QoS-Constrained Resource Allocation for a Grid-Based Multiple Source Electrocardiogram Application

Transcription

1 QoS-Constained Resouce Allocation fo a Gid-Based Multiple Souce Electocadiogam Application Dong Su Nam 1,5, Chan-Hyun Youn 1,3, Bong Hwan Lee 2, Gai Cliffod 3, and Jennife Healey 4 1 School of Engineeing, Infomation and Communications Univesity Munji-dong, Yusong-gu, Daejeon , Koea {dsnam, chyoun}@icu.ac.k 2 Dept. of Infomation and Communications Engineeing, Daejeon Univesity Daejeon , Koea blee@dju.ac.k 3 Havad-MIT Division of Health Science Technology, MIT, Cambidge, MA 02139, USA {chyoun, gai}@mit.edu 4 Dept. of Tanlational Medicine, Havad Medical School/BIDMC, Boston, MA 02215, USA jhealey@bidmc.havad.edu 5 Dept. of Infomation Assuance, National Secuity Reseach Institute, 52-1 Hwaam-dong, Yusong-gu, Daejeon , Koea dsnam@eti.e.k Abstact. QoS-constained policy has an advantage to guaantee QoS equiements equested by uses. Quoum systems can ensue the consistency and availability of eplicated data despite the benign failue of data epositoies. We popose a Quoum based esouce management scheme, which esouce Quoum includes middlewae entity and netwok entity, both can satisfy equiements of application QoS. We also suggest a heuistic configuation algoithm in ode to optimize pefomance and usage cost of Resouce Quoum. We evaluate both simulations and expeiments based on the electocadiogam (ECG) application fo health cae, because this application equies tansfeing giga-bytes of data and analyzing complicated signal of ECG. Simulation esults show that netwok capabilities ae moe impotant than computing capabilities, as both sizes of tansfeed data and computation task inceases. Expeimental esults show that ou scheme can educe the total execution time of ECG application by using poposed heuistic algoithm compaed to policy based management scheme. 1 Intoduction Gid Computing have poposed to extend distibuted computing infastuctue fo advanced science and engineeing. Reseaches have made much pogess in constucting such an infastuctue and extending and applying it to a boad ange of A. Laganà et al. (Eds.): ICCSA 2004, LNCS 3043, pp , Spinge-Velag Belin Heidelbeg 2004

2 QoS-Constained Resouce Allocation 353 computing poblems. As a esult, gid has enteed the compute science vocabulay to denote middlewae infastuctue, tools, and applications concened with integating geogaphically distibuted computational esouces. [1] Gid computing lets netwoked computes shae and apply thei esouces towad solving a given poblem [2]. Gid technology must theefoe include a function that can divide pieces of a poblem ove multiple computes and then integate the esulting patial solutions. It must also be able to povide naming, secuity, and data etieval functions acoss multiple computes. In this pape, we popose QoS-constained Quoum configuation scheme fo Reliable Resouce Management. In ode to apply the Quoum based model to ou scheme, we define elements in Quoum as QoS constained middlewae entities and netwok entities, epesented as a binay fom. Poposed QoS Quoum means the condition including QoS entities equied by use and Resouce Quoum means the esouce including entities that can satisfy the condition of QoS Quoum. Theefoe Resouce Quoum, mapped with QoS Quoum, can guaantee use s QoS equiements. We also suggest a heuistic configuation algoithm in ode to optimize pefomance and usage cost of Resouce Quoum. We cay out both simulations and expeiments based on the electocadiogam (ECG) application fo health cae, because this application needs tansfeing lots of data and analyzing complicated signal of ECG.. Simulation esults show that netwok capabilities ae moe impotant than computing capabilities, as both sizes of tansfeed data and computation task inceases. Expeiment esults show that ou scheme can educe the total execution time of ECG application by using poposed heuistic algoithm compaed to policy based management scheme,. We expect that this esult can be contibuted to take advantage of moe eliable esouces in Gid. 2 Model Desciption fo QoS-Constained Resouce Entity A vaiety of entities exist in heteogeneous envionments such as computational Gids. The entities of esouces can be divided as middlewae esouces and netwok esouces. Middlewae esouce entities ae elated to a single compute system and include the CPU speed, memoy size, stoage capacity and I/O devices. Netwok esouce entities, such as bandwidth, delay and jitte, ae based on a souce-destination pai. We define the entity of both QoS equiements and esouces, ae consideed system middlewae and netwok elements fom vaious Gid esouces. Ou esouce management model is based on some assumptions that simplify the poblem fomulation: QoS dimensions have a one to one coespondence to system esouces metics. System esouces ae modeled as limited buckets of capacity. The total esouce utilization in the system cannot exceed the available amount. A netwok esouce is modeled as a limited bucket associated to a pai of between boke o client uses and system esouces. Netwok links ae bi-diectional. Connections in both diections shae the same netwok esouces.

3 354 D.S. Nam et al. Resouces ae independent of each othe. Resouces ae not pobabilistic and the system guaantees the contacted QoS. 2.1 Optimization Poblem of Application QoS Constaints An application, A, can be epesented by an undiected gaph as a function of the computing tasks and communication elations as compising a numbe S, of tasks, T, such that { 1,..., } { k, kl k kl k l A = T Ts = < Vt Et > V and t Et = T T, k land kl, = 1,..., s}. k kl V means the vetices of each computing node and E means the edge fo the communication between V and k kl k E. l means all communication pees elated to the V. We similaly define an available esouce univese compising n esouce entities, R = { R1,..., R n }. Which can also be epesented as a undiected gaph k kl K kl k l R = { < V, E > V and E = R R, k l and kl, = 1,..., n} M m The QoS equest is descibed by vecto matices MQ and MQ fo the middlewae constaints, and also NQ and NQ define the maximum, M m espectively, minimum netwok esouce equiements fo each connection (, i j ), fo which tasks T i and T j communicate. Fo the middlewae constaints: M M M MQ = ( mq ij ), i= 1.. k, j= 1.. s NQ = ( nq M ij ) i= 1.. k, j= 1.. s (1) m m m m MQ = ( ), NQ = ( nq ij ) i= 1.. k, j= 1.. s (2) mq ij i= 1.. k, j= 1.. s whee i is the numbe of entities and j is the numbe of esouce nodes. This esouce model assumes that thee is a path in the netwok between any two nodes and that all esouce allocations fo connections ae independent. The optimal allocation is designed to maximize the application QoS equiements defined as a function of the combined QoS atio fo all tasks. The oveall application QoS equiement is a linea combination of the middlewae task utility and the s netwok task utility. The middlewae utility of task T s fo esouce Ri is mui and the s s netwok utility of task Ts between esouce Ri and R j is nui nu j. Maximizing the followed function satisfied application QoS is the optimization of the middlewae entity utility and the netwok entity utility, whee s is the numbe of tasks n n n s s s i i j i= 1 i= 1 j= 1, i j Q( f ) = mu + nu nu 2.2 Quoum Vectos (3) An assetion is a set of QoS attibutes that ae equied fo sevice delivey and ageed on with the sevice equeste. Each sevice equeste must specify its QoS equiements fo the esouce manage. Theefoe QoS Quoum can be detemined fom SLAs containing use s esouce on demand that is composed of middlewae and

4 QoS-Constained Resouce Allocation 355 netwok esouce equiements. The QoS Quoum is configued of binay foms that mean must satisfied minimum QoS equiements fo esouce entities = 1 o don t cae satisfied minimum QoS equiements fo esouce entities = 0 [8]. The QoS Quoum can be defined as: a QoS Quoum set Q QoS ={Q MQ,Q NQ} is a collection of subsets Q MQ,QNQ U of a finite univese U. Both QoS quoum QMQ and QNQ consists of middlewae QoS vectos netwok QoS vectos. Similaly we define a esouce Quoum as the subset of esouces satisfying the conditions equied by the QoS Quoum. The esouce manage allocates this subset of Gid esouces to the application to allow the application tasks to be executed. Resouce Quoum can be defined as, Q R ={Q MR,Q NR} consisting of both middlewae esouce vectos (m) and netwok vectos (N). If a specified entity can satisfy the coespondent QoS equiement it can be 1 othewise 0. 3 Heuistic Optimization Scheme in Resouce Allocation Basically, Resouce Quoum set has the chaacteistics to guaantee minimal QoS equiements as defined in the pevious section. We can select the specified available esouces in Resouce Quoum set. Fist of all, minimal QoS constaints ceated by the SLAs make up two goups of vectos in QoS Quoum and Resouce Quoum. QoS Quoum is made fo the sevice class coespondent with one of QoS sevices. Simultaneously, Resouce Quoum is detemined fom whethe satisfying QoS constaints o not. Afte the configuation of two types of Quoum, we can ceate Resouce Quoum sets fo guaanteeing each QoS sevice. We suggest a heuistic algoithm to maximize pefomance though capabilities of esouces and to minimize the usage cost of esouces. The sum of both entities middlewae and netwok in a esouce can be epesented as eithe the capability o the usage cost of the esouce. As the sum of entities is lage, the esouce can impove its capacity. Also, as the sum of entities is small, the usage cost of the esouce is less. When we allocate supeio esouces in a Resouce Quoum set to Gid applications, we can expect esultant eductions in both computation time and communication time. Theefoe we can optimize the pefomance and the usage cost, by using the sum of entities in esouces allocated in Resouce Quoum set. At this time, eithe the middlewae entity o netwok entity is given pecedence. We will show the impact as the pecedence of entities changes using simulation. We assume that all Gid applications could be downloaded fom the application epositoy. Each task of an application is executed at distibuted esouces. To minimize the usage cost of esouces, we select the esouce having the lowest value of summation. Likewise to maximize the pefomance of computing, we select the esouces having the highest value of summation. Note that all of esouces satisfy the minimum QoS equiements. We assume that all Gid applications could be downloaded fom the application epositoy. Each task of an application is executed at distibuted esouces. To minimize the usage cost of esouces, we select the esouce having the lowest value of summation. Likewise to maximize the pefomance of computing, we select the esouces having the highest value of summation. Note that all of esouces satisfy the minimum QoS equiements. The pefomance maximization pocedue could

5 356 D.S. Nam et al. minimize the execution time by allocating application tasks to esouces having supeio capabilities. When uses want to execute thei applications in the deadline time, we can apply this scheme fo the task scheduling. In ode to minimize execution time of tasks, we select sequentially the esouces by soting in ode of the maximum values of vecto sum and then we configue Resouce Quoum. The usage cost minimization scheme is to educe usage cost of esouces by allocating application tasks to esouces having poo capabilities. When uses want to execute thei applications with the lowest budget, we can apply this scheme fo the task scheduling. In ode to minimize the usage cost of esouces, we select sequentially the esouces by soting in ode of the minimum values of vecto sum and configue Resouce Quoum. Although these esouces have some infeio middlewae and netwok capabilities, these esouces ae able to satisfy the minimal QoS equiements. 4 Pefomance Evaluations To evaluate the poposed eliable esouce management scheme, we simulate it with Simgid and discuss some measuement esults in intenational Gids. 4.1 Simulation Using Simgid To analyze the poposed scheme, we have used the useful tool, Simgid [8], which povides coe functionalities that can be used to build simulatos fo the study of application scheduling in distibuted envionments. The scenaio 1 shows that the andomly selected Quoum satisfying the minimum QoS equiements is allocated to Gid application and the scenaio 2 shows that the Quoum both satisfying the minimum QoS equiements and having bette CPU capabilities than netwok capabilities is allocated the Gid application. The scenaio 3 shows that the Quoum both satisfying the minimum QoS equiements and having bette netwok capabilities than CPU capabilities is allocated Gid application. In the simulation esults, we have known that the computation capabilities affected the execution time only fo the small size of data such as 3Mbytes o 30Mbytes. Howeve, as the size of data became lage such as 300Mbytes, the effect of netwok capabilities became moe impotant. Figue 1 shows that netwok capabilities moe impotant than computing capabilities as the size of data and the amount of computation tasks ae lage. The esult also showed that the poposed Quoum configuation scheme could impove the pefomance, compaed with the andom configuation of esouces. Fig. 1. Total elapsed time (sec) of scenaio 1, 2, and 3

6 QoS-Constained Resouce Allocation Expeiment Results The testbed to implement fo this expeiment have shown in Figue 2, which consists of five systems at Infomation and Communications Univesity (ICU) and each one system at Hanyang Univesity, Koea Univesity and MIT in USA. Also, we have used nine DBs fo sub tasks such as subjob1={db1, DB2, DB3}, subjob2={db4, DB5, DB6}, and subjob3={db7, DB8, DB9} among the 72 MIMIC DBs which contains ECG signals [7]. Fig. 2. Gid testbed fo the ECGs expeiments Minimum QoS equiement is obtained fom the SLAs equested by use. The PQRM geneates the sevice classes of QoS Quoum such as QoS-1, QoS-2, and QoS-3. Thee of QoS sevice classes ae assumed as the Guaanteed Sevice, the Contolled load Sevice and the Best effot Sevice, espectively. In the QoS Quoum, the netwok equiement is classified into two pats since it needs two links to pefom the ECG applications, one is the link between PQRM and esouces and the othe is the link between the esouces and MIMIC DB. Figue 3 shows the ceation of QoS Quoum. Minimum QoS equiement detemines the Available Resouces Vecto fo each esouce. The measuement was pefomed with the Netwok Weathe Sevice (NWS) [3]. 8 systems with Linux O/S wee used in this expeiment, which ae 5 systems located in the Local Aea, and 3 systems in the Wide Aea. Each value of vectos is detemined owing to the satisfaction of the minimum QoS equiement. Afte the ceation of the Resouce vecto, Resouce Quoum set is geneated fom QoS Quoum. In Figue 3, we can see that thee is no esouce to satisfy QoS-1 sevice. Instead, we knew the esouces satisfying QoS-2 and QoS-3 sevice. In ou expeiments, the esouces satisfying QoS-2 sevice have become fou systems such as ICU1, MIT, ICU4, and ICU5. Also, the esouces satisfying QoS-3 sevice have become eight systems such as HYU, KU, MIT, ICU1, ICU2, ICU3, ICU4, and ICU5.

7 358 D.S. Nam et al. Fig. 3. The ceation of Resouce Quoum set fo each QoS Quoum To guaantee QoS-3 sevice we also have configued ten Resouce Quoums and each Resouce Quoum consists of thee esouces selected fom eight esouces such as ICU1, ICU2, ICU3, ICU4, ICU5, HYU, KU, and MIT. Resouce Quoums fom Quoum-1 to Quoum-8 wee configued andomly. The Quoum-9 (Min_Cost Quoum) was configued by cost minimization configuation scheme that select in ode of the minimum value of esouce vectos and the Quoum-10 (Max_Pef Quoum) was configued by pefomance maximization configuation scheme that select in ode of the maximum value of esouce vectos. The total execution time of the ECGs application at the Quoum-10 is smalle than at othe Quoums. Also Quoum-9, the Min_Cost Quoum, educe total execution time bette than Q-4, Q-5, Q-6, Q-7, and Q- 8. Note that all of the Quoums satisfy the minimum QoS equiements. Figue 4 shows the compaison of execution time fo the ECGs application. Fig. 4. Expeiment esults fo QoS-3 sevice

8 QoS-Constained Resouce Allocation Conclusions The management of the oveall Gid system itself in a flexible way is becoming moe and moe impotant. Howeve, this is a elatively new field that has so fa been undestudied. Especially, policy based the SLAs can povide Quality of Sevice to Gid application uses and a flexible way. In this pape, we poposed QoS constained Quoum configuation scheme fo eliable esouce management and we also applied the taditional Quoum system to esouce management scheme in Gid, since the Quoum system can impove availability and eliability of esouces. Initially, we defined Quoum entities consisting of middlewae esouces and netwok esouces. We also defined a QoS Quoum as a set of conditions fo including QoS entities equied by the use and a esouce Quoum as the set of esouce entities that can satisfy the conditions of the QoS Quoum. Secondly we poposed two kinds of Resouce Quoum configuation scheme fo optimizing the pefomance and the usage cost. In this scheme, we configued a Resouce Quoum set to adequately allocate esouces to the Gid application afte soting in ode of the summation of both entities of middlewae and netwok. Finally, we caied out simulations using Simgid and expeiments based on the ECG application fo health cae since this application needs tansfeing lots of data and analyzing complicated signal of ECG. Simulation esults showed that netwok capabilities ae moe impotant than computing capabilities, as both sizes of tansfeed data and computation task incease. Expeiment esults showed that ou scheme could educe the total execution time of ECG application by using poposed heuistic algoithm, compaed to a policy based management scheme. We expect that the poposed esouce entities model fo management can be applied fo moe eliable esouces allocation in Gid. Refeences 1. F.Douglis, I.Foste, The Gid Gows Up Intenet Computing IEEE 2003, pp I. Foste and C. Kesselman, Globus : A Metacomputing Infastuctue Toolkit, Intenational J. Supecomputing Applications, vol. 11, no. 2, 1997, pp R. Wolski. Dynamically Foecasting Netwok Pefomance to Suppot Dynamic Scheduling Using the Netwok Weathe Sevice In Poceedings of the 6th High-Pefomance Distibuted Computing Confeence, August K.Yang, A. Galis, C. Todd A Policy-based Active Gid Management Achitectue, Poceedings of 10th IEEE Intenational Confeence on Netwoks (ICOIN02), pp , IEEE Pess. August I.Liabotis, et al, Self-oganising management of Gid envionments 6. D. Malkhi and M. Reite. Byzantine quoum systems. In Poceedings of the 29th ACM Symposium on Theoy of Computing (STOC), May A. Golbege, L. Amaal, L. Glass, J.M. Hausdoff et al, PhysioBank, PhysioToolkit, and PhysioNet :Component of a New Reseach Resouce fo Complex Physiologic Signals, Ciculation 101 (23), June, H. Casanova, Simgid: A Toolkit fo the Simulation of Application Scheduling, Poceedings of the Fist IEEE/ACM Intenational Symposium on Cluste Computing and the Gid (CCGid 2001), May 15-18, 2001, Bisbane, Austalia.