Application of Improved Fish Swarm Algorithm in Cloud Computing Resource Scheduling
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1 , pp Applcaton of Improved Fsh Swarm Algorthm n Cloud Computng Resource Schedulng Yu Lu, Fangtao Lu School of Informaton Engneerng, Chongqng Vocatonal Insttute of Engneerng, Chongqng Abstract. An mproved fsh-swarm algorthm s proposed for resource schedulng n cloud computng. The mprovement process s dvded nto the followng aspects, The expermental results show that the mproved fsh-swarm algorthm s more sutable for resource schedulng n cloud computng because of ts hgh accuracy, short teratve process and fast resource schedulng tme. Keywords: Cloud resource schedulng, fsh-swarm algorthm, umpng behavor, obectve functon 1 Introducton From the brth of the frst computer to the present, the computer model has gone through three mportant stages, namely a sngle host mode, server / clent mode and cloud computng model[1]..cloud computng has changed the tradtonal server/clent model and proposed a new calculaton model. It s another revoluton n the computer feld. Cloud computng has changed the orgnal model whch focus on the task delvered to the large processor, and acheved a new type of computng on demand dstrbuton model [2]. Wth the rapd development of computer technology, users ncreasngly hgh demand for the Internet, the scale of cloud computng becomes more and more large, and the user scale s magnanmous [3]. Therefore, how to adapt to the dynamc changes n demand, to acheve hgh effcency and low cost of ratonal allocaton of resources has become a research focus of cloud computng. The key of the whole cloud resource allocaton s to select a reasonable resource allocaton algorthm. A good resource allocaton algorthm can not only mprove the calculaton and transmsson speed, but also reduce the load on the cloud computng network and reduce the energy consumpton. So that t can make the network load as soon as possble to acheve balance and enhance the performance of the entre cloud computng system [4]. Wood analyzed QoS Guded Mn-Mn and ts mproved algorthm. In vew of the fact that these algorthms are not balanced, he proposed a QoS task schedulng algorthm. The algorthm dvdes the tasks nto general tasks and specal tasks. The general tasks are greedy and the specal tasks are dspatched by Mn-Mn algorthm, whch makes the system load balance [5]. The cloud envronment has the ISSN: ASTL Copyrght 2017 SERSC
2 characterstcs of complex and changeable, and the demand of users wll change accordng to the tme. Ths flexble and vrtualzed characterstc makes the QoS constrant algorthm dffcult to adapt to the envronment and the change of user's requrement. In order to solve ths problem, Goyal consdered the task's trust QoS, proposed a task schedulng mechansm n the cloud computng envronment, and used vrtual machne mgraton technology to acheve dynamc load balancng [6]. In order to mprove the qualty of servce, Tan developed an algorthm QoPA for resource allocaton, whch s based on the needs of users to servce qualty. The algorthm solves the problem of user senstve to qualty on the bass of analytc herarchy process. At the same tme, a fuzzy comprehensve evaluaton method was used to evaluate the replca nodes wth hgh satsfacton, and then the FCE_RS algorthm [7]. In vew of the servce performance evaluaton of cloud computng, Matos ntroduced the queung theory nto servce performance modelng, and appled ths model to the update delay evaluaton of SmpleDB Amazon. The expermental results verfed the feasblty of the model and also showed the dependency relaton of performance of devce and servce of cloud computng servce[8].. 2 Fsh-swarm algorthm and ts mprovement strategy In order to make artfcal fsh-swarm algorthm adapt to the problem of resource allocaton n cloud computng envronment, ths paper proposes an mproved artfcal fsh-swarm algorthm. Ths algorthm cancels the stochastc behavor of the orgnal algorthm, adds the umpng behavor, and ntroduces the lfe cycle and survval ndex to mprove the global optmzaton ablty and effcency of the algorthm. (1) Jumpng behavor In order to further enhance the ablty of artfcal fsh to search the global optmum, ths paper ntroduces the umpng behavor of artfcal fsh. In the artfcal fsh-swarm algorthm, the umpng behavor s manly used to solve the problem of local extrema. That s, when the artfcal fsh nto a local optmal value, even after much teraton, ts ftness value wll not change. At ths pont, we need to model the parameters of the algorthm to make some changes, and the artfcal fsh out of the local extreme areas. The specfc descrpton s: Frst, set the ump value of the fsh ump, determne whether the obectve functon of artfcal fsh s less than the dfference n ump after n teratons, where the dfference between the current value s of the optmal value now and before teraton. If the condtons are satsfed, some artfcal fsh are randomly selected and the parameters of these obects are set to ump out of the current local optmum state. The mathematcal expresson s as follows: Y ( t 1) Y ( t) Vsual Rand() (1) Heren, may be set as a mutaton parameter as requred. Copyrght 2017 SERSC 41
3 (2) Lfe cycle and survval ndex The survval tme of artfcal fsh s lfe cycle T, the mathematcal expresson s as follows: Here, max F T max F s the optmal food concentraton n fsh and (2) s the proporton coeffcent. s the unt tme consumpton of food, referred to as the consumpton factor. The survval ndex h s descrbed as follows: F h T Where, T s the lfe cycle of artfcal fsh; F s the current locaton of artfcal fsh food concentraton; s the consumpton factor. After the ntroducton of lfe cycle, the vablty of artfcal fsh wth ts locaton changes. When the global extremum s near, the lfe cycle of the artfcal fsh s the longest. When t s at the local extremum, the artfcal fsh wll de out and reborn n the ntal state. The advantage of ths method s that the algorthm has a wder search space, and mproves the effcency of optmzaton, saves the ncrease n the number of populatons brought about by the huge storage space. (3) 3 The mplementaton of mproved fsh-swarm algorthm In the cloud resource allocaton model, cloud resources and cloud customers are subect to the followng restrctons: Frst, a cloud server can accept multple tasks. Although the order of servce requests for each customer has been determned, the server does not consder the prorty of the task when t runs multple tasks on the same server. Second, n a certan perod of tme, the cloud server and customer requests are one-to-one relatonshp, and the servce does not allow nterrupts. Thrd, each task can only be executed once on the same server. In the cloud resource allocaton mathematcal model, the specfc constrants are as follows: v 1 v 1 v 1 s s s h h h d d d (4) 42 Copyrght 2017 SERSC
4 The ultmate goal of resource allocaton s to get the mnmum number U of physcal machnes partcpatng n the vrtual machne allocaton: In the above process, s U, s q u 1 and s memory and bandwdth confguraton respectvely. assgned to resource, d, d and d (5) represents the frst task, h represents the task represents the -th resources, memory and bandwdth confguraton respectvely. represents the total number of resources. Among them, the task executon tme constrants refers to the resources to perform the task of the tme spent. The bandwdth allocaton constrant refers to the maxmum bandwdth that can be allocated. The network delay constrant s the maxmum network delay that can be provded. Defne the obectve functon as: R A ET B ND C q (6) ET max ET s. t. mn ND max ND (7) 4 Cloud computng resource schedulng experment Ths experment s dvded nto the performance test of the algorthm and the resource allocaton smulaton on the CloudSm platform. The algorthm uses the smulaton method to test the performance of the algorthm.in the experment, the tradtonal fsh school algorthm s selected as the comparson algorthm of ths paper. The number of ndvduals n the fsh populaton s 100, 150, 200, 250, 300, 350, 400, respectvely, and the number of teratons s 200. The ntal state percepton dstance Vsual = 8, the crowdng factor 0. 6, the maxmum number of probes 100, the mproved algorthm 0. 15, ump value ump = 0.5, ump teraton number n = 15, mutaton parameter The teraton tmes of the two algorthms to acheve the optmal soluton and the optmal soluton are shown n Table 1, where the results are obtaned by repeatng the averagng of 20 experments. Copyrght 2017 SERSC 43
5 Table 1. Comparson of teratve tmes and optmal solutons of two algorthms Tradtonal fsh-swarm Improved fsh-swarm Sze of fsh algorthm algorthm (Number of Number of Optmal Number of Optmal artfcal fsh) teratons soluton teratons soluton Fg. 1. Comparson of teratve tmes of two algorthms Frst, we observe the comparson between the tradtonal fsh-swarm algorthm and the mproved fsh-swarm algorthm n the teraton tmes, as shown n Table 1 and Fgure 1. It can be clearly seen that the mproved fsh-swarm algorthm has fewer teratons, that s, t does not fall nto the local mnmum and can not fnd the global optmum. In ths experment, the optmal soluton s 45. From the expermental results, we can see that the optmal soluton obtaned by the tradtonal fsh-swarm algorthm s close to 50, and the mproved fsh-swarm algorthm s very close to Copyrght 2017 SERSC
6 From the above two groups of comparson results, we can see that after the mprovement of ths paper, the performance of the fsh algorthm has been mproved. 5 Conclusons Amng at the problem of resource schedulng n cloud computng, an mproved fshswarm algorthm s proposed based on the tradtonal fsh-swarm algorthm. The expermental results show that the mproved fsh swarm algorthm proposed n ths paper s more accurate and the number of teratons s shorter, and the tme of completng cloud resource schedulng s shorter. References 1. Yook Juhye, Km Jnsul. The advanced Korea-computer access assessment system(k- CAAS) on smart moble cloud envronment[j]. Multmeda Tools and Applcatons, 31(5): (2014) 2. Guo-nng G, Tng-le H, Shua G. Genetc smulated annealng algorthm for task schedulng based on cloud computng envronment[c] Internatonal Conference on Intellgent Computng and Integrated Systems (2010) 3. Gan Abdullah, Nayeem Golam Mokatder. A revew on nterworkng and moblty technques for seamless connectvty n moble cloud computng[j]. Journal of Network and Computer Applcatons, 43: (2014) 4. Janfang Cao, June Chen, Qngshan Zhao. An optmzed schedulng algorthm on a cloud workflow usng a dscrete partcle swarm[j]. Cybernetcs and Informaton Technology, 14(1): (2014) 5. Wood J, Romoser B, Zecena I, Zong Z. B-Maps: a self-adaptve resource schedulng framework for heterogeneous cloud systems[c]. ACM Internatonal Conference Proceedng Seres, ( 2013) 6. Goyal Sudhr, Bawa Seerna, Sngh Bhupnder. Expermental comparson of three schedulng algorthms for energy effcency n cloud computng[c] IEEE Internatonal Conference on Cloud Computng n Emergng Markets, (2014) 7. Tan W, Zhao Y, Zhong Y, et al. A dynamc and ntegrated load-balancng schedulng algorthm for Cloud datacenters[c]. IEEE Internatonal Conference on Cloud Computng and Intellgence Systems(CCIS 2011) (2011) 8. Matos Rubens, Arauo Jean. Senstvty analyss of a herarchcal model of moble cloud computng[j]. Smulaton Modellng Practce and Theory, 14(5): ( 2014) Copyrght 2017 SERSC 45
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