Pofit Models To solve the poblem of esouce allocation in the cloud simulation platfom, this section descibes how we establish the analytic model to estimate the pefomance in tems of SLA. In cloud simulation envionment, diffeent SCs with diffeent pefomance equiements may use simulation sevices suppoted by ISPs. What s moe, the ISPs objective is to seve equests with diffeent equiements such that the ISPs pofit can be maximized and the SCs pefomance equiements can be also guaanteed. In this section, we explain the system pofit model. The popeties defined in the SLA ae as follows: Request Type: It defines the SCs equest types, which ae Gold, Silve and Bonze. The coesponding pefomance equiements ae diffeent fo diffeent equest types. Response Time: It is defined as the actual time taken fo a SA equest to go though the thee-tieed system. The value of esponse time is diffeent in each type, and specified in SLA. VM Type: Thee ae thee VM types, which include Lage, Medium and Small. They ae coesponding to thee kinds of SCs. VM Pice: Pice of VM is decided by the numbe of seved equests pe time unit. It includes the physical equipment, powe, netwok and administation pice.

5 VM Penalty: The coesponding penalty caused by violations of SLA. Violation occus when actual un time exceeds pe-defined esponse time in SLA. Fo each equest, a linea utility function specifies the pe equest penalty Penalty incued by the coesponding aveage end-to-end esponse time R. If sevices povided by ISPs violate SLA tems, it has to pay fo the penalty accoding to the clauses defined in the SLA. Let R denote the numbe of SCs and denotes SC s id. Let M denote the numbe of ties fo a typical multi-tie simulation application and m denotes tie id. Let I be the numbe of initiated VMs, and i indicates the VM id. Let K denote the types of VMs. Let n m denote numbe of initiated VMs in tie m. Pofit, VMCostikm,, and Penalty denote the pofit, VM cost and penalty cost fo seving equest using VM ikm espectively. usetime denotes the duation time used by SC. PiceSev denotes ISPs chage fom SC. Ou goal is to maximize the pofit of ISPs. The global pofit function can be fomulated as: R R R Pofitikm,, PiceSev usetime Costikm,, (1) whee, R,1 i nm, k K, m M Let Cost indicate the cost fo seving SC equest with VM. It depends on the VM cost ( VMCost ) and penalty ( Penalty ). It can be fomulated as: Cost,, =,, + ikm VMCostikm Penalty (2) whee, R,1 i nm, k K, m M The VM cost depends on the VM type k, the pice of VM i with type k in tie m ( PiceVM ), the initiation Time ( initimesa ) and duation time of SC equest ( usetime ). The VM cost is defined as: VMCost = PiceVM initime + usetimeikm,, (3) whee, R,1 i nm, k K, m M The SLA violation penalty model is simila to othe elated utility functions [14, 15]. The penalty Penalty function penalizes the ISPs by educing the utility. The esouce allocation poblem in question is how to dynamically allocate the CPU among VMs with the goal of maximizing the global pofit function. In this pape, we adjust VM allocation stategy fo vitualized simulation envionments. 3 Vitual Machine Allocation Stategy The main objective of ou wok is to maximize the pofit fo ISPs by minimizing the cost of VMs using an effective vitualized esouce allocation stategy. To achieve this objective, we popose and examine a 2-step vitualized esouce allocation algoithm (HoiVeScale) which allows a cost effective usage of esouces in cloud simulation envionment to satisfy dynamically changing SCs equiements in line with SLAs.

6 3.1 Allocation Stategy In cloud simulation envionment, SCs with diffeent pefomance levels may equest sevices of the same simulation application. In this case, SCs in each tie of a typical multi-tie simulation application have diffeent pefomance levels. In ode to seve these diffeent equests in each tie, VM with diffeent capacities should be allocated to equests fom diffeent pefomance levels, e.g., equests with the highest level should be seved by VM with the highest capacity. In this way, the pefomance equiement of coesponding equests can be satisfied. What s moe, SCs with the same pefomance level may equest sevices in the same tie of a multi-tie simulation application. In this case, the same VM with enough capacity can be eused and shaed by diffeent SCs of this level. Fo ISPs, initiation of a new VM is moe costly than euse of initiated VM. And this pape consides that esizing of VMs is much cheape than stating a new VM. Thee ae existing possible solutions fo vetical scaling including migating a vitual machine to anothe physical seve o dynamically inceasing the vitualized esouce allocated to the vitual machine. So we assume that the available numbe of VMs in each tie is limited, denoted as C m. Let C mg, C ms and C mb denote uppe limit of gold type, silve type and bonze type in mth tie espectively. Note that C m is the sum of C mg, C ms and C mb. The next poblem fo ISPs comes that how to accuately detemine C mg, C ms o C mb in mth tie fo thee kinds of SCs. As shown in Fig. 2, when the numbe of initiated VMs fo equests fom specific consume type in mth tie eaches coesponding limit, e.g. C mg fo gold type, to guaantee coesponding pefomance equiements while minimizing VMs costs. The VM with the maximum emaining capacity will be scaled vetically by a cetain amount, denoted as sv%, which assues that the vetically scaled VM can exactly satisfy equiement of that equests. Note that when the sum of emaining and scaled esouces in a VM eaches the theshold (85%) of physical machine, the VM should be migated to anothe physical seve. In this way, new aival equests can always be seved by a cetain VM. Fig. 2. An example of VM vetical scaling fo gold consumes in Web tie, whee C mg is set 3. In the poposed 2-step algoithm HoiVeScale, we fistly popose the algoithm to specify the uppe limit of available VMs numbe fo a specific SC in mth tie including C mg, C ms and C mb. Then in ode to seve new aival equests, we popose

7 the algoithm to choose a suitable VM and specify the amount of the added vitualized esouce. In this way, a specific VM can be vetically scaled instead of initiating a new VM. Altogethe, the 2-step algoithm fistly hoizontally scales VMs to initiate moe VMs until the coesponding limit of numbe of VMs fo a specific kind of SCs in mth tie is eached. And secondly the algoithm vetically scales the selected VM by inceasing vitualized esouces o migating to anothe physical seve instead of initiating new VMs. 3.2 Poposed Algoithm The algoithm fistly detemines the uppe limit of initiated VMs. The equests aival ate of thee kinds of SCs can be detemined espectively by online monitoing. Togethe with known diffeent capacities of VMs, the algoithm can detemine minimum numbe of VMs to assue that total pocessing capacity of VMs is no less than total equests aival ate. Then the algoithm checks whethe the pefomance equiement specified in SLA is violated. If so, new VMs will be initiated until SCs pefomance equiement can be satisfied. The final initiated numbe of VMs fo specific SCs in mth tie can be set as C mg, C ms and C mb espectively. Algoithm 1 descibes the poposed HoizontalScaleLimit algoithm, which is the fist step of 2- step algoithm. Algoithm 1. HoizontalScaleLimit Input: equests aival ate fo a specific kind of SCs in mth tie (e.g. λ m,g,i fo gold type) and pocessing capacity of coesponding VM, µ m,g,i, i=1,2,,c m,g Output: The uppe limit of initiated VMs fo a specific kind of SCs in mth tie, e.g., C mg fo gold type. Function: HoizontalScaleLimit (λ m,g, µ m,g ){ 1 fo m=1 to M do 2 C mg =1 3 end fo 4 fo m=1 to M do C mg 5 mg, =, mgi,, i 1 6 mg, while( 1) do mg, 7 C, C, 1 mg 8 mg, mg C mg =, mgi,,. i 1 9 end while 10 end fo 11 fo m=1 to M do 12 while(pefomance equiement can NOT be satisfied)

8 13 Cmg, Cmg, 1 14 end while 15 end fo Then the second step of 2-step algoithm is descibed as follows. The algoithm checks the equest type of SC c. Accoding to the equest type, the algoithm finds the VM i with type t (VM it ) that can satisfy the pefomance equiement of the equest. Then, it checks whethe thee is aleady initiated VM it as SC c equests. If thee is an initiated VM it, then the algoithm checks whethe this VM it has enough capacity to seve the equest of SC c accoding to equested esouce on this VM. If thee ae moe than one VM it with enough available capacity to seve the equest c, then the equest c is assigned to the machine with minimum available capacity. If thee is no initiated VM with type t, a new VM of coesponding type is initiated. When the numbe of initiated VMs fo equests fom a specific SC type in mth tie eaches coesponding limit, e.g. C mg fo gold type, in ode to seve new aival equests, no moe VMs will be initiated and the VM with the maximum emaining capacity will be scaled vetically by a cetain amount to assue that the vetically scaled VM can exactly satisfy equiement of that equests. Note that when the sum of emaining and scaled esouces in a VM eaches the theshold (85%) of physical machine, the VM should be migated to anothe physical seve. Othewise, vitualized esouces can be diectly allocated to vetically scale the VM. In this way, new aival equests can always be seved by a cetain VM. Algoithm 2 descibes the VeScaleLimit algoithm. Algoithm 2. VeScaleLimit Input: equest c Output: VM seving equest c Function: VeScale (c){ 1 if(thee is no initiated VMs which can satisfy equiement of equest c) { 2 initiate new VM with coesponding type as equest c equied 3 } 4 else if(thee is initiated VM i with type k which matches to the VM type equested by c){ 5 if(the numbe of initiated VMs fo equests fom a specific SC type in mth tie don t each coesponding limit){ 6 initiate new VM with coesponding type as equest c equied 7 }else{ 8 fo each initiated VM i with type k (VM i,k ) { 9 if (VM i has enough capacity equied by equest c) { 10 put VM i into vmlist 11 } 11 } 13 if(vmmax is not empty){ 14 schedule equest c to VMmin, which has minimum emaining capacity 15 }else{ //vetical scaling

9 16 select VM with maximum emaining capacity, VMvs 17 if(scaled VM does not each the theshold of physical machine){ 18 scale VM by inceasing vitualized esouce and assue that the VM can exactly seve equest c. 19 }else{ 20 migate the VM to anothe physical machine with available vitualized esouces 21 } 22 } 23 } 24 } 25 } ISPs can maximize the pofit by minimizing the esouce cost, which depends on the numbe and type of initiated VMs. Theefoe, this 2-step algoithm HoiVeScale is designed to minimize the numbe of initiated VMs by utilizing aleady initiated VMs. 4 Pefomance Evaluation This section pesents the pefomance esults obtained fom an extensive set of expeiments. The poposed VM allocation algoithm, HoiVeScale have been evaluated and compaed with the existing algoithm, MinRemCapacity [16]. MinRemCapacity also euse initiated VM to seve aival equests, but it initiates new VMs when existing initiated VMs cannot satisfy equiement of equests instead of scaling existing VMs. In this section, we fistly descibe ou expeiment setting, and give the analysis of expeimental esults. 4.1 Expeimental Setting We evaluate ou poposed algoithm fo VM allocation in ou cloud simulation platfom consisting of six heteogeneous physical machines. To decease the numbe of ecods and keep the vaiation chaacteistic, we take a sample fom evey 120 equests. The equest aival ates vay with time fom 0 minute to 1440 minutes. All the paametes used in the expeiments study ae given in Table 1. Table 1. SLA Chaacteistics fo Simulation Application in Cloud Simulation Platfom. SC Type VM Type Response Time Theshold(msec) Pice($/hou) Gold Lage Silve Medium Bonze Small

10 4.2 Effectiveness of Poposed Algoithm In this section, we compae its pefomance esults fom diffeent pespectives. Fig. 3 shows the vaiation of aveage numbe of initiated VMs in thee ties including web tie, application tie and database tie of ou simulation application. As illustated, the numbe of initiated VMs with HoiVeScale algoithm is less than MinRemCapacity algoithm in all 3 ties. The compaison of numbe of total initiated VMs shows that poposed HoiVeScale algoithm can educe nealy 13% of initiated VMs. So the ISPs can educe cost by using less initiated VMs while satisfying SCs pefomance equiement. Fig. 3. Compaison of aveage numbe of initiated VMs. Fig. 4. Aveage pecentage of SLA Violations.

11 Fig. 4 shows the aveage pecentage of SLA Violations using HoiVeScale and MinRemCapacity algoithms. As illustated, HoiVeScale algoithm shows bette pefomance, esponse time of which is smalle than that of MinRemCapacity algoithm in aveage. So SLA violations in 3 ties can be all educed with HoiVeScale algoithm. In addition, the compaison of numbe of total SLA violations shows that poposed HoiVeScale algoithm can educe nealy 9.5% of SLA violations. Fig. 5 shows compaison of the educed total cost using HoiVeScale and MinRemCapacity algoithms. In aveage, the algoithm HoiVeScale pefoms bette to educe total cost by using less VMs compaed with MinRemCapacity algoithm. Because it costs less with less o equal numbe of VMs but geneates less numbe of SLA violations. So, duing the vaiation of aival ate, the HoiVeScale algoithm can educe the SLA violations and total cost in the context of esouce shaing. Fig. 5. Reduced total cost using HoiVeScale and MinRemCapacity algoithms. Fig. 6. CPU utilization of initiated vitual machines. Fig. 6 shows compaison of aveage CPU utilization of initiated vitual machines in ou simulation application. Based on bette VM allocation mechanism, duing the

12 vaiation of aival ate fom 0 to 2000, as illustated, the HoiVeScale algoithm pefoms bette and can assue highe aveage CPU utilization than MinRemCapacity algoithm. As shown in Fig. 7, it shows the thoughput vaiation of the RUBiS system as equest aival ates vay with time. Above 80% of aival equests can be seved by eithe HoiVeScale algoithm o MinRemCapacity algoithm at any time. At some time, 90% of aival equests can be seved. This shows that both algoithms can effectively seve most of aival equests. Howeve, compaed with the MinRemCapacity algoithm, the HoiVeScale algoithm shows supeio pefomance and coesponding thoughout is slightly highe at most of time fom 0min to 2000min. The shows that HoiVeScale algoithm can seve moe equests and can effectively utilize VM esouces. Fig. 7. Thoughput of the system using HoiVeScale and MinRemCapacity algoithms. Fig % of the aveage esponse time using HoiVeScale and MinRemCapacity.

13 As shown in Fig. 8, it shows the 98% of the aveage esponse time vaiation using HoiVeScale and MinRemCapacity algoithms. This only shows SLA theshold fo consumes with gold level, which is set 200msec. As illustated, though MinRemCapacity algoithm shows geat pefomance at most of the time, in some time, esponse time of MinRemCapacity algoithm violate SLA theshold. Howeve, HoiVeScale algoithm shows bette pefomance, esponse time of which is smalle than that of MinRemCapacity algoithm at most of the time. In addition, HoiVeScale algoithm can also assue that esponse time can be in the limit of coesponding SLA theshold. 5 Conclusion This pape focuses on the poblem of vitualized esouce allocation fo SA with the goal of maximizing the SLAs evenue while minimizing enegy costs in cloud simulation platfom. Accoding to the pefomance metics specified in the SLA, the system pofit model is poposed. Based on this model, in ode to dynamically allocate esouce to minimize SLA violations and infastuctue cost, a VMs allocation algoithm is poposed and compaed with the existing algoithm. The expeimental evaluation with a ealistic wokload in cloud simulation platfom demonstates the feasibility of the algoithm. This allows a cost effective usage of esouces in cloud. In futue wok, we will look into the simulation esouces and capabilities shaing on demand fo mass uses. We would also focus on the application eseach of cloud simulation system. Acknowledgment This wok was suppoted in pat by a gant fom the China Postdoctoal Science Foundation (No. 2014M550068). Refeences 1. Baham P., Dagovic B., ase K., Hand S., Hais T., Ho A., Neugebaue R., Patt I., Wafield A.: Xen and the At of Vitualization. In: Poceedings of the 19th ACM Symposium on Opeating Systems Pinciples, SOSP 2003, pp Bolton Landing, NY, USA, (2003) 2. Ambust M., Fox A., Giffith R., et al: Above the Clouds: A Bekeley View of Cloud Computing. Technical Repot No. UCB/EECS , Univesity of Califonia Bekley, USA, Feb. 10 (2009) 3. Buyya R., Yeo C.S., Venugopal S., et al: Cloud Computing and Emeging IT Platfoms: Vision, Hype, and Reality fo Deliveing Computing as the 5th Utility, Futue Geneation Compute Systems, 25(6), pp Elsevie science, Amstedam, the Nethelands (2009)

14 4. Bo Hu Li, et al.: A netwoked modeling and simulation platfom based on the concept of cloud computing Cloud Simulation Platfom. Jounal of System Simulation. 12, pp (2009) 5. Bo Hu Li, Xudong Chai, et al: New Advances of the Reseach on Cloud Simulation. Poc. of AsiaSim. (2011) 6. Aoun R., Doumith E. A., Gagnaie M.: Resouce Povisioning fo Eniched Sevices in Cloud Envionment. In: Poceedings IEEE CloudCom Confeence, pp (2010) 7. Yazi Y.O., Matthews C., Faahbod R., Neville S., Guitouni A., Ganti S., Coady Y.: Dynamic Resouce Allocation in Computing Clouds using Distibuted Multiple Citeia Decision Analysis. In: Poceedings IEEE CLOUD Confeence, pp (2010) 8. Bi, J., Zhu Z.L., Tian R.X., Wang Q.B.: Dynamic Povisioning Modeling fo Vitualized Multi-tie Applications in Cloud Data Cente. IEEE 3d Intenational Confeence on Cloud Computing, pp Miami, USA, (2010) 9. Fu Y., Vahdat A.: SLA Based Distibuted Resouce Allocation fo Steaming Hosting Systems, Yamolenko V., Sakellaiou R.: An Evaluation of Heuistics fo SLA Based Paallel Job Scheduling. In: Poceedings of the 3d High Pefomance Gid Computing Wokshop (in conjunction with IPDPS 2006). Rhodes, Geece (2006) 11. Lee Y.C., Wang C., Zomaya A.Y., Zhou B.B.: Pofit-diven Sevice Request Scheduling in Clouds. In: Poceedings of the Intenational Symposium on Cluste and Gid Computing, (CCGid 2010), Melboune, Austalia (2010) 12. White S.R., Hanson J.E., Whalley I., et al.: An achitectual appoach to autonomic computing. In: Poceedings of the Intenational Confeence on Autonomic Computing (2004) 13. Kephat, J.O. Chess D.M.: The vision of autonomic computing, IEEE Compute. 36 (1), pp (2003) 14. Walsh W.E., Tesauo G., Kephat J.O., Das R.: Utility Functions in Autonomic Computing. In: Poceedings of the IEEE Intenational Confeence. Autonomic Computing (ICAC 04), May pp New Yok, NY (2004) 15. Bennani M.N., Menasc e D.A.: Resouce Allocation fo Autonomic Data Centes Using Analytic Pefomance Models. In: Poceedings of the IEEE Intenational Confeence on Autonomic Computing, June pp Seattle, WA (2005) 16. Wu L.L., Gag S.K., Buyya R.: SLA-based Resouce Allocation fo Softwae as a Sevice Povide (SaaS) in Cloud Computing Envionments. In: Poceedings of the 11th IEEE/ACM Intenational Confeence Symposium on Cluste, Cloud and Gid Computing (CCGid 2011), May pp (2011)

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