Computing Pool: a Simplified and Practical Computational Grid Model

Transcription

1 Computing Pool: a Simplified and Pratial Computational Grid Model Peng Liu, Yao Shi, San-li Li Institute of High Performane Computing, Department of Computer Siene and Tehnology, Tsinghua University, Beijing, , China Pengliu@ieee.org Abstrat. Even though grid researh is prosperous in an extensive ontext, few grid platforms for high performane omputing are pratial and in operation so far. Sine most appliations are moderate ones, trying to deompose these appliations among distributed superomputers will results in omplexity in programming and optimizing, and heavy ost of ommuniations. In this paper we advoate omputing pool whih shares distributed superomputers but does not deompose appliations. Analysis proves that it is a pratiable omputing platform, whih an greatly improve quality of servie and utilization of resoures. 1 Introdution The ultimate goal of grid [1] is to build an information proessing infrastruture on the base of the rapidly improving information transporting infrastruture (e.g. information highway), by onneting enormous omputers, instruments, databases and people in the world together and make them be an organi maroosm, providing nontrivial quality of servies [2], for example, immense omputing power, enormous storage apaity, smart instrument aessing apability and intensive information proessing/retrieving ompetene. Presently, dozens of grid researh projets have being arried out, whih inlude Globus [3], Legion [4], Condor [5], IPG [6], DOE Siene Grid [7], GriPhyN [8] and TeraGrid [9] in Ameria, DataGrid [10], UNICORE [11] and DAS-2 [12] in Europe, Nimrod/G [13] and EoGrid [14] in Australia, and Ninf [15] in Japan, et. Traditionally, people hope a grid an aumulate omputing power of many superomputers, to form an unpreedented virtual superomputer (whih we all it full-fledged grid). In fat, many efforts have being seen towards this goal, for example, SF Express projet [16] that had ahieved a reord setting level of performane running military simulations, and Catus projet [17] that had got remarkable ahievements in numerial relativity researh [18] whih won the 2001 Gordon Bell Prize in Superomputing 2001 meeting [19]. So far as we know, however, most of the grid appliations are still in their tentative stages: they are for speifi appliations, and few of them an meet the requirement of a long-term servie for general appliations. For instane, the SF Express projet hasn t reported

2 any obvious progresses after it had simulated 100,298 battle units on 13 omputers among 9 sites in 1998, although it is said that it will ontinue to inorporate emerging omputational grid tools and tehniques into the distributed interative simulation environment [20]. As to the numerial relativity appliations on Catus, some obvious drawbaks an also be found out, aording to its representative paper [17]: (1) although four superomputers had been used, it is not a ompliated heterogeneous omputing platform, sine three of the superomputers are of the same type and at the same site; (2) there is a onfliting situation that higher performane was got from smaller system: a performane of 249 GFlop/s had been ahieved from the four superomputers (the effiieny was 63.3%), yet a 14.7% better performane of GFlop/s had been ahieved from two of the four superomputers (this figure an be alulated from its remarkable effiieny of 88% as it was reported); (3) its sale annot prove the advantages of grid, sine there are only 1500 proessors used by the appliation, while ASCI White, a giant omputer made by IBM, has 8192 proessor in the same year; and (4) part of the suess of the work should be owed to the harateristis of the appliation, whih an be optimized to keep the ommuniations exist only between adjaent proessors, making the long distane link between NCSA and SDSC does not seem to be a problem. In the rest of this paper, we will first analyze what have been limiting the pratiability of full-fledged grids. We then bring forth omputing pool, whih stays between full-fledged grid and traditional superomputer enters. Further analysis will show how it shuns the disadvantages of full-fledged grid, and how it greatly improves quality of servie for users and effiieny of superomputers in the pool. 2 The Lateny and Bandwidth Problem Why is full-fledged grid not pratial at present? We think the major reason is that only a few appliations an be divided into independent smaller parts whih need zero or little ommuniations between one another. Though there does exist this type of appliations, they may be more suitable to be run in a muh more powerful, looser and heaper platform, the P2P omputing environment, whih an aggregate power of millions of personal omputers. For instane, SETI@home (the Searh for Extraterrestrial Intelligene at home) projet [21] has aroused more than 4 millions ontributors sine July 1999, and has onsumed more than one million years of CPU time for free. Compared to the amount of thousands of proessors integrated by grid urrently, P2P omputing seems to be a better solution for appliations that an be partitioned into independent parts. When an appliation is run on a full-fledged grid, the main problems ome from lateny and bandwidth. Lateny is really a tough problem, sine it is limited by the speed of light, whih is 300,000 km/s, and it will take 10ms for light to over 3000 km. In addition, as there are many gateways, swithes and routers lies between the sender and reeiver, and TCP/IP protool is inevitable and ineffiient, the real lateny will be even more. Compared to a delay of several miroseonds in SAN (System Area Network) in a luster or MPP, the lateny in grid is at least 3 orders of magnitude higher than that within a superomputer. Relatively, bandwidth problem is

3 easier to overome, as bakbones ordinarily have Gbps bandwidth and an easily reah Tbps in the near future thanks to DWDM tehnology [22]. Currently, however, bandwidth is still a problem, sine bakbones are always shared by many servies, and the ost of renting lines must be taken in aount when grid platform will be applied for daily operations. Compared to lateny of several miroseonds and bandwidth of 2Gbps of Myrinet [23] and 10Gbps of Infiniband [24], the System Area Networks (SAN) often used in lusters, the ommuniation effiieny in WAN annot beat LAN or SAN. It is true that we have many methods to relieve the influene of ommuniation problems, by using adaptive algorithms to hide part of the lateny or ut down data transmission, so as to relieve the dependeny on ommuniation. For example [17], we an overlap omputation and ommuniation; we an inrease granularity of omputation or use redundant omputation to ommute ommuniation; we an redue times of transmission by inreasing size of eah transmission; we an ompress data before transmitting it and deompress it after reeiving; we an find a balane between auray of omputation and ost of ommuniation; or we an adjust some protool parameters (e.g. parameters of TCP/IP protool) to optimize transmission, et. Nevertheless, all the above optimizations should be based on an intensive study on properties of appliations and grids, and there does not have any universal solutions so far. In short, there are still a lot of works on algorithms, protools, and middleware to do, before an effiient ommuniation platform for general grid appliations may emerge and eliminate the need of additional optimization works for eah appliation. 3 Computing Pool As a generi full-fledged grid may not be realisti presently, how an we build a pratial omputing infrastruture for general appliations? Fortunately, most of our high performane appliations are relatively moderate ones, whih an be solved on only one superomputer in a tolerable time limit. In this ondition, it is more pratial to run an appliation on a sole superomputer instead of many different superomputers, sine the former will save a lot of ommuniation time of the appliation and a lot of programming, debugging and optimizing time of the programmers. That is why superomputer enters are still towers of strength for today s high performane omputing appliations, though grid seems to be getting popular. However, we an see drawbaks of traditional superomputer enters too. First, they are not sharing their omputing resoures among distributed sites. Eah superomputer enter has its own users. The users of site A often do not visit site B to run their appliations due to geographial and poliy limits. Thus a ondition may often happen: superomputer A is underloaded while superomputer B is overloaded. Seond, it is not onvenient for users that they may need reservation, traveling, pending and more ost to use a superomputer. Third, although this does not always happen, the user s appliation may be too big to be run on the superomputer he an aess.

4 Here we put forward an idea of Computing Pool, whih may ease the disadvantages of both full-fledged grids and superomputer enters. Computing pool is an elementary grid whih follows three rules: (1) dynamially shares resoures among superomputers in distributed sites; (2) never divide one appliation into smaller parts to run on different superomputers. Instead, it will find a suitable superomputer in the pool for the appliation; and (3) it will provide a web portal for users to aess the omputing resoures. As may be proved in the following ontext, rule (1) will greatly improve utilization of the superomputers in the pool, and provides a muh better quality of servie by sharply reduing user s pending time. Also, it is possible for users to solve bigger problems that annot be settled at loal superomputer enter sine there will averagely be 50% superomputers in the pool have more power than the loal one. In addition, rule (2) will shun the omplexity problem of deomposing appliations among distributed sites, and what is more, it will ut most of the ommuniation ost by restriting all ommuniations happen loally. This makes general appliations an be easily and heaply run on the platform. Finally, rule (3) provides a onvenient interfae to users. They need not travel to and stay in a superomputer enter. Instead, they an hand in their appliations at home in 24 hours. Generally speaking, the ommuniation ost of task submission, ontrolling, and result retrieving is muh lesser than the ommuniation ost among subtasks. 4 Performane Analysis Theoretially, the behavior of omputing pool an be desribed as followings. Suppose omputing pool S has N superomputers, whose omputing power are P 1, P 2,, and P N. When a user put forward a task A that requires omputing resoure of M A, the omputing pool will assign a proper superomputer i (instead of several superomputers) to A, to meet the requirement of P i *T M A. Here T is an aeptable duration time to the user. The dominant advantages of omputing pool are (1) it an serve users more quikly than independent superomputers, and (2) evidently improves the throughput of superomputers in the pool. This an be proved as followings. To simplify the problem, suppose eah superomputer has the same omputing power, i.e. P 1 =P 2 = =P N. If there is no omputing pool, eah superomputer works independently for speifi users. Suppose both arrival rate of users appliations and servie time of superomputers are negative exponentially distributed (i.e. Poisson proess). λ 1 is average arrival rate, and µ 1 is average servie rate. In short, it is a M/M/1 queuing system. So, the average waiting time W q1 for an appliation on the superomputer will be: λ1 = µ 1( µ 1-1) w (1) q 1 λ

5 If we have a omputing pool of superomputers, then it will be a M/M/ queuing system that has parallel servers. In this system, the average waiting time for an appliation Wq will be: in whih a is loaded ratio, W q a C(, a) = (2) µ ( a) λ λ 1 = = (3) µ µ 1 and C(,a) is the probability of all the superomputers are busy when an appliation arrives and must wait. C a a!(1 a / ) (, ) = 1 n a a + n= 0 n!!(1 a / ) (0 a<) Using different values of, λ 1 and µ 1 for formula (2), we an get a set of urves shown as Fig (4) Wq (average waiting time of appliations) Parameters for the urves from top to bottom are: λ1=9 µ1=10 λ1=8 µ1=9 λ1=7 µ1=8 λ1=6 µ1=7 λ1=5 µ1=6 λ1=4 µ1=5 λ1=3 µ1=4 λ1=2 µ1=3 λ1=1 µ1= (number of superomputers in the omputing pool) Fig. 1. Relationship between volume of omputing pool and average waiting time From the figure, we an see distint differenes between independent superomputers (=1 for eah superomputer) and a omputing pool of superomputers (>1). For example, suppose λ 1 =9 and µ 1 = 10, i.e. a superomputer

6 an averagely see 9 arriving appliations while it an arry out 10 appliations every hour. If =1, arriving appliations should wait 0.9 hour in average when the superomputer is 90% (=λ 1 /µ 1 ) busy; if =4, however, appliations only need 0.2 hour of waiting time in average. All the urves in Fig. 1 show that as the number of superomputers in the pool inreases, the waiting time of the arriving appliations gets shorter and shorter, i.e. quality of servie is improved. Now we will examine how omputing pool improves throughput of superomputers. When µ 1 is fixed (µ 1 >λ 1 ), a M/M/ queuing system will have the following properties: If does not hange, Wq will inrease with the growth of λ 1, i.e. Wq is a monotoni inreasing funtion of λ 1 ; on the other hand, if λ 1 does not hange, Wq will derease with the growth of, i.e. Wq is a monotoni dereasing funtion of. Then we an imagine there will be a balaned ondition: Wq an be kept unhanged, while λ 1 an be inreased with the growth of. This ondition will be illustrated in Fig. 2, in whih µ 1 =10 and Wq =0.1. If the superomputers are independent ones, λ 1 an only be 5, whih means 50% of the omputing power an be utilized; but if it is a omputing pool of 4 superomputers, λ 1 an be as high as 8.3, whih means 83% of the omputing power an be utilized. The values of λ 1 in Fig. 2 are figured out by using formula (2) in a reversed manner in MATLAB. λ1 (the allowed arriving rate of appliations) (number of superomputers in the omputing pool) Fig. 2. Relationship between the volume of omputing pool and its throughput In another word, if we use distributed superomputers to form a omputing pool, the throughput of eah superomputer an be signifiantly improved by inreasing λ 1 (the rate of reeiving users appliations) while keeps the same quality of servie Wq (the average waiting time of appliations). 5 Conlusions Deomposing appliations to distributed omputers is one of the ore ideas of grid to solve grand hallenges problems [25]. Nevertheless, most live appliations are relative moderate ones whih an be settled easily on one of the superomputers.

7 Negleting this fat and dividing appliation any way, will result in severe ommuniation ost and many redundant programming and optimizing work. On the other hand, many superomputer enters are still operated in traditional ways. They are not onvenient to users, and their QoS and effiieny annot ompete with omputing pool. Analysis shows that if a omputing pool has four idential superomputers and eah one an handle ten appliations in average, the waiting time for appliations an be redued from 0.9 hour to 0.2 hour, or utilization of eah superomputer an be inreased from 50% to 83% without prolonging waiting time. Although omputing pool is a simple idea, it is worth emphasizing its advantages (as shown in Table 1) and importane here. There are some grid appliations have been developed, tried, and then abandoned, sine they are designed for the future. But now it is time to deploy the pratiable, inexpensive, general-purpose omputing pool, to enhane QoS and performane of traditional superomputer enters. Table 1. A omparison of the three high performane omputing platforms Computing Pool Full-fledged Grid Computing Center Good for tasks that have Medium to large Huge Medium this size Utilization of resoures High High Low Quality of servie High High Low Complexity of Medium High Low programming and optimizing Communiation Cost Medium High Low Referenes 1. Foster, I., Kesselman, C., Tueke, S.: The Anatomy of the Grid: Enabling Salable Virtual Organizations. International J. Superomputer Appliations, 15(3), Foster, I.: What is the Grid? A Three Point Cheklist, Grid Today, July 22,, 2002: Vol. 1 No. 6, 3. Foster, I., Kesselman, C.: Globus: A Metaomputing Infrastruture Toolkit. Intl J. Superomputer Appliations. 1997, 11(2): Grimshaw, A. S., Wulf, W. A.: the Legion team. The Legion Vision of a Worldwide Virtual Computer. Communiations of the ACM, 1997, 40(1): Litzkow, M. J., Livny, M., Mutka, M. W.: Condor - A hunter of idle workstations. 8th International Conferene on Distributed Computing Systems, 1988, Johnston, W., Gannon, D., Nitzberg, B.: Grids as prodution omputing environments: The engineering aspets of nasa's information power grid. In Pro. Eighth IEEE International Symposium on High Performane Distributed Computing, DOE Siene Grid, 8. GriPhyN Grid Physis Network, 9. TeraGrid, Segal, B.: Grid Computing: The European Data Projet, IEEE Nulear Siene Symposium and Medial Imaging Conferene, Lyon, Otober 2000.

8 11. Almond, J., Snelling, D.: UNICORE: uniform aess to superomputing as an element of eletroni ommere, Future Generation Computer Systems 15(1999) , NH- Elsevier. 12. The Distributed ASCI Superomputer 2 (DAS-2), Buyya, R., Abramson, D., Giddy, J.: Nimrod/G: An Arhiteture for a Resoure Management and Sheduling System in a Global Computational Grid. In: Pro. of 4th Int l Conf. on High Performane Computing in Asia-Paifi Region, Beijing, 2000, Buyya, R., Abramson, D., Giddy, J.: Eonomi Models for Resoure Trading in a Servie Oriented Grid Computing Environments, Monash University, Ot 2000 (in publiation). 15. Sato, M., Nakada, H., Sekiguhi, S., et al.: Ninf: A Network based Information Library for a Global World-Wide Computing Infrastruture. In: Proeedings of HPCN'97 (LNCS- 1225), 1997, Brunett, S., Davis, D., Gottshalk, T., Messina, P., Kesselman, C.: Implementing distributed syntheti fores simulations in metaomputing environments. In Proeedings of the Heterogeneous Computing Workshop, pages IEEE Computer Soiety Press, Allen, G., Dramlitsh, T., Foster, I., Karonis, N., Ripeanu, M., Seidel, E., Toonen, B.: Supporting Effiient Exeution in Heterogeneous Distributed Computing Environments with Catus and Globus, Superomputing Benger, W., Foster, I., Novotny, J., Seidel, E., Shalf, J., Smith, W., Walker, P.: Numerial relativity in a distributed environment. In Proeedings of the Ninth SIAM Conferene on Parallel Proessing for Sienti Computing, Apr Awards Cap SC2001 HPC and Networking Conferene, NPACI & DSC Online, Volume 5, Issue 24 - November 28, 2001, Syntheti Fores Express, SETI@home: Searh for Extraterrestrial Intelligene at home, Elmir, J. M. H., Muftah. H. T.: All-optial wavelength onversion: tehnologies and appliations in DWDM networks. IEEE Communiation Magazine, 2000, 38(3): 86~ Myriom Home Page, InfiniBand Trade Assoiation Home Page, Committee on Physial, Mathematial and Engineering Sienes, Grand Challenges: High Performane Computing and Communiations, Offie of Siene and Tehnology Poliy, Washington, D.C