Presentation Advanced Operating System Maekawa vs RicartAgrawala By

Presentation Advanced Operating System Maekawa vs. Ricart-Agrawala By Rizal M Nor

Introduction n My project implements two DMX algorithms, Ricart-Agrawala and Maekawa. n The Maekawa algorithm is expected to be faster than Ricart. Agrawala algorithm q Maekawa reduces the number of processes that must be contacted n Objective: q Implement the algorithms described above. q To study the performance of the algorithm. q To understand behavior of the algorithm.

Algorithms Short Review n Ricart-Agrawala: q q q n A modification to Lamport’s DMX Algorithm (Request, Reply, Release) Delay replies of request, therefore only (Request, Reply) messages Reduces message complexity to: Maekawa: q q In Maekawa algorithm, each process is in a member of a set of neighbors (quorum). Quorum: n n q q The set of Si can be found by solving finite projective planes of N points Symmetric when, N=K*(K-1)+1 (where k is a power of a prime) A process enters critical section if it succeeds in acquiring locks from its entire quorum. (Request, Locked Release) This reduces the message complexity of Maekawa algorithm to: q q However, recall that Maekawa’s algorithm has 6 types of messages (Request, Locked Release, Failed, Inquire, Relinquish) Hence in very high load, performance could go as large as:

Experiment Description n Ricart-Agrawala: q q n vary the size of the system, number of nodes(N) from 5 to 50 nodes in increments of 5. for each system Size N, vary the size of contending nodes L for 1(node), 25%, 50%, 75% and 100% load. For Maekawa, q vary the size of the system, number of nodes(N) according to: n symmetric pairwise nonnull intersecting set quorum sizes q q Using preloaded value from a text file. The sizes chosen is 7, 13, 21, 31, 57, 73, 91, 133 , 183, 307 , 381, 553, 871, 993 These values are chosen because creating non-symmetric sets were difficult for each system Size N, vary the size of contending nodes L for 1(node), 25% , 50%, 75% and 100% load.

Experiment Setup n Run the Simulator program with parameters: $ java –Dalgo=ricart –Dnodes=5 –Dload=5 Simulator > results. txt n Extracting Results Using grep and wc program to capture tags in output q The command below calculates the amount of messages send by processor 0 to reach critical section $ grep “Processor * sends *” | wc –l 13 q n Save output into excel q q Calculate average number of messages sent by each processor requesting critical section. Plot results in excel.

Results (Ricart-Agrawala) n Fixed N, Varying Load from the table below: q q q M/Critical Section N L=(1 node) L=25 L=50 L=75 L=100 5 8 8 8 10 18 18 18 15 28 28 28 20 38 38 38 25 48 48 48 30 58 58 58 35 68 68 68 40 78 78 78 45 88 88 88 50 98 98 98 n the size of message complexity does not change for ricart-agrawala. In fact, it is exactly the same. as expected since even though there might be more contending nodes, the amount of messages sent and receive by each node requesting critical section remains the same. Varying N, Fixed Load from the table below: q q q The Message complexity changed linearly Can be seen in graph above. It can be seen the change is exactly 2*(N-1) without any variance. As expected because each node requires to send to exactly N number of nodes twice for Request and Reply. Hence. The linear growth.

Results (Maekawa) n Fixed N, Varying Load from the graph: q q the size of message per CS required seems to increase as the load increases for any number of fixed N As expected, the growth starts from for light load n However q q n n Results are not as expected. Growth is not linear with respect to load. In fact, the growth does not reach Looking at the generated output file, it can be seen that at higher load (50%), most nodes requesting nodes will fail and can’t enter CS. Extra FAILEDs messages needs to be sent. Number of Nodes

Results (Maekawa Continue) n Varying N, Fixed Load from the graph: q q n However, as the load increases(50% below) q q q n The Message complexity changed in the order of square root of N. Regardless of the load size, there is a trend of growth for N. The growth is close to a square root of N. This behavior is because as N increases it will require more messages to be sent to the quorum. The quorum size is roughly square root of N. there is a constant change in the graph. This is particularly because an increase in load will cause some constant amount of failed messages in the system. Thus increasing the number of messages needed for CS. At high loads(50% above), most of the nodes are requesting CS and hence most probably will be Locked or by itself or others.

Future Research n Study Maekawa’s algorithm, when the quorum size is not entirely symmetric. q q n generate a symmetric quorum from an optimized value of N greater that the desired size N. Addition of nodes and how it affects the network Making Ricart-Agrawala capable to work in an unreliable network. Where nodes gets removed out of the network. Reply responses are lose.

Code Design n Design q Simulator n q The simulator is responsible to handle creation of nodes, selecting nodes by random to be run, and assigning channels to the nodes Processor Class n n n An interface class to be implemented by any algorithm. Class Maekawa and Ricart_Agrawala implements this class. This design allows abstractioni from the Simulator main class to run the processes.

Code Difficulty n Design q Simulator n n PRNG used for random no. is not purely random. Each instance of a class is seeded with the millisecond system time * 100 * process ID, however, I feel it is still lack the randomness I need. Difficulty q Quorum Data Set n n n q Not explained in the paper. Found out to be non-trivial. Used data downloaded from a mathematical website. Found out that the order of K=41 would take a long time to generate. There is none to be found right now. Queues n Maekawa Priority Queue q n Requires a priority queue, hence a special comparator is needed to be overriden by the default comparator to handle priority by seq. no and process id Ricart-Agrawala Message Queue q q The queue is non-fifo. During send, insert the element in random order of the queue. This is to simulate messages arriving not in order sent.

Object Oriented Design Simulator <Uses> Processor <Implements> <Uses> Ricart_Agra Maekawa Member Set Lock. Queue Comparator