Preemptive Priority Research Articles

Modeling and Performance Analysis of Cognitive Radio Networks Using Stochastic Timed Colored Petri Nets

Cognitive radio (CR) is currently one of the most promising information transmission technologies to deal with the problem of spectrum scarcity and spectrum underutilization in wireless communications. CR networks aim to enhance spectrum efficiency to meet the ever-increasing demands of end users. The principle is to provide the opportunity for unlicensed users (secondary users, SUs) to temporarily and dynamically access the unused or sparsely used bandwidth while ensuring that it never interferes or degrades the performance of the incumbent license holders, commonly called primary users (PUs). This raises several challenges to be addressed in CR networks and performance of secondary users is one of the critical issues tackled in this paper. That is, we propose to devise CR networks as a retrial queueing system where PUs have preemptive priority over SUs. To calculate performance measures of the devised model under quite general assumptions about the model parameters, analytical methods are known to require hard calculations and the obtained results are generally not exploitable. For this reason, simulation modeling becomes the last resort to assess the dependability indicators. To this extend, we build the simulation model of the queueing system using Timed Stochastic Colored Petri Nets. Various useful results will be hence drawn while varying network conditions. Both exponential and Erlang distributions are considered for modeling service time of SUs. The obtained results with restrictive assumptions fit the analytical outcomes experienced for quite similar queuing models, which demonstrate the effectiveness of the proposed STCPN simulation model.

Stochastic analysis of a preemptive retrial queue with orbital search and multiple vacations

This paper deals with a preemptive priority M/G/1 retrial queue with orbital search and exhaustive multiple vacations. By using embedded Markov chain technique and the supplementary variable method, we discuss the necessary and sufficient condition for the system to be stable and the joint queue length distribution in steady state as well as some important performance measures and the Laplace–Stieltjes transform of the busy period. Also, we establish a special case and the stochastic decomposition laws for this preemptive retrial queueing system. Finally, some numerical examples and cost optimization analysis are presented.

A novel efficient cross layer-based bandwidth reservation mechanism for next generation wireless networks

In this paper, a cross-layer-based efficient bandwidth reservation mechanism (CLBRM) is proposed for next generation wireless networks. Application layer and network layer are involved in the design of the cross-layer architecture. Cross-layer architecture is used to reduce the delay. Shared database is used to implement the cross-layer architecture which allows to share the information among the application layer and the network layer. Bandwidth is reserved based on the type of requests and past history of the users. Four types of requests are used in this paper and priority is assigned among these requests. Two queues are maintained for last two priority requests and pre-emption of these requests is incorporated in order to serve the highly prioritised requests in a better way. The QoS parameters like delay, throughput, blocking probability, and dropping probability are used to evaluate the performance of the proposed mechanism. The performance of the proposed mechanism, CLBRM is compared with the data driven allocation (DDA) scheme (Fan et al., 2016) and proved to perform better.

Service priority queuing model-based internet of things middleware for load balancing among fog computing centres

The fog computing architectural component meets the major requirements of the internet of things (IoT) applications. Fog computing nodes are mainly used to handle delay sensitive requests generated by IoT devices. An increase in the service arrival rate at the fog computing centre leads to an overload of fog nodes. Offloading delay sensitive requests from the overloaded fog node to the cloud introduces significant communication delays. This may lead to a violation of the service deadline in the service level agreement (SLA). This paper proposes horizontal collaborative load balancing among fog nodes in close geographical proximity. The authors use the preemptive priority queuing model for the processing of delay sensitive requests. The paper introduces an efficient message exchange among fog nodes to reduce computational overhead. These messages are used for load balancing. The result of the simulation shows that the proposed approach demonstrates the superiority of the proposed mechanism in minimising the response time of delay sensitive requests. Moreover, 11% reduction in the offloading of delay sensitive requests to neighbouring fog nodes is a significant improvement over existing methods.

An unreliable discrete-time retrial queue with probabilistic preemptive priority, balking customers and replacements of repair times

This paper deals with a discrete-time $Geo/G/1$ retrial queueing system with probabilistic preemptive priority and balking customers, in which the server is subject to starting failures and replacements in the repair times may occur with some probability. If the server is found busy at an arrival epoch, the newly arriving customer either interrupts the customer in service to begin its own service with probability $p$ or enters the orbit with probability $1-p$. When an arriving customer (external or repeated) finds the server free, he must turn on the server. If the server is activated successfully, the customer receives service immediately. Otherwise, the server undergoes a repair process. If an external arrival finds that the server is under repair, he decides either to join the orbit with probability $q$ or leaves the system completely (balking) with probability $1-q$. Applying the supplementary variable method and the generating function technique, we analyze the Markov chain underlying the considered queueing model and derive the stationary distributions under different system states, the generating functions for the number of customers in the orbit and in the system, as well as some crucial performance measures in steady state. Especially, some corresponding results under special cases are directly obtained by setting appropriate parameter values. Further, some numerical examples are provided to examine the effect of various system parameters on queueing characteristics. Finally, an operating cost function is formulated to discuss numerically a cost optimization problem.

Scheduling algorithm based on preemptive priority and hybrid data structure for cognitive radio technology with vehicular ad hoc network

There are different types of messages containing different priorities in vehicular ad hoc networks. Hence, queue rescheduling is required according to priorities of arrived messages. In this study, a data structure with less computational complexity is proposed to minimise queuing delay. Further, to maintain quality of service, preemptive priority is applied to time-bound safety messages by transferring non-safety messages to other bands using the concept of cognitive radio technology. The time-bound messages are transmitted using the dedicated short-range communication spectrum without the need for spectrum sensing by vehicles. The other messages with no deadline constraint are switched to other bands near-dedicated short-range communication spectrum. The results show that 6.25% improvement in packet delivery ratio of cognitive radio-enabled preemptive priority is achieved in comparison to existing cognitive radio protocol. The delay shows a slight increment of 1.1%. The packet delivery ratio of cognitive radio-enabled non-preemptive priority is improved by 3.24% while the delay is improved by 3.17%. The data storage required for storing sensing data of 50 channels for 10 days is only 45 Mb.

Workshop Queue System Modification Through Multi Priority Strategy

This research focused on the modification of a queuing system in a workshop practice that repairs crankshafts using multi priority strategy. The random distribution of the inter arrival time of the crankshafts as well as the service time were statistically determined using Chi square goodness of fit test. The results obtained show that all the classes conform to Poisson distribution. For the non-preemptive priority, the mean waiting time in queue results for the first, second and third classes are 0.066, 0.09 and 0.224 day, respectively, while for the preemptive priority the three classes show 0.007, 0.036 and 0.258. Besides, the mean waiting time in queue for no priority system is 0.17 day. Arrivals that are of higher priority classes in preemptive priority systems enjoy huge improvement when compared with non-preemptive system. On the other hand, the improvement gained in higher priority classes has detrimental effect on the low priority class. Similar scenario plays out when the results of the mean waiting time in the system was analyzed. It is therefore advocated that priority strategy should be adopted for a system that may have urgent or semi-urgent jobs among the pool of jobs that needs repair to avoid possible losses arising from frequent reneging or balking of such jobs.

A Delay Optimal Multiclass Packet Scheduler for General M2M Uplink

The uplink data arriving at the Machine-to-Machine (M2M) Application Server (AS) via M2M Aggregators (MAs) is fairly heterogeneous along several dimensions such as maximum permissible packet delay, arrival rate, and payload size, thus necessitating the design of Quality-of-Service (QoS) aware packet schedulers. In this paper, we classify the M2M uplink data into multiple QoS classes and use sigmoidal function to map the delay requirements of each class onto utility functions. We propose a delay-optimal multiclass packet scheduler at AS obtained by iteratively searching for the optimal fraction of time-sharing between all preemptive priority scheduling policies, such that it maximizes a proportionally fair system utility metric. The iterative optimization process leads to reduced complexity, thus facilitating its online implementation. We then extend this work to determine a jointly optimal packet scheduler at the MAs and AS. We significantly reduce its computational complexity by iteratively solving a distributed optimization problem, independently at MAs and AS. Using Monte Carlo simulations, we show that the proposed joint packet scheduler outperforms other state-of-the-art packet schedulers and results in (desirable) near-minimal delay-jitter for delay-sensitive traffic at the expense of (tolerable) higher delay-jitter for delay-tolerant traffic.

A Combined Preemptive SJF and Preemptive Priority Algorithm to Enhance CPU Utilization

A Combined Preemptive SJF and Preemptive Priority Algorithm to Enhance CPU Utilization

Limited Search Space-Based Algorithm for Dual Resource Constrained Scheduling Problem with Multilevel Product Structure

This study addresses the dual resource constrained flexible job shop scheduling problem (DRCFJSP) with a multilevel product structure. The DRCFJSP is a strong NP-hard problem, and an efficient algorithm is essential for DRCFJSP. In this study, we propose an algorithm for the DRCFJSP with a multilevel product structure to minimize the lateness, makespan, and deviation of the workload with preemptive priorities. To efficiently solve the problem within a limited time, the search space is limited based on the possible start and end time, and focus is placed on the intensification rather than diversification, which can help the algorithm spend more time to find an optimal solution in a reasonable solution space. The performance of the proposed algorithm is compared with those of a genetic algorithm and a hybrid genetic algorithm with variable neighborhood search. The numerical experiments demonstrate that the strategy limiting the search space is effective for large and complex problems.

Non-Cooperative Spectrum Access Strategy Based on Impatient Behavior of Secondary Users in Cognitive Radio Networks

In the cognitive radio network (CRN), secondary users (SUs) compete for limited spectrum resources, so the spectrum access process of SUs can be regarded as a non-cooperative game. With enough artificial intelligence (AI), SUs can adopt certain spectrum access strategies through their learning ability, so as to improve their own benefit. Taking into account the impatience of the SUs with the waiting time to access the spectrum and the fact that the primary users (PUs) have preemptive priority to use the licensed spectrum in the CRN, this paper proposed the repairable queueing model with balking and reneging to investigate the spectrum access. Based on the utility function from an economic perspective, the relationship between the Nash equilibrium and the socially optimal spectrum access strategy of SUs was studied through the analysis of the system model. Then a reasonable spectrum pricing scheme was proposed to maximize the social benefits. Simulation results show that the proposed access mechanism can realize the consistency of Nash equilibrium strategy and social optimal strategy to maximize the benefits of the whole cognitive system.

Mathematical Analysis of Queueing-Inventory Model with Compliment and Multiple Working Vacations

In this paper, we provide independent continuous review of stock for two commodities, namely commodity 1(C1) and 2 (C2). The type 1(T1) customer demands C1 and Type 2(T2) customer demands C2 but C2 is also given to T1 customer as a compliment (i.e. C2 is also given as a compliment to C1). The arrival pattern of customers is from a finite source of population. The ordering policies of both commodities are independent and also, each customer demands service with positive time. A single finite retrial orbit is allowed for any customer, when the demanding item is stocked out or the server is busy. The pre-emptive priority service policy is assigned to T1 customer over T2 customer. The server may go for the working vacation as C1 becomes zero, in which T2 customer who is taking his service is allowed to orbit whenever T1 customer demands the service with both commodities and when the orbit is not full. The joint limiting distributions of four random variables are studied under steady state. Long run expected total cost rate and measures of system performance are derived.

Integrated energy management system employing pre-emptive priority based load scheduling (PEPLS) approach at residential premises

Human work has been automated and replaced by various smart appliances at residential buildings with an objective to save time and energy. So, there is a massive upsurge of energy consumption everywhere and in due course, it resulted in increased electrical bills than before. In order to meet the growing utilization of more electricity by consumers at residences, internalization of renewable energy systems are firmly recommended for a better transformation. Hither, the IEMS is principally aiming to truncate the rate of electricity bills to consumers. Likewise, scheduling the usage of smart appliances and implementing load management practices prudently, a peak load reduction can be certainly accomplished by effectively using Solar Power, Batteries and Utility Grid. Already existing TOU tariff is employed in the process along with restructured components and renewable resources to manage the loads of different smart appliances according to their usage priority at different times of a day. This automated system is able to manage multiple heavy loads smartly with the help of renewable energy resources even at peak hours of a day.

A two-priority single server retrial queue with additional items

In this paper, we study a priority queueing-inventory problem with two types of customers. Arrival of customers follows Marked Markovian arrival process and service times have phase-type distribution with parameters depending on the type of customer in service. For service of each type of customer, a certain number of additional items are needed. High priority customers do not have waiting space and so leave the system when on their arrival a priority 1 customer is in service or the number of available additional items is less than the required threshold. Preemptive priority is assumed. Type 2 customers, encountering a busy server or idle with the number of available additional items less than a threshold, go to an orbit of infinite capacity to retry for service. The customers in orbit are non-persistent: if on retrial the server is found to be busy/idle with the number of additional items less than the threshold, this customer abandons the system with certain probability. Such a system represents an accurate enough model of many real-world systems, including wireless sensor networks and system of cognitive radio with energy harvesting and healthcare systems. The probability distribution of the system states is computed, using which several of the characteristics are derived. A detailed numerical study of the system, including the analysis of the influence of the threshold, is performed.

Scalability analysis of request scheduling in cloud computing

Rapid advancement of distributed computing systems enables complex services in remote computing clusters. Massive applications with large-scale and disparate characteristics also create high requirements for computing systems. Cloud computing provides a series of novel approaches to meet new trends and demands. However, some scalability issues have to be addressed in the request scheduling process and few studies have been conducted to solve these problems. Thus, this study investigates the scalability of the request scheduling process in cloud computing. We provide a theoretical definition of the scalability of this process. By modeling the scheduling server as a stochastic preemptive priority queue, we conduct a comprehensive theoretical and numerical analysis of the scalability metric under different structures and various environment configurations. The comparison and conclusion are expected to shed light on the future design and deployment of the request scheduling process in cloud computing.

Performance Analysis of Preemptive Priority Retrial Queueing System with Disaster under Working Breakdown Services

In this investigation, a novel sort of retrial queueing system with working breakdown services is introduced. Two distinct kinds of customers are considered, which are priority and ordinary customers. The normal busy server may become inadequate due to catastrophes at any time which cause the major server to fail. At a failure moment, the major server is sent to be fixed and the server functions at a lower speed (called the working breakdown period) during the repair period. The probability generating functions (PGF) of the system size is found using the concepts of the supplementary variable technique (SVT). The impact of parameters in system performance measures and cost optimization are examined numerically.

Performance Simulation of Finite-Source Cognitive Radio Networks with Servers Subjects to Breakdowns and Repairs

The present paper deals with the performance evaluation of a cognitive radio network with the help of a queueing model. The queueing system contains two interconnected, not independent sub-systems. The first part is for the requests of the Primary Units (PU). The number of sources is finite, and each source generates high priority requests after a exponentially distributed time. The requests are sent to a single server unit or Primary Channel Service (PCS) with a preemptive priority queue. The service times are assumed to be exponentially distributed. The second sub-system is for the requests of the Secondary Units (SU), which is finite sources system too; the inter-request times and service times of the single server unit or Secondary system Channel Service (SCS) are assumed to be exponentially distributed, respectively. A generated high priority packet goes to the primary service unit. If the unit is idle, the service of the packet begins immediately. If the server is busy with a high priority request, the packet joins the preemptive priority queue. When the unit is engaged with a request from SUs, the service is interrupted and the interrupted low priority task is sent back to the SCS. Depending on the state of the secondary channel, the interrupted job is directed to either the server or the orbit. In case the requests from SUs find the SCS idle, the service starts, and if the SCS is busy, the packet looks for the PCS. In the case of an idle PCS, the service of the low-priority packet begins at the high-priority channel (PCS). If the PCS is busy, the packet goes to the orbit. From the orbit it retries to be served after an exponentially distributed time. The novelty of our investigation is that each server is subject to random breakdowns, in which case the interrupted request is sent to the queue or orbit, respectively. The operating and repair times of the servers are assumed to be generally distributed. Finally, all the random times included in the model construction are assumed to be independent of each other. The main aim of the paper is to analyze the effect of the nonreliability of the servers on the mean and variance of the response time for the SUs by using simulation.

Model Probabilistik Fuzzy Goal Programming Berdistribusi Pareto dengan Urutan Prioritas pada Permasalahan Produksi Kue

Yulis’s home industry is one of the Small and Medium Bussiness (UKM) on Palembang city. Yulis’s home industry produces bolu kukus, kue lapis, kue pare, kumbu kacang, srikaya, and wajik. Most of cakes that will be produced by Yuli is still based on the cake sale on the last day, so that the profit is still not optimal condition. This research aims to create preemptive priority additive approach to Fuzzy Goal Programming Probabilistic (PFGP) model with Pareto distribution for maximize profits, minimize the production of perishable cake, and maximize best sellers cake. The results are obtained maximum profit is Rp. 8.488.569 on a week , the amount of perishable cake is 1,295 pieces on a week, and optimal production for best seller cake as much as 8,489 pieces on a week.

A joint maxmin-lexicographic maximisation approach in fuzzy goal programming using dominance possibility and necessity criteria

In this paper, a new approach for solving fuzzy goal programming problems is introduced. The coefficients and the aspiration level of each fuzzy goal are considered either trapezoidal or triangular fuzzy numbers. Four dominance criteria (dominance possibility, strict dominance possibility, dominance necessity, and strict dominance necessity) are utilised for comparing the fuzzy numbers. The proposed approach is based on merging the maxmin approach and the lexicographic approach in a two-phase process. The first phase applies the maxmin technique by maximising the minimum achievement degree of the fuzzy goals. The second phase lexicographically maximises the achievement degrees of the fuzzy goals according to their preemptive priorities. This methodology provides the decision maker with the advantage of improving the results of his preemptive priority structure model by initially maximising the lowest achievement of the fuzzy goals, and hence guarantee that the ultimate achievement of any fuzzy goal will never be lower than a specific percentage of the achieved maxmin value. The suggested approach is illustrated by a numerical example.

A joint maxmin-lexicographic maximisation approach in fuzzy goal programming using dominance possibility and necessity criteria

In this paper, a new approach for solving fuzzy goal programming problems is introduced. The coefficients and the aspiration level of each fuzzy goal are considered either trapezoidal or triangular fuzzy numbers. Four dominance criteria (dominance possibility, strict dominance possibility, dominance necessity, and strict dominance necessity) are utilised for comparing the fuzzy numbers. The proposed approach is based on merging the maxmin approach and the lexicographic approach in a two-phase process. The first phase applies the maxmin technique by maximising the minimum achievement degree of the fuzzy goals. The second phase lexicographically maximises the achievement degrees of the fuzzy goals according to their preemptive priorities. This methodology provides the decision maker with the advantage of improving the results of his preemptive priority structure model by initially maximising the lowest achievement of the fuzzy goals, and hence guarantee that the ultimate achievement of any fuzzy goal will never be lower than a specific percentage of the achieved maxmin value. The suggested approach is illustrated by a numerical example.