A Fluid-Diffusion-Hybrid Limiting Approximation for Priority Systems with Fast and Slow Customers

The paper “Fluid-Diffusion-Hybrid (FDH) Approximation” proposes a new heavy-traffic asymptotic regime for a two-class priority system in which the high-priority customers require substantially larger service times than the low-priority customers. In the FDH limit, the high-priority queue is a diffusion, whereas the low-priority queue operates as a (random) fluid limit, whose dynamics are driven by the former diffusion. A characterizing property of our limit process is that, unlike other asymptotic regimes, a non-negligible proportion of the customers from both classes must wait for service. This property allows us to study the costs and benefits of de-pooling, and prove that a two-pool system is often the asymptotically optimal design of the system.

SRPT Scheduling Discipline in Many-Server Queues with Impatient Customers

The shortest-remaining-processing-time (SRPT) scheduling policy has been extensively studied, for more than 50 years, in single-server queues with infinitely patient jobs. Yet, much less is known about its performance in multiserver queues. In this paper, we present the first theoretical analysis of SRPT in multiserver queues with abandonment. In particular, we consider the [Formula: see text] queue and demonstrate that, in the many-sever overloaded regime, performance in the SRPT queue is equivalent, asymptotically in steady state, to a preemptive two-class priority queue where customers with short service times (below a threshold) are served without wait, and customers with long service times (above a threshold) eventually abandon without service. We prove that the SRPT discipline maximizes, asymptotically, the system throughput, among all scheduling disciplines. We also compare the performance of the SRPT policy to blind policies and study the effects of the patience-time and service-time distributions. This paper was accepted by Baris Ata, stochastic models & simulation.

Active Queue Management in RED Considering Critical Point on Target Queue

The basic philosophy behind RED is to prevent congestion. When the average queue length exceeds the minimum threshold, packets are randomly dropped, or the explicit congestion notification bit is marked. Since network requirements differ significantly, it is not an optimal approach to establish RED parameters with constant value. There is a new algorithm we are proposing called Critical Point on Target Queue (AQM-RED-CPTQ), provide greater congestion management over the network while also preserving the value of RED. To overcome the problem in RED without changing queue weight parameter, we have proposed few models to control the congestion by introducing range parameter with probability and control mechanism which will belong between minimum and maximum threshold. The current queue size is controlled together with average queue size. A new range variable has been introduced to improve the performance of priority queue of existing RED based algorithm which improves the overall performance of networks. For each packet, minimum and maximum threshold has been updated and dropped with probability (Pa) for a special condition. Instead of multiplicative increase and decrease the maximum probability, the scheme uses additive-increase and multiplicative-decrease. Once the AVG queue length is close to the minimum threshold value, our approach automatically sets queue parameter according to queue conditions and handles queuing delay and improve throughput. The simulated results proof that our approaches are better than RED in terms of throughput, end to end delay, packet delivery ratio and goodput.

Path Planning and Collision Avoidance in Unknown Environments for USVs Based on an Improved D* Lite

Path planning and collision avoidance during autonomous navigation in unknown environments is a crucial issue for unmanned surface vehicles (USVs). This paper improves the traditional D* Lite algorithm and achieves multi-goal path planning and collision avoidance for USVs in unknown and complex environments. By expanding the adjacent search range and setting a safe distance for USVs, we solve the issue of limited steering maneuverability in USVs with fewer DOF during autonomous navigation. We propose an approach to optimize the planned path during navigation by comparing the estimated distance with the actual distance between the current waypoint and the goal waypoint. A minimum binary heap is used to optimize the priority queue of the D* Lite and significantly reduce the path search time. Simulation results show that the improved D * Lite can significantly reduce the path planning time, optimize the planned path and solve the issue of limited steering maneuverability in USVs. We apply the algorithm to a real USV for further tests. Experimental results show that the USV can plan an optimized path while avoiding both static and dynamic obstacles in complex environments with a safe distance during autonomous navigation.

High-performance simplification of triangular surfaces using a GPU

Due to advances in high-performance computing technologies, computer graphics techniques—especially those related to mesh simplification—have been noticeably improved. These techniques, which have a strong impact on many applications, such as geometric modeling and visualization, have been well studied for more than two decades. Recent advances in GPUs have led to significant improvements in terms of speed and interactivity. In this paper, we present a mesh simplification algorithm that benefits from the parallel framework provided by recent GPUs. We customize the halfedge data structure for adaption with the dynamic memory restrictions of CUDA. The proposed algorithm is fully parallelized by employing a lock-free skip priority queue and a set of disjoint regions of the mesh. The proposed technique accelerates the simplification process while preserving the topological properties of the mesh. Some results and comparisons are provided to verify the efficiency of the proposed algorithm.

MMAP/(PH,PH)/1 Queue with Priority Loss through Feedback

In this paper, we consider two single server queueing systems to which customers of two distinct priorities (P1 and P2) arrive according to a Marked Markovian arrival process (MMAP). They are served according to two distinct phase type distributions. The probability of a P1 customer to feedback is θ on completion of his service. The feedback (P1) customers, as well as P2 customers, join the low priority queue. Low priority (P2) customers are taken for service from the head of the line whenever the P1 queue is found to be empty at the service completion epoch. We assume a finite waiting space for P1 customers and infinite waiting space for P2 customers. Two models are discussed in this paper. In model I, we assume that the service of P2 customers is according to a non-preemptive service discipline and in model II, the P2 customers service follow a preemptive policy. No feedback is permitted to customers in the P2 line. In the steady state these two models are compared through numerical experiments which reveal their respective performance characteristics.