. A UML/MARTE DETECTION OF STARVATION AND DEADLOCKS AT THE DESIGN LEVEL IN CONCURRENT SYSTEM

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1 . A UML/MARTE DETECTION OF STARVATION AND DEADLOCKS AT THE DESIGN LEVEL IN CONCURRENT SYSTEM C.Revath Department of computer science an Engineering, Karunya University, comibatore,inia.rcswathiathi@gmail.com Prof.M.Mythily, Department of Computer Science an Engineering, Karunya University, comibatore, inia. mythily.m@gmail.com. Abstract Concurrency problem is when more than one job is execute in parallel. Concurrency problems such as ealock an starvation shoul be ientifie in esign process. The existing work is base on the analysis of esign moels expresse in the Unifie Moelling Language (UML).It uses a genetic algorithm to etect concurrency problem. Concurrency information is extracte from system UML moels that comply with the UML Moeling an Analysis of Real Time Embee System (MARTE) profile. Genetic Algorithms (GA) is use for optimizing the search space to efficiently etect ealock an starvation, Even though it hanles large threa execution chromosomes, it faile to achieve accuracy an complexity. To overcome these issues, a Particle Swarm Optimization algorithm (PSO) is propose which will reuce complexity an increase accuracy. The PSO can optimize threa execution interleaving that have a high probability of revealing ealock an starvation faults. The concurrency problem such as ealock an starvation approach can be implemente in JAVA. The result can be compare to the GA an PSO algorithm. Keywors: Particle Swarm optimization (PSO), RAG, Dealock an Starvation. 1. Introuction During the past ecaes Concurrency problem was is ientifie in the esign phase of software Engineering process. It is mae progressively ifficult in larger more complex systems. The fining of concurrency issues is base on the esign moels articulate in UML. Once the UML representation is not sufficient to completely moel a system for a particular purpose, the representation is extene by profiles. The ajustment of the MARTE (Moeling an Analysis of Real Time an Embee Systems) profile [4] aresses omain specific parts of real time concurrent system moeling. The objective of this paper is to etect the concurrency faults (such as ealocks, starvation) using PSO optimization. Particle swarm was originate in 1995 by Kenney an Eberhart [3] after stuying the social behavior of birs. In previous works, GA was employe to etect the concurrency problems such ealock an starvation base on the analysis of the esign moels expresse in the Unifie Moeling Language (UML).GA can be aresse by ealock an starvation an also can be easily tailore to other concurrency issues [2].The existing work have tailore a GA for the etection of ealocks [5] an starvation [9]. This paper, proposes a particle swarm optimization to etect the ealock an starvation using Resource Allocation Graph (RAG).The current paper aresses both ealock an starvation, which have a lot of in common. First starvation is aresse them. The aress of Starvation an ealock is ifference.pso reuces the complexity an increase the accuracy spee compare to GA. Available online@ 279

2 Next a summary of relate work is presente followe by a summary about the PSO an ealock an starvation. Dealock an starvation etection using PSO. Finally, result of GA an PSO is compare. 3. Relate Work Concurrency problem can be etecte by using more techniques like Genetic Algorithm (GA), Ranom Search (RS), Hill Climbing (HC) an Simulate Annealing (SA). In concurrency issues area, Ranom search space has large number of points. RS poorly etects the ealock an starvation etection rate is very low compare to other two techniques (i.e GA, HS). Hence, RS can be use only in small search space. Avantages of the RS are goo response time an starvation which can be etecte in any search space by Shousha et.al an Brain et.al. Similarly for Hill climbing total execution time is very less. HC etects the issues in large an small search space Shousha et.al an Brain et.al [11],[1],[2].A ifferent UML profile is use, instea of the SPT profile in [1], Genetic algorithm can be applie to the UML esign moel with help of SPT profile. Here, SPT profile can be etecte by ealock using GA. CFD tool can use for SPT profile [1].Other profile is MARTE.A UML extension profile is calle MARTE.GA has been applie to the UML/MARTE to etecting the ealock an starvation, an also etect the other concurrency issues by Shousha,Brian an Labiche et.al[11],[2].ga can be etect by ataraces using CFD tool[11].in this paper, PSO technique can be applie in concurrency problem an the effectiveness of the propose metho has been teste an the results are iscusse. 3.1 Particle Swarm Optimization ((PSO) PSO was initially establishe by Kenney an Eberhart in 1995[3],[7] it is a population base evolutionary algorithm. PSO is evelope from research inspire from the nature social behavior an ynamic movements of insects, birs an fish. The concept PSO uses a number of particles that establish a swarm moving aroun in the search space looking for the best solution. Each particle in search space ajusts its flying accoring to its own flying events as well as the flying events of other particles. Each particle in the swarm is efine by velocity an position. The particle (each particles keep track) pbest value an gbest value. The pbest (personal best) value is efines the best solution. The gbest (global best) value efines any value of any particles. The proceure for implementing the global version of PSO is given by the following steps [6]: (i) Initialize a (population) of particles with ranom positions an velocities in the n-imensional problem space using a uniform probability istribution function; (ii) Evaluate its fitness value (for each particle in the swarm); (iii) Compare each particle s fitness evaluation with the current particle s pbest. If current value is better than pbest, set its pbest value to the current value an the pbest location to the current location in n-imensional space; (iv) Compare the fitness evaluation with the population s overall previous best. If current value is better than gbest, then reset gbest to the current particle s array inex an value; (v) Velocity an position of the particles can change accoring to Equ (1) an (2). Particle swarm has two primary operators: Velocity upate an Position upate as given by Kenney an Eberhart [10]. v c (1) (2) v 2 ran x c ran x 1 g v x p x Available online@ 280

3 Where i= 1, 2, P, an P is the total number of the particle in a swarm, which is calle population size. c 1 an c 2 are constants, ran is ranom function with range [0,1]. is the inertia weight use to control the impact` of the previous velocities on the current velocity, motivating the mutual benefit between the global an local experiences. A particle s new velocity is calculate accoring to its previous velocity an the istance from its current position to its local best an global best using Equation (1). A particle s new position is calculate by evelop its experience (ie., local best) an the best experience of all particles (ie., global best) using Equation (2). 4. Dealock an Starvation Dealock is efine as the circular wait for the resources. Dealocks ensure when a threa is unable to continue its execution because it is blocke waiting for a lock that is hel forever by another threa [9]. Consier the Resource Allocation Graph (RAG) which epicts the allocation of resources to threas [8] in Fig. 1. Threa T1 locks L1 (as specifie by the soli arrow) an requests access to L2 (as inicate by the otte arrow). This request places T1 in L2 s conceptual wait queue because L2 is currently hel by T2, which is also requesting access to L1. Neither T1 nor T2 can procee because each is waiting for the locks hel by the other. In general, four conitions must be true before a ealock occurs: 1. Threas share information that is place uner a lock, 2. Threas acquire a lock while waiting for more locks, 3. Locks are non preemptible, an 4. There exists a circular chain of requests an locks (the Circularity conition), as ientifie in a RAG [8]. Threa starvation has generally been efine in two contexts: a) the operating system context an b) the concurrency context. The operating system point of view, threa starvation refers to the incapability of threas to access plenty CPU cycles to complete their execution [12].In the context of concurrency, starvation is relate to locks, an is therefore coine lock starvation [10]. Much like CPU starvation, where threas wait for CPU access inefinitely, lock starvation occurs when some threas wait for lock access inefinitely [10], making them relate to a ealock situation. For example, as seen in Fig. 1, a situation arises where a writer (W1) appears when reaers (T1 an T3) are executing in the critical section, an so W1 is place in the wait queue. Before all reaers take off the critical section, an aitional reaer appears an is grante access to the critical section. As long as a new reaer appears before the ones in the critical section complete their execution, the writer will never be accepte to access the critical section. Unlike a ealock situation, some threas are executing (T3) while others (W1) are blocke access to the critical section [9]. 5. Dealock an Starvation Detection using particle Swarm Optimization (PSO) Fig.1 Dealock an Starvation showe on RAG In previous work, we ha starte with GA for optimizing the search space to efficiently etect ealock an starvation, Available online@ 281

4 even though it hanles large threa execution chromosomes it faile to achieve accuracy an complexity [2].so, to overcome these issues, we are proposing Particle Swarm Optimization algorithm which will reuce complexity an increase accuracy. The PSO can optimize threa execution interleaving that have a high probability of revealing ealock an starvation faults. These interleaving are achieve through threa interleaving of particular access times to locks.pso algorithm is as follows A) Initialize Search Space Ranomly initialize the threas, locks an particle velocity an particle position. Each particle contains K ranomly selecte. Format of PSO particle is [T, L, A, V] T - Threas L Locks T Access time V-Velocity B) Fitness calculation Calculate fitness value of each particle, the fitness value is calculate base on its T an locks. C) Parameter Upating Moel In this phase, upate various parameters such as pbest, gbest, velocity an location. Pbest an gbest values are calculate base on fitness value calculate in the previous step. Velocity upate is one base on the location of the pbest an gbest particle. Location upate is one base on the new velocity value of the particle. Finally upate the fitness value of the newly positione particle. D) Dealock /Starvation Detection The best fitte particles were taken as input to the Dealock an starvation etection moule. In this moule scheule the lock an Threas accoring to their access time unit an execute the process by using RAG. Dealock/Starvation etection is performe using a RAG whenever a particles results in at least two threas waiting on locks. If a cycle is foun, ealock will occur. If a cycle is performe in RAG then it is calle as ealock. 5. The propose Approach The propose methoology introuces the PSO algorithm to improve the accuracy spee an reuce the complexcity.pso algorithm etect the ealock an starvation base on the threas, locks an access time. Resource Allocation Graph (RAG) is very useful to ientify the ealock an starvation problem. Fig.2. Represent the system implementation of PSO algorithm: 5.1 Particle Swarm Optimization stages (PSO) Step 1: Particle Initialization A Particle contains totally 4 fiels, Threas, lock, access time, Velocity. Eg: [T1, L2, 5, 3.8] Step2: Rearranging (Decoing) the particles base on threas, locks.eg: Before Rearrange :[T7,L15,25],[T2,L25,15],[T10,L2,2].. After Rearrange: [T1,L1,12],[T5,L1,34],[T2,L3,45].. Available online@ 282

5 1, if i=0 an A exec Threas i an A watingthreas i if A exec Threas i an A waiting Threas i 0, otherwise Fig.2. System Implementation Step3: Implementing Scheuler in the resultant Particles an Scheule the Threas to the particular lock base upon execution time. Step 4: From the result of scheuler we are going to evaluate the fitness value of base on the following objective functions Step 4(a): Starvation Detection Starvation involves the target threa waiting in the target lock s wait queue, the target threa in wait queue means initially the iteration value is 0, particular target threa is waiting the queue means the fitness value is 1.subsequently,the target threa is not in the wait queue means the fitness value is 0. a) No threa is waiting for access to the target lock orb) Threas are waiting for access to the target lock, but the target threa is not one of them. If the target threa gains access to the target locks once, later accesses in subsequent time units shoul still be checke for starvation. Base upon this objective function we ha esigne a fitness function is esigne [2]: f (c)= The variable A represents the target threa.variables starttime an entime enote the time interval start an en times, respectively.the set of threas executing within the target lock at time unit i is enote execthreasi an waitingthreasi is the set of threas waiting for access to the target lock at time i.the sets execthreasi an waitingthreasi are obtaine after scheuling threas an are calculate for every time unit of the time interval. Step (b) 4: Dealock Detection Dealocks involve at least two waiting threas; the fitness of scenarios where at least two threas are waiting on locks is always greater than the fitness of scenarios where zero or one threa is waiting. The fitness function is riven by the number of locks locke, i.e., an aitional threa executing in a lock shoul increase the fitness. The fitness function is riven by the number of threas waiting on locks, i.e., an aitional threa waiting for access to a lock shoul increase the fitness. Base Upon this objective function we ha esigne a fitness function for Dealock: f ( c)= #llockexecs+threawating,if threawating<2 #LockExecs+threaWating +threawating #Lock capacities, if threawating 2 #LockExecs is the total number of threas executing within all locks. It is the summation of the slots in all locks that are occupie. Where lock Capacities is the summation of all lock capacities. Available online@ 283

6 Step 5: Initialize best fitness value before optimization to pbest Step 6: Upating time (1) an Velocity (2) v c x 2 it x it v it 1 1 (1) it x it 1 v it c1 Ran (0,1) pb it Ran (0,1) gb it x it. 6. Result (2) Results for the etection of both ealocks an starvation are presente in Fig.3. The initialization swarm of the locks, threas an access time are the three inputs that is use to etect the concurrency problem using PSO.Here the swarm size is the 10 an maximum iteration is 100.The etection rate of Dealock an Starvation is base on the iteration an lock capability. Acess time Dealock an Starvation Starvatio n Dealoc k Fig.3. Dealock an Starvation Each iteration base on the access time, so it can easily etect the Starvation problems. Lock capability can be calculate by Dealock fitness value base on the total number of locks. The each particle has the pbest value an gbest value. Whenever PSO is applie, the best fitness value can be choose. The maximum fitness value can be one by initializing the pbest an gbest value. For example the fitness values are 35, 46, 65 when means the maximum fitness value can be choose as i.e.65.after that, velocity base on the iteration is calculate. Finally, the best particle can be applie to the RAG to etect the ealock an starvation. 7. Conclusion Concurrency abouns in many software systems, where systems usually inclue threas that access share resources an ifficult threa communication. If not hanle accurately, such access can cause ealock an starvation situation, which might elay system execution. In the existing work, GA is use to etect the concurrency problem such as ealock, starvation an also etect other concurrency issues. Detection rate time is very slow an the accuracy spee is very less[2]. In this paper, concurrency fault etection using PSO optimization has been explaine an implemente using JAVA. The PSO optimization problems reuce the complexity an increase the accuracy spee. The maximum fitness value can be use to fin out the best particle value. The best particles value can be applie to the RAG algorithm to etect the ealock an starvation problems. 8. References 1. M. Shousha, L. Brian, an Y. Labiche, A UML/SPT Moel Analysis Methoology for Concurrent Systems Base on Genetic Algorithms, Proc. ACM/IEEE Int l Conf. Moel Driven Eng.Languages an Systems, pp , M. Shousha, L. Brian, an Y. Labiche, A UML/MARTE Moel Analysis Metho for Uncovering Scenarios Leaing to Starvation an Dealocks in Concurrent, Proc. ACM/IEEE Int l Conf. Moel Driven Eng.Languages an Systems, pp , Kenney. J an Eberhart. R (1995), Particle Swarm Optimization, Proceeings of IEEE International Conference on Neural Networks, vol.4, pp Kleppe A., Warmer J. an Bast W., MDA Explaine - The Moel Driven Architecture: Practice an Promise, Aison-Wesley, Available online@ 284

7 5. Shousha, M., Brian, L.C., Labiche, Y., A UML/MARTE Moel Analysis Me-thoology for Detection of Starvation an Dealocks in Concurrent Systems, Carleton University, Technical Report SCE-09-01, squall.sce.carleton.ca, Shi Y, Eberhart RC. A moifie particle swarm optimizer. In: Proceeings of the IEEE international conference on evolutionary computation. Piscataway, NJ: IEEE Press; Eberhart RC, Kenney JF. A new optimizer using particle swarm theory. In: Proceeings of the sixth international symposium on micromachine an human science, Nagoya, Japan; p J. Bacon, Concurrent Systems Operating Systems, Database an Distribute Systems: An Integrate Approach, secon e., Aison- Wesley, A.B. Downey, The Little Book of Semaphores, secon e. Green Tea Press, S. Oaks an H. Wong, Java Threas, thir e., O Reilly Meia Inc., MATre)M. Shousha, L. Brian, an Y. Labiche, A UML/MARTE Moel Analysis Metho for Detection of Data Races in Concurrent Systems, Proc. ACM/IEEE Int l Conf. Moel Driven Eng.Languages an Systems, pp , R. Sinclair, Coenotes for Java: Intermeiate an Avance Language Features, G. Bill, e. Ranom House, Available online@ 285