Low-priority Queue Research Articles

Instant queue occupancy used for automatic traffic scheduling in data center networks

Datacenter applications desire low latency for short messages to provide a better user experience. Therefore, one of the goals of datacenter networks is to minimize flow completion time (FCT), especially for short flows. Multiple scheduling disciplines have been proposed to achieve this goal. In this paper, we develop a Dynamic Longer Stay Less Priority (D-LSLP) which looks at the current queue occupancy to adjust the demotion threshold for the packets of the flows arriving in the strict priority queues. Initially, D-LSLP considers every flow to be a short flow, and with the passage of time, the flow is demoted to the lower priority queue, similar to Multilevel Feedback Queue (MLFQ) Scheduling. It enables short flows to be completed in a couple of higher-priority queues. In contrast, large flows are demoted to the lower priority queues after remaining active in the queues for a certain amount of time (demotion threshold). However, for the different traffic patterns with different distribution of flow lengths, the demotion threshold for the flows should be adapted automatically. In this paper, we leverage the programmable nature of the P4 switches to measure the instant queue occupancy in the P4, which can be used to adjust the demotion threshold accordingly for the highest priority queue. This enables D-LSLP to increase or decrease its highest priority demotion threshold based on the instant queue status. D-LSLP allows multiple traffic patterns to coexist without manually tuning the demotion thresholds. Furthermore, it reduces the tail drop in the highest priority queue when many short flows overwhelm the highest priority queue. The performance evaluation shows that it works equally well for different traffic patterns without operator intervention.

Delay and size‐dependent priority‐aware scheduling for IoT‐based healthcare traffic using heterogeneous multi‐server priority queueing system

AbstractInternet of Things (IoT) based healthcare applications are time‐sensitive and any delay can cause alarming situations, including death of patients. The Earliest Deadline First (EDF) scheduling scheme has been proposed for use in IoT‐based healthcare applications. However, the EDF scheme performs poorly under overloaded conditions due to giving highest priority to packets that are close to missing their deadlines. Some studies have proposed the use of Priority EDF to overcome the challenges of EDF; however, Priority EDF still favours higher priority queues which increases the waiting times of lower priority queues. In order to overcome the limitation of EDF and its variants, this paper proposes a system model for a prioritized scheduling (PS) scheme. The PS scheme is an improvement of the Earliest Deadline First (EDF) scheme and its variants for IoT‐based healthcare applications. The PS scheme uses a heterogeneous multi‐server priority queuing system to provide service differentiation by prioritizing short packets over large packets and delay sensitive packets are serviced before delay tolerant packets. Numerical results demonstrate that the PS scheme minimizes the mean slowdown for both delay sensitive short and large packets at low and high load values. Additionally, the PS scheme performs better than the EDF and Priority EDF schemes in terms of reducing mean slowdown of packets and the PS scheme performs better than the EDF in terms of throughput for all packet sizes at both low and high load values. The performance improvement in terms of throughput is more pronounced at high load values. This addresses the challenge of the EDF scheme which performs poorly under overloaded conditions and the challenge of the Priority EDF scheme which favours higher priority queues at the expense of low priority queues.

Analytical Study of Two Serial Channels with Priority and Reneging

Most of the studies of queuing theory, which are useful in our daily life has been investigated by many researchers. The present research is the study of pre-emptive priority queuing system consisting two serial channels in stochastic environment. The impatient behavior of customer’s will be discussed with exponential service distribution and Poisson arrivals. Higher priority customers have pre-emptive priority over the low priority customers. The G.F. technique is used to derive the performance measures of high & low priority queues and assuming FCFS discipline in busy schedule of higher priority class. Also evaluate queue behavior graphically and discussed a special case at the end which shows utilization of channels.

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.

Improving QoS for Real-time Traffic using Multiple Low Latency Queueing Scheduling Mechanisms

In this work, a Multiple Low Latency Queuing scheduling mechanism model is developed to improve the QoS performance for real time and critical mission data traffic in LTE mobile networks. The main objective of this model is to achieve minimum delay and improve the QoS for real time applications (like Live Video and Voice over LTE). In addition, issues likestarvation of lower priority queues and bandwidth allocation are addressed. The model is composed of four components, first, classifier to classify the incoming traffic in router interface. Second, four Class Based Weighted Fair Queues (CBWFQ) scheduling mechanisms, with activation of strict priority feature in the first two queues. Third, two separate rate limiters (policers), one for each strict priority queue. Two scenarios are designed and simulated using Optimized Network Engineering Tool (OPNET). The results show that, in the case of Multiple Low Latency Queuing scheduling mechanism model, the real time traffic suffers less delay compared to the case of existing scheduling mechanisms like (Custom Queuing, Priory Queuing, CBWFQ and Low Latency Queuing). On the other hand, the model also addressed the starvation of lower-priority queuesproblem.

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.

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.

Effects of Transport Network Slicing on 5G Applications

Network slicing is considered a key technology in enabling the underlying 5G mobile network infrastructure to meet diverse service requirements. In this article, we demonstrate how transport network slicing accommodates the various network service requirements of Massive IoT (MIoT), Critical IoT (CIoT), and Mobile Broadband (MBB) applications. Given that most of the research conducted previously to measure 5G network slicing is done through simulations, we utilized SimTalk, an IoT application traffic emulator, to emulate large amounts of realistic traffic patterns in order to study the effects of transport network slicing on IoT and MBB applications. Furthermore, we developed several MIoT, CIoT, and MBB applications that operate sustainably on several campuses and directed both real and emulated traffic into a Programming Protocol-Independent Packet Processors (P4)-based 5G testbed. We then examined the performance in terms of throughput, packet loss, and latency. Our study indicates that applications with different traffic characteristics need different corresponding Committed Information Rate (CIR) ratios. The CIR ratio is the CIR setting for a P4 meter in physical switch hardware over the aggregated data rate of applications of the same type. A low CIR ratio adversely affects the application’s performance because P4 switches will dispatch application packets to the low-priority queue if the packet arrival rate exceeds the CIR setting for the same type of applications. In our testbed, both exemplar MBB applications required a CIR ratio of 140% to achieve, respectively, a near 100% throughput percentage with a 0.0035% loss rate and an approximate 100% throughput percentage with a 0.0017% loss rate. However, the exemplar CIoT and MIoT applications required a CIR ratio of 120% and 100%, respectively, to reach a 100% throughput percentage without any packet loss. With the proper CIR settings for the P4 meters, the proposed transport network slicing mechanism can enforce the committed rates and fulfill the latency and reliability requirements for 5G MIoT, CIoT, and MBB applications in both TCP and UDP.

ASSESSMENT OF THE PERFORMANCE OF A BUFFER MANAGEMENT SYSTEM TO IMPROVE HIGH PRIORITY MESSAGE DELIVERY TIME LIMIT

In vehicular delay-tolerant networks, buffer management systems are developed to improve overall performance. However, these buffer memory management systems cannot simultaneously reduce network overload, reduce high priority message delivery time limit, and improve all priority class message delivery rates. As a result, quality of service is not guaranteed. In this paper, we propose a drop policy based on the constitution of two queues according to message weight, the position of the node in relation to the destination and the comparison of the oldness between the high-priority message and the messages in the low-priority queue. The results of the simulations show that compared to the existing buffer management policy based on time-to-live and priority, our strategy simultaneously reduces network overload, reduces the delivery time limit of high-priority messages and allows for an increase in the delivery rate of messages regardless of their priority.

Load Balancing Using SJF-MMBF and SJF-ELM Approach

This paper studies the delay-optimal virtual machine (VM) scheduling problem in cloud computing systems, which have a constant amount of infrastructure resources such as CPU, memory and storage in the resource pool. The cloud computing system provides VMs as services to users. Cloud users request various types of VMs randomly over time and the requested VM-hosting durations vary vastly. A multi-level queue scheduling algorithm partitions the ready queue into several separate queues. The processes are permanently assigned to one queue, generally based on some property of the process, such as memory size, process priority or process type. Each queue has its own scheduling algorithm. Similarly, a process that waits too long in a lower-priority queue may be moved to a higher-priority queue. Multi-level queue scheduling is performed via the use of the Particle Swarm Optimization algorithm (MQPSO). It checks both Shortest-Job-First (SJF) buffering and Min-Min Best Fit (MMBF) scheduling algorithms, i.e., SJF-MMBF, is proposed to determine the solutions. Another scheme that combines the SJF buffering and Extreme Learning Machine (ELM)-based scheduling algorithms, i.e., SJF- ELM, is further proposed to avoid the potential of job starva¬tion in SJF-MMBF. In addition, there must be scheduling among the queues, which is commonly implemented as fixed-priority preemptive scheduling. The simulation results also illustrate that SJF- ELM is optimal in a heavy-loaded and highly dynamic environment and it is efficient in provisioning the average job hosting rate.

Real-Time Communication Model Based on OPC UA Wireless Network for Intelligent Production Line

With the continuous development of intelligent manufacturing technology, the intelligent production line for the Industrial Internet of Things occupies an important position in the field of industrial intelligence. OPC UA is a standard for communication data exchange between intelligent production line devices. OPC UA can establish a unified information model for production line devices and improve the connectivity of heterogeneous networks. However, in industrial wireless network application scenarios, there are various types of sensing information and large amounts of data to be transmitted. Each type of data has different requirements for real-time. The traditional OPC UA communication method is difficult to achieve real-time and reliable transmission in intelligent production lines and can’t meet the transmission requirements of time-sensitive data. In this paper, we propose a real-time communication model of the OPC UA wireless network for intelligent production lines (UAMPDS). IEEE 802.15.4e TSCH is used as the wireless communication infrastructure, and OPC UA protocols are fused to this model. Meanwhile, we design a load-aware time-slot scheduling algorithm to dynamically allocate and schedule time slots according to the network topology and traffic load of each node. We also implement a dynamic preemptive resource scheduling strategy based on service differentiation to ensure real-time transmission of time-sensitive data. The experimental results show that this model achieves differentiated services based on data with different latency tolerance and effectively reduces the transmission latency of real-time service data under the constrained network resources. It can avoid the starvation phenomenon of low priority queues, and improves the overall quality of service of intelligent production line communication networks.

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.

Low Latency Big Data Processing without Prior Information

Job scheduling plays an important role in improving the overall system performance in big data processing frameworks. Simple job scheduling policies, such as Fair and FIFO scheduling, do not consider job sizes and may degrade the performance when jobs of varying sizes arrive. More elaborate job scheduling policies make the convenient assumption that jobs are recurring, and complete information about their sizes is available from their prior runs. In this paper, we design and implement an efficient and practical job scheduler for big data processing systems to achieve better performance even without prior information about job sizes. The superior performance of our job scheduler originates from the design of multiple level priority queues, where jobs are demoted to lower priority queues if the amount of service consumed so far reaches a certain threshold. In this case, jobs in need of a small amount of service can finish in the topmost several levels of queues, while jobs that need a large amount of service to complete are moved to lower priority queues to avoid head-of-line blocking. Our new job scheduler can effectively mimic the shortest job first scheduling policy without knowing the job sizes in advance. To demonstrate its performance, we have implemented our new job scheduler in YARN, a popular resource manager used by Hadoop/Spark, and validated its performance with experiments in both real testbeds including Amazon EC2 and large-scale trace-driven simulations. Our experimental and simulation results have strongly confirmed the effectiveness of our design: our new job scheduler can reduce the average job response time of the Fair scheduler by up to 45 percent and achieve better fairness at the same time.

A Lazy Bailout Approach for Dual-Criticality Systems on Uniprocessor Platforms

A challenge in the design of cyber-physical systems is to integrate the scheduling of tasks of different criticality, while still providing service guarantees for the higher critical tasks in the case of resource-shortages caused by faults. While standard real-time scheduling is agnostic to the criticality of tasks, the scheduling of tasks with different criticalities is called mixed-criticality scheduling. In this paper, we present the Lazy Bailout Protocol (LBP), a mixed-criticality scheduling method where low-criticality jobs overrunning their time budget cannot threaten the timeliness of high-criticality jobs while at the same time the method tries to complete as many low-criticality jobs as possible. The key principle of LBP is instead of immediately abandoning low-criticality jobs when a high-criticality job overruns its optimistic WCET estimate, to put them in a low-priority queue for later execution. To compare mixed-criticality scheduling methods, we introduce a formal quality criterion for mixed-criticality scheduling, which, above all else, compares schedulability of high-criticality jobs and only afterwards the schedulability of low-criticality jobs. Based on this criterion, we prove that LBP behaves better than the original Bailout Protocol (BP). We show that LBP can be further improved by slack time exploitation and by gain time collection at runtime, resulting in LBPSG. We also show that these improvements of LBP perform better than the analogous improvements based on BP.

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.

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

From Exhaustive Vacation Queues to Preemptive Priority Queues with General Interarrival Times

Abstract We consider the discrete-time G/GI/1 queueing system with multiple exhaustive vacations. By a transform approach, we obtain an expression for the probability generating function of the waiting time of customers in such a system. We then show that the results can be used to assess the performance of G/GI/1 queueing systems with server breakdowns as well as that of the low-priority queue of a preemptive MX+G/GI/1 priority queueing system. By calculating service completion times of low-priority customers, various preemptive breakdown/priority disciplines can be studied, including preemptive resume and preemptive repeat, as well as their combinations. We illustrate our approach with some numerical examples.