Scheduling Workflow Applications on the Heterogeneous Cloud Resources

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1 Indan Journal of Scence and Technology, Vol 8(2, DOI: /jst/205/v82/57984, June 205 ISSN (rnt : ISSN (Onlne : Schedulng Workflow Applcatons on the Heterogeneous Cloud Resources R. Bagher and M. Jahanshah 2* Departent of Coputer Engneerng, Gazvn Branch, Islac Azad Unversty, Tehran, Iran 2 Departent of Coputer Engneerng, Central Tehran Branch, Islac Azad Unversty, Tehran, Iran; jahanshah@auctb.ac.r Abstract As cloud coputng odel recently becoe prosng and enables users to obtan ther requred servces, any users desrous to run ther workflow applcatons on t. Schedulng workflow s one of the ost portant challenges n the cloud. For optal use of the capabltes of the dstrbuted syste, an effcent schedulng algorth s needed. Addressng the proble of schedulng workflow applcatons onto Cloud envronent s the an contrbuton of ths paper. Heterogenety of resource types s one of the ost portant ssues whch sgnfcantly affect workflow schedulng n Cloud envronent. On the other hand, a workflow applcaton s usually consstng of dfferent tasks wth the need for dfferent resource types to coplete whch we call t heterogenety n workflow. The an dea n ths paper s to atch the heterogenety n workflow applcaton to the heterogenety n Cloud envronent. To obtan ths objectve a new schedulng algorth s ntroduced, whch s based upon the dea of detectng the set of tasks that could run concurrently and dstrbute the nto dfferent sub-workflows and then allocate each sub-workflow n resource cluster nstead of allocatng ndvdual tasks. Ths can reduce nter-task councaton cost and thus prove workflow executon perforance. Frst we perfor global schedulng and then conduct local schedulng. On the Global-schedulng to acheve hgh parallels the receved DAG partton nto ultple sub-workflows that s realzed by WRC algorth. On the Local-schedulng, sub-workflows were generated at the global level are dspatched to selected resource clusters. We used the sulaton to evaluate the perforance of the proposed algorth n coparson wth three well-known approaches. The results show that the proposed algorth outperfors other approaches n dfferent QoS related ters. Keywords: Cloud Coputng, Cluster resources, Schedulng Algorth, Workflow. Introducton Cloud coputng s the servce-focused that delvers hardware nfrastructure and software applcaton as servces wth low cost and hgh qualty,2. These technology advances have led to the possblty of usng geographcally dstrbuted to solve large-scale probles n scence, engneerng, and coerce. Currently, cloud coputng servces are categorzed nto three classes: Infrastructure as a Servce (IaaS, latfor as a Servce (aas and Software as a Servce (SaaS. These servces are avalable n a pay-per-use on deand odel 3,4. Soe researchers consder the benefts of usng cloud coputng for executng scentfc workflows 5 8. Several features that are dstnct cloud coputng fro other coputng envronents consst of: ( The type and nuber of copute resources assgned to a workflow are deterned by servce requests. (2 Copute resources n Cloud are exposed as servces that provde a standardzed nterface for servces to access over the network 9. Workflows consttute a coon odel for descrbng a wde range of scentfc applcatons n dstrbuted systes. Usually, a workflow can be represented by a Drected Acyclc Graph (DAG n whch each coputatonal task s represented *Author for correspondence

2 Schedulng Workflow Applcatons on the Heterogeneous Cloud Resources by a node, and each data or control dependency between tasks s represented by a drected edge between the correspondng nodes. Workflow schedulng s the way of choosng a sutable resource for each task. Wth Growng up and coplexty of workflow, the need of workflow s schedulng be felt ore than ever and has becoe one of the ost portant challenges n the cloud. The workflow scheduler has to schedule and allocate each task accordng to the dependency of the workflow s tasks. As task schedulng s a well-known N-coplete proble 0. Many heurstc ethods have been proposed for dstrbuted syste. Most of the try to nze the total copleton te of all tasks (ake span and cost of the workflow 4. Zeng et al. proposed a budget-conscous scheduler to nze any-task workflow executon te wthn a certan budget 5. In 6, Abrsha et al. desgned a QoS-based workflow schedulng algorth based on artal Crtcal aths (C n SaaS clouds to nze the cost of workflow executon wthn a user defned deadlne. Workflow schedulng algorths are classfed nto four classes: lst-based, 2 duplcaton-based, 3 clusterng-based, and 4 level-based. Most prevous workflows schedulng research are based on a lst-based heurstc approach. Lst-based schedulng s a class of schedulng heurstcs n whch tasks are assgned wth prortes and placed n a lst ordered n decreasng agntude of prorty. An portant ssue n DAG schedulng s how to rank the nodes. The rank of a node s used as ts prorty n the schedulng and then allocates each ndvdual task onto processors. Many lst-based heurstcs proposed n the lterature 4,7,8. In ths paper, we propose an algorth accordng to the clusterng-based heurstc approach. Clusterng s another effcent way to reduce a councaton delay n DAGs by groupng heavly councatng tasks to the sae labeled clusters 9. In general, clusterng algorths have two phases: the task clusterng phase that parttons the orgnal task graph nto clusters for allocaton nstead of allocatng ndvdual tasks. Ths can reduce nter-task councaton cost and thus prove workflow executon perforance and post-clusterng phases whch can refne the clusters produced n the prevous phase and get the fnal taskto-resource ap. The rest of the paper s organzed as follow: Secton 2, descrbes the schedulng archtecture and odel used by algorth. Secton 3, the proposed schedulng algorths are explaned. Secton 4, evaluates sulaton results. Secton 5, concludes. 2. Workflow Schedulng Model The proposed workflow schedulng odel conssts of a Cloud applcaton odel and a cloud resource odel. A Cloud applcaton s odeled by a Drected Acyclc Graph (DAG, G = (V, E, q, w, n whch V = {t =, 2,..., } be the fnte set of tasks t and E be the set of drected arcs of the for e(t, t j, An edge e (t,t j represents the councaton fro t to t j where t s called a pror of t j, and t j s a successor of t and q represents the coputatonal cost of task t. The weght w of e (t,t j represents the councaton cost fro task t to t j. In cloud resource odel, resources have been clustered that fored wth ultple VMs. Resource clusters are connected by a WAN, and wthn a cluster, coputatonal nodes are connected by hgh speed LAN. Usually, the councaton cost by WAN s uch hgher than LAN. We nae the cost of councaton between two resources n dfferent cluster as external councaton cost slarly; we nae the cost of councaton wthn a cluster, nternal councaton cost. The nternal data transfer n cloud slar to the ost recently publsh s neglgble. Each resource cluster represented by C (, where s the unque dentfcaton nuber of the cluster and s the nuber of clusters. When a workflow receves, t wll break nto subgraphs, accordng to knowledge about avalable resources, by executng the Workflow artton Resource Clusters (WRC algorth and then scheduler wll run the local level schedulng and ap tasks n subgraph to local coputatonal node. A coputatonal resource s denoted as R,j where s the resource cluster d to whch ths resource belongs and j s the resource d wthn ts cluster. For a resource cluster C, the nuber of resources n C s represented by n. Let p,j s the expected perforance of R,j so s total coputaton power of C, whch s the su of the coputatonal power of all resources n C : n =,j j= ( The average perforance of all avalable Coputatonal resources n C s gven by: = n (2 - s the average coputatonal power of all resource clusters that copute by: 2 Vol 8 (2 June Indan Journal of Scence and Technology

3 R. Bagher and M. Jahanshah = << We assued that councaton cost between resource cluster C and C j s represented by Co Cost_C,j and the average cross-cluster councaton cost s defned as: Co Cost C = nl 2 j, = Co Cost C, j We assued that the coputaton power of resource effect on the te requred copletng a task and the te to fnsh a data transfer s coensurate wth the councaton cost of the lnk. 2. WRC: Schedulng Algorth (3 (4 Hgh parallels eans to dspatch ore tasks sultaneously to dfferent resources. To acheve ths, the an task graph needs to be parttoned nto subgraphs and each subgraph has to be assgned to a resource cluster. The an paraeter ust be deterned n parttonng of graph, s the nuber of parttons should be ade (N. To deterne N, CTC paraeter s used. CTC s the rato of councaton-to-coputaton. A hgh CTC value eans a task graph s coputaton ntensve. Forally, CTC s defned as: CTC = q (5 Where q s the average processng requreent of all tasks. As the CTC ncreases, hgh parallels s preferred because ore coputatonal power s requred. WRC deterne the nuber of graph parttons to be created, N, accordng to dfferent workflow patterns and councaton costs: N = n, CTC/ ( CoCost C β (6 β = p p = ( ( 2 << (7 (8 Here β s the acceleratng factor and p s the standard devaton on the coputatonal power of dfferent resource clusters. It s clear that N s always no greater than the nuber of avalable resource clusters. Wth the nuber of subgraphs, to be created, WRC s to specfy how tasks n the an graph should be assgned. To acheve hgh parallels and avod nessental external councaton, the sze of a subgraph assgned to a resource cluster should be as large as possble under a certan threshold value. The weghts of edges connectng dfferent subgraphs should be as sall as possble to nze councaton cost. Accordng to purpose, we need to detect the set of tasks that could run concurrently and dstrbute the nto dfferent subgraphs and then specfy the axu nuber of nodes that could run concurrently when assgned to the sae resource cluster that call Maxu Concurrent Node (MCN. To get MCN, two paraeters are defned: For a node t n a DAG, ts Earlest Start Te (EST s defned as follows: EST (t = ax (ET (t j + e (t j, t t j red (t (9 Where pred (t s the set of edate predecessors of t and Executon Te (ET of t j s defned as: ET( t j = q j (0 The Earlest Fnsh Te (EFT of t s defned as follows: EFT (t = EST (t + ET (t ( Now, we can specfy whch nodes could run concurrently. We call t and t j parallel peers, f followng equaton s satsfed to one of the: f the EST( t after EST (t j and before the EFT (t j or the EFT (t after EST (t j and before the EFT (t j. By checkng parallel peers of every node, we can fnd the largest set of concurrent nodes n task graph G, whose sze s the value of MCN. The sze of a partton s also related wth the coputatonal power of resource clusters. We assue that the set of resource cluster servces that are offered nclude: S= {S, S 2 S } (2 If the nuber of resources n cluster that offer servce j s n,sj,the average nuber of all avalable resources n cluster for servce j s gven by: n,sj N = (3 n Su of Coputng power of all resources n the cluster that offer servce j s Coputaton power (p sj, the average Coputaton power of all avalable resources n cluster for servce j s gven by: Vol 8 (2 June Indan Journal of Scence and Technology 3

4 Schedulng Workflow Applcatons on the Heterogeneous Cloud Resources sj = sj = s (4 Resource falures to be statstcally ndependent and follow a constant falure rate Fr j for each resource j. We consder the relablty of an actvty as the robablty of successful copleton on a resource j, odeled usng an exponental dstrbuton 20,2 : Frj exete( T, Rj RT (, Rj= e (5 Total relablty of all resources n the cluster that offer servce j s gven by: ( j R S = Rj resource delver servce j RT (, R j (6 Where, T s the average of tasks coputaton. The average relablty of all avalable resource resources n cluster for dfferent servce s gven by: R( S j = R j = ( Sj R( Sj (7 Then for each resource cluster, a weght (W can be obtaned for each type of servce n the resource cluster by the followng equaton: W N Coputaton power R S (8 = + ( + ( j sj j After we get the weght of all servces n the all resource clusters, we then create the clusters atrx: s s2... s cls W W2... W cls2 W2 W22... W clsn Wn Wn2... Wn In whch W j s weght of the resource cluster for the servce j. To be adaptve to the dynac and heterogeneous nature of the coputatonal cloud, WRC ntroduces two paraeters to descrbe the related propertes of a resource cluster, naely the Cluster Rank (R and arallel Threshold (T. Thus R n = + W d n S 2 j= (,j (9 Where, d,j s the standard devaton of the perforance fluctuaton of,j n var ous te slots, and W s su of weghts of all resources n cluster for varous servces that can be obtan by the followng equaton: W = j= W,j And ΔS s the standard devaton of the coputatonal capacty of resources n r (so a larger d,j eans the perforance of p,j s ore unstable and a larger ΔS ples that r s ore heterogeneous. After resource clusters are ranked, N out of of the, havng the N hghest ranks s selected for the current job. We assue these clusters are r, r N. Then the ntal threshold value T ' of r s defned as: t = n R The above equaton, the parallel coputaton power of each cluster s also taken nto account. Then the parallel threshold value T of each subgraph to be created s gven by: The pseudo-code for the WRC s gven n Algorth. WRC s descrbed as follows. When a scheduler receves a job, t frst traverses the job s DAG G to copute ts CTC, the nuber of parttons to create N, and the level of each task node. Then, the scheduler selects N resource clusters whose ranks are the hghest N out of, accordng to ts knowledge. A graph partton teraton checks every reanng nodes n G to deterne whether the node can be put nto a subgraph G. Algorth WRC t N = j= W j= (20 Input: A task DAG G (V, E and avalable Cloud resource clusters C C. Output: A subgraph of G and assgned to resource cluster C.. Copute CTC, N and EFT and EST of each node n G, and cluster ranks R and threshold values T; 2. Mark all nodes unassgned; j, Wj, N (2 T = N MCN( G (22 t = 4 Vol 8 (2 June Indan Journal of Scence and Technology

5 R. Bagher and M. Jahanshah 3. Select the resource cluster C wth the hghest threshold value T; 4. Fnd the largest edge e (a, b n whch a, b have not been checked; 5. IF Br (G + a T and Br (G + b T { 6. Add a and b to G and ark a and b as checked; 7. } 8. G = G - G ; 9. ut (G, C n the output set. WRC tself does not gve the task-to-resource ap, but on the global level only parttons orgnal workflow to dfferent subgraphs and then dspatches subgraphs to dfferent resource clusters. So, on the local level, another schedulng algorth s needed to get the fnal schedule of the receved subgraphs. For schedulng a subgraph n specal resource cluster need to rank all nodes and then assgn node to resource accordng to ts prorty. Usually, the prorty of a task node can be obtaned by fndng the axu dstance fro ths node to the startng node. Dstance eans the su of coputatonal and councaton costs along a certan path. To estate the copleton te of nodes, we use the average perforance value of resource cluster. The rank or prorty of a node s defned as: q j rank ( t ax rank ( t j+ = e,j nter p + ( t red t j ( For the tasks to resources appng, ntally prorty of each task s coputed. Then at each step, a task that has the hghest prorty n the ready queue RQ s selected. The task s placed n a ready queue RQ f all ts predecessors have been pleented and the ddle results are provded. Once the task node s selected, the functon Select rocessor (t for choce sutable source s called. Mappng algorth adopts a forward-lookng approach to decson-akng based on only the current state of resources and task. Functon Select rocessor (t copute the executon te of task t for all the resources that provde the requred servce t. It can be obtaned fro the followng equaton: After the task executon te were coputed fro all sources, then the resource, where s nze, s selected. After each assgnent, the rank of tasks wll cost (23 ( (24 ET t = q/ be update. The pseudo-code for the Mappng s gven n Algorth 2. Algorth 2 Mappng Input: A task graph G and a set of resources R R n Output: A appng of tasks to resources. Copute rank for each task; 2. Intalze the ready queue RQ wth the entry task; 3. WHILE (there are unscheduled nodes { 4. Select the hghest prorty task t n RQ 5. Call Select rocessor (t to assgn task t 6. Update prortes of all tasks 7. } Select rocessor (task t. FOR all avalable resource R j that delver servce type x (t request servce type x { 2. Copute ET(t 3. } 4. Select the resource that has n ET (t ; 5. Insert t to selected Resource; 6. Update avalable resource; 3. Dscusson In ths secton, we wll present our sulaton of the Workflow artton Resource Clusters algorth. 3. Sulaton Model We evaluate the perforance of WRC usng CloudS 22, whch has been wdely adopted for the odelng and evaluaton of cloud-based solutons. In the experents, three resource clusters are used. Each cluster conssts of dfferent resources nuber connected by a LAN. The resource clusters are connected by a WAN. In each resource cluster, the resources n t have dfferent coputng power and delvered dfferent type of servces. In ters of nput task graphs, We used rando graph generaton wth the ablty of generatng a varety of task graphs accordng to dfferent confguraton paraeters, such as average nuber of task nodes of each graph, average outgong and ncong degrees for each node n a graph, and coputatonal and councaton cost for each type of task nodes and edges. The count of nodes n each workflow graph set between 25 and 00 nodes. Each graph has a sngle entry and a sngle ext node. We used the rando Vol 8 (2 June Indan Journal of Scence and Technology 5

6 Schedulng Workflow Applcatons on the Heterogeneous Cloud Resources graph generator dscussed n 7. Ths rando graph generator requres followng nput paraeters: V: The nuber of task n the DAG. Out degree: The rato of axu out edges of a node to total nodes of the DAG. Councaton to Coputaton Rato (CCR. It s the rato of the average councaton cost to the average coputaton cost. Β: The coputatonal heterogenety factor of resources. α: The depth paraeter of the DAG. Ths paraeter ndcates the depth of a DAG by usng the unfor dstrbuton wth the ean value equal to V. α The values for the nput paraeters are shown n Table. The followng four etrcs are used to evaluate the perforance of the proposed algorth: Fgure. Avg Makespane wth dfferent nuber of task. 3.2 Sulaton Results To evaluate the perforance of the proposed algorth we copared t wth the HEFT 7, the QRS 23 and the FAS 24 whch are well-known ethods for DAG schedulng. The perforance etrc used n the sulaton s the average ake span. To test our proposed algorth, the followng paraeters are consdered n the experent: The average nuber of task nodes n a graph v 2 The rato of the average degree of a task node to the total nuber of tasks n a graph (Edge densty n a graph 3 CCR. Fgure present the average ake span of our approach over the others approach wth respect to dfferent nuber of task. Fgure 2 present the average akespan of our approach over the others approach wth respect to dfferent degree. Fgure 3 present the average ake span of our approach over the others approach wth respect to dfferent CCR. The result of sulaton ndcates that the average ake span of workflow's graph wth our approach algorth s better than other algorths. Table. The values for the nput paraeters araeter Value V 20, 40, 60, 80, 00 Out degree 0., 0.2, 0.3, 0.4, 0.5 CCR 0., 0.5,.0, 5.0, 0.0 α 0.5,.0, 2.0 Β 0., 0.25, 0.5, 0.75,.0 Fgure 2. Fgure Further Analyss Because our proposed algorth s based on clusterng ethod and t s base on concurrent tasks, we defne tasks Graph Concurrency degree (GC a that can be obtaned fro the followng equaton: GC = MCN NT a Graph Concurrency Avg Makespane wth dfferent degree. Avg Makespane wth dfferent CCR. (25 6 Vol 8 (2 June Indan Journal of Scence and Technology

7 R. Bagher and M. Jahanshah Fgure 4. Avg Makespane of workflows wth dfferent GC and ncreasng Dfference the Avg Makespane wth ncreases GC. Where MCN s the axu nuber of tasks n a workflow's graph that can be executed n parallel and NT s the Nuber of tasks n a workflow. Sulaton results n Fgure 4 show that whatever aount GC becoes larger (rato the nuber of concurrent tasks to the total tasks Dfference n perforance of the proposed algorth wth other algorths wll be ore and proveent of the proposed algorth copared to other algorths wll be ore. 4. Concluson We propose a new Workflow artton algorth WRC for a workflow schedulng n the cloud. Schedulng s n two phases, on the global level, WRC clusters workflow to acheve hgh parallels and on the local level, Sub workflows was generated at the global level are then dspatched to selected resource clusters. It not only consders the heterogenety and dynas of cloud resources, but also uses an adaptve strategy accordng to dfferent workflow patterns and resource topologes. When t parttons a task graph, WRC try to nzng the cost of workflow executon and the ake span. Future work ncludes provng the current strategy, and uses other paraeters for clusterng the workflow. 5. References. Buyya R, Yeo CS, Venugopal S, Broberg J, Brandc I. Cloud coputng and eergng IT platfors: Vson, hype, and realty for delverng coputng as the 5th utlty. Future Generaton Coputer Systes. 2009; 25(6: Rajath A, Saravanan N. A survey on secure storage n cloud coputng. Indan Journal of Scence and Technology. 203; 6(4: Vera A, Kaushal S. Cloud coputng securty ssues and challenges: a survey. Advances n Coputng and Councatons. 20; 93: al AS, attnak BK. Classfcaton of vrtualzaton envronent for cloud coputng. Indan Journal of Scence and Technology. 203; 6(: Deelan E. Grds and clouds: akng workflow applcatons work n heterogeneous dstrbuted envronents. Internatonal Journal of Hgh erforance Coputng Applcatons. 200; 24(3: Hoffa C, Mehta G, Freean T, Deelan E, Keahey K, Berran B, et al. On the use of cloud coputng for scentfc workflows. escence, 2008 escence 08 IEEE Fourth Internatonal Conference; IEEE; Juve G, Deelan E, Vah K, Mehta G, Berran B, Beran B, et al. Scentfc workflow applcatons on Aazon EC th IEEE Internatonal Conference on E-Scence Workshops; IEEE; Sathck KJ, Jaya A. Natural Language to SQL Generaton for Seantc Knowledge Extracton n Socal Web Sources. Indan Journal of Scence and Technology. 205; 8(: Ln C, Lu S. Schedulng scentfc workflows elastcally for cloud coputng. 20 IEEE Internatonal Conference on Cloud Coputng (CLOUD; IEEE; 20; p Yu J, Buyya R, Raaohanarao K. Workflow schedulng algorths for grd coputng. Metaheurstcs for schedulng n dstrbuted coputng envronents. 2008; 46: Gu Y, Wu Q. Optzng dstrbuted coputng workflows n heterogeneous network envronents. Dstrbuted Coputng and Networkng. 200; 5935: Rahan M, Venugopal S, Buyya R. A dynac crtcal path algorth for schedulng scentfc workflow applcatons on global grds. IEEE Internatonal Conference on e-scence and Grd Coputng; IEEE; Wu Q, Gu Y. Optzng end-to-end perforance of data-ntensve coputng ppelnes n heterogeneous network envronents. Journal of arallel and Dstrbuted Coputng. 20; 7(2: Sakellarou R, Zhao H. A hybrd heurstc for DAG schedulng on heterogeneous systes roceedngs 8th Internatonal arallel and Dstrbuted rocessng Syposu; IEEE Zeng L, Veeravall B, L X. Scalestar: Budget conscous schedulng precedence-constraned any-task workflow applcatons n cloud. 202 IEEE 26th Internatonal Conference on Advanced Inforaton Networkng and Applcatons (AINA; IEEE; 202. Vol 8 (2 June Indan Journal of Scence and Technology 7

8 Schedulng Workflow Applcatons on the Heterogeneous Cloud Resources 6. Abrsha S, Naghbzadeh M. Deadlne-constraned workflow schedulng n software as a servce cloud. Scenta Iranca. 202; 9(3: Topcuoglu H, Harr S, Wu M-Y. erforance-effectve and low-coplexty task schedulng for heterogeneous coputng. IEEE Transactons on arallel and Dstrbuted Systes. 2002; 3(3: Kwok Y-K, Ahad I. Dynac crtcal-path schedulng: An effectve technque for allocatng task graphs to ultprocessors. IEEE Transactons on arallel and Dstrbuted Systes. 996; 7(5: Yang T, Gerasouls A. DSC: Schedulng parallel tasks on an unbounded nuber of processors. IEEE Transactons on arallel and Dstrbuted Systes. 994; 5(9: Dogan A, Ozguner F. Tradng off executon te for relablty n schedulng precedence-constraned tasks n heterogeneous coputng. roceedngs 5th Internatonal arallel and Dstrbuted rocessng Syposu; IEEE; Yu J, Buyya R, Tha CK. Cost-based schedulng of scentfc workflow applcatons on utlty grds. e-scence and Grd Coputng, 2005 Frst Internatonal Conference; IEEE Calheros RN, Ranjan R, Beloglazov A, De Rose CA, Buyya R. CloudS: a toolkt for odelng and sulaton of cloud coputng envronents and evaluaton of resource provsonng algorths. Software: ractce and Experence. 20; 4(: Chunln L, Xu ZJ, Layuan L. Resource schedulng wth conflctng objectves n grd envronents. J Netw Coput. 2009; 3: Dong F, Akl SG. FAS: a resource-perforance-fluctuaton-aware workflow schedulng algorth for Grd Coputng IDS 2007 IEEE Internatonal arallel and Dstrbuted rocessng Syposu; IEEE Vol 8 (2 June Indan Journal of Scence and Technology