Parallel discrete event simulation: A shared memory approach

With traditional event list techniques, evaluating a detailed discrete event simulation model can often require hours or even days of computation time. Parallel simulation mimics the interacting servers and queues of a real system by assigning each simulated entity to a processor. By eliminating the event list and maintaining only sufficient synchronization to insure causality, parallel simulation can potentially provide speedups that are linear in the number of processors. A set of shared memory experiments is presented using the Chandy-Misra distributed simulation algorithm to simulate networks of queues. Parameters include queueing network topology and routing probabilities, number of processors, and assignment of network nodes to processors. These experiments show that Chandy-Misra distributed simulation is a questionable alternative to sequential simulation of most queueing network models

Design of deadlock detection and prevention algorithms in distributed systems

A distributed system consists of a collection of processes which communicate with each other by exchanging messages to achieve a common goal. One of the key problems in distributed systems is the possibility of deadlock. Processes are said to be deadlocked when some processes are blocked on resource requests that can never be satisfied unless drastic systems action is taken. Two distributed deadlock detection algorithms handling multiple outstanding requests is proposed and are proven to be correct: it detects all cycles and does not detect false deadlocks. The algorithms are based on the concept of chasing the edge of the waitfor graph (probe-based). Simulation results show that the proposed algorithm performs very well compared to some existing algorithms. A deadlock prevention algorithm based on the notion of coloring the nodes of the waitfor graph is also proposed. Rollback is quite less compared to some existing algorithms

Self-stabilizing deadlock algorithms in distributed systems

A self-stabilizing system is a network of processors, which, when started from an arbitrary (and possibly illegal) initial state, always returns to a legal state in a finite number of steps. Self-stabilization is an evolving paradigm in fault-tolerant computing. This research will be the first time self-stabilization is used in the areas of deadlock detection and prevention. Traditional deadlock detection algorithms have a process initiate a probe. If that probe travels around the system and is received by the initiator, there is a cycle in the system, and deadlock is detected. In order to prevent deadlocks, algorithms usually rank nodes in order to determine if an added edge will create a deadlock in the system. In a self-stabilizing system, perturbances are automatically dealt with. For the deadlock model, the perturbances in the system are requests and releases of resources. So, the self-stabilizing deadlock detection algorithm will automatically detect a deadlock when a request causes a cycle in the wait-for graph. The self-stabilizing prevention algorithm prevents deadlocks in a similar manner. The self-stabilizing algorithms do not have to be initiated by any process because the requests and releases create a perturbance which is dealt with automatically

Attributes of fault-tolerant distributed file systems

Fault tolerance in distributed file systems will be investigated by analyzing recovery techniques and concepts implemented within the following models of distributed systems: pool-processor model and user-server model. The research presented provides an overview of fault tolerance characteristics and mechanisms within current implementations and summarizes future directions for fault tolerant distributed file systems