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Permissioned blockchains are increasingly used in areas like supply chain management, financial transactions, and medical data sharing, where ensuring data consistency and security is critical. However, these systems are vulnerable to threats such as DDoS attacks, forged transactions, and certificate authority compromises, primarily due to inadequate network layer admission control. Existing solutions, like static whitelisting, struggle with scalability and adaptability in dynamic environments. This paper proposes a novel admission control mechanism based on identity-based cryptography, utilizing multi-level anonymous identifiers and decentralized private key generation to enhance user authentication and privacy. The mechanism dynamically updates whitelists and selectively filters network traffic, ensuring a balance between security and performance. Experimental results validate its effectiveness in mitigating key threats while maintaining operational efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of admission control for a stream of jobs arriving at two single-server queues working in parallel, with general inter-arrival time distribution and exponential service-time distribution. The jobs are classified into three types: two of these types are dedicated to being processed completely by one of the two servers. The third job is a fork type wherein each admitted job upon arrival splits into two sub jobs and each of which enters one of the two queues. In addition, each type of jobs is further segmented into multiple classes according to the revenue earned on job acceptance. We formulate the admission control problem of the above queueing system as a finite-horizon stochastic dynamic program. Using value iteration, we characterize the structure of the optimal policy as a set of monotone switching curves. Further, we extend our analysis to the batch arrival case, the infinite-horizon case, and the case with more than two parallel queues. To address the computational issues of the multi-dimensional dynamic programming associated with the models, we develop an efficient improved control policy to solve the problem. Finally, numerical examples are used to illustrate the monotone structure of switching curves and compare the performance of the optimal and improved control policies. [ABSTRACT FROM AUTHOR]

We study the routing and admission control problem in a parallel queueing system with heterogeneous servers serving multiple types of customers. The system makes admission decision regarding whether to admit a customer upon arrival as well as routing decision of the queue to which the admitted customer is assigned. The objective is to maximize the expected profit, which includes customer-dependent revenues and holding cost and server-dependent cost. We first characterize the structure of the optimal policy for the case with two servers and two types of customers that have the same holding cost. We show that the optimal admission and routing policy has a complex non-monotone structure; however, we show that this non-monotone structure is the result of overlapping of three pairwise dominant policies that have a monotone structure. Utilizing the above structure, we propose three heuristics for the general case of multiple servers and multiple types of customers. Through a numerical study, we demonstrate the effectiveness of our heuristics, and provide conditions under which each heuristic performs well. Lastly, we provide insights on the effect of holding cost on customer rejection and the effect of fixed production cost on capacity allocation. [ABSTRACT FROM AUTHOR]

Off-service placement is a common capacity-pooling strategy that hospitals utilize to address mismatches in supply and demand that arise from the day-to-day variation in patient demand. This strategy involves placing patients in a bed in a unit that is designated for another specialty service. Building on prior work that documents the negative first order effects of off-service placement on patients who are placed off service themselves, we quantify the spillover effects of this practice on patients who are actually placed on service. Using an estimation strategy that combines the Heckman correction procedure and a heteroskedasticity-based identification approach, we find that off-service placement has substantial negative spillover effects on the efficiency of care delivered to on-service patients. In particular, we find that a 10 percentage point increase in the level of off-service placement during a patient's hospitalization is associated with a 10.9% increase in length of stay. Through a series of counterfactual analyses, we propose alternate routing and capacity-planning policies that could meaningfully improve the efficiency of care in the inpatient setting. This paper was accepted by Stefan Scholtes, healthcare management. Funding: This work was supported by Claude Marion Endowed Faculty Scholar Award; Fishman-Davidson Center for Service and Operations Management. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02202. [ABSTRACT FROM AUTHOR]

In this study, we aim to optimize hospital bed allocation to enhance medical service efficiency and quality. We developed an optimization model and algorithms considering cross-departmental bed-sharing costs, patient waiting costs, and the impact on medical quality when patients are assigned to non-primary departments. First, we propose an algorithm to calculate departmental similarity and quantify the effect on patients’ length of stay when admitted to non-primary departments. We then formulate a two-stage cost minimization problem: the first stage involves determining bed allocation for each department, and the second stage involves dynamic admission control decisions. For the second stage, we apply a dynamic programming method and approximate the model using deterministic linear programming to ensure practicality and computational efficiency. Numerical studies validate the effectiveness of our approach. Results show that our model and algorithms significantly improve bed resource utilization and medical service quality, supporting hospital management decisions. [ABSTRACT FROM AUTHOR]

We develop an efficient reinforcement learning algorithm that learns the optimal admission control policy in a partially observable queueing network. Specifically, only the arrival and departure times from the network are observable, optimality refers to the average holding/rejection cost in infinite horizon, and efficiency is with respect to regret performance. While reinforcement learning in partially-observable Markov Decision Processes (MDP) is prohibitively expensive in general, we show that the regret at time T induced by our algorithm is O ~ T log (1 / ρ) where ρ ∈ (0 , 1) is connected to the mixing time of the underlying MDP. In contrast with existing regret bounds, ours does not depend on the diameter (D) of the underlying MDP, which in most queueing systems is at least exponential in S, i.e., the maximal number of jobs in the network. Instead, the role of the diameter is played by the log (1 / ρ) term, which may depend on S but we find that such dependence is "minimal". In the case of acyclic or hyperstable queueing networks, we prove that log (1 / ρ) = O (S) , which overall provides a regret bound of the order of O ~ TS . In the general case, numerical simulations support the claim that the term log (1 / ρ) remains extremely small compared to the diameter. The novelty of our approach is to leverage Norton's theorem for queueing networks and an efficient reinforcement learning algorithm for MDPs with the structure of birth-and-death processes. [ABSTRACT FROM AUTHOR]

In the domain of cognitive radio (CR), unlicensed users have the opportunity to efficiently use available spectrum bands without interfering with licensed primary users (PUs). Our study addresses the challenge of secondary user (SU) spectrum shortage due to high arrival rates of licensed users. We propose two models aimed at improving the average total waiting time for SUs in such scenarios. These models incorporate non-acquired and preemptive priority mechanisms within the M/D/1 model of a PU delay system. Through quantitative evaluations and Monte Carlo simulations, we evaluate the performance of these models. Our findings show significant improvements in average waiting time for both PUs and SUs, especially under the priority scheme. Furthermore, we explore these models in the context of real-time systems, considering the limited buffer capacity for both user types. This further improves the average waiting time for PUs and SUs in both priority schemes. Our contribution lies in providing effective solutions to mitigate SU shortages in CR networks, providing insight into priority-based approaches and real-time system considerations.

We study a multi-server queueing system where a customer is satisfied (and generates a unit revenue) if their queueing time is at most a given constant. If the queueing time of the admitted customer exceeds this constant, the customer gets served, but is unsatisfied and generates no revenue. Such queueing systems arise in the context of modeling service systems where excessive delays are of concern. A key challenge is how to design an admission control policy to maximize the number of satisfied customers per unit time in the long run, assuming that we can observe the number of customers in the system at any time. We call this the binary reward structure system and show that a threshold-type admission policy is optimal. The optimal threshold policy has to be computed numerically. Hence we propose a square-root admission policy to approximate the optimal admission control policy, and compare the performance of these two policies. We derive an analytical upper bound on the performance of optimal admission control policy by deriving an optimal admission policy assuming we have full information over the queueing time of the admitted customers. This is equivalent to a queueing system where customers abandon the queue (i.e., leave without service) if their queueing time exceeds the given constant. We demonstrate that the optimal policy that includes customer abandonment, or alternatively, the optimal policy under full information, the optimal threshold policy, and the square-root admission policy, all exhibit identical performance in the asymptotic regions of the parameter space. Our numerical results indicate that the worst optimality gap of the square-root admission policy is within 3.9% of the optimal revenue, and implementing the square-root admission policy in the observable queueing system leads to a revenue loss that is at most 5.6% of the maximum possible revenue rate in the full information system. We also compare the binary reward structure with the more common linear reward structure where the system incurs holding cost per unit queueing time per customer. In addition, we also show that the analysis based on queueing time is applicable to the system time as well. [ABSTRACT FROM AUTHOR]

In the domain of cognitive radio (CR), unlicensed users have the opportunity to efficiently use available spectrum bands without interfering with licensed primary users (PUs). Our study addresses the challenge of secondary user (SU) spectrum shortage due to high arrival rates of licensed users. We propose two models aimed at improving the average total waiting time for SUs in such scenarios. These models incorporate non-acquired and preemptive priority mechanisms within the M/D/1 model of a PU delay system. Through quantitative evaluations and Monte Carlo simulations, we evaluate the performance of these models. Our findings show significant improvements in average waiting time for both PUs and SUs, especially under the priority scheme. Furthermore, we explore these models in the context of real-time systems, considering the limited buffer capacity for both user types. This further improves the average waiting time for PUs and SUs in both priority schemes. Our contribution lies in providing effective solutions to mitigate SU shortages in CR networks, providing insight into priority-based approaches and real-time system considerations. [ABSTRACT FROM AUTHOR]

Permissioned blockchains are increasingly used in areas like supply chain management, financial transactions, and medical data sharing, where ensuring data consistency and security is critical. However, these systems are vulnerable to threats such as DDoS attacks, forged transactions, and certificate authority compromises, primarily due to inadequate network layer admission control. Existing solutions, like static whitelisting, struggle with scalability and adaptability in dynamic environments. This paper proposes a novel admission control mechanism based on identity-based cryptography, utilizing multi-level anonymous identifiers and decentralized private key generation to enhance user authentication and privacy. The mechanism dynamically updates whitelists and selectively filters network traffic, ensuring a balance between security and performance. Experimental results validate its effectiveness in mitigating key threats while maintaining operational efficiency.

Problem definition: Do physicians exhibit predictable behavioral bias when making admission control decisions under patient diagnosis uncertainty with stochastic arrivals and lengths of stay? How can we structure feedback to help improve their decision making? Methodology/results: We use a behavioral model to theorize how a diagnosis anchoring and insufficient adjustment heuristic may lead to an over-rationing bias, and we hypothesize when this bias is greatest. We then propose that feedback for rejected patients—above and beyond feedback for admitted patients—is critical for mitigating this bias. This is because feedback for only admitted patients may suffer from a type of path dependency that prevents decision makers from receiving the most helpful disconfirming feedback. We provide evidence supporting these hypotheses using preregistered experiments in which medical students, Amazon Mechanical Turk workers, or Prolific workers manage admissions for simulated hospital units. Managerial implications: Our results (1) illuminate an important anchoring bias in admission control under diagnosis uncertainty, (2) identify rejected-patient feedback as a critical component for mitigating this bias, and (3) provide insight into the circumstances under which these phenomena are likely to be most significant. Funding: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) [Grant 2022R1F1A1076045]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0194. [ABSTRACT FROM AUTHOR]

In this paper, we establish a single server retrial queueing system with two types of customers, admission control, balking, emergency vacation, differentiate breakdown, and restoration. There are two distinct factors which must be considered when classifying priority and ordinary customers. The non-preemptive priority discipline proposed by this concept. Ordinary and priority customers arrive in accordance with Poisson processes. For both priority and ordinary customers, the server continuously offers a single service that is distributed arbitrarily. In this study, we compute the Laplace transforms of the time-dependent probabilities of system states using a probability generating function and the supplementary variable technique. The sensitivity analysis of system descriptions is assisted by study of numerical findings. [ABSTRACT FROM AUTHOR]

Providing connectivity to high-density traffic demand is one of the key promises of future wireless networks. The open radio access network (O-RAN) is one of the critical drivers ensuring such connectivity in heterogeneous networks. Despite intense interest from researchers in this domain, key challenges remain to ensure efficient network resource allocation and utilization. This paper proposes a dynamic traffic forecasting scheme to predict future traffic demand in federated O-RAN. Utilizing information on user demand and network capacity, we propose a fully reconfigurable admission control framework via fuzzy-logic optimization. We also perform detailed analysis on several parameters (user satisfaction level, utilization gain, and fairness) over benchmarks from various papers. The results show that the proposed forecasting and fuzzy-logic-based admission control framework significantly enhances fairness and provides guaranteed quality of experience without sacrificing resource utilization. Moreover, we have proven that the proposed framework can accommodate a large number of devices connected simultaneously in the federated O-RAN. [ABSTRACT FROM AUTHOR]

This research studies the problems of stochastic dynamic scheduling in production systems with batch processes and process queue time (PQT) constraints. The production systems consist of upstream batch processing machines and downstream single processing machines. Under the PQT constraint, waiting time in the downstream queue is constrained by an upper limit and violating this constraint causes scraps of jobs. The batch process increases the probability of PQT constraint violation because a batch of work-in-processes (WIPs) will move simultaneously into the downstream queue after the service completion of batch processes and suffer from higher waiting time variance. A batch process admission control (BPAC) model is developed using Markov decision processes to minimize the sum of long-run average waiting and scrap costs. The proposed BPAC model explicitly considers uncertain factors in production systems given that uncertainties are major reasons for PQT constraint violation. These uncertain factors include job arrival, processing time, and machine breakdown/repair. To cope with these uncertain factors, the BPAC control decisions change dynamically with the real-time machine health and WIP distribution. The performance of BPAC is validated using discrete event simulation, and the simulation results confirm the significant performance improvement in a wide range of batch production environments. [ABSTRACT FROM AUTHOR]

This paper considers a manufacturing system in which products are produced in both make‐to‐stock (MTS) and make‐to‐order (MTO) modes. Production of MTS and MTO products is done in batches, incurs a setup cost, and is non‐preemptive. The inventory of MTS products fulfills the demand of multiple classes, and each class demand can be satisfied or rejected. Customer orders for MTO production can be accepted or rejected, and their size is the same as the production batch. The primary goal of this paper is to study a policy that coordinates inventory rationing, admission control, and production capacity allocation to maximize the system's profit. We formulate the problem as a Markov decision process model and identify the structure of optimal control policies. We investigate the effect of inventory rationing on the profit by comparing its performance to that of the system with a first‐come‐ first‐serve policy to allocate inventory to multiple demand classes and study the extent to which the benefit of inventory rationing can be affected by system parameter changes. We also propose a heuristic that manages control decisions from linear threshold functions. Our test results from numerical examples show that the average percentage difference between the optimal and heuristic policies is within 1.2%. [ABSTRACT FROM AUTHOR]

Low-Earth orbit (LEO) satellites have limited on-board resources, user terminals are unevenly distributed in the constantly changing coverage area, and the service requirements vary significantly. It is urgent to optimize resource allocation under the constraint of limited satellite spectrum resources and ensure the fairness of service admission control. Therefore, we propose an intelligent hierarchical admission control (IHAC) strategy based on deep reinforcement learning (DRL). This strategy combines the deep deterministic policy gradient (DDPG) and the deep Q network (DQN) intelligent algorithm to construct upper and lower hierarchical resource allocation and admission control frameworks. The upper controller considers the state features of each ground zone and satellite resources from a global perspective, and determines the beam resource allocation ratio of each ground zone. The lower controller formulates the admission control policy based on the decision of the upper controller and the detailed information of the users' services. At the same time, a designed reward and punishment mechanism is used to optimize the decisions of the upper and lower controllers. The fairness of users' services admissions in each ground zone is achieved as far as possible while ensuring the reasonable allocation of beam resources among zones. Finally, online decision-making and offline learning were combined, so that the controller could make full use of a large number of historical data to learn and generate intelligent strategies with stronger adaptive ability while interacting with the network environment in real time. A large number of simulation results show that IHAC has better performance in terms of a successful service admission rate, service drop rate, and fair resource allocation. Among them, the number of accepted services increased by 20.36% on average, the packet loss rate decreased by 17.56% on average, and the resource fairness increased by 17.16% on average. [ABSTRACT FROM AUTHOR]

Conventional micro-batch streaming systems introduce high tail latency for the following two reasons. First, they trigger data processing on a fixed time interval, without taking into account the data ingestion rate and data analysis processing rate. Second, in a multi-query environment, their scheduling methods do not consider the real-time characteristics of the application and each query. This paper presents zStream, a low latency micro-batch streaming system implemented on Apache Spark. The main idea of zStream's design is to use a reference value called deadline for each query to constrain latency around a specific time. Considering the deadline information of each query, zStream controls the admission of every micro-batch dynamically and schedules them by their priorities, significantly reducing the average and tail latency while maintaining throughput. Experimental results show that the tail latency of zStream is reduced by a maximum of 34% compared to the original Spark. Also, zStream reduces the average latency per query by up to 48% while stably maintaining throughput compared to the original Spark. [ABSTRACT FROM AUTHOR]

Cloud computing is a pay-as-you-go model that provides users with on-demand access to services or computing resources. It is a challenging issue to maximize the service provider's profit and, on the other hand, meet the Quality of Service (QoS) requirements of users. Therefore, this paper proposes an admission control heuristic (ACH) approach that selects or rejects the requests based on budget, deadline, and penalty cost, i.e., those given by the user. Then a service level agreement (SLA) is created for each selected request. The proposed work uses Particle Swarm Optimization (PSO) and the Salp Swarm Algorithm (SSA) to schedule the selected requests under budget and deadline constraints. Performances of PSO and SSA with and without ACH are evaluated and compared using CloudSim. The simulation results prove that admission control maximizes profit by minimizing the number of task rejections and SLA violations. It also improves resource utilization while balancing makespan. [ABSTRACT FROM AUTHOR]

With the growing demand for massive access and data transmission requests, terrestrial communication systems are inefficient in providing satisfactory services. Compared with terrestrial communication networks, satellite communication networks have the advantages of wide coverage and support for massive access services. Satellite–terrestrial integrated networks are indispensable parts of future B5G/6G networks. Challenges arise for implementing and operating a successful satellite–terrestrial integrated network, including differentiated user requirements, infrastructure compatibility, limited resource constraints, and service provider incentives. In order to support diversified services, a multi-domain network slicing approach is proposed in this study, in which network resources from both terrestrial and satellite networks are combined to build alternative routes when serving the same slice request as virtual private networks. To improve the utilization efficiency of limited resources, slice admission control is formulated as a mechanism design problem. To encourage participation and cooperation among different service providers, a multi-sided ascending-price auction mechanism is further proposed as a game theory-based solution for slice admission control and resource allocation, in which multiple strategic service providers maximize their own utilities by trading bandwidth resources. The proposed auction mechanism is proven to be strongly budget-balanced, individually rational, and obviously truthful. To validate the effectiveness of the proposed approach, real-world historical traffic data are used in the simulation experiments and the results show that the proposed approach is asymptotically optimal with the increase in users and competitive with the polynomial-time optimal trade mechanism, in terms of admission ratio and service provider profit. [ABSTRACT FROM AUTHOR]

Fifth generation (5G) networks support ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB). The coexistence of URLLC and eMBB is often organized by non-orthogonal multiple access (NOMA), giving priority to URLLC and resulting in eMBB quality of service (QoS) degradation. In this paper, we address the issue of joint URLLC and eMBB transmission, focusing on the problem from the perspective of delay-tolerant eMBB. Due to the priority given to URLLC, we assume that an eMBB session may be interrupted if there are no free resources available for URLLC or delayed when a new eMBB session arrives. To make the scheme more flexible, we propose that interrupted and delayed eMBB sessions periodically check for free resources, rather than continuously. To analyze this scenario, we propose a retrial queuing system with two retrial buffers (orbits) for interrupted and delayed eMBB sessions. The stationary probability distribution, provided in matrix form by recursive formulas, is presented. The paper concludes with a numerical example showing that the scheme with two buffers, compared to one buffer, practically doubles the average number of active eMBB sessions while keeping the interruption probability below 0.001. We provide an illustration of the configuration of eMBB retrial rates to meet its QoS requirements. [ABSTRACT FROM AUTHOR]

Network Slicing in 5G networks enables allocation and sharing of the underlying network resources of an infrastructure provider (INP) among multiple tenants of the INP. Each tenant generates slice requests specifying the resources required and the INP collects revenue from the tenants for hosting the admitted slices. In this paper, we have used the Prioritised Experience Replay-based Deep Q-Network with N step return (N-PERDQN) Reinforcement learning (RL) approach to solve the slice admission control and associated resource allocation problem, in a dynamic environment. The slices are classified as elastic and inelastic, with flexible and rigid service guarantee requirements respectively. We have also used Long Short-Term Memory (LSTM) to predict the admitted elastic slices’ future resource requirements to share resources among elastic slices more efficiently. This enables the INP to accept more slice requests to increase its revenue while maximizing system utilization. The system has been modeled using the SimPy discrete-event simulator and TensorFlow machine learning. Performance metrics studied include INP revenue generated, class-specific slice acceptance rate and resource utilization were studied on Materna data center trace. The proposed N-PERDQN approach performs better than other RL and heuristic methods by up to 10% on average, in terms of generating INP revenue. Also, N-PERDQN+LSTM accepts around 6% more slice requests than N-PERDQN without LSTM. We have also tested the performance of the proposed algorithm with an offline algorithm, that has complete set of incoming slice request information available beforehand. The proposed algorithm is able to achieve close to 82% to 86% in terms of revenue gain of the offline algorithm. The proposed mechanism was also studied using the Alibaba data center trace and similar improvements were observed. [ABSTRACT FROM AUTHOR]

Problem definition: Motivated by ride-hailing platforms such as Uber, Lyft and Didi, we study the problem of matching riders with self-interested drivers over a spatial network. We focus on the performance impact of two operational platform controls—demand-side admission control and supply-side repositioning control—considering the interplay with two practically important challenges: (i) spatial demand imbalances prevail for extended periods of time; and (ii) self-interested drivers strategically decide whether to join the network, and, if so, whether to reposition when not serving riders. Methodology/results: We develop and analyze the steady-state behavior of a novel game-theoretic fluid model of a two-location, four-route loss network. First, we fully characterize and compare the steady-state system equilibria under three control regimes, from minimal control to centralized control. Second, we provide insights on how and why platform control impacts equilibrium performance, notably with new findings on the role of admission control: the platform may find it optimal to strategically reject demand at the low-demand location even if drivers are in excess supply, to induce repositioning to the high-demand location. We provide necessary and sufficient conditions for this policy. Third, we derive upper bounds on the platform's and drivers' benefits caused by increased platform control; these are more significant under moderate capacity and significant cross-location demand imbalance. Managerial implications: Our results contribute important guidelines on the optimal operations of ride-hailing networks. Our model can also inform the design of driver compensation structures that support more centralized network control. Supplemental Material: The e-companion and Supplemental Material are available at https://doi.org/10.1287/msom.2023.1221. [ABSTRACT FROM AUTHOR]

Currently, streaming communications are widespread through YouTube and other media. Streaming communication focuses on real-time communication and requires flow admission control to ensure communication quality. Many existing studies have solved the system-side problems of streaming communications. However, few have considered user behavior, even though users also use the network. For example, if the selected bandwidth changes due to a selection error made by the user, the network traffic load should also change. However, previous studies have not taken into account the frustration caused by such errors in daily life. In this paper, we first model user bandwidth selection considering selection errors based on stochastic evolutionary game theory. Using the distribution of the derived user bandwidth selection, we propose a flow admission control method based on queueing theory. Furthermore, we define user dissatisfaction using the call loss probability derived by queueing theory, and model selection error behavior using this dissatisfaction. By numerical calculation, we drive the optimal threshold value that results in the highest overall user satisfaction when selection error is taken into account, thereby improving the overall user satisfaction.

In the current era, numerous products are accessible online from anywhere and every minute. This work used encryption with access management because authenticities, anonymity, and security over accessibility are mandatory. The proposed design introduced a significantly better encryption strategy to ensure better protection of overall resource admissions. This proposed work uses Improved RSA-based role-based access control (RBAC) with extendable access connectivity markup language (XACML) to encrypt information and maintain privileges. This approach enables storing information within the online computer using cryptographic ideas and information available via a basic admission management mechanism. To ensure the overall protection of sensitive information, the encryption method is employed that merged the conventional homogeneous encryption procedure with unstable information distribution method. This hybrid technique provides user to get advantage from retrieved information in a protected manner. The overall execution of proposed work is considerably quicker than other existing encryption methods.

Device-to-device (D2D) communication has become a key complementary technology in emerging next-generation communication paradigm. It offers a number potential use cases including peer to peer communication, cellular offloading, device-relaying and content sharing etc. Efficient resource management is one of the key factor for boosting overall network data rate. This paper provides an effective resource management approach for D2D networks that not only maximises the network's overall throughput but also maximises the number of admitted users while adhering to power and interference limits. The optimization problem considered in this work is NP-complete and belongs to the category of mixed integer non-linear programming problems. The complexity of problem is proportional to the number of users, exhaustive search of solution is not feasible for large network size. In this work, the resource management problem solved using Binary Particle Swarm Optimization (BPSO) algorithm with eight different types of transfer functions which facilitate for calculation of probability values from velocities of the particles. The performance results are presented for different networks scenarios and compared with classical evolutionary algorithm. The simulation results show that the proposed solution achieves a better sum rate and faster convergence as compared to other solution. [ABSTRACT FROM AUTHOR]

In the current era, numerous products are accessible online from anywhere and every minute. This work used encryption with access management because authenticities, anonymity, and security over accessibility are mandatory. The proposed design introduced a significantly better encryption strategy to ensure better protection of overall resource admissions. This proposed work uses Improved RSA-based role-based access control (RBAC) with extendable access connectivity markup language (XACML) to encrypt information and maintain privileges. This approach enables storing information within the online computer using cryptographic ideas and information available via a basic admission management mechanism. To ensure the overall protection of sensitive information, the encryption method is employed that merged the conventional homogeneous encryption procedure with unstable information distribution method. This hybrid technique provides user to get advantage from retrieved information in a protected manner. The overall execution of proposed work is considerably quicker than other existing encryption methods. [ABSTRACT FROM AUTHOR]

The shortage of inpatient beds causes surgery cancellations, treatment delays, and inappropriate ward arrangements. That is why studying how to make admission decisions on the wards of crowded units under premature discharge decisions is essential. In this study, we set up an infinite‐horizon total discounted cost Markov decision process model to minimize the total expected cost to the hospital. By analyzing the structural properties of the optimal value function, we find that there is an optimal admission control policy of the threshold type for low‐acuity emergency patients (type‐2). Numerical experiments are done to examine how optimal dynamic policy depends on key system parameters. Furthermore, we evaluate the performance of the optimal dynamic policy with a benchmark policy without premature discharge decisions. The results indicate that the optimal admission control policy helps hospitals in maintaining low costs and enables patients with high‐acuity diseases to enter primary wards more quickly. [ABSTRACT FROM AUTHOR]

The frequent replacement requirement of UAVs for recharging outputs an extreme number of messaging for admission control of end-users. There are many studies that try to optimize the network capacity in an energy-efficient manner. However, they do not consider the security of data and control channels, which is the urgent requirement of 5G. Blockchain handles secure systems. However, the high numbered transactions in blockchain may cause bottlenecks while considering computational delay and throughput of end-user. In UAVs, a high percentage of battery is consumed for computational tasks instead of communication tasks. Therefore, to handle security by considering the computational needs, this paper proposes a novel two-layered blockchain-based admission control in UAV networks. It has two layers named as side-chain for UAVs and off-chain for end-users building in a decentralized manner. In the former one, the data bearer of end-users is protected by the side-chain of each transaction which is signed by admitted end-users and UAVs. Here, each transaction includes a unique transaction id, nonce value, IMSI of the user, RNTI of UAV, signed data, and hash value. In the latter one, the off-chain is built by signing admission contracts between users in a decentralized manner and the Merkle tree is constructed for the off-chain where the root hash is only stored in a side-chain. Then, the number of transactions that should be validated is extremely decreased. Moreover, PoS is used for the validation of transactions where the validator is selected randomly instead of the competition requirement as in PoW. A novel blockchain-based admission control algorithm isolates readmission control of end-users to 5G core-side during UAV replacement. According to performance evaluation, the proposed approach serves end-users by 64.2% better QoS than the conventional one during UAV replacement. Moreover, it keeps edge delay in acceptable range by using Merkle tree and PoS. [ABSTRACT FROM AUTHOR]

System-of-Systems (SoS) offer unprecedented potential for new types of emerging services, which significantly exceed the capabilities of the constituting systems. SoS in safety-critical domains (e.g., medical applications, smart grid, disaster recovery, defense) are prominent examples, but they have stringent real-time and reliability requirements. Therefore, a suitable temporal and spatial allocation of resources is required both within each constituent system and in the wide area networks between them. This paper introduces an algorithm for admission control and resources’ allocation, which considers these requirements and the autonomy of the constituent systems. To simulate a realistic admission control and resources’ allocation process of a typical SoS network, a simulated case study with eight constituent systems, six services, and twenty-five processes/requests is developed. The suggested admission control and resources’ allocation process’s performance is measured in terms of gain in the execution time and blockage probability. A sensitivity analysis is carried out to evaluate the influence of the number of constituent systems and the number of services sought by the received processes/requests on the efficacy of the proposed process. The results show that the proposed admission control and resources’ allocation process have very low blockage probability, high gain in the execution time, and high resources’ utilization.

With real-time communication being a key part of the fourth industrial revolution, the need for Quality of Service (QoS) in industrial networks is gaining increasing importance. Time-Sensitive Networking (TSN) faces this need, for example, by introducing new scheduling mechanisms. The Credit-Based Shaper (CBS) has been introduced to TSN to offer low delays for multiple traffic classes by applying rate limitations. Currently, flows are reserved decentrally in CBS networks using a Stream Reservation Protocol (SRP). In contrast, the new TSN standard IEEE 802.1Qcc allows for a centralized architecture to favor short reconfiguration latencies. However, no online admission control scheme which offers safe delay bounds has been proposed for this central architecture. To close this gap, we propose two models for admission control in TSN networks using CBS. Both models offer deadline-guaranteeing flow allocation, including routing and prioritization of flows, and configure forwarding devices while eliminating packet loss. Our models utilize the mathematical framework of Network Calculus to calculate worst-case flow delays and buffer sizes. We show how our models allow for more reservations than the decentralized standard approach by improved resource utilization. We validate our models both in synthetic and industrial network scenarios. Additionally, we compare the effects and parameters of our two models, providing guidance on when to choose them.

With the growing demand for massive access and data transmission requests, terrestrial communication systems are inefficient in providing satisfactory services. Compared with terrestrial communication networks, satellite communication networks have the advantages of wide coverage and support for massive access services. Satellite–terrestrial integrated networks are indispensable parts of future B5G/6G networks. Challenges arise for implementing and operating a successful satellite–terrestrial integrated network, including differentiated user requirements, infrastructure compatibility, limited resource constraints, and service provider incentives. In order to support diversified services, a multi-domain network slicing approach is proposed in this study, in which network resources from both terrestrial and satellite networks are combined to build alternative routes when serving the same slice request as virtual private networks. To improve the utilization efficiency of limited resources, slice admission control is formulated as a mechanism design problem. To encourage participation and cooperation among different service providers, a multi-sided ascending-price auction mechanism is further proposed as a game theory-based solution for slice admission control and resource allocation, in which multiple strategic service providers maximize their own utilities by trading bandwidth resources. The proposed auction mechanism is proven to be strongly budget-balanced, individually rational, and obviously truthful. To validate the effectiveness of the proposed approach, real-world historical traffic data are used in the simulation experiments and the results show that the proposed approach is asymptotically optimal with the increase in users and competitive with the polynomial-time optimal trade mechanism, in terms of admission ratio and service provider profit.

This article addresses the admission control of a loss queueing network, or shortly loss network, of $N$ parallel multiserver stations with no waiting rooms. $M$ classes of customers arrive at a Poisson rate and require an exponential service time with common mean. A customer can be served by any free server of a station in the set of stations determined by the class of the customer. Admission of a customer to a station brings different rewards that depend on customer class and the preference order of that station. Our objective is to find the optimal admission control policy that maximizes the average reward. We adopt a research strategy that evolves from simpler networks to more complicated ones. First, we consider a one-station $M$ -class loss network whose results form a fundamental basis for the analysis of more complicated networks. Second, we consider a two-station $M$ -class loss network for which we establish the existence of an optimal threshold admission policy. Finally, we consider a general $N$ -station $M$ -class loss network and propose an iterative approximation policy (IAP) and a mixed-integer linear programming model that is proven to compute an upper bound. We demonstrate the near-optimal performance of the proposed IAP and the tightness of the upper bound on several numerical instances motivated by real-life networks, such as healthcare emergency service networks and emergency call centers. Note to Practitioners—This article is mainly motivated by real-time scheduling of emergency service networks, such as emergency healthcare and telecommunication networks. In such networks, emergent demands need to be served promptly and are lost otherwise. They can be served by multiple hospitals/operator groups but have their own preferences. The decision maker has to decide dynamically whether to keep the capacity of a hospital/operator for its own customers to meet customer preference or serve the incoming demand to improve the resource utilization. We set the problem as the admission control of a multiclass multistation loss network, study the properties of the optimal policy, and propose an iterative approximation policy that is proven to be near-optimal for large-size loss networks. The proposed policy is found to improve by up to 16% and 52% the no-control policy admitting the customer to the most preferred available station and the no-overflow policy with admission to only the most preferred station, respectively. Managerial insights and extensions are also discussed. [ABSTRACT FROM AUTHOR]

This paper investigates a long-term average total energy cost minimization problem via resource management, including admission control, power allocation, and Energy Sharing (ES) of renewable energy in Heterogeneous Cellular Networks powered by Grid-connected Smart Micro Grids (GSMG-HCNs). In GSMG-HCNs, both renewable and grid energy power the base stations. Unlike existing works, we consider the cost of both renewable and grid energy and formulate the power line loss process caused by ES into our model. To solve the proposed problem, we transform it into a real-time issue by the Lyapunov technique. The proposed Cost-Aware Online Resource Management (CAORM) algorithm decouples the real-time issue into two sub-problems, one of which is linear and the other is addressed based on the successive convex approximation approach. We theoretically prove the asymptotic optimality of the CAORM algorithm and a tradeoff between the average total energy cost and the average queue length. Simulation results reveal that the CAORM algorithm outperforms benchmarks in reducing total energy cost and can make appropriate decisions according to different unit costs of renewable energy. Besides, the designed distance-related ES loss rate can help obtain better solutions with lower ES losses. [ABSTRACT FROM AUTHOR]

Network slicing has emerged as a key concept in 5G systems, allowing Mobile Network Operators (MNOs) to build isolated logical networks (slices) on top of shared infrastructure networks managed by Infrastructure Providers (InP). Network slicing requires the assignment of infrastructure network resources to virtual network components at slice activation time and the adjustment of resources for slices under operation. Performing these operations just-in-time, on a best-effort basis, comes with no guarantee on the availability of enough infrastructure resources to meet slice requirements. This paper proposes a prioritized admission control mechanism for concurrent slices based on an infrastructure resource reservation approach. The reservation accounts for the dynamic nature of slice requests while being robust to uncertainties in slice resource demands. Adopting the perspective of an InP, reservation schemes are proposed that maximize the number of slices for which infrastructure resources can be granted while minimizing the costs charged to the MNOs. This requires the solution of a max-min optimization problem with a non-linear cost function and non-linear constraints induced by the robustness to uncertainties of demands and the limitation of the impact of reservation on background services. The cost and the constraints are linearized and several reduced-complexity strategies are proposed to solve the slice admission control and resource reservation problem. Simulations show that the proportion of admitted slices of different priority levels can be adjusted by a differentiated selection of the delay between the reception and the processing instants of a slice resource request. [ABSTRACT FROM AUTHOR]

Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to run complex applications. This can be realized through efficient computing data offloading and yet enhanced while simultaneously using multiple wireless interfaces for D2D, MEC and cloud offloading. In this work, we propose user-centric real-time computation task offloading and resource allocation strategies aiming at minimizing energy consumption and monetary cost while maximizing the number of completed tasks. We develop dynamic partial offloading solutions using the Lyapunov drift-plus-penalty optimization approach. Moreover, we propose a task admission solution based on support vector machines (SVM) to assess the potential of a task to be completed within its deadline, and accordingly, decide whether to drop from or add it to the user’s queue for processing. Results demonstrate high performance gains of the proposed solution that employs SVM-based task admission and Lyapunov-based computation offloading strategies. Significant increase in number of completed tasks, energy savings, and cost reductions are resulted as compared to alternative baseline approaches. [ABSTRACT FROM AUTHOR]

Network slicing is a promising technology for providing customized logical and virtualized networks for the fifth-generation (5G) use-cases (enhanced mobile broadband, ultrareliable low-latency communications, and massive machine-type communications), which pose distinct quality of service (QoS) requirements. Admission control and resource allocation mechanisms are pivotal for realizing network slicing efficiently, but existing mechanisms focus on slicing the radio access network. This article proposes an approach encompassing intelligent and efficient mechanisms for admission control and resource allocation for network slicing in the 5G core network. The admission control mechanism introduces two solutions, one based on reinforcement learning (called SARA) and the other based on deep reinforcement learning (called DSARA). SARA and DSARA consider the QoS requirements of 5G use-cases, differentiate network core nodes from edge nodes, and process slice requests in time windows to favor the service provider’s profit and resource utilization. Results show that SARA and DSARA overcome existing mechanisms for managing admission control and resource allocation in 5G core network slicing. [ABSTRACT FROM AUTHOR]

The multiplicative weight update (MWU) schema is a powerful tool to design online routing algorithms. However, even the latest research still employs or incorporates the inefficient online single-path routing (OSR) algorithm. In this article, we design an online multipath routing (OMR) algorithm via the MWU schema in order to exploit the network path diversity. Compared to OSR and comparative multipath solutions, OMR has the same worst-case performance guarantee in theory and also has significant performance advantage in practice. [ABSTRACT FROM AUTHOR]

This paper proposes an energy-efficient framework to provide a solution to the joint admission control, mode selection, and energy-efficient resource (channel and power) allocation (JACMSEERA) problem for D2D communication underlaying cellular networks. The JACMSEERA problem is a non-deterministic polynomial (NP) hard problem, whose computational complexity scales exponentially with the increase in the number of users. The allocation of channel and power in JACMSEERA problem depends on the mode selection. Such problems require two-step solution and are called bi-level optimization problems. Bi-level optimization increases the complexity and computation time. We propose a modified version of single-level artificial bee colony (ABC) algorithm to allocate the cellular, and reuse modes to the DUs with channel, and energy-efficient power allocation to solve the JACMSEERA problem. Majority of the existing literature decomposes such resource allocation problems into sub-problems by separating mode selection, and resource allocation. Consequently, such solutions are unable to satisfy the stringent constraints leading to inferior solutions. The success of nature-inspired optimization algorithms to solve resource allocation problems has motivated us to use the swarm intelligence based ABC algorithm to solve the JACMSEERA problem. The JACMSEERA problem's objective is to maximize the number of DUs admitted and energy-efficiency under power, interference, and rate constraints. A simple, scalable, low complexity solution is obtained for the JACMSEERA problem using a single variable, represented by the DUs, for joint admission control, mode selection, and energy-efficient resource allocation. The efficacy of the ABC aided approach is validated by numerical investigations under different simulation scenarios and provides an enhancement in energy efficiency to the extent of 20 % as compared to results reported in literature. [ABSTRACT FROM AUTHOR]

This paper considers optimal admission and routing control in multi-class service systems in which customers can either receive quality regular service which is subject to congestion or can receive congestion-free but less desirable service at an alternative service station, which we call the self-service station. We formulate the problem within the Markov decision process framework and focus on characterizing the structure of dynamic optimal policies which maximize the expected long-run rewards. For this, value function and sample path arguments are used. The congestion sensitivity of customers is modeled with class-independent holding costs at the regular service station. The results show how the admission rewards of customer classes affect their priorities at the regular and self-service stations. We explore that the priority for regular service may not only depend on regular service admission rewards of classes but also on the difference between regular and self-service admission rewards. We show that optimal policies have monotonicity properties, regarding the optimal decisions of individual customer classes such that they divide the state space into three connected regions per class. [ABSTRACT FROM AUTHOR]

Due to the rapid development and wide application of mobile devices, mobile edge computing (MEC) technology improves system performance by offloading vehicle computing tasks to the edges of the network. In order to save device power, shorten transmission delay, save network bandwidth, or shorten execution time, many related works have been proposed. Different from these works under the constraints of system resources, we adopt another method to maximize the admission of mobile users' tasks to meet the service requirements. Compared with these works, this method can allocate and utilize all system resources more effectively and admit as many tasks as possible. Further, we prove that the approximate ratio of its solution to the optimal solution of the problem of maximizing task admission in MEC is bounded. The above claim is verified by the conducted simulations and the simulation results show that there is a small difference between its solution and the optimal one. On the other hand, if the system resources can be increased at a bounded rate, another algorithm is proposed to control the compromise between the overall system performance and the robustness of its obtained solution. [ABSTRACT FROM AUTHOR]