High Priority Queue Research Articles

Priority based energy efficient hybrid cluster routing protocol for underwater wireless sensor network

<p class="Affiliation"><span lang="EN-US">A little change in the environment that goes unnoticed in an underwater communication network might lead to calamity. A little alteration in the environment must also be adequately analyzed in order to deal with a potential crisis. A priority-based routing protocol is required to ensure that the vital data perceived by the sensor about the environment changes. The priority-based routing system guarantees that vital data packets are delivered at a quicker pace to the destination or base station for further processing. In this work, we present a priority-based routing protocol based on the energy efficient hybrid cluster routing protocol (EEHRCP) algorithm. The suggested approach keeps two distinct queues for lower and higher priority data packets. In order to ensure that these packets get at their destination without any information loss and at a quicker rate, all of the crucial sensed data is passed through a higher priority queue. Test findings show that the suggested technique increases throughput, delivery percentage, and reduces latency for the crucial data packets.</span></p>

Optimization assisted frequent pattern mining for data replication in cloud: Combining sealion and grey wolf algorithm

It is critical in cloud computing to have excellent data accessibility and system performance. To improve system availability, commonly used data should be duplicated to many places, allowing users to access it from a nearby site. Deciding on a sensible number and location for replicas is a difficult problem in cloud computing. Therefore, a novel Data Replication system based on data mining techniques is being proposed in this research work. The data replication is done here by locating commonly utilized data patterns in a node's massive database. This will be accomplished using an optimization-assisted frequent pattern mining approach, with a novel hybrid algorithm performing the best threshold selection. The proposed hybrid algorithm referred to as Greywolves Updated Exploration and Exploitation with Sealion Behaviour (GUEES), hybrids the concept of Sealion Optimization Model (SLnO) and Grey wolf optimization (GWO) algorithms. Apart from this, the mining will be carried out under the defining dual constraints such as (i) Prioritization and (ii) Cost. The prioritization falls under two cases: queuing both high and low-priority data, and the cost relies on the evaluation of storage demand. The high-priority queues are optimized with the GUEES model. Finally, a comparative validation is carried out to validate the efficiency of the adopted model. Accordingly, when the number of requests=1000, the network usage of the proposed model is 35.07%, 34.9%, 30.5%, 29.23%, 24.57%, 16.8%, and 16.85% higher than the existing methods like SMO, LA, ROA, GWO, SLnO, PSO, HCS, respectively.

Analysis on D2D Heterogeneous Networks with State-Dependent Priority燭raffic

In this work, we consider the performance analysis of state dependent priority traffic and scheduling in device to device (D2D) heterogeneous networks. There are two priority transmission types of data in wireless communication, such as video or telephone, which always meet the requirements of high priority (HP) data transmission first. If there is a large amount of low priority (LP) data, there will be a large amount of LP data that cannot be sent. This situation will cause excessive delay of LP data and packet dropping probability. In order to solve this problem, the data transmission process of high priority queue and low priority queue is studied. Considering the priority jump strategy to the priority queuing model, the queuing process with two priority data is modeled as a two-dimensional Markov chain. A state dependent priority jump queuing strategy is proposed, which can improve the discarding performance of low priority data. The quasi birth and death process method (QBD) and fixed point iteration method are used to solve the causality, and the steady-state probability distribution is further obtained.Then, performance parameters such as average queue length, average throughput, average delay and packet dropping probability for both high and low priority data can be expressed. The simulation results verify the correctness of the theoretical derivation. Meanwhile, the proposed priority jump queuing strategy can significantly improve the drop performance of low-priority data.

Design, implementation, and performance evaluation of an earliest-deadline-first packet scheduling scheme in P4 hardware switches

Real-time applications such as video conferencing, virtual reality (VR), high-speed train controls, nuclear plant controls, etc. impose stringent limitations on the maximum end-to-end latency that their packets can tolerate. In such applications, if a packet arrives at its destination node but misses its deadline, it will be useless. On a network, a packet often needs to pass multiple switches on its way from its source node to its destination node. In some of these switches, the packet may experience large queuing delays and the accumulation of these delays may cause the packet to miss its deadline at its destination node. In this work, we design and implement an earliest-deadline-first (EDF) packet scheduling scheme in P4 hardware switches to overcome this problem. We implement two versions of our scheme that use two queues and six queues respectively for real-time packets to study how the number of queues may affect the performance of our scheme. We evaluate the performance of our scheme under different levels of congestion conditions. Experimental results show that, compared with the baseline scheme that dispatches all real-time packets to the same high-priority queue, our scheme greatly reduces the deadline miss rate of real-time packets when the real-time traffic ratio in the network is between 40% and 80%.

FAIFO: UAV-assisted IoT programmable packet scheduling considering freshness

UAVs have the qualities of large coverage, flexible deployment, scalability, low cost and easy programming. Myriad UAVs are flexibly deployed in the air, on the ground and even on submarines, enabling ubiquitous connectivity. Thus, UAV-assisted IoT is considered a potential solution to support time-sensitive applications in sixth-generation (6G) networks. Such as the Industrial Internet of Things (IIoT), smart homes, smart cities and self-driving vehicles. A large amount of sensor data will be collected and carried by drones for transmission to destination nodes for further processing and analysis. Unlike traditional broadband services, most IoT applications transmit short and time-varying data packets carrying sensor updates. Many studies on time-sensitive applications at this stage still focus on optimization of latency, reliability and other metrics. Less attention has been paid to the freshness of the updates and the timeliness of the data. Age of Information (AoI) is a new metric introduced for time-sensitive applications in recent years. Unlike latency, which represents real-time, AoI utilizes the concept of freshness to depict the timeliness of data. The larger the AoI, the less fresh and timely the information is. To better serve time-sensitive applications, we cannot reduce the stringent requirements for latency while ensuring data timeliness. Therefore, we propose Freshness-based Admission in First Out (FAIFO), a novel fine-grained programmable packet scheduling scheme. FAIFO can guarantee the freshness of information for time-sensitive applications while meeting their low-latency requirements. FAIFO first uses the concept of admission control to rapidly filter high-priority (low-latency) packets into queue A to meet the real-time requirements of time-sensitive applications. Subsequently, depending on the freshness level of the packets, FAIFO will decide whether the discarded packets need to be cached in queue B to meet the timeliness requirements of time-sensitive applications. Finally, we improve the credit-based shaper (CBS) algorithm to perform traffic shaping on packets in queue A and queue B to ensure that high-priority queue A is forwarded first and freshness queue B is forwarded in a timely manner. Simulation experiments compare FAIFO with the ideal benchmark Push-In-First-Out (PIFO), state-of-the-art SP-PIFO, and AIFO. The results indicate that our FAIFO can also obtain the approximate PIFO effect, and the delay guarantee is comparable to SP-PIFO and AIFO. More importantly, FAIFO’s guarantee of information freshness is significantly better than other compared algorithms.

An Analysis of a Two-State Markovian Retrial Queueing Model with Priority Customers

Objective: This study considered a system of retrial queues with two types of customers: high-priority and low-priority. This study deals to find the time dependent probabilities of exact number of arrivals and departures from the system when server is free or busy. Numerical solution and graphical representation will also be presented. Method: For this model, we solved difference differential equations recursively and used Laplace transformation to obtain the transient state probabilities of exact number of arrivals and departures from the system when server is free or busy. Findings: Timedependent probabilities of exact number of arrivals (primary arrivals, arrivals in high priority queue, arrivals in low priority queue) in the system and exact number of departures (primary departures, departures from high priority queue, departures from low priority queue) from the system by a given time for when the server is idle and when the server is busy are obtained. Various interesting performance measures along with some special cases are also obtained. Conversion of two state model into single state model was discussed. Numerical illustrations are also presented using MATLAB programming along with the busy period probabilities of the system and server. Novelty: In past research, models considered arrivals and departures from the orbit whereas in present model arrivals and departures from the system are studied along with the concept of retrial and priority customers. Applications: Priority retrial queues are used in many applications like real time systems, operating systems, manufacturing system, simulation and medical service systems. Keywords: Arrivals; Departures; Probability; Priority; Retrial

Modeling and Performance Analysis of Channel Assembling Based on Ps-rc Strategy with Priority Queues in CRNs

Based on two types of priority queues, this paper proposes a polling scheduling strategy with reserved channel (Ps-rc strategy) for predefined priority services in cognitive radio networks (CRNs). Channel assembling (CA) technology and spectrum adaptation (SA) technology are adopted to dynamically adjust the assembled channels of secondary users (SUs) to improve the performance of the secondary network. Specifically, the SUs in CRNs are divided into two queues with different priorities; based on polling scheduling, a part of the idle channel is reserved for high-priority queue during the polling stage of the low-priority queue. The purpose is to increase the service quality (QoS) of high priority on the basis of providing fair scheduling. Furthermore, the CA-based channel access process of the proposed strategy is presented and modeled by continuous time Markov chain (CTMC). Then, the process of resource flow between users is mapped on CTMC, and the transition conditions and parameter sets of channel assembling covering all user activities of the system are derived. Finally, the system performance of the proposed CA-based Ps-rc scheduling strategy is simulated and evaluated, including network capacity, spectrum utilization, blocking probability, and forced termination probability. Numerical results show that the proposed strategy can improve the QoS of the predefined high-priority service without causing excessive starvation problem of low-priority service.

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.

Time dependent retrial queueing model with orbital search under non-preemptive priority services

Transient solution of Single server Non-preemptive priority Retrial model with orbital search is studied using eigenvalues and eigenvectors. In this model, customers are arriving in Poisson process. Arrival rates of low priority and high priority customers are respectively λ 1 and λ 2 . The service times for low and high priority customers follow exponential distribution with parameters µ 1 and µ 2 respectively. Customer finding the system busy, on arrival, goes to the orbit and form a virtual queue. In this model of orbital search, when the server becomes free, he has two options. Either the server search for the customer in the orbit with probability p to provide service for a customer in the orbit or remains idle with the probability 1-p. Whenever the server is free, customer from orbit try for service, under classical retrial policy with rate σ which follows Poisson process. In this paper, transient solution of average number of customers in the orbit and high priority queue, the probability of server being idle, the probabilities of server being busy with low and high priority customers for various values of λ 1, λ 2, µ 1 , µ 2 , σ, p and t are estimated.

Pause-triggered episode classification algorithms and high-priority review queue concept for cardiac monitoring devices

Abstract Background Cardiac monitoring technologies often utilize monitoring centers or services in which technicians adjudicate arrhythmia episodes. Arrhythmias with a high prevalence of false detections (such as pauses) can slow down the review process while also delaying review of time-sensitive true episodes. Objective To develop algorithms that can stratify device-detected pause episodes into low- and high-priority queues to facilitate monitoring center review. Methods The high priority queue algorithm identifies episodes which are likely to be true pause episodes. The algorithm consists of four conditions, which identify features that were found to be predictive of true pauses, including the number of beats in the episode, the noise status of the last pre-pause beat, and the relative flatness of the ECG signal. Using a similar set of features, a set of criteria was determined that would identify pause-triggered episodes that were highly unlikely to be true pauses. 11,567 pause episodes were used for development with 19,520 separate episodes used for validation. All episodes were adjudicated by a cardiac monitoring center. The validation dataset consisted of 18,280 (93.6%) false pauses and 1240 (6.4%) true pauses. Results The high-priority queue algorithm identified true pause episodes with a sensitivity of 82.3% and specificity of 96.8% in the validation dataset (see table). The low-priority queue algorithm flagged 78.4% of all pause episodes as low-priority in the validation dataset, with only 4 true pause episodes (<0.1%) flagged as low priority. Conclusion The high-priority queue algorithm for device detected pause episodes could potentially identify ∼82% of all true pause notifiable episodes, expediting their review process. The low-priority queue successfully identifies a large percentage (∼80%) of false pause-triggered episodes, which would help to improve monitoring center efficiency as false pause-triggered episodes are a large driver of artifact / normal sinus rhythm episodes. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Medtronic

A Geo/Geo/1 Inventory Priority Queue with Self Induced Interruption

This paper considers discrete time (s, S) inventory Geo/Geo/1 priority queue formed by the customers’ induced interruption during service. An arriving customer enters into a high priority queue of infinite capacity. During his/her service, the service may be interrupted due to own reasons with some probability. The interrupted customer is moved to a lower priority queue of infinite capacity. An item in the inventory is supplied whenever a customer leaves from high priority queue after interrupting/completing the service. The customer in the lower priority queue is served according to preemptive priority discipline. Inter-arrival time, service time and lead time are considered to be geometrically distributed. We use the matrix analytic method to analyse the model. The necessary and sufficient condition for the stability of the system is obtained. The marginal distributions of both higher and lower priority queue lengths are studied. Numerical experiments are incorporated to draw special attention to the importance of the model.

Utilizing priority-based packet scheduling based on preemptive priority scheduling in wireless sensor networks

In this paper, each node has three levels of priority queues. Real-time packets are placed into the highest-priority queue and can preempt data packets in other queues. Non-real-time packets are placed into two other queues based on a certain threshold of their estimated processing time. Leaf nodes have two queues for real-time and non-real-time data packets since they do not receive data from other nodes and thus, reduce end-to-end delay. The priority packet scheduling scheme outperforms conventional schemes in terms of average data waiting time and end-to-end delay and also it reduces sensor energy consumption.

864 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: E6080018520 /2020©BEIESP DOI:10.35940/ijrte.E6080.018520 Journal Website: www.ijrte.org Multi Level Queue Scheduling With Particle Swarm Optimization (Mlqs-Pso) Of Vms in Queueing Heterogeneous Cloud Computing Systems

This article investigates in cloud computing systems about problem of delay optimal Virtual Machine (VM) scheduling holds constant resources with full infrastructure like CPU, memory and storage in the resource pool. Cloud computing offers users with VMs as utilities. Cloud consumers randomly demand different VM types over time, and the usual length of the VM hosting differs greatly. A scheduling algorithm for a multi-level queue divides the prepared queue towards lengthy and various queues. System is allocated with single queue in to several longer queues. The systems are allocated to one queue indefinitely, usually on any basis of process property, like memory size, process priority, or process sort. Every queue will have its self-algorithm for scheduling. Likewise, a system that’s taking in a less preference queue is so lengthy, a high-priority queue can be transferred. Using Particle Swarm Optimization Algorithm (MQPSO), Multi-level queue scheduling has been done. To evaluate the solutions, it explores both Shortest-Job-First (SJF) buffering and Min-Min Best Fit (MMBF) programming algorithms, i.e., SJF-MMBF. The scheme incorporating the SJF-ELM-specific scheduling algorithms depending SJF buffering and Extreme Learning Machine (ELM) is also being proposed to prevent work hunger in an SJF-MMBF system. Furthermore, the queues must be planned, which is usually used as a preventive fixed priority schedule. The results of the simulation show that the SJF-ELM is ideal inside strong duty as well as maximum is environment dynamically, with an efficient average employment hosting rate.

Design and Provision of Traffic Grooming for Optical Wireless Data Center Networks

Traditional wired data center networks (DCNs) suffer from cabling complexity, lack flexibility, and are limited by the speed of digital switches. In this paper, we alternatively develop a top-down traffic grooming (TG) approach to the design and provisioning of mission-critical optical wireless DCNs. While switches are modeled as hybrid optoelectronic cross-connects, links are modeled as wavelength division multiplexing capable free-space optic channels. Using the standard TG terminology, we formulate the optimal mixed-integer TG problem considering the virtual topology, flow conversation, connection topology, non-bifurcation, and capacity constraints. Thereafter, we develop a fast yet efficient sub-optimal solution, which grooms mice flows (MFs), mission-critical flows (CFs), and forward on predetermined rack-to-rack (R2R) lightpaths. On the other hand, elephant flows (EFs) are forwarded over dedicated server-to-server express lightpaths whose routes and capacity are dynamically determined based on the availability of wavelength and capacity. To prioritize the CFs, we consider low and high-priority queues and analyze the delay characteristics such as waiting times, maximum hop counts, and blocking probability. As a result of grooming, the sub-wavelength traffic and adjusting the wavelength capacities, numerical results show that the proposed solutions can achieve significant performance enhancement by utilizing the bandwidth more efficiently, completing the flows faster than delay sensitivity requirements, and avoiding the traffic congestion by treating EFs and MFs separately.

Analysis of priority queueing system with working breakdown, repair, immediate feedback and bernoulli vacation

We analyze an M[X1], M[X2]/G1,G2/1 queue with two classes of non-preemptive priority service based on working breakdown, repair, immediate feedback and Bernoulli vacation. The server may subject to random breakdown with parameter α, during high priority service (type I), then the server will complete the service for current customer at a slower service rate compared to the regular service rate. On the other hand, during low priority service (type II), it should go for repair immediately. After the completion of each high priority service, there are two choices. First, the server can go for vacation with probability θ, secondly, its serves the next customer which has the probability (1 – θ). In case of customer dissatisfaction after completion of high priority service, immediately they receive service again without joining queue with probability r, or else the customer gets an option to discard, which has a probability of (1 – r). We use the established norm, which is the corresponding steady state results for the time dependent probability generating functions. Along with that, the expected time of wait for the expected number of customers in the high and low priority queues are computed. Numerical results along with the graphical representations are shown elaborately.

HPQS: A Fast, High-Capacity, Hybrid Priority Queuing System for High-Speed Networking Devices

In this paper, we present a fast hybrid priority queue architecture intended for scheduling and prioritizing packets in a network data plane. Due to increasing traffic and tight requirements of high-speed networking devices, a high capacity priority queue, with constant latency and guaranteed performance is needed. We aim at reducing latency to best support the upcoming 5G wireless standards. The proposed hybrid priority queuing system (HPQS) enables pipelined queue operations with almost constant time complexity in practice. The proposed architecture is implemented in C++, and is synthesized with the Vivado High-Level Synthesis (HLS) tool. Two configurations are proposed. The first one is intended for scheduling with a multi-queuing system for which implementation results of 64 up to 512 independent queues are reported. The second configuration is intended for large capacity priority queues, that are placed and routed on a ZC706 board and a XCVU440-FLGB2377-3-E Xilinx FPGA supporting a total capacity of 1/2 million packet tags. The reported results are compared across a range of priority queue depths and performance metrics with existing approaches. The proposed HPQS supports links operating at 40 Gb/s.

Packets routing and bandwidth sensing in a network traffic: Ant colony optimization tactic.

Packets routing and bandwidth sensing in a network platform remains an integral part of the study of signal flow.The algorithm to route packets in a network link called the AntNet algorithm was inspired by the behavior of real ant colonies. At each node in the network, a forward ant deposits some amount of pheromones at different links that responds to the node’s queue length. In this paper, we propose the inclusion of the computation of paths to adapt with the Depth Search Ant Explorer Network (DS-ANTENet) algorithm for discrete problems as an IP based mechanism. This method is tested and the efficiency is compared to the original AntNet algorithm and the Link-State algorithm to check the transmission of computing traffic flows between the nodes. We then made comparison with the algorithms proposed in the literature. The protocols were sorted out in terms of average number of lost packets ranging from the higher priority queue to the lower priority queue which then resulted to the fact that; First, AntNetBW (loss ratios reduction of 9.6% when compared to the AntNet and the Link-State algorithm respectively. Secondly, SANTENetBW (loss ratios reduction of 8.3% and 36.7% when compared to the AntNet and the Link-State algorithm respectively. Finally, DS-ANTENet (loss ratios reduction 0.7% and 33.2% when compared to the AntNet and the Link- State algorithm respectively.Keywords: Packets Routing, Bandwidth Sensing, Network Traffic, Ant Colony Optimization Algorithm, AntNet

Research on the Problem of Big Datagram Outburst Transmission

This paper analyses the transmission character of the high priority big data-gram in some network architecture adopting tunnel technology. Describe the second separation mechanism in the transmission progress. Design experiments to test the performance of router. Through the data analysis and experiments validation, get the reasons of packet loss in the outburst transmission, introduces how the high priority queue influence the lower. Some solutions have been proposed.

Transient Analysis of $$M^{[X_{1}]},M^{[X_{2}]}/G_{1},G_{2}/1$$ M [ X 1 ] , M [ X 2 ] / G 1 , G 2 / 1 Retrial Queueing System with Priority Services, Working Vacations and Vacation Interruption, Emergency Vacation, Negative Arrival and Delayed Repair

This paper deals with the analysis of two classes of batch arrivals, one is high priority and other is low priority(retrial) customers with non-preemptive priority service, working vacations, negative arrival, emergency vacation for an unreliable server, which consists of a break-down by negative arrival and delay time to start repair. When there are no customers in the queue and in the orbit at the time of service completion for a positive customers, the server goes for a working vacation. The server works at a lower service rate during working vacation period. If there are no customers in the system at the end of vacation, the server becomes idle and ready for serving the new arrivals with probability p(single working vacation) or it remains on vacation with probability $$q=(1{-}p)$$ (multiple working vacations). Here we assume that customers arrive according to compound Poisson process in which high priority customers are assigned to class one and class two customers are of a low priority type. The priority customers that find the server busy are queued and then served in accordance with FCFS discipline. The arriving low priority customers on finding the server busy are queued in the orbit in accordance with FCFS constant retrial policy. The arrival of negative customer remove the customer being in service and makes the server breakdown and the server fail for a short interval of time. When the server fails it will send to repair. But the repair time do not start immediately, there is a delay time to start the repair process. We consider an emergency vacation which means that during the service time the server suddenly go for a vacation and the interrupted customer waits to getting the remaining service. The retrial time, service time, emergency vacation time, delay time and repair time are all follows general(arbitrary) distribution, working vacation time and negative arrival follows exponential distribution. The time dependent probability generating functions have been obtained in terms of their Laplace transforms and the corresponding steady state results are obtained explicitly. Also some performance measures of our model such as the system state probabilities, reliability indices, the average number of customer in the high priority queue and the low priority in the orbit and the average waiting time are derived. Numerical results are computed and graphical representations are presented.

Smart systems approach for development of explicit congestion marking and traffic engineering model for Diffserv/MPLS networks

The scheduling, control, and management of traffic load in multi-protocol label switching (MPLS) are important aspects supporting the quality of service (QoS) and optimizing the network performance. In addition, ensuring the QoS in the Internet of Things (IoT) is one of the main issues of current research in this area. The IoT provides the ability for humans and computers to learn and interact with billions of things, including sensors, actuators, services, and other Internet-connected objects. MPLS offers the possibility for effective traffic control mechanisms within limited network capabilities. Traffic engineering in the MPLS domain provides alterations of the traffic routes to improve network resources or avoid network traffic congestion. Static management of traffic flows has failed to enhance the QoS of the network performance. Therefore, many researchers have attempted to use a dynamic management of traffic flow to enhance the QoS; however, more enhancements are needed. In this paper, we propose a new scheme to solve the problem of dynamic management of traffic flow through various links and routers in the network, and describe its application to an MPLS network. The proposed scheme works by utilizing queues, re-distribution, and re-balancing of streams before or during periods of congestion. It solves the problem of QoS violation, and tags, upgrades, or downgrades the traffic between queues based on the availability of free space of other queues and QoS parameters. We simulate the proposed scheme using OPNET, and the effect of implementing our method on low- and high-priority queues is analyzed and finally validated in conjunction with the ITU QoS measures.