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

Simulationsgestützte Lösung von Deadlocks bei fahrerlosen Transportsystemen mit Hilfe von Deep Reinforcement Learning

This paper discusses the use of deep reinforcement learning to resolve deadlocks in material flow systems with automated guided vehicles (AGVs). The paper proposes a strategy for dealing with deadlocks based on a single Agent reinforcement learning approach (SARL). The agent will find the optimal solution strategy in real time. The proposed approach is evaluated using a material flow simulation for a real use case in industry. The effectiveness in reducing the occurrence of deadlocks as well as the number of collisions in the system is demonstrated. This study highlights the potential of deep reinforcement learning for improving the performance and efficiency of material flow systems with AGVs

GEM: a Distributed Goal Evaluation Algorithm for Trust Management

Trust management is an approach to access control in distributed systems where access decisions are based on policy statements issued by multiple principals and stored in a distributed manner. In trust management, the policy statements of a principal can refer to other principals' statements; thus, the process of evaluating an access request (i.e., a goal) consists of finding a "chain" of policy statements that allows the access to the requested resource. Most existing goal evaluation algorithms for trust management either rely on a centralized evaluation strategy, which consists of collecting all the relevant policy statements in a single location (and therefore they do not guarantee the confidentiality of intensional policies), or do not detect the termination of the computation (i.e., when all the answers of a goal are computed). In this paper we present GEM, a distributed goal evaluation algorithm for trust management systems that relies on function-free logic programming for the specification of policy statements. GEM detects termination in a completely distributed way without disclosing intensional policies, thereby preserving their confidentiality. We demonstrate that the algorithm terminates and is sound and complete with respect to the standard semantics for logic programs.Comment: To appear in Theory and Practice of Logic Programming (TPLP