Banker Algorithm Simulation Software

Banker's algorithm is an algorithm that models a bank in a small town dealing with a set of customers. This banker algorithm is used to deal with queuing problems in banking. In this case, one of them is how to simulate queues in banking. In this study, the design and manufacture of simulation software is used to help simulate whether a system is in a safe state or an unsafe state, in a safe condition the process is continued but if the process is unsafe the process is delayed until the system is in a safe state. The result of this research is a banker algorithm simulation software that models a banker who is dealing with a group of customers in a bank.

Deadlock Free Resource Management Technique for IoT-Based Post Disaster Recovery Systems

Disasters are inevitable, but their impact can be mitigated with careful planning. An IoT-based network with limited resources can be used in the post-disaster recovery. However, the resource of common interest creates contention among its contenders. This contention leads to tussle which in turn may lead to a deadlock. Some of the existing techniques prevent or avoid deadlock by performing stringent testing with significant testing overhead. While others propose recovery action after the deadlock is detected with significant overhead. A deadlock leads to a breakdown of the post-disaster recovery system while testing overhead implies delayed response either case can lead to catastrophic losses. This paper presents a new class of techniques that do not perform stringent testing before allocating the resources but still ensure that the system is deadlock-free and the overhead is also minimal. The proposed technique suggests reserving a portion of the resources to ensure no deadlock would occur. The correctness of the technique is proved in the form of theorems. The average turnaround time is approximately 18% lower for the proposed technique over Banker’s algorithm and also an optimal overhead of O(m).

Robust supervisory control policy for automated manufacturing systems with a single unreliable resource

Over the past two decades, the development of supervisory controllers that guarantee deadlock-free operation for automated manufacturing systems (AMSs) has been an active area of research. Most work to date assumes that the system resources are reliable. This paper focuses on the robust supervisory control problem of AMSs with a single unreliable resource. Our objective is to develop a robust supervisory control policy under which the system can continue producing in the face of the unreliable resource’s failure or recovery. To do so, we integrate an optimal deadlock avoidance policy based on a Petri net with a modified Banker’s Algorithm and present a novel robust supervisory control policy. It is proven to be of polynomial complexity and more permissive than two existing policies. Also, experimental results on a set of AMSs generated randomly indicate its superiority over all other existing policies.