IMPLEMENTATION OF OBJECT ORIENTED APPROACH TO MODIFIED ANT ALGORITHM FOR TASK SCHEDULING IN GRID COMPUTING

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1 International Jornal of Modern Engineering Research (IJMER) Vol.1, Isse1, pp ISSN: IMPLEMENTATION OF OBJECT ORIENTED APPROACH TO MODIFIED ANT ALGORITHM FOR TASK SCHEDULING IN GRID COMPUTING CH SOMESWARA RAO 1, V MNSSVKR GUPTA 2, K.V.S. MURTHY 3, J RAJANIKANTH Department of CSE, S.R.K.R Engg. College, Affiliated to Andhra University, Bhimavaram, W.G.District, Pin , A.P. INDIA Abstract Now a day s Resorce management and tas schedling are very important and complex problems in grid compting environment. It is necessary to predict resorce state to get proper tas schedling. In this paper we propose obect oriented modified ANT algorithm is a new heristic algorithm. The inherent parallelism and scalability mae the modified ANT algorithm very sitable to be sed in grid compting tas schedling, whose strctre is changes dynamically almost all the time. The scalability of proposed algorithm is validating by proposing a simple Grid simlation architectre for resorce management and tas schedling. The algorithm when implemented in simlation environment prodced good reslts in terms of minimal response time; maximm resorce average tilization and tas flfill proportion. Index Terms Grid Compting, Tas Schedling, ANT Algorithm, Modified ANT Algorithm, Resorce Management. I. INTRODUCTION Grids enable the sharing, selection, and aggregation of a wide variety of resorces inclding sper compters, storage systems, data sorces, and specialized devices that are geographically distribted and owned by different organizations for solving large-scale comptational and data intensive problems in science, engineering, and commerce. Grid compting, most simply stated, is distribted compting taen to the next evoltionary level [1,2]. Resorce management and tas schedling are very important and complex problems in grid compting [3]. In grid environment, atonomy resorces are lined throgh internet to form a hge virtal seamless environment. The resorces in the grid are heterogeneos and the strctre of the grid is changing almost all the time. In a grid, some resorces may fail, some new resorces enroll the grid, and some resorces resme to wor. So, it is necessary to do resorce state prediction to get proper tas schedling. Since the static algorithms lie rond robin algorithm, fastest processor-largest tas first algorithm etc, are no longer sitable for the effective tilization of the grid, a good tas schedling method shold be sed which is distribtable, scalable and falt tolerant[4,5]. The aim of this paper is to simlate and analyze a tas schedling algorithm which is based on well nown ant-algorithm. The tas schedling algorithm shold wor better in the grid environment, in which the strctre of the grid changes dynamically almost all the time. It shold redce the total exection time of obs sbmitted to the grid, by effectively schedling the obs on to the appropriate resorces in the grid. It shold also redce the cost of sing the resorces to execte the obs. So, both the total exection time of the obs and the cost of execting the obs shold be taen into consideration in schedling the obs on to the resorces. If the cost of resorces is not a factor modified forms of ant-algorithm, which can frther redce the total exection of the obs. A. ANT ALGORITHM ANT algorithm is a new heristic, predictive schedling algorithm; it is based on the behavior of real ants. When the blind insects, sch as ants loo for food, every moving ant lays some pheromone on the path, then the pheromone on shorter path will be increased qicly, the qantity of pheromone on every path will affect the possibility of other ants to select path. At last all the ants will choose the shortest path. When a resorce enrolls the Grid, ( 0) m p c / s where m is the No. of PEs, p is the MIPS of one PE, c is the size of the parameters, s is the parameter transfer time from resorce to resorce monitor. The pheromone on path from schedle centre to the corresponding resorce will be changed as new old. is the change of pheromone on path from the schedle center to resorce ; ρ is the permanence of pheromone ( 0 < ρ <1 ) 1 - ρ is evaporation of pheromone when tas is assigned to resorce, Manscript received September 4 th, 2011 F. A. Athor is with the SRKR Engineering College, Andhra Pradesh, INDIA S. B. Athor is with the SRKR Engineering College, Andhra Pradesh, INDIA

2 International Jornal of Modern Engineering Research (IJMER) Vol.1, Isse1, pp ISSN: = -, is the compte and transfer qality of the tas. when tas sccessflly retrned from resorce, = Ce x, Ce is the encorage factor. when tas failed retrned from resorce, = Cp x, Cp is the pnish factor. The possibility of tas assignment to every resorce will be recompted as: (t) [ ( t)] [ ( t)] *[ ( t)] = 0 others is the pheromone intensity on the path from schedle center to resorce (t) is the innate performance of the resorce; is the importance of the pheromone; is the importance of resorce innate attribtes; B. Drawbacs of the ANT algorithm The schedler schedles a tas based on the possibilities of the resorces. The problem with this algorithm is, it may schedle a tas to a resorce with low possibility even if the resorces with high possibility are free. Bt this randomness is reqired becase if the tass are always schedled to the resorce with high possibility, then the load on the resorce may be increased and the obs may be ept waiting in the qee for the resorce to be free. It ses centralized schedling scheme. II. RELATED WORD The efficient schedling of independent comptational obs in a heterogeneos compting (HC) environment sch as a comptational grid is clearly important if good se is to be made of a valable resorce. Schedling algorithms can be sed in sch a system for several different reqirements [6]. The first, and most common, is for planning an efficient schedle for some set of obs that are to be rn at some time in the ftre, and to wor ot if sfficient time or comptational resorces are available to complete the rn a priori. Static schedling may also be sefl for analysis of heterogeneos compting systems, to wor ot the effect that losing (or gaining) a particlar piece of hardware, or some sb networ of a grid for example, will have. Static schedling techniqes can also be sed to evalate the performance of a dynamic schedling system after it has rn, to chec how effectively the system is sing the resorces available. The Ant Colony *[ ] Optimization (ACO) meta-heristic was first described in[7] as a techniqe to solve the traveling salesman problem, and was inspired by the ability of real ant colonies to efficiently organize the foraging behavior of the colony sing external chemical pheromone trails as a means of commnication. ACO algorithms have since been widely employed on many other combinatorial optimization problems inclding several domains related to the problem in hand, sch as bin pacing and ob shop schedling, bt ACO has not previosly been applied to finding good ob schedles in an HC environment. Althogh varios classification of tas schedling strategies exist, depending on the type of the grid, the schedler organization and tas schedling strategy has to be chosen sch that the resorces in the grid are effectively tilized. Along with the schedling organization, state estimation is also necessary for the dynamic grid. To achieve maximm resorce tilization and high ob throghpt, re-schedling of the obs on different resorces is also sometimes reqired. Since the static algorithms lie rond robin algorithm, fastest processor-largest tas first algorithm etc, are no longer sitable for the effective tilization of the grid, a good tas schedling method shold be sed which is distribtable, scalable and falt tolerant. In high throghpt compting, the Grid is sed to schedle large nmbers of loosely copled or independent obs, with the goal of ptting nsed processor (resorce) cycles to wor. Bild on the Internet and World Wide Web, the grid has emerged as a new class of infrastrctre. By discovering and obtaining access to remote resorces, the grid can provide scalable, secre, high performance calclating resorces to mae it possible for distribted scientific grops to wor together to share resorces in a large scale and to solve complex scientific problem that never practice in the previos time. In grid, resorces are distribted, heterogeneos, dynamic and instability. Dorigo et.al proposed [8], which is a new heristic algorithm and based on the behavior of real ants. When the blind insects, sch as ants loo for food, the moving ant lays some pheromone on the grond, ths maing the path it followed by a trail of this sbstance. While an isolated ant moves essentially at random, an ant encontering a previosly laid trail can detect it and decide with high probability to follow it, ths reinforcing the trail with its own pheromone. The collective behavior that emerges means where the more the ants are following a trail, the more that trail becomes attractive for being followed. The process is ths characterized by a feedbac loop, where the probability with which an ant chooses an optimm path

3 International Jornal of Modern Engineering Research (IJMER) Vol.1, Isse1, pp ISSN: increases with the nmber of ants that chose the path in the preceding steps. These observations inspired a new type of algorithm called ant algorithms or ant systems. The tas-schedling algorithm shold wor better in the grid environment, in which the strctre of the grid changes dynamically almost all the time. It shold redce the total exection time of obs sbmitted to the grid, by effectively schedling the obs on to the appropriate resorces in the grid. It shold also redce the cost of sing the resorces to execte the obs. So, both the total exection time of the obs and the cost of execting the obs shold be taen into consideration in schedling the obs on to the resorces. If the cost of resorces is not a factor (sitations lie, all the resorces of or interest belong to the same organization), modified forms of ant-algorithm, which can frther redce the total exection of the obs. III. SYSTEM ARCHITECTURE In Grid environment, the client nodes can enroll the Grid at any time, deliver reqests to the schedle center, and monitor the implementation of themselves tass. There are five important modles in the schedle center; the architectre of the schedler center can be expressed as shown in the Fig 1. Each node denotes a client or a Grid resorce, and each edge denotes a lin between nodes. When a grid client delivers a reqest, the grid wors as follows: The client delivers a reqest that contains an application description to the tas receiver. The description is abot the wor load of the application, commnication load, and time limit, etc. The tas receiver qees the tass in priority, and delivers the first tas in the qee to schedling manager. The receiver maintains an nschedled tas qee; record the client name and ser reqirements, application worload, commnication load, and time limit, etc. The schedling manager selects a most appropriate schedling scheme from all schemes according to the resorce graph and ser reqirement, then delivers the scheme to tas dispatcher and inform resorce monitor. This is the most important and complex schedling in the schedle center, there are many schedling strategies can be pt in the schedling manager. We design the ant algorithm based strategy, and will discss it in the following section. Tas dispatcher delivers the tas to the selected resorce, and gives transfer delay and actal tas assignment reslt to the resorce monitor. The dispatcher maintains a schedled tas qee; record the assigned resorce name, application wor load, commnication load, and time limit, etc. When some tas is finished or failed, the dispatcher delete the tas in the qee or pt the tas bac to nschedled tas qee, and notifies the resorce monitor. The resorce monitor maintains the p to date of every resorce and revises the resorce graph. Fig 1 Detailed System Design

4 We se a graphic to express the grid environment, each node denotes a client or a grid resorce (nmber of processors, speed of each processor, I/O bandwidth, RAM capability, and dis capacity, etc.), and each edge denotes a lin between nodes (bandwidth, delay, qantity of pheromone, etc.). A. Improvements to the above said ANT algorithm The algorithm can be modified by sing a threshold to minimize the total processing time. The processing costs of the tass also can be controlled. B. Modified Ant Algorithm When a resorce enrolls the Grid, ( 0) m p c / s where m is the No. of PEs p is the MIPS of one PE c is the size of the parameters s is the parameter transfer time from resorce to resorce monitor. The pheromone on path from schedle centre to the corresponding resorce will be changed as new old. is the change of pheromone on path from the schedle center to resorce ; ρ is the permanence of pheromone ( 0 < ρ <1 ) 1 - ρ is evaporation of pheromone. when tas is assigned to resorce, = -, is the compte and transfer qality of the tas. when tas sccessflly retrned from resorce, = Ce x, Ce is the encorage factor. when tas failed retrned from resorce, = Cp x, Cp is the pnish factor. The possibility of tas assignment to every resorce will be recompted as: (t) [ ( t)] [ ( t)] = 0 others *[ ( t)] (t) is the pheromone intensity on the path from schedle center to resorce (t) is the innate performance of the resorce; is the importance of the pheromone; is the importance of resorce innate attribtes; The schedler finds a resorce on which a new tas is to be schedled at time t with a *[ ] probability eqal to it s corresponding (t) ntil ( (t) h (t) ) T h. where h is the resorce with highest (t). T h is the threshold which will be taen as 1/ (no. of resorces). The schedler finds a resorce i taing one resorce at a time in the ascending order of C i / (MIPS i ) ntil (t) i (t) T i * l where C i is the cost of the resorce i per second MIPS i is the total MIPS of the resorce i T i = Th ( (t) h (t) ) and l is the cost redction factor which is selected by the grid ser who sbmits the tas. 0 l 1. The schedler schedles the new tas on to the resorce i. IV. IMPLEMENTATION In this paper we consider Grid Simlator(GridSim) toolit, which spports modeling and simlation of a wide range of heterogeneos resorces, sch as single or mltiprocessors, shared and distribted memory machines sch as PCs, worstations, SMPs, and clsters managed by time or space-shared schedlers. GridSim can be sed for modeling and simlation of application schedling on varios classes of parallel and distribted compting systems sch as clsters, Grids, and P2P networs. The resorces in clsters are located in a single administrative domain and managed by a single entity whereas, in Grid and P2P systems, resorces are geographically distribted across mltiple administrative domains with their own management policies and goals. The schedlers in clster systems focs on enhancing overall system performance and tility, as they are responsible for the whole system. The GridSim toolit provides a comprehensive facility for simlation of different classes of heterogeneos resorces, sers, applications, resorce broers, and schedlers. It can be sed to simlate application schedlers for single or mltiple administrative domain(s) distribted compting systems sch as clsters and Grids. Application schedlers in Grid environment, called resorce broers, perform resorce discovery, selection, and aggregation of a diverse set of distribted resorces for an individal ser. A. GridSim Entities GridSim spports entities for simlation of single processor and mltiprocessor, heterogeneos resorces that can be configred as time or space shared systems. It allows setting their cloc to

5 different time zones to simlate geographic distribtion of resorces. It spports entities that simlate networs sed for commnication among resorces. The design and implementation isses of GridSim entities are discssed below: i. User Each instance of the User entity represents a Grid ser. Each ser may differ from the rest of the sers with respect to the following characteristics: o Types of ob created e.g., ob exection time, nmber of parametric replications, etc., o Schedling optimization strategy e.g., minimization of cost, time, or both, o Activity rate e.g., how often it creates new ob, o Time zone, and o Absolte deadline and bdget. ii. Broer Each ser is connected to an instance of the Broer entity. Every ob of a ser is first sbmitted to its broer and the broer then schedles the parametric tass according to the ser s schedling policy. Before schedling the tass, the broer dynamically gets a list of available resorces from the global directory entity. Every broer tries to optimize the policy of its ser and therefore, broers are expected to face extreme competition while gaining access to resorces. iii. Resorce Each instance of the Resorce entity represents a Grid resorce. Each resorce may differ from the rest of resorces with respect to the following characteristics: o Nmber of processors; o Cost of processing; o Speed of processing; o Internal process schedling policy e.g., time shared or space shared; o Local load factor; and o Time zone. iv. Grid Information Service It provides resorce registration services and maintains a list of resorces available in the Grid. v. Inpt and Otpt The flow of information among the GridSim entities happen via their Inpt and Otpt entities. Every networed GridSim entity has I/O channels, which are sed for establishing a lin between the entity and its own Inpt and Otpt entities. Graphical ser Interface. We give appropriate data for sorce info, resorce info and ser info, based on the given inpt we have apply the ANT, Rond Rabin, and Modified ANT. Finally Fig 6 shows the Modified ANT algorithm is better in performance wise. Fig 2. Sorce info Fig 3 Resorce Information V. RESULTS Graphical User Interface (GUI) is sed for better sability of the system. In order to facilitate software (class) rese, the GUI is solely implemented in a separate class. Another class is sed for reading data (resorce information) from ser specified resorce file. Resorce information, ser information, and tas information shold be maintained in the

6 threshold vale in the modified ant-algorithm is very important. As a ftre wor, this schedling method can be pt into actal Grid environment for validation to mae QOS schedling. This algorithm can be made more sitable for wide se if pricing factor will also be inclded. The ant algorithm can also be applied to search for optimized path in compter networs. Fig 5 User information Fig 6. Comparison of different schedling algorithms reslts: VI. CONCLUSION AND FUTURE WORK Since the strctre of the grid dynamically changes, there is no particlar schedling algorithm, which can effectively tilize all the resorces in a grid. Bt, the algorithm shold be distribtable, scalable and falt tolerant. It shold also estimate the state of the resorces, which are crrently in the grid. And predictive state estimation is better than non-predictive state estimation becase it ses the crrent state as well as the historical stats of the resorces. So, the static algorithms lie rond robin schedling are no longer sitable. The Ant algorithm is a heristic predictive state estimating schedling algorithm, which is distribtable, scalable, and falt tolerant. The inherent parallelism and scalability mae the algorithm very sitable to be sed in grid compting tas schedling. The modified form of ant-algorithm can be sed if cost of sing the resorces is not a concern. The selection of References [1] Foster I, Kesselman C (eds.). The Grid: Bleprint for a Ftre Compting Infrastrctre. Morgan Kafmann: San Francisco, CA, 1999 [2] Chetty M, Byya R. Weaving comptational Grids: How analogos are they with electrical Grids? Jornal of Compting in Science and Engineering (CiSE) 2001; (Jly Agst). [3] Sinha PK. Distribted Operating Systems: Concepts and Design. IEEE Press: New Yor, NY, [4] Eemecic I, Tartala I, Miltinovic V. A srvey of heterogeneos compting: Concepts and systems. Proceedings of the IEEE 1996; 84(8): [5] Byya R, Abramson D, Giddy J. Nimrod/G: An architectre for a resorce management and schedling system in a global comptational Grid. The 4th International Conference on High Performance Compting in Asia-Pacific Region (HPC Asia 2000), Beiing, China, IEEE Compter Society Press: Los Alamitos, CA, [6] Bran TD, Siegel HJ, Bec N, Boloni LL, Maheswaran M, Rether AI, Robertson JP, Theys MD, Yao B. A taxonomy for describing matching and schedling heristics for mixed-machine heterogeneos compting systems. Proceedings IEEE Worshop on Advances in Parallel and Distribted Systems, October [7] M. Dorigo et L.M. Gambardella, Ant Colony System : A Cooperative Learning Approach to the Traveling Salesman Problem, IEEE Transactions on Evoltionary Comptation, volme 1, nméro 1, pages 53-66, 1997 [8] M. Dorigo, V. Maniezzo, et A. Colorni, Ant system: optimization by a colony of cooperating agents, IEEE Transactions on Systems, Man, and Cybernetics--Part B, volme 26, nméro 1, pages 29-41, 1996.