Effective Task Scheduling and Dynamic Resource Optimization based on Heuristic Algorithms in Cloud Computing Environment

Transcription

1 KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL 11, NO 12, Dec Copyrgh c2017 KSII Effecve Task Schedulng and Dynamc Resource Opmzaon based on Heursc Algorhms n Cloud Compung Envronmen Frederc NZanywayngoma 1, Yang Yang 2 12 Deparmen of Compuer Scence and Communcaon Engneerng, Unversy of Scence and Technology Beng Beng, Chna [e-mal: nzanywafre@yahoofr] [e-mal: yyang@usbeducn] *Correspondng auhor: Frederc Nzanywayngoma Receved May 10, 2016; revsed November 12, 2016; revsed February 28, 2017; revsed May 11, 2017; revsed July 4, 2017; acceped Augus 9, 2017; publshed December 31, 2017 Absrac Cloud compung sysem consss of dsrbued resources n a dynamc and decenralzed envronmen Therefore, usng cloud compung resources effcenly and geng he maxmum profs are sll challengng problems o he cloud servce provders and cloud servce users I s mporan o provde he effcen schedulng To schedule cloud resources, numerous heursc algorhms such as Parcle Swarm Opmzaon (PSO), Genec Algorhm (GA), An Colony Opmzaon (ACO), Cuckoo Search (CS) algorhms have been adoped The paper proposes a Modfed Parcle Swarm Opmzaon (MPSO) algorhm o solve he above menoned ssues We frs formulae an opmzaon problem and propose a Modfed PSO opmzaon echnque The performance of MPSO was evaluaed agans PSO, and GA Our expermenal resuls show ha he proposed MPSO mnmzes he ask execuon me, and maxmzes he resource ulzaon rae Keywords: Modfed Parcle Swarm Opmzaon (MPSO), Vrual Resource Schedulng, PSO, Global Opmzaon, Cloud Compung The auhor would lke o hank he revewers for her valuable commens and suggesons ha helped us o mprove he qualy and he correcness of hs work Ths work was suppored by he Naonal Scence Foundaon of Chna (Gran Nos , , , , and , Fundamenal Research Funds for he Cenral Unverses [FRF-TP A2]) hps://doorg/103837/s ISSN :

2 KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL 11, NO 12, December Inroducon Nowadays, users have realzed ha he PCs bough few years ago can move wh he developmen of sofware; hey requre a hgher speed CPU, a larger capacy hard dsk, and a hgher performance Operaon Sysem (OS) Tha s he magc of Moore s Law whch needs user o upgrade her PCs consanly, o cope wh developmen of echnques[1] Cloud Compung Servces such as Infrasrucure as a Servce (IaaS), Sofware as a Servce (SaaS), Plaform as a Servce (PaaS) came o gve users he vrual unlmed pay-per-use compung resources (eg, nework resources, servers, and sorage resources) wh a mnmum effor o manage avalable resources effcenly and gan maxmum prof The PCs ha wan o access he cloud servces us need o have less memory, a lgh operang Sysem and browser Users can use he sofware drecly from he cloud, can sore daa on cloud, can make her applcaons on cloud plaform whou nsallng any sofware on her machnes[1, 2] Accordng o NIST defnon, cloud compung s a model for enablng ubquous, convenen, on-demand nework access o a shared pool of confgurable compung resources ha can be rapdly provsoned and released wh mnmal managemen effor or servce provder neracon[3] Cloud compung has become one of he ndusry buzz words and a maor wdely used n varous felds of IT world I s an emergng echnology and processes a large amoun of daa so ha he ask schedulng mechansms work as a val role The problem of ask schedulng has become a ho opc A he same me, s also NP hard problem Therefore, he man goal of he effcen ask schedule algorhm s o decrease oal compleon me, overall execuon me, average response me and o mprove he resource ulzaon rae of he enre sysem under he condon of meeng he Qualy of Servce Cloud compung echnology allows users o pay as you need and has he hgh performance The renal cos s relaed o hardware and sofware, such as he number of CPU frequency, number of core, memory, he sze of hard dsk and nework dsk, operang sysem, daabases, nework bandwdh, manenance cos, and locaon of rened servers, ec[4] The effcen usage of resources and achevng an opmal allocaon of user s asks are he key ssues of ask schedulng n cloud compung Therefore, how o use cloud compung resources effcenly and ge he maxmum profs becomes he fundamenal goals of cloud compung servce provders[5] Schedulng an nensve daa or compung an nensve applcaon, s acknowledged ha opmzng he ransferrng and processng me s crucal o an applcaon program In our paper, a Task Schedulng Opmzaon based on Heursc Algorhms s proposed Nowadays, cloud compung envronmen s mosly bul accordng o MapReduce programmng model, whch s an effcen ask schedulng model especally for he generaon and processng of large daa ses Therefore, for cloud compung provders, ask schedulng and vrual resource allocaon wh dynamc characerscs s sll a challengng problem In order o fnd a good mehod of solvng hs problem, research s conduced on varous heursc approaches Thus, key pons and dffcules of cloud compung are how o reasonably carry ou he ask schedulng and vrual resource allocaon a he same me There are more facors ha are affecng he cloud servce performance such as he cloud servce cos, real-me server load rae, bandwdh ulzaon especally when users requre a hgher communcaon such as n mulmeda sreamng, relably of he sysem, nework delay, and ask compuaon complexy Therefore, hese above-menoned facors yeld o

3 5782 Frederc NZanywayngoma&Yang Yang: Effecve Task Schedulng and Dynamc Resource Opmzaon based on Heursc Algorhms n Cloud Compung Envronmen ncrease oal ask compleon me, overall ask execuon me, average ask response me, and decrease he resource ulzaon rae of he enre cloud compung sysem [6] In hs paper, we mnmze he cos of he processng by formulang a model for ask schedulng and proposng a modfed parcle swarm opmzaon algorhm whch s based on small poson value rule The radonal parcle swarm opmzaon algorhms have been negraed no he ask schedulng model n he cloud compung envronmen o mprove he qualy of servce and he fness funcon[7]; however, s common o encouner ssues such as local opmzaon and no neracon beween parallel swarms[2] We resolve hese dsadvanages of he sandard parcle swarm opmzaon (SPSO) algorhm by descrbng a modfed parcle swarm opmzaon (MPSO) algorhm able o denfy opmal soluons for he cloud resource schedulng problems Our conrbuons o hs paper are ced as he followngs: (1) we formulae a model for ask schedulng n cloud compung o mnmze he overall me of execung and ransmng asks (2)We desgn a MPSO algorhm o solve ask schedulng based on he proposed mahemacal model, compare and analyze wh oher exsng algorhms We compare he MPSO resuls wh he radonal PSO We organzed hs paper as follows: Secon (1) nroduces he cloud compung and he basc of PSO opmzaon Secon (2) shows relaed work Secon (3) saes and formulaes he problem Secon (4) presens ask schedulng algorhm ha uses MPSO and formulae he mahemacal model Secon (5) shows he smulaon resuls of he proposed algorhm Las bu no lease, secon (6) concludes he paper 2 Relaed Work The mos crucal requremen for cloud compung s an effecve ask schedulng and a vrual resource opmzaon Task schedulng s very mporan o cloud compung resources and has been a challengng problems Recenly, many researches are beng conduced o mprove he effcency of ask schedulng and vrual resource ulzaon Mos of he proposed search algorhms, dsregarded he oal ask compleon me, and he resource allocaon a he same me The msuse of he avalable resource leads o he ncreasng of server compung me and he user wang me Heursc algorhms have been proposed o solve hese complex opmzaon problems whch are mosly non-lnear or non-dfferenable or combnaoral opmzaon problems[8] Due o he complexy naure of opmzaon problems, consan and lnear me-varyng values may no work well n many cases Therefore, usng a non-lnear coeffcen for MPSO could yeld o a beer performance n some exen In hese days, many heursc evoluonary opmzaon algorhms have been proposed o resolve he ask schedulng problems [9] These nclude Parcle Swarm Opmzaon(PSO), Genec Algorhm(GA), Deferenal Evoluon(DE), An Colony(AC), Tabu search algorhm, Smulaed Annealng Algorhm [10], Arfcal bee colony algorhm(abc) [8, 11, 12], Harmony Search Algorhm[13], and Gravaonal Search algorhm(gsa) [14] The purpose for hem s o fnd he bes global opmum among all possble npus [15] In [16], Swa Agrawal, consderes a modfed PSO o address he problem of premaure convergence Parcle Swarm Opmzaon (PSO) echnque suffers from a maor drawback of a possble premaure convergence Here, he focus s o free PSO from local opmum soluons; enable o progress owards he global opmum searchng over wder area For hs modfed PSO, resuls have been compared wh he base PSO, whch s nera wegh PSO

4 KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL 11, NO 12, December Numercal expermens done on some benchmark funcons compare nera wegh PSO and modfed PSO Also n [17], Sad Labed e al, propose a modfed hybrd Parcle Swarm Opmzaon (MHPSO) algorhm ha combnes some prncples of Parcle Swarm Opmzaon (PSO) and Crossover operaon of he Genec Algorhm (GA) wh wo ams: o propose a new hybrd PSO algorhm; o prove he effecveness of he proposed algorhm n dealng wh NP-hard and combnaoral opmzaon problems The es resuls were smulaed n malab7 o assess he effcency and performance of he MPSO algorhm Shaf Ullah Khan e al [18] propose a MPSO for global opmzaons of Inverse problems In hs work, he modfed PSO s presened by nroducng a muaon mechansm and usng dynamc algorhm parameers The expermenal resuls on dfferen case show ha he proposed PSO ges he bes resuls among he esed algorhms In [18], A-Qn Mu e al, use he dea of smulaed annealng algorhm o propose a modfed algorhm whch makes he mos opmal parcle of every me of eraon evolvng connuously By he esng of hree classc esng benchmark funcons, s concluded ha he modfed PSO algorhm has he beer performance of convergence and global searchng han he orgnal PSO The work n [19], akes no consderaon boh compuaon cos and daa ransmsson cos Pandey s e al, has presened a parcle swarm opmzaon (PSO) based heursc o schedule applcaons o cloud resources In [11] auhor presens a parcle swarm opmzaon n a muldmensonal complex space Ther paper analyzes a parcle s raecory n dscree and n connuous me wh he goal of ncreasng he ably of he swarms n order o fnd he opmum soluon The work n [20] used hybrd (PSO and GA) heursc algorhms o enhance power sysem sably Oher heursc algorhms n [21] have been used combned wh MapReduce parallel programmng model for hgh performance compung a scale n cloud compung Ther work evaluaes sysem desgn alernaves and capables aware ask schedulng for large-scale daa processng on acceleraor-based dsrbued sysems The paper [22] proposed he mulple applcaon co-exs (MACE) mehod, whch mnmzes muual-nerference and maxmzes resource ulzaon for resource sac allocaon, dynamc supplemen and resource reserved mechansm [23] Therefore, we wll focus on reducng he processng cos, power consumpon, and ransmng me beween vrual resources n he cloud compung wh he purpose of mnmzng he cos and me of he obs submed by users Energy consumpon, processng me, and cos The cos s relaed o he compung power of he CPU, he memory sze, and he bandwdh Auhor [24] suggesed a ask schedulng usng a mul-obecve nesed Parcle Swarm Opmzaon (TSPSO) o opmze energy and processng me o reduce power consumpon and mprove he prof of servce provders by reducng processng me and auhor [25] focused on opmzng he energy effcency n daacener by usng effcen ask schedulng o physcal servers To mprove he resource allocaon n cloud compung, an allocaon model usng he shores ask compleon me and he lowes cos as he consrans o mnmze cos and compleon me was proposed Paper [26] proposed a Parallel Bee Colony Opmzaon Parcla Swarm Opmzaon(PBCOPSO) approach wh obecve o mnmze oal execuon me and o opmze he resource ulzaon The auhor consdered Bee Colony Opmzaon(BCO) n parallel wh PSO o schedule ndependen asks In her expermenal resuls, auhors consdered o use 4 resources o schedule 40 o 60 asks Ther developed approach was compared wh Mn-Mn and IBCO (Improved Bee Colony Opmzaon) Compared o Mn-Mn algorhm, he proposed new mehod mproved he resource ulzaon by an average of 5038% and 3724% compared wh IBCO To show he performance of he proposed algorhm (MPSO), our expermenal resuls wll be compared agans GA and PSO n erms of ask schedulng mercs There exss many

5 5784 Frederc NZanywayngoma&Yang Yang: Effecve Task Schedulng and Dynamc Resource Opmzaon based on Heursc Algorhms n Cloud Compung Envronmen leraure on comparson of GA and PSO PSO and Genec Algorhm (GA) are wo evoluonary heurscs populaon-based search mehods bu GA s dscree varables based whch s suable for combnaoral problems PSO has o be modfed o cope wh dscree varables GA and PSO echnques sar wh a group of a randomly generaed populaon and use a fness value o evaluae he populaon They all updae he populaon and search for he opmum wh random echnques The man dfference beween he PSO and GA approach s ha PSO does no possess genec operaors such as crossover, selecon, and muaon Compared o GAs, he advanages of PSO are ha PSO s easy o mplemen and here are few parameers o adus [20, 27-29] Paper [30]compared he resuls of GA, PSO, ACO, shuffled frog leapng, and memec algorhms n erms of processng me, convergence speed and qualy of he resuls PSO was found he bes performer han oher algorhms n erms of processng me The elemens used for PSO are: Parcles, fness funcon, local Bes, global bes, velocy updae, poson updae And GA employs hree operaors (crossover, Selecon, and Muaon) o propagae s populaon from one generaon o anoher We have explored how PSO/GA work and how hey are appled o solve NP-complee problems of ask schedulng n cloud compung 3 Problem Saemen 31 Assumpons and problem formulaon The ask schedulng echnques [31] s o assgn ncomng asks o he avalable resources Accordng o he schedulng sraeges, he poor ask schedulng algorhms can sgnfcanly affec he effcency of he whole Cloud Compung sysem Le assume ha a cloud servce provder assgns n asks o m machnes wh ( m < n) Le ( 1, 2,, n) represens he npu asks wang o be scheduled Then he ask scheduler based on schedulng polcy and QoS requremens decdes whch ask o be assgned o whch machne from he collecon of vrual machnes as shown n Fg31 The VMs are represened by vmd, mps, ram, bw, cpu vmd represens VM number, mps represens vrual machnes speed n mllon nsrucons per second, ram represens he memory of VM, bw represens he bandwdh, cpu represens he cpu of he VM To model our ask schedulng problem, we assume ha he number of swarm parcles correspond wh a se of ask numbers The asks are modeled as a drec acyclc graph (DAG) where G= (V, E) A se of verces V represens he VMs and asks and a se of edges E represens he connecons beween he asks and VMs and her communcaon cos Edge s a par ( v, ) whv, V We denoe Tn as he number of asks and Rm he number of avalable heerogeneous compung dynamc vrual resources The obecve o model he ask schedulng problem s o mnmze he compleon me and o fnd he bes vrual resource ulzaon We evaluae boh obecve funcons usng fness funcon Here he fness of a parcle s measured wh execuon me and communcaon cos for all asks Le G = ( V, E) be a graph wh V = { 1, 2,, n } as a se of asks nodes/verces, and E s a se of edge weghs beween wo asks and k and s denoed he nformaon exchange beween hese pared asks An arc s n he form of < T, T > E, wheret enry s called he paren ask and s he chld ask Here, we consder ha all asks are compuaonal and are consdered o be dependen o each oher accordngly We consder a complee graph whch

6 KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL 11, NO 12, December means ha here s an edge beween every par of verces The graph sars wh roo node and ends wh end node The node wh no paren/roo node s called an enry node Tenry and a node wh no chld/end node s called an ex node T ex Inpu asks Sar of he process Task1 Task2 Task n Task Scheduler Task Assgnmen Sysem Schedulng Polcy QoS Requremens VM1 VM2 VM Oupus End of he process Fg 31 Represens a Task Schedulng model n cloud compung envronmen[32] I s assumed ha a chld ask canno be execued unl all of s paren asks have been compleed Fg 31 and Fg 32 llusrae clearly he scenaro Le us consder Tenry andt ex as wo dummy asks a he begnnng and a he end of he weghed drec acyclc graph (DAG) wh zero execuon me We calculae he communcaon cos C, by referencng o he amoun of daa o be ransmed beween resources The schedulng s consdered as a non-preempve schedulng o mean ha here s no nerrupon when he asks are processng T enry T 1 e 12 e 13 T 3 e 34 T 2 T 4 e 2n T n e 4n T ex Fg 32 Task graph wh n asks

7 5786 Frederc NZanywayngoma&Yang Yang: Effecve Task Schedulng and Dynamc Resource Opmzaon based on Heursc Algorhms n Cloud Compung Envronmen The mappng of he se of asks o he avalable heerogeneous resources n Fg 33 helps us o compue he maxmum compleon me of he asks, mnmum execuon me, and he execuon cos The cos vares dependng on he compung power of he CPU, memory, and bandwdh To map he above-menoned se of resources, we consder R m number of avalable heerogeneous compung resources, and b he bandwdh beween resources Then we calculae he avalable bandwdh B = ( b ) NxN for he avalable resource We suppose ha = o a = 1, 2 r m and a large number of par of asks and VM resources The Fg 33 depcs an llusrave example of he ask assgnmen Assume ha ask 1 s assgned o resource r 1 and r 3, ask 2 s assgned o resource r 2, r 4, and r, ask 3 s assgned o resource here exs a fne number of possble mappngs from a collecon T { 1, 2,, n } collecon R { r r,, } r4 and r, and 4 s assgned o r respecvely m T 1 T 2 T 3 T 4 T n R 1 R 2 R 3 R m Fg 33 Mappng of he asks o avalable resources[33] We consder a dscree-me model wh a collecon Rm of machnes ndexed1,2,, m Tasks come n wh a agged random mappng number and each ask s assocaed wh m number of avalable resources and hey are flocked ogeher accordng o her ndces n an ncreasngly order no a vecor m {, r1 < r2 < rm } ( r1 r2,, rm ) {,2,, } 1 (1) V M <

8 KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL 11, NO 12, December The dsrbuon relaonshp beween ask se Tn and s defned as x x χ = x n x = m1 x x x m2 = 1 else n x = 1 = 0 x x 1n 2n x nm f s runnng on x represens he poson of a parcle such ha x k + 1 VM hen x = 1 else x = 0 1 = 0 To avod he convergence of he swarm he velocy v s lmed by Vmax and Vmax = ( x x x ), [ 1, m], [ 1, n] X,,, 1 2 n and The wegh of a node s c, and he compuaon ably of vrual machne s Ca Each ask can be execued on dfferen vrual machnes and he execuon me of ask equals o he raon of he workload and compuaon ably of he resource r Execuon me of he runnng on MI[] denoes he lenghs of MI[ ] VM s expressed as me = MIPS[ ] and MIPS[ ] s he processng speed of VM where The marx of he execuon me s defned as: ET ET ET = ET T = n m { { r m1 ET ET ET m2 ET ET ET 1n 2n mn 1 n }represens a se of n asks R = 1 m } represens a se of m resources ET wh 1 n, 1 m } represens a marx of expeced execuon mes of ask on resources r

9 5788 Frederc NZanywayngoma&Yang Yang: Effecve Task Schedulng and Dynamc Resource Opmzaon based on Heursc Algorhms n Cloud Compung Envronmen 32 Obecve funcon formulaon To formulae he obecve funcon, we suppose ha user U s assgned o vrual resource R o subm a se of askst The fnshng me oft can be calculaed as he summaon of sar me and me requred execung askt : FT (, r ) = (, r ) + ST (, r ) So he oal me spends o complee he user s ob by R can be defned as Makespan = max{ FT} The obecve funcons of he new model are expressed as mnmzng makespan ( = 1,2,, m) The Scheduler opmzer s mplemened by MPSO algorhm o generae an opmal schedulng sraegy The MPSO s an effcen schedulng sraegy o face he characersc of he ask cloud compung problem 4 Mehodology 41 Parcle Swarm Opmzaon Algorhm overvew PSO was frs proposed by Kennedy and Eberhar [34, 35] hrough smulang of a smplfed socal behavor model of brd flockng o fnd food source or fsh schoolng o proec hemselves from a predaor Parcle Swarm Opmzaon (PSO) [3] also known as a heursc opmzer ha opmzes a problem ha eravely res o mprove a canddae soluon based on adapve searchng echnques PSO uses he followng parameers: Inal populaon, swarm, populaon sze, search space, maxmum generaon As a powerful opmzer, PSO s appled for un-processor heerogeneous and preempve real-me sysems PSO presens he mers of parallel dsrbuon, scalably, easy o realze wh hgh flexbly and srong robusness n dynamc envronmens PSO solves many combnaoral opmzaon problems successfully [5, 15] PSO pus more emphass on exploaon han exploraon PSO concenraes on searchng around a promsng area n order o refne a canddae soluon and explores dfferen regons of he search space n order o locae a good opmum PSO depends on good nal posonng of he parcles n he soluon space [36] Wh her exploaon and exploraon, he parcle swarms fly hrough he problem space and have wo reasonng capables: he memory of her own bes poson pbes and knowledge of he global or neghborhood s bes poson gbes [34, 37] The same as n cloud compung, each ask runs on vrual machne where he resources are dsrbued vrually lke he way parcle swarm fly hrough problem space manan useful nformaon of her local poson and global poson The poson of parcle s nfluenced by velocy and has o be updaed each me he parcle moves from one pon o he nex poson We assume ha he asks are compleely dfferen and are dependen as parcles move n swarm and all asks need o use resources such as CPU, memory, bandwdh, o be execued and hey mus be measured n erms of cos The more accurae coss, he more he profs are [38] PSO has fas speed, bu low convergence accuracy PSO's dsadvanages are as follows: he mehod easly suffers from he paral opmsm PSO uses a number of parcles (canddae soluons) whch fly around n he search space o fnd bes soluon In PSO populaon represens he number of parcles n he search space Each parcle n PSO should consder he curren poson X = x, x,, x ] vecor, he curren velocy V = v, v,, v ], he [ 1 2 D [ 1 2 D

10 KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL 11, NO 12, December dsance o pbes, and he dsance o gbes o modfy s poson Take pbes pbes gbes gbes = ( x x ) and gbes ( x,, x ) pbes,, n = n as he bes poson of a parcle and s neghbors' bes poson Then, he velocy and poson of every parcle s updaed usng he wo Equaons below[39]: + 1 v = ωv + c1 rand1 ( pbes x ) + c2 rand2 ( gbes x ) (5) x = x + v (6) where v s he velocy of he h parcle a h k eraon, andω s a weghng funcon c1 and rand and rand 2 are he c2 are he weghng facor whch ncrease he performance of PSO, 1 random numbers beween 0 and 1whch gve he PSO a more randomzed search ably; he curren poson of he h parcle a he h k eraon; x s pbes s he varable o sore he bes h soluon obaned by he parcle; gbes represens he parcle poson or global poson as well To acheve a hgh performance, we se he nera wegh as ( ωmn ωmax ) ω = ωmax + er (7) er max ωmn and ωmax are he sarng and endng nera wegh whch are responsble o conrol he PSO algorhm s sably Ther bes values are beween 02 and 09 respecvely er and ermax represen he curren and maxmum erave me whch we se o 1000 ω s se o 04 The maxmum number of nera wegh s assgned o parcles wh fness values greaer han average fness value whle mnmum value of nera wegh s assgned o parcles wh fness values lesser han fness average value The frs par of he above-menoned formula, ω v, provdes exploraon ably for PSO The second and hrd pars, c1 rand ( pbes x ) and c2 rand ( gbes x ), represen prvae hnkng and collaboraon of parcles respecvely The nera wegh ω balances he global opmzaon capably and he local opmzaon capably Y Y X (+1) X (+1) V() V gbes gbes V() w*v() V (+1) gbes X () V pbes pbes X() gbes V pbes V pbes The orgnal PSO X The sandard PSO X Fg 41 The movemen of parcles: a concep of modfcaon of a searchng pon by PSO

11 5790 Frederc NZanywayngoma&Yang Yang: Effecve Task Schedulng and Dynamc Resource Opmzaon based on Heursc Algorhms n Cloud Compung Envronmen 42 The mplemenaon of MPSO Algorhm for Task Schedulng and Vrual Resource Allocaon n Cloud Compung Cloud compung echnology brngs he compung resources and sorage resources n dfferen geographcal posons no a resource pool hrough vrual echnology The users have o use hem and hen need o release hem so ha hey can be reused In hs way, we can calculae he average compuaon cos and compuaon me of all asks on all he resources The MPSO can be used o solve hs non-lnear complex opmzaon problem due o s advanages of beng less complex operaons and parameers MPSO presens a good deal n cloud compung because cloud compung server cluser can fas realze resource dscovery, resource machng, schedulng producon, and ask average runnng me [5] The proposed MPSO enlarges he scope of excellen posons and enhances global search ably n order o mprove he performance of parcle swarms In hs work, MPSO aduss he value of nera wegh ω, parcle velocy, and updaes of parcle poson The nera wegh ω balances he global opmzaon capably and he local opmzaon capably Durng he resource dscovery, and schedulng process, he cloud sysem uses pbes whch s he bes locaon he parcle has acheved so far pbes can be vewed as he parcle's memory and does no depend only on he value of fness funcon I also depends on oher consrans gbes s he bes poson ha neghbors of a parcle have acheved so far gbes akes he whole populaon as he neghbors of each parcle The selecon of gbes consss of hree seps: deermne he neghborhood, selec he gbes among he neghbors, and compare fness values among neghbors In hs MPSO algorhm, mos of he seps are he same as PSO algorhm bu ours ams a enhancng he global search ably, swarm dversy by ncreasng he chance o fnd a beer soluons and exploaon ably In frs seps, all he parcles are nalzed The fness value of all parcles s calculaed The pbes and gbes are calculaed The process of he MPSO algorhm s shown n he Fg 42 below: As we can see from he Fg 42 above, he heursc algorhms are essenal o solve real-world and complex problems PSO and our proposed MPSO are close o each oher srucurally bu he movemen of parcles n neghborhood represens a soluon for he problem The parcle moves by he drecon on he pbes and gbes unl reachng he maxmal number of eraon The algorhms are descrbed as follows:

12 KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL 11, NO 12, December Sar Sar Inalze PSO parameers Inalze parcles parameers Inalze populaon Evaluae fness funcon Check and updae pbes Check and updae gbes Evaluae fness funcon of each parcle Updae he opmal poson of he swarm Calculae he average fness value Updae he poson and velocy of each parcle Updae parcle velocy & poson Termnaon crera reached? End Yes No fness reached he hreshold? End Yes No Fg 42 Flow char of Sandard PSO and MPSO Algorhm1 Pseudo code of Parcle Swarm Opmzaon (PSO) algorhm 1: Inalzaon: Sar nalzng parcles wh random posons 2: Converson: Conver he connuous poson vecor o dscree vecor 3: Fness: Calculae fness value for each parcle usng fness funcon If curren fness s greaer han fness pbes, hen 4: Calculang pbes : Calculae he bes parcle pbes and assgn s bes poson value o gbes 5: Calculae velocy for each parcle 6: Updang: Updae he parcle velocy and he swarm bes known poson unl reachng he ermnaon condon based on formula (6) and (7): + 1 v = ωv + c1 rand1 ( pbes x ) + c2 rand2 ( gbes x ) x = x + v

13 5792 Frederc NZanywayngoma&Yang Yang: Effecve Task Schedulng and Dynamc Resource Opmzaon based on Heursc Algorhms n Cloud Compung Envronmen Where ω =nera c 1,c 2 =unformly dsrbued random numbers pbes =bes poson of each parcle gbes =bes poson of he enre parcles n a populaon = eraon 7: Repea 2 o 6 unl he soppng condon s sasfed (reachng maxmum number of eraons or when no change n fness value for a consecuve eraon) 8: Oupu: Prn he fnal soluon as he bes parcle Algorhm2 Pseudo code of modfed Parcle Swarm Opmzaon(MPSO) algorhm 1: Ge he bes soluon of he parcles 2: Se parcle dmenson as equal o he sze of avalable asks 3: Randomly, nalze parcles poson and velocy of each parcle; 4: Calculae he bes fness value of each parcle, concern he parcle wh he beer fness value, and compare s fness value wh he fness of s pbes ; f F X ( )) < F( pbes ) F ( ( pbes ) = F( X ( pbes = X () 5: Calculae he bes parcle as gbes wh he bes fness value, )) f F X ( )) < F( gbes ) ( gbes ) = F( X ( )) = X () F( gbes 6: Calculae velocy and updae her posons MPSOupdaevelocy() //change he velocy of he parcle accordng o (5) MPSOupdaeposon() //change he poson of he parcle accordng o (6) 7: Check f he soppng condon or he maxmum eraon s reached oherwse, repea from sep3 unl a soppng creron s sasfed Algorhm3- MPSO-Based Vrual Resource Schedulng 1: Se he vrual resource parameers and he wegh of he nodes and edges from he DAG; 2: Selec asks o be allocae o he avalable resources r R accordng o her prores p 3: For every r R, do 4: Schedule all asks from r 6: End 7: End 8: For he nodes r R 9: The ask s assgned o a vrual resource r for execuon based on he MPSO algorhm 10: End

14 KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL 11, NO 12, December Algorhms Descrpons In hs secon, he deals of he proposed MPSO algorhms are explaned clearly The above ask schedulng algorhms n he cloud-compung envronmen are descrbed based on PSO elemens as follows: The parcles are defned as he avalable real asks n ; he fness funcon s defned as he funcon used o fnd he opmal soluon; local bes s defned as he bes poson of a parcle among s all posons vsed so far; global bes s defned as he poson where he fness s acheved among all he parcles vsed so far; Inera Wegh s defned as he value used o balance he exploraon-exploaon rade off; he velocy updae s defned as a vecor o calculae he speed and drecon of he parcle; and hen he poson updae s defned as a global opmal poson of a parcle The PSO parameer sengs are as follows: Inal populaon s a se of parcles a a sarng me and are generaed randomly; he swarms are dsorganzed of movng parcles ha end o cluser ogeher whle each parcle seems o be movng n a random drecon; populaon sze s he number of parcles whch can be fxed accordngly; search space s he range n whch he algorhm compues he opmal conrol varables We se he lower bound and upper boundary o 0 and 1; maxmum generaons are he maxmum number of generaons allowed for he fness o converge wh he opmal soluon From he nalzaon sage, he algorhm ses he number of parcles, nalzes he parcle poson vecor and velocy vecor of each parcle n he parcle swarm search space, max r r r, vr v r The maxmum speed of parcle n n-dmensonal has o be se n order o lm he velocy of max ur lr a parcle vr = β ( ) (8) ur + lr where β [ 0,1], and v r s he velocy of he n h dmenson parcles, u r and l r are he upper bound and lower bound of he r-dmensonal search space The speed lm can cause he convergence of he parcle o he global opmal poson The second sep calculaes he max max nera weghω and updaes he velocy of he parcle If vr v r, hen v r = v r, f max max v < v r, hen vr = v r Updae he parcle poson from he nal poson up o he nex poson If x r < lr, hen se x r = lr, f x r > ur, se x r = ur The algorhm calculaes he where n x [ l, u ] fness of each parcle and updaes he pbes and gbes opmal of each parcle poson n he parcle swarm When reaches o he maxmum eraon or fnds he deal resul, wll close up he process oherwse wll repea he process from sep2 o updae he velocy of each parcle unl he soppng creron s sasfed Le us assume ha we know he sze of npu and sze of oupu of each asks and assgned as edge wegh e, e n Fg 32 Dependng on he number of asks compleed, he ready ls s k 1 k 2 updaed, whch wll now conan he asks whch parens have compleed execuon I wll updae he average values for communcaon beween resources accordng o he curren bandwdh When he remoe resource managemen sysems are no able o assgn ask o resources accordng o our mappngs sraegy due o resource unavalably, he compuaon of MPSO makes he heursc dynamcally balanced o oher asks' mappng Therefore, n hs problem, he parcles are he asks o be assgned and he dmensons of he parcles are he number of asks n a cloud sysem The value assgned o each dmenson of

15 5794 Frederc NZanywayngoma&Yang Yang: Effecve Task Schedulng and Dynamc Resource Opmzaon based on Heursc Algorhms n Cloud Compung Envronmen parcles s he compung resource o a ask The performance of each parcle s evaluaed by fness funcon descrbed above where represens he number of parcles and represens he vrual resource node number The parcle calculaes her velocy and updaes her poson accordngly The evaluaon sep s carred ou unl he specfed number of eraons s reached The performance of MPSO vares accordng o he varaon of he compung resource cos Ths varaon depends on he way cloud servce vares prcng polces dependng on he ype and capables of he vrual resources 5 Smulaon and Analyss of he Resuls 51 Smulaon Envronmen In order o prove he feasbly and o show he performance of he proposed algorhm, we chose o use a comparson mehod of MPSO algorhms agans GA, and sandard PSO We ake no consderaon varous parameers lke number of asks, number of CPU, number of VMs, number of eraons, swarm sze, populaon sze, as shown n Table51 below The smulaon was conduced no wo scenaros: 1) Usng he Malab program runnng on Inel(R) dual-core(tm) CPU@330GHz, wh 600GB of memory and 2) Usng CloudSm oolk runnng on Wndows 7 operang sysem wh Inel(R) dual-core(tm) CPU@330GHz, wh 600GB nsalled memory We consdered usng a maxmum of 10 vrual machnes Table 51 Parameers used for parcle swarm opmzaon and genec algorhm Populaon sze 40 PSO parameers ω max 09 ω mn 01 C 1 20 C 2 10 Number of eraons 1000 Populaon sze 40 GA parameers Crossover probably 07 Muaon probably 001 Number of eraons 1000 The reason we have chosen hese parameers s ha c 1 and c 2 are he weghng facors ha ncrease he performance of MPSO The bes range for c 1 s 15 o 2 and he bes range for c 2 s 1 o 20 We se he nera weghωmn andω max as he sarng and endng nera wegh whch are responsble o conrol he MPSO algorhm s sably Ther good range values lay beween 01 and 09 ermn and ermax represen he curren and maxmum erave me whch we se o 1000 The populaon sze s fxed a 100 parcles bu can be any number For purpose of comparng PSO and MPSO, our populaon sze s fxed a 100 parcles Our research has found ou ha fxng he values as hey are represened n Table51 provdes he bes convergence rae for all es problems consdered Scenaro 1: Ieraon of he parcle poson and velocy: In each PSO eraon sep, each parcle moves from one poson o he nex poson based on s velocy; by movng reaches o dfferen prospecve of he problem The basc parcular equaon was represened n (5) and (6) Each parcle was frs evaluaed o fnd he parcle

16 KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL 11, NO 12, December obecve funcon value The resul s revealed n Fg 51 below Bes Cos Ieraon Fg 51 MPSO represenaon of he bes cos a 1000 eraons The Fg 51 smulaes he bes cos for he proposed MPSO algorhm The cos vares gradualy due o he ncrease of number of eraon Increased number of eraon ncreases he qualy of soluon whch lead o he good cloud resource soluon Comparson resuls MPSO PSO fness value No of eraons Fg 52 Performance comparson of MPSO and PSO algorhms In Fg 52, he horzonal axs represens he number of eraon and he vercal axs represens he fness values The red lne ndcaed he modfed parcles swarm opmzaon mprovemen Ths means ha when he wo algorhms compleed he same ask, he modfed PSO performs beer han sandard PSO The plo resuls show ha MPSO converges faser han he PSO algorhm In each MPSO s eraon, he poson and he velocy of all parcles are updaed and her fness s evaluaed ogeher wh her dmenson number n The fness funcon complexy s based on he schedulng algorhm and also depended on he number of asks The convergence me s nfluenced by he number of parcles and he number of vrual resources consdered The numbers n Table 52 ndcae he average run me of bes funcon values n 20 runs Several expermens and dfferen parameers were se o evaluae he effcency of MPSO, so MPSO algorhm sus more o cloud compung Therefore, he average run me of he MPSO algorhm s shorer han ha of PSO algorhm Table 52 Comparson resuls for 20 runs of parcle swarm opmzaon algorhm echnque Algorhm Fness value The number of eraons Avg Run me(ms) PSO MPSO

17 5796 Frederc NZanywayngoma&Yang Yang: Effecve Task Schedulng and Dynamc Resource Opmzaon based on Heursc Algorhms n Cloud Compung Envronmen Scenaro 2: Analyss of he resuls based on CloudSm Smulaons: In hs secon, we consdered o smulae our resuls wh CloudSm plaform [40] and o evaluae he performance of he proposed algorhm We consdered some parameers such as compleon me, execuon me, and resource ulzaon The process of classfyng hese parameers s known as Task parameerzaon In our expermen, we used fve vrual machnes, and as well as en vrual machnes The comparson expermens were carred ou by usng MPSO, PSO, and GA algorhms We compared he performance of he hree algorhm from ask compleon me, execuon me, and resource ulzaon We consdered number of asks and as he number of VMs Tasks are he servce requess n cloud compung envronmen and need o be allocaed o VMs n order o be processed The VMs have he confgured processng capably such as processor, memory, and capacy sze whch are dynamcally vared We se ask lengh beween 1000MI o 15000MI wh he proceedng speed of vrual machne 150MIPS o 300MIPS (MIPS s he mllon nsrucons per second of VMs) and (MI s he mllon nsrucons of asks) Oher parameers used are from Table51 above The bellow fgures show he varaon of processng me of parcles (asks), execuon me and resource ulzaon wh respec o her number of VMs o complee he work Fg 53 shows ha he compleon me decreases when he number of resources ncreases The compleon me of MPSO s shorer han PSO, and GA Ths shows ha MPSO has grea advanage and can easly fnd very good soluon space o reduce he processng me he asks ake o complee he process Fg 53 shows he compleon me for MPSO, PSO, and GA wh respec o 10 vrual machnes Fg 53 Compleon me for MPSO, PSO, and GA wh respec o 10 vrual machnes To demonsrae he performance for he algorhms (GA, PSO, and MPSO), we use 5 vrual machnes and 10 vrual machnes for 10, 15, 25, 40 cloudles Table 53 Task Schedulng comparson based on compleon me (sec) GA PSO MPSO VM Cloudle

18 KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL 11, NO 12, December The resuls show ha he MPSO algorhm ouperforms he GA and PSO wh he respec o he execuon me Fg54 Represenaon of Execuon Tme for MPSO, PSO, and GA The fgure above shows he execuon me for GA, MPSO, and PSO algorhms when usng 5 vrual machnes and seng 10, 15, 25, 40 cloudles Table54 Task Schedulng comparson based on compleon me(sec) GA PSO MPSO VM Cloudle Based on he resuls n Fg 55 whch shows he execuon me of GA, MPSO, and PSO algorhms when usng 10 vrual machnes and varous cloudles, he proposed algorhm ouperforms he curren PSO and GA respecvely

19 5798 Frederc NZanywayngoma&Yang Yang: Effecve Task Schedulng and Dynamc Resource Opmzaon based on Heursc Algorhms n Cloud Compung Envronmen Fg 55 Represenaon of Execuon Tme for MPSO, PSO, and GA when consderng 10 VMs GA PSO MPSO VM Cloudle Table 55 shows he resource ulzaon for GA, MPSO, and PSO algorhms whle consderng 5 vrual machnes and 10, 15, 25, 40 cloudles Based on he resuls n Fg 56, MPSO algorhm ouperforms PSO and GA wh respec o he resource ulzaon for 5 VMs Fg 56 The smulaon resuls of he resource ulzaon for MPSO, PSO, and GA based on 5 vrual machnes and varous cloudles

20 KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL 11, NO 12, December Table 56 Comparson resuls of MPSO, PSO, and GA wh respec o he resource ulzaon GA PSO MPSO VM Cloudle Based on he resuls n Fg 57, MPSO algorhm ouperforms PSO, and GA wh respec o he resource ulzaon whle consderng 10 vrual machnes and 10, 15, 25, 40 cloudles Fg 57 Represenaon of resource ulzaon based on 10 VMs and varous Cloudles The research shows how o use evoluonary echnques o ener a schedule as a search soluon We refer o he Fg 33 o show he mappng of asks o he avalable resources Consderng he proposed algorhm, each parcle represens a ask for whch s randomly assgned o he avalable resource The fgures show he assgnmen of varous cloudles o fve and en vrual machnes 5 Concluson In a cloud envronmen, here are many asks runnng on vrual resources, so hs paper presens a modfed parcle swarm opmzaon algorhm appled o opmze ask schedulng and vrual resource allocaon n cloud compung wh wo consrans for me, cos, and resource ulzaon rae From he smulaons, s shown ha hs algorhm could quckly and dynamcally opmze vrual resources wh a reduced oal me for ask schedulng n he cloud envronmen The MPSO also can perform beer han he PSO mehod proposed n he earler researches n erms of processng me, cos and resource ulzaon rae The PSO can fal o acheve he requred opmum soluon n cases when he problem o be solved seems o be complcaed or complex bu hs can be fxed by usng MPSO algorhm Usng MPSO algorhm n cloud compung resource allocaon, he smulaon resuls show ha MPSO presens advanages n convergence speed, n fndng global opmal, and n smplcy ably

21 5800 Frederc NZanywayngoma&Yang Yang: Effecve Task Schedulng and Dynamc Resource Opmzaon based on Heursc Algorhms n Cloud Compung Envronmen As we can see from he resuls, a MPSO opmzaon processes he bes fness value, decreases rapdly and converges whle he number of eraon convergen o he hgher value To show how he evoluon process s gong on for boh MPSO, he convergence of he average fness values s also shown n Fg 52, from whch s clear ha MPSO seems o perform beer In he changng envronmen, lke vrual cloud compung resources need o be operaed n opmally manner Therefore, he MPSO opmzaon algorhm can quckly allocae resources under he dynamc envronmen, and ulzes effecvely he sysem resources o reduce cos, and makespan for he number of vrual resources and user obs References [1] Q, H and A Gan, Research on moble cloud compung: Revew, rend and perspecves, n Proc of Dgal Informaon and Communcaon Technology and 's Applcaons (DICTAP), 2012 Second Inernaonal Conference on, IEEE, 2012 Arcle (CrossRef Lnk) [2] Weng, C, e al, The hybrd schedulng framework for vrual machne sysems, n Proc of he 2009 ACM SIGPLAN/SIGOPS nernaonal conference on Vrual execuon envronmens, ACM, 2009 [3] Mell, P and T Grance, The NIST defnon of cloud compung, 2011 hps://wwwnsgov/publcaons/ns-defnon-cloud-compung [4] Luo, Y and B Plale, Herarchcal mapreduce programmng model and schedulng algorhms, n Proc of he h IEEE/ACM Inernaonal Symposum on Cluser, Cloud and Grd Compung (ccgrd 2012), IEEE Compuer Socey, 2012 Arcle (CrossRef Lnk) [5] Zhan, S and H Huo, Improved PSO-based ask schedulng algorhm n cloud compung, Journal of Informaon & Compuaonal Scence, 9(13): p , 2012 [6] Nng, W, e al, A ask schedulng algorhm based on qos and complexy-aware opmzaon n cloud compung, n Proc of Informaon and Communcaons Technology 2013, Naonal Docoral Academc Forum on, IET, 2013 Arcle (CrossRef Lnk) [7] Zhu, K, e al, Hybrd genec algorhm for cloud compung applcaons, n Proc of Servces Compung Conference (APSCC), 2011 IEEE Asa-Pacfc, IEEE, 2011 Arcle (CrossRef Lnk) [8] Ln, S-W, K-C Yng, and C-Y Huang, Mulprocessor ask schedulng n mulsage hybrd flowshops: A hybrd arfcal bee colony algorhm wh b-dreconal plannng, Compuers & Operaons Research, 40(5), p , 2013 Arcle (CrossRef Lnk) [9] Al-maamar, A and FA Omara, Task Schedulng Usng PSO Algorhm n Cloud Compung Envronmens, Inernaonal Journal of Grd and Dsrbued Compung, 8(5): p , 2015 Arcle (CrossRef Lnk) [10] Xu, S-H, e al, A Combnaon of Genec Algorhm and Parcle Swarm Opmzaon for Vehcle Roung Problem wh Tme Wndows, Sensors, 15(9), p , 2015 Arcle (CrossRef Lnk) [11] Karaboga, D, Arfcal bee colony algorhm scholarpeda, 5(3), p 6915, 2010 [12] Kumar, RS and S Gunasekaran, Improvng ask schedulng n large scale cloud compung envronmen usng arfcal bee colony algorhm, Inernaonal Journal of Compuer Applcaons, 103(5), 2014 Arcle (CrossRef Lnk) [13] Akkoyunlu, MC, O Engın, and K Büyuközkan, A harmony search algorhm for hybrd flow shop schedulng wh mulprocessor ask problems, n Proc of Modelng, Smulaon, and Appled Opmzaon (ICMSAO), h Inernaonal Conference on, IEEE, 2015 Arcle (CrossRef Lnk) [14] Mrall, S and SZM Hashm, A new hybrd PSOGSA algorhm for funcon opmzaon, n Proc of Compuer and nformaon applcaon (ICCIA), 2010 nernaonal conference on, IEEE, 2010 Arcle (CrossRef Lnk) [15] Ll, X, e al, An mproved bnary PSO-based ask schedulng algorhm n green cloud compung, n Proc of h Inernaonal Conference on Communcaons and Neworkng n Chna, , 2014 Arcle (CrossRef Lnk)

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23 5802 Frederc NZanywayngoma&Yang Yang: Effecve Task Schedulng and Dynamc Resource Opmzaon based on Heursc Algorhms n Cloud Compung Envronmen [36] Chapman, B, When clouds become green: he green open cloud archecure, Parallel Compung: From Mulcores and GPU's o Peascale, 19, p 228, 2010 Arcle (CrossRef Lnk) [37] Sedghzadeh, M, e al, Parameer opmzaon for a PEMFC model wh parcle swarm opmzaon, In J Eng Appl Sc, 3, p , 2011 [38] Lu, C-Y, C-M Zou, and P Wu, A ask schedulng algorhm based on genec algorhm and an colony opmzaon n cloud compung, n Proc of Dsrbued Compung and Applcaons o Busness, Engneerng and Scence (DCABES), h Inernaonal Symposum on, IEEE, 2014 Arcle (CrossRef Lnk) [39] Rosam, A and M Lashkar, Exended PSO algorhm for mprovemen problems K-Means cluserng algorhm, Inernaonal Journal of Managng Informaon Technology, 6(3), p 17, 2014 Arcle (CrossRef Lnk) [40] Calheros, RN, e al, CloudSm: a oolk for modelng and smulaon of cloud compung envronmens and evaluaon of resource provsonng algorhms, Sofware - Pracce and Experence, 41(1), p 23-50, 2011 Arcle (CrossRef Lnk) Frederc Nzanywayngoma was born n Rwanda, Sepember, 1981 He receved hs BS degree n Elecronc and Communcaon Sysems Engneerng from Naonal Unversy of Rwanda n 2010 and hs MS degree n Informaon Communcaon Engneerng from Unversy of Scence and Technology Beng, Chna n 2013 Currenly, he s a PhD canddae a he same unversy Hs research neress nclude Machne o Machne Communcaons, Cloud Compung, Schedulng Algorhms and Opmzaon Mehods Professor Yang Yang graduaed from Beng Iron and Seel Insue of Auomaon Sysem n 1982 He receved hs PhD n Informaon Engneerng, from Unversy of Scence and Technology n Llle, France, n 1988 He has been a professor a Unversy of Scence and Technology Beng snce 1988 He has publshed n ournals and conferences boh a home and abroad more han 200 papers, compleed books Hs research neress nclude Servce Scence and Cloud Compung, Inellgen Conrol, Image Processng and Paern Recognon, Mulmeda Communcaon, Grd Technology