1 International Journal of Couter Networs and Counications Security VOL. 2, NO. 8, AUGUST 2014, Available online at: ISSN C N C S A ulti obective virtual achine laceent ethod for reduce oerational costs in cloud couting by genetic Reza Soohtsaraei 1, Mirorsal Madani 2, Atena Kavian 3 1 Faculty eber of Paya Noor University, eartent of couter engineering and inforation technology, Tehran, Iran 2 Faculty eber of Korduy Islaic Azad University, earten of Couter Engineering, Korduy, Iran 3 Airabir University, eartent of Couter Engineering, Tehran, Iran E-ail: ASTRACT Increase of deand in using cloud couting caused increase of oerational costs consution energy and resources consued. As a result oreover satisfying services with quality requested through service level agreeent SLA, we ust reduce consuing energy and cost resulting fro resources used. According to too uch researches conducted on reducing consution energy, in this article we will focus on reducing oerational costs resulting fro wasting resources using technique of aing virtual achines to service roviders whose the ost iortant one is using on deand rovisioning odel which will revent fro wasting resources and ore exloitation of cloud couting and reduction in oerational costs. One of the ey asects of the rocess is considering load feature of virtual achines. ut ost tass done in this field do not care about it and on the other hand only otiization is noticed. ue to existence of faults and also high level of exloitation of genetic algoriths in finding fine results in searching sace, in this article a ethod was resented based on ulti-urose genetic algorith, where by considering tas feature of virtual achines. Several obectives are considered for otiizing during aing rocess. Coaring results gained fro algorith suggested with a rando algorith and one algorith in the first ultiurose choice, we can coe to this conclusion that the algorith suggested will establish better results on rovidence in resources and reducing oerational costs. Keywords: Cloud couting, virtual achine, ulti-urose genetic algorith, otiization. 1 INTROUCTION In recent years cloud couting has been nown as the ost oular calculation bed for hosting and roviding services based on internet , so that ost organizations chose this technology as their calculation atterns in solving robles related to IT. To aintain accetance established in alying cloud couting, we have to tae u soe easures so that two sides (roviders and clients) involved in this technology can reach the axiu level of satisfaction in using it. ased on this, to reach client satisfaction and for eeting service quality requested by the, big cloud couting roviders such as Aazon, Google, Microsoft, IM and so on created new data centers in different geograhical areas so that by establishing redundancy and trust caability they can reove errors ade during resenting services and this way they can irove quality level and reliability in these services. On the other hand, as deand in using cloud couting is increased, gaining roviders` satisfaction is not an easy tas due to increase of oerational costs (consuing energy, resources consued). In  it is shown that with increase of deand energy consution in data center of cloud couting had a 400-ercent increase coared to the revious decade. As a result, to control oerational costs, roviders of cloud couting have to reduce consuing energy
2 251 R. Soohtsaraei et. al / International Journal of Couter Networs and Counications Security, 2 (8), August 2014 and costs resulting fro resources used, oreover eeting services with the quality requested through SLA. Since too uch research investigated energy consution reduction in cloud couting [4-10], we focus on reducing oerational costs resulting fro wasting resources. One of the ost iortant technologies along with cloud couting is virtualization. In this technology we can share resources of a achine between several alications by using erforance isolated latfors called virtual achines. One of the ost roinent advantages of virtualization is alying on deand rovisioning odel. The odel guarantees that users ust be rovided the resources they need whose results will be reventing fro waste of resources and ore exloitation of cloud couting and also reduction in oerational costs. esite virtualization, one of the subects raised is rocess of aing virtual achines to service roviders. The rocess is highly iortant since it has a great effect on exloitation of resources. If the rocess of aing virtual achines to service roviders is not conducted accurately and with full awareness with resource condition, then oerational costs will increase due to waste of resources. As a result, the rocess was raised as one of the ost iortant research fields on cloud couting [4,9-11,13-22] and to solve it various ethods are used such as linear rograing[13,14,5], constraint rograing [16,17], bin acing [18,19,20], Ant colony algorith and genetic algorith. One of the ey asects in aing virtual achines to service roviders which can cause increase of efficient in cloud couting is considering feature of woring load of virtual achines during the rocess , but ost of tass done on this do not care about it. Also in ost researches conducted on this, only otiization is cared, while in real robles we can consider various obectives. ue to defects entioned earlier, a ethod was resented in this article which cares about several goals for otiization by considering feature of woring load of virtual achines. Since ultiurose otiization roble is aong NP-hard robles, due to high level of genetic algoriths in finding desirable results in searching sace we used this tye of algorith for finding the best resonse in this article. y coaring results gained fro siulating suggested ulti-urose genetic algorith with a rando algorith and one ultiurose algorith, we can coe to this conclusion that suggested algorith can establish better results on resource rovidence and in reducing oerational costs. Next, the article is organized as follows: in section 2 several recent activities conducted on aing virtual achines to service roviders were resented and their faults are entioned. In section3, rocess of aing and forulas used is resented officially. In section4, suggested ultiurose genetic algorith is shown. In secton5, siulation results are resented and in the end the article is finished by roviding conclusion and exressing future wors. 2 WORK RELATE In this section several researches conducted on relacing virtual achines to hysical ones are resented. We investigate wors done in this section fro three views: a) Wors in which overloading of hysical achines are not considered: In , an Ant colony algorith is resented which deals with aing roble in erutation for fro virtual achines to hysical ones. The ai of the algorith is to resent a solution so that it can reduce level of consued energy and resources wasted siultaneously. In this algorith, virtual achines are aed based on level of desirability and robability of oveent to hysical achines which are selected randoly. In , an algorith of dynaic resource allocation was resented which was based on threshold where it allocates virtual achines based on their worload changes to hysical achines. To do this it alies a ethod based on threshold for otiizing the rocess. The algorith reestablishes virtual achines in dynaic for based on needed worload changes in cloud alications and based on this it creates rovidence in using resources and can increase efficiency. b) Wors in which rovidence in wasted resources is not considered: In a develoed algorith First Fit ecreasing (FF) was suggested in which each hysical achine was rovided one grade as an advantage and based on this virtual achines are aed to hysical achines with higher level of score. To revent fro deficient iigration, two values of threshold are deterined as Rlow and Rhigh. If a hysical achine consues ore than Rhigh, it is called Highly-loaded and if it consues less than Rlow, it is called Lowly-loaded. Then virtual achines which are only laced in these two tyes of achines will have the erission to igrate. In  a odified bin acing algorith is suggested in which virtual achines can be divided into two sets of Cu intensive and ata intensive. The algorith as virtual achines to hysical achines ased on two rules so that the least nuber of hysical achines can be deterined.
3 252 R. Soohtsaraei et. al / International Journal of Couter Networs and Counications Security, 2 (8), August 2014 The first rule is in this way that the axiu nuber of virtual achine which is focused on data aed on a hysical achine should not be ore than, since too uch aing this tye of virtual achine to a hysical one can increase coetition for availability to dis. The second rule is that the axiu resource deanded by virtual achines should not be ore than the axiu nuber of resources resent in hysical achines. C) Activities in which hysical achine being overhead and rovidence in wasted resources are not considered: In  algoriths of Hybrid and ynaic Round Robin (RR) are resented whose ai is rovidence in consuing energy using ixture. RR has been an iroveent in algorith RR in which two rules are used for cobining virtual achines. The first rule is so that if a virtual achine ended and other virtual achines are being erfored on that hysical achine then the hysical achine does not establish a new virtual achine. The second rule indicates that if one hysical achine does not accet a new virtual achine for a long tie, then all the virtual achines will ove into anther hysical achine and the achines will turn off. In Green couting algorith is resented in which condition of all the virtual achines are suervised and added hysical achines will turn off, if the syste efficiency does not becoe low. In  an architecture called Green Cloud is shown which can reduce consuing energy of data centers. This architecture is caable of suervising cloud eleents in on-line for and corehensively, it can ae virtual achines ove in live for and also it can otiize aing of virtual achines. In a Fraewor called VM lanner was suggested for reducing energy costs resulting fro the networ eleents resent in data center (lins and routing ). Using dynaic igration of virtual achines and routing based on rograable flow-based routing resent in odern data centers, this fraewor can reduce the consution resulting fro networ eleents oreover eeting traffic needs. To reduce consuing energy, VM lanner considers toological features and traffic atterns and based on this it tries to turn off unnecessary networ eleents as uch as ossible. In  an architecture called Energy and Carbon- Efficient (ECE) was resented in which an interface called ECE Cloud roer can a virtual achines to hysical achines so that consuing energy of data center can be otiized and as a result Carbon Footrint is reduced. roer does the aing rocess based on data such as consued energy and rate of carbon roduction by energy resources in a data center. The activity conducted in this article is different with revious ones. In aing virtual achines to service roviders we suggest a ultiurose genetic algorith using it we can reduce nuber of overloaded service roviders and also the level of wasted resources. 3 PROLEM EXPRESSING In a cloud, alications are erfored in storage of service roviders which were virtualized totally. The tas of aing virtual achines to storage of service roviders can be considered a tye of in acing roble in which virtual achines are in fact ites and service roviders are ins. Feature of each ite and bin can be described with a leash set in for of CPU, Meory and andwidth all of which indicate resources requested and leash of each bin indicates level of resources resent. Next we will rovide forulas used in aing rocess and the reason of using it, also sybols used are shown in table 1. Variable S S TR TR UR UR ` RR RR l U i i Table1. araeters used efinition Set all servers All of virtual achines that are assigned to the - server The total rocessing caacity of -server The total ain eory caacity of -server Used -server rocessing caacity Used -server ain eory caacity The reaining rocessing caacity of -server The reaining ain eory caacity of -server The threshold for all rocessors The threshold for the ain eory of all the servers Required bandwidth between and l virtual achine andwidth allocated between i and virtual achine Available bandwidth between i and servers Processing caacity required by the -virtual achine A eory request by -virtual achine andwidth threshold
4 253 R. Soohtsaraei et. al / International Journal of Couter Networs and Counications Security, 2 (8), August 2014 a) Obective functions Assue that we laced n virtual achines in three naes of Cu_VMset, Meory_VMset and I/O_VMset which indicate set of virtual achines focused on rocessor, focused on eory and inut and outut, resectively. These sets are deendent on each other, in other words a virtual achine ay include features of all three sets. n of virtual achine ust be aed on service. For silicity we assue that resources requested of a virtual achine are not ore than resources which a service rovider rovides. The ai of aing virtual achines to service roviders in this article is siultaneous otiization of three following forulas (1, 2, 3) Min S 1 RR RR (1) Using forula (1), we can gain level of wasted resource. The ore iniu the forula (1), the less the value. e.g. if in a service rovider level of using the ain eory is ore than rocessor, then aing of the other virtual achine to this service rovider will face sae robles due to eory shortage and if the roble still exits the reaining rocessing resource in this service rovider ay not be usable and it will be considered as wasted resource. Using forula (2) we can identify service roviders overloaded and calculate level of overloading. For calculating value of this forula, service rovider is divided into five grous. The way of calculating level of overload for each grou is different to other grou. This grouing is conducted based on hrases of (4) to (13). Feature of each grou is u next. Min S O 1 M ( ) TR 1 TR M ( i) M ( ) 1 l 1 i M ( ) i TR 1 TR and (3) and (4) i, S M ( ) TR 1 S (1) and (5) TR M ( ) TR 1 S (2) and (7) TR M ( i) M ( ) i, 1 l 1 l O i, S (3) and (6) i M ( ) M ( i) M ( ) M ( ) TR TR i 1 1 l 1 l 1 TR i TR i, S, and and [0,0.5] ((1) and (8)) or ((3) and (9)) or ((2) and (10)) 0 else 1 M ( ) 0 S X X Min 1 0 else (3) l (1) and (2)
5 254 R. Soohtsaraei et. al / International Journal of Couter Networs and Counications Security, 2 (8), August 2014 These five grous include: The first grous include service roviders whose level of used resources was ore than valid value and also soe virtual achines focused on rocessor, focused on eory and I/O are aed on the. This grou is deterined based on forulas (4) to (7). The second grous include service roviders whose level of rocessor usage was ore than threshold and also they have soe virtual achines focused on rocessor. These grous can be deterined using stateents (4) and (8). The third grous includes service roviders whose level of eory usage was ore than level deterined and also they have soe virtual achines focused on eory. This grou can be secified using stateents (5) and (10). The forth grou includes service roviders whose connection lins are ore than threshold level and also soe virtual achines with deand of counication (focused on I/O) are aed on the. This grou is secified based on stateents (6) and (9). The fifth grou (using stateents (4),(11),(6),(12),(5) and (13)) includes service roviders in which one or several resources in the ay be ore than authorized level. They don`t have virtual achines to be able to influence on those resources. TR UR TR UR M( i) M( ) i, S, i 1 l 1 l, l, z M ( ), I / O _ VMset and l Cu _ VMset and z Meory_ VMset l z or l z M( ), Cu _ VMset S M( ), I / O _ VMset S M( ), Meory_ VMset S M( ), Cu_ VMset S M( ), I / O _ VMset S M( ), Meory_ VMset S (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) The oint that is worth to ention is that soe service roviders ay have feature of several grous together which in this situation value of all of these grous in forula (2) should be calculated for the service rovider. Next art ulti-urose genetic algorith is resented. 4 ESCRIING SUGGESTE MULTI- PURPOSE GENETIC ALGORIM Since aing of virtual achines to service roviders is a tye of bin acing, we can aly grou genetic algorith (GGA) which was suggested by Falenauer . As the nae of GGA suggests, it is a develoent of noral genetic algorith which was coared for solving grouing robles. There are also other tyes of genetic algorith for solving grouing robles but what ade GGA roinent is a secial coding which was suggested by Falenauer. As a result, in this article we used GGA. Suggested ulti-urose genetic algorith code is whons u next Inut: Set of VMs with their associated resource deands (Cu, Meory, andwidth ) and set of hosts with their existing resources(cu E, Meory E, andwidth E ) Outut: Set P contains the aing inforation of virtual achines to servers /* Initialization Phase* 1. Set Values of araeters, P,,,,,, NV ( nuber of virtual achines), NI( nuber of iteration), NC( nuber of chroosoes in each 2. Grouing virtual achines in three categories. ( Cu _ VMset, Meory _ VMset, I / O _ VMset ) 3. escending sorting virtual achines based on the forula Creating initial oulation based on aing of resorted virtual achines to servers that are randoly selected. 5. For all Chroosoes in first oulation calculate ran of each Chroosoes based on su of forulas (1),(2),(3) and save the in oldran array /* Iterative Phase*/ 6. While (nuber of iteration<=ni) 7. For all Chroosoes in re oulation 8. Randoly select two Chroosoes. 9. Merge two chroosoes are selected based on the grouing crossover *. 10. Iort two new chroosoes in a teorary oulation. 11. End for oulation)
6 255 R. Soohtsaraei et. al / International Journal of Couter Networs and Counications Security, 2 (8), August o the grouing utation * oeration for all chroosoes in teorary oulation. 13. For all Chroosoes in teorary oulation calculate ran of each Chroosoes based on su of forulas (1),(2),(3) 14. For new oulation: Select K 1 % of new oulation fro to raning chroosoes of teorary oulation. Select K 2 % of new oulation fro to raning chroosoes of old oulation. Select K 3 % of new oulation randoly generated chroosoes. 15. End while /* Solution Phase */ 16. Select toost chroosoe fro final oulation as Solution * Grouing crossover and grouing utation has been roosed in . Figure 1. Suggested ulti-urose genetic algorith S TR 1 S TR 1 S S 1i 1 i VM _ List (14) Figure suggested ulti-urose genetic algorith is shown. Fro now on suggested algorith is called CMPGGA. This algorith is fored on three stes of initialization, iterative and solution. In the first ste at first araeters needed are valued and then three grous of virtual achines are created. next oblivious of grouing of revious ste and based on forula 14, all the virtual achines are ordered in falling for. After arranging, virtual achines are aed to service roviders who were selected randoly and those with enough resources needed by virtual achines so that the first oulation can be gained. In the last hase of initialization, ran of each chroosoe is calculated and saved in the first oulation. In the iterative ste in each reetition a new oulation is created. In each reetition for all chroosoes of the revious generation, act of grou ixing is done. Selection of each chroosoe for ixing is in rando fro. Two chroosoes created in each ixture activity, enter a teorary oulation. After the end of ixing and entrance of all the chroosoes created in teorary oulation, act of grou utation will be done for all the chroosoes of the oulation. Then ran of each chroosoes of teorary oulation is calculated and saved. The last hase of Iterative ste, is establishing new oulation. Selection of chroosoes of new oulation is based on figure3. The reason behind rando selection of the new oulation is not lacing in the local iniu and creation of generations so that we can cover ore areas of searching doain. Fig. 2. Foring new oulation In the final stage; Solution, aong chroosoes of final oulation, the chroosoes with the highest level of significance is selected as the best VMPGGA solution. 5 ANALYSIS OF EFFICIENCY To analyze efficiency of VMPGGA we created a siulator based on Java. To this, we consider a data center with 20 single-ato hysical service. Features of these hysical service roviders are selected based on unifor distribution of values shown in table 2. Table2. Features of hysical service roviders Resource CPU (MIPS) Meory (G) Networ andwidth (Gbs) Caacity Also 50to100virtual achines are considered for aing on this data center. Features of virtual achines are also selected based on the sae distribution of values resented in table 3. Table 3. Features of virtual achines Resource CPU (MIPS) Meory (M) Networ andwidth (Mbs) eand
7 256 R. Soohtsaraei et. al / International Journal of Couter Networs and Counications Security, 2 (8), August 2014 Reaining needed araeters are selected based on table4 Table 4. Values of reaining araeters used Paraeters,, NC ( nuber of chroosoes in each oulation NI ( nuber of iteration) Values TR i 0.005TR i i Fig. 4. level of wasted eory to various nubers of virtual achine To analyze efficiency of VMPGA, we coare it with two algoriths of Multi Obective First Fid ecreasing and Rando. In latter algorith first virtual achines are ordered based on value gained out of forula 14 in falling for and then in the list of service roviders which are ordered in falling for and based on a forula siilar to forula 14, the first service rovider with sufficient sace for the virtual achine is selected and then virtual achine will be aed in it. Next, results gained out of assessent will be resented. Fig. 5. Level of wasted bandwidth to various nubers of virtual achine Fig. 3. level of wasted rocessing to various nubers of virtual achine Fig. 6. Nuber of service roviders used to various nubers of virtual achine
8 257 R. Soohtsaraei et. al / International Journal of Couter Networs and Counications Security, 2 (8), August CONCLUSION Fig. 7. Level of request of overload creator in using rocessor in service roviders used to various nubers of virtual achine. Since ost of tass done in cloud couting do not care about worload of virtual achines and only care about otiization of an obective, in this article we focused our discussion on reducing oerational costs resulting fro wasting resource. y investigating tass in which overloading of hysical achines are not considered  , rovidence in resources wasted is not iosed   also by activities in which overloading of hysical achines and rovidence in resources wasted are not iosed     we resented a ethod which deals with several goals for otiizing in aing rocess by considering feature of worload of virtual achines. Then by coaring results gained fro siulating suggested ulti-urose genetic algorith with a rando algorith and an algorith of the first ulti-urose choice based on factors such as level of rocessing loss, eory and bandwidth, nuber of service roviders and also level of request of overload creator in using rocessor and service roviders, we cae to this conclusion that the algorith suggested can establish better results on rovidence in resources and reducing oerational costs by resenting an efficient ethod we hoe to be able to achieve soe results with less oerational overload, less level of resources usage and at the sae tie the least energy consution in cloud couting. 7 REFERENCES Fig. 8. Level of request of overload creator in using eory in service roviders used to various nubers of virtual achine. Fig. 9. Level of request of overload creator in using bandwidth in service roviders used to various nubers of virtual achine.  A. eloglazov, J. Abaway, R. uyya, Energy-aware resource allocation heuristics for efficient anageent of data centers for cloud couting, Future Generation Couter Systes, vol. 28, no. 5, , Elsevier Science,  A. asterdi, S. Garg, R. uyya, QoS-aware deloyent of networ of virtual aliances across ultile clouds, Proceedings of the 3th IEEE International Conference on Cloud Couting Technology and Science,  A. Khosravi, S. Garg, R. uyya, Energy and carbon-efficient laceent of virtual achines in distributed cloud data centers, Proceedings of the 19th International Euroean Conference on Parallel and istributed Couting, . Seita, M. ichler, A atheatical rograing aroach for server consolidation robles in virtualized data centers, IEEE Trans. Services Couting, vol. 3, no. 4, ,
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