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This paper investigates the sub-channels and power allocation issue in the multi-carrier NOMA communication system. To maximize the sum rate of the system, we design a fast dynamic resource allocation (FDRA) algorithm. The algorithm can quickly identify the interference nodes with poor channel conditions, and reduces the search complexity of the algorithm. By jointly optimizing sub-carrier and power resource allocation, the proposed scheme can take full advantage of the channel capacity, and maximize the system performance while ensuring the quality of service (QoS) of each user. Simulation results show that when compared with the joint resource allocation (JRA) algorithm and the genetic algorithm (GA), the FDRA algorithm can achieve the best performance on the complexity and the sum rate of the system. Thus, the proposed algorithm can provide a fast and efficient resource allocation scheme for the downlink multi-carrier NOMA systems. [ABSTRACT FROM AUTHOR]

Federated Learning (FL) is a promising privacy-preserving distributed machine learning paradigm. However, communication inefficiency remains the key bottleneck that impedes its large-scale implementation. Recently, hierarchical FL (HFL) has been proposed in which data owners, i.e., workers, can first transmit their updated model parameters to edge servers for intermediate aggregation. This reduces the instances of global communication and straggling workers. To enable efficient HFL, it is important to address the issues of edge association and resource allocation in the context of non-cooperative players, i.e., workers, edge servers, and model owner. However, the existing studies merely focus on static approaches and do not consider the dynamic interactions and bounded rationalities of the players. In this paper, we propose a hierarchical game framework to study the dynamics of edge association and resource allocation in self-organizing HFL networks. In the lower-level game, the edge association strategies of the workers are modelled using an evolutionary game. In the upper-level game, a Stackelberg differential game is adopted in which the model owner decides an optimal reward scheme given the expected bandwidth allocation control strategy of the edge server. Finally, we provide numerical results to validate that our proposed framework captures the HFL system dynamics under varying sources of network heterogeneity. [ABSTRACT FROM AUTHOR]

This paper proposes a dynamic pricing and revenue-driven service federation strategy based on a Deep Q-Network (DQN) to instantly and automatically decide federation across different service provider domains, each introduces dynamic service prices offering to its customers and towards other domains. A dynamic pricing model is considered in this work based on the analysis of real pricing data collected from public cloud provider, and upon this a dynamic arrival process as a result of the price changes is proposed for formulating the service federation problem as a Markov Decision Problem (MDP). In this work, several reinforcement learning algorithms are developed to solve the problem, and the presented results show that the DQN method reached 90% of the optimal revenue and outperformed existing state-of-the-art strategies, and it can learn the federation pricing dynamics to make optimum federation decisions according to price changes. [ABSTRACT FROM AUTHOR]

In recent years, distributed machine learning in WANs (DML-WANs), i.e., collaboratively training a high-quality ML model cross geo-distributed micro-clouds or edge devices, has attracted attention and been widely applied. Compared with cloud-centric training, DML-WANs avoids the high cost of transferring large amounts of raw data to a central cloud and privacy concerns. However, performing DML-WANs still faces challenges. Model synchronization, an essential step of DML-WANs, is accompanied by a lot of model communication cross limited-bandwidth WANs, which generates high communication overhead. Moreover, the parameter server system, which has been widely used, performs model synchronization in a centralized manner, resulting in serious communication in-cast problem. Such communication in-cast further raises the communication overhead, leading to the low efficiency of DML-WANs. To alleviate the communication in-cast, existing researches attempt to build tree-based communication overlays over the parameter server and workers. However, we identify that these approaches can not adapt to the dynamic and heterogeneous network of DML-WANs, resulting in insufficient improvements. This paper proposes TSEngine, an adaptive communication scheduler for efficient communication overlay of the parameter server system in DML-WANs. Its core idea is to dynamically schedule the communication logic over the parameter server and workers based on the active network perception. Specifically, we propose novel communication scheduling protocols for model distribution and model aggregation, respectively. We have implemented TSEngine in a mainstream parameter server system and verified its effectiveness in DML-WANs testbeds. [ABSTRACT FROM AUTHOR]

The electric power system is a complicated dynamic system with a range of operating states and parametric uncertainties, especially due to change of the network topology, load increment and generation scheduling. Under this circumstance, traditional power system transient stability analysis methods may not always be appropriate. This paper presents the development of a computational methodology for evaluating the effect of parametric uncertainties on the small-signal stability analysis of power systems. A probabilistic approach is applied as a metric for the dynamic performance of the damping ratio of critical eigenvalues. The method is based on a Monte Carlo simulation for the analysis of automatic control of generation. The methodology is used for the performance evaluation of three classical controller tuning techniques: Frequency Response, Approximate Method and Ziegler-Nichols. The results show that the methodology is valid and potentially useful for quantifying the effect of parametric uncertainties in power systems dynamics simulations. [ABSTRACT FROM AUTHOR]

Millimeter wave (mmWave) communication enables ultra-high rate data transmission in wireless environments, but its performance depends on blockages between the transmitter and receiver significantly. To overcome the blockage problem, we consider two approaches: multi-connectivity and network densification. The multi-connectivity opens up a new paradigm for user centric communication, where each user autonomously chooses multiple base stations (BSs) to attain high quality services. The network densification allows network operators to deploy many BSs in a limited area, thereby providing qualified communication services to users. In this paper, we study user equipment (UE) association control in user centric mmWave communication environments. When a UE arrives, the network provides the UE with information on the resource status of adjacent BSs, and the UE generates a set of candidate BSs. For access control, we formulate two optimization problems that aim to minimize peak load and sum load, respectively. They are non-convex and combinatorial, so their computational complexity grows exponentially with the problem size. As a solution, we develop an access pricing and peak load limit control scheme. Through numerical experiments, we investigate its impact on the performance of mmWave communication systems. The results show that our proposed scheme successfully provides high quality services to users and allocates the network load fairly over the network. [ABSTRACT FROM AUTHOR]

Due to line-of-sight communication links and distributed deployment, Unmanned Aerial Vehicles (UAVs) have attracted substantial interest in agile Mobile Edge Computing (MEC) service provision. In this paper, by clustering multiple users into independent communities based on their geographic locations, we design a 5G-enabled UAV-to-community offloading system. A system throughput maximization problem is formulated, subjected to the transmission rate, atomicity of tasks and speed of UAVs. By relaxing the transmission rate constraint, the mixed integer non-linear program is transformed into two subproblems. We first develop an average throughput maximization-based auction algorithm to determine the trajectory of UAVs, where a community-based latency approximation algorithm is developed to regulate the designed auction bidding. Then, a dynamic task admission algorithm is proposed to solve the task scheduling subproblem within one community. Performance analyses demonstrate that our designed auction bidding can guarantee user truthfulness, and can be fulfilled in polynomial time. Extensive simulations based on real-world data in health monitoring and online YouTube video services show that our proposed algorithm is able to maximize the system throughput while guaranteeing the fraction of served users. [ABSTRACT FROM AUTHOR]

Wireless charging is one of the most important issues in wireless sensor networks (WSNs), which aims to cope with the energy limitation problem of the sensors. Many of the existing researches applied the mobile charger to recharge the sensors for maintaining the perpetual lifetime of sensor networks. However, when choosing sensors to be recharged, the coverage contribution of sensors was ignored. This paper proposes a recharging scheme, called CAERM, which considers the coverage contribution of each requested sensor and constructs the recharging path to maximize the coverage of the whole networks. Two algorithms, including Simple Recharging Coverage Benefit (S-RCB) and Chain-Effect Recharging Coverage Benefit (CE-RCB) algorithms, are proposed to evaluate the coverage contribution of the requested sensors effectively. Through extensive simulations, experimental study shows that the proposed algorithm improves the recharging efficiency, while maximizing the coverage contribution and monitoring quality of the given sensor networks. [ABSTRACT FROM AUTHOR]

Wireless body area network (WBAN) has become a promising technology, which can be widely applied in health monitoring, and so on. However, the performance of a practical WBAN may severely suffer from the degradation caused by dynamic nature of wireless channels with the movements of human body. Traditional communication frameworks cannot catch up with the channel variation of dynamic WBANs, which may severely degrade the performance, so an accurate channel prediction model is necessary for developing an efficient transmission strategy. In this paper, we propose a DeepBAN communication framework for dynamic WBANs. In our proposed framework, a temporal convolution network (TCN) based deep learning approach is adopted for channel prediction, the computationally intensive task of which is processed by mobile edge computing (MEC), to reduce the response time. Given the predicted channel conditions, we propose a joint power control, time-slot allocation, and relay selection algorithm to maximize the energy efficiency of the system, taking into account the transmission reliability and end-to-end latency requirements. We evaluate the performance of DeepBAN, and the results show that it can achieve energy-efficient, reliable, and low-latency data transmission in dynamic WBANs, which can improve the system energy efficiency by 15% compared with the stochastic scheduling scheme. [ABSTRACT FROM AUTHOR]

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In this paper, we design an adaptive multi-homing resource allocation algorithm for time-varying heterogeneous wireless networks (HetNet), where the algorithm iteration timescale is the same to the network state acquisition timescale. First, the network utility maximization is characterized by a stochastic optimization model. Second, the multi-homing resource allocation (MHRA) algorithm is developed to accommodate the dynamic wireless network states, i.e., time-varying wireless channels between the access points (AP) and mobile terminals and as well the queuing dynamics at the APs. Then, we investigate the tracking error between the MHRA algorithm output and the target optimal resource allocation solution. Based on these results, an adaptive-compensation multi-homing resource allocation (AMRA) algorithm is proposed to offset the tracking error so as to enhance the network utility. Specifically, we give a sufficient condition that the AMRA algorithm asymptotically tracks the moving equilibrium point with no tracking errors. Finally, we derive a tradeoff between network utility and media transmission delay, where the increase of average delay is approximately linear in $V$ and the increase of network utility is at the speed of $1/V$ with the control parameter $V$ . Simulation results validate the theoretical analysis of our proposed scheme. [ABSTRACT FROM PUBLISHER]

In this paper, we study a dynamic pricing and energy consumption scheduling problem in the microgrid where the service provider acts as a broker between the utility company and customers by purchasing electric energy from the utility company and selling it to the customers. For the service provider, even though dynamic pricing is an efficient tool to manage the microgrid, the implementation of dynamic pricing is highly challenging due to the lack of the customer-side information and the various types of uncertainties in the microgrid. Similarly, the customers also face challenges in scheduling their energy consumption due to the uncertainty of the retail electricity price. In order to overcome the challenges of implementing dynamic pricing and energy consumption scheduling, we develop reinforcement learning algorithms that allow each of the service provider and the customers to learn its strategy without a priori information about the microgrid. Through numerical results, we show that the proposed reinforcement learning-based dynamic pricing algorithm can effectively work without a priori information about the system dynamics and the proposed energy consumption scheduling algorithm further reduces the system cost thanks to the learning capability of each customer. [ABSTRACT FROM PUBLISHER]

Cellular communications are evolving to facilitate the current and expected increasing needs of quality of service, high data rates, and diversity of offered services. Toward this direction, radio access network (RAN) virtualization aims at providing solutions of mapping virtual network elements onto radio resources of the existing physical network. This paper proposes the Resources nEgotiation for NEtwork Virtualization (RENEV) algorithm, which is suitable for application in heterogeneous networks in Long-Term Evolution Advanced (LTE-A) environments, consisting of a macro evolved Node B overlaid with small cells (SCs). By exploiting radio resource management principles, RENEV achieves slicing and on-demand delivery of resources. Leveraging the multitenancy approach, radio resources are transferred in terms of physical radio resource blocks among multiple heterogeneous base stations, which are interconnected via the X2 interface. The main target is to deal with traffic variations in geographical dimension. All signaling design considerations under the current Third-Generation Partnership Project LTE-A architecture are also investigated. Analytical studies and simulation experiments are conducted to evaluate RENEV in terms of the network's throughput and additional signaling overhead. Moreover, we show that RENEV can be applied independently on top of already proposed schemes for RAN virtualization to improve their performance. The results indicate that significant advantages are achieved both from the network's and users' perspective and that it is a scalable solution for different numbers of SCs. [ABSTRACT FROM AUTHOR]

Among most of the message scheduling strategies for low-density parity-check (LDPC) codes, the dynamic scheduling strategy behaves best in error correction performance. Dynamic selection is an integral part of dynamic scheduling decoding, which plays a decisive role throughout the decoding process. Usually, the dynamic selection strategy based on the message residuals only is employed in dynamic decoding algorithms, while other potentials of the dynamic selection strategy are rarely cared about. In this paper, we propose the triple judgment dynamic selection strategy combined with a Stability Criterion. Interestingly, the new strategy can be well applied to two different dynamic algorithms, namely, the V-VCRBP and the V-CVRBP algorithms. The proposed strategy has a great advantage: locating the message to be preferentially updated is extremely quick and accurate. Simulation results demonstrate that the V-VCRBP algorithm outperforms existing decoding algorithms in terms of BER performance and convergence speed, while the V-CVRBP algorithm has good error correction performance with a lower computational complexity. [ABSTRACT FROM AUTHOR]

Hyper-heuristics have recently emerged as a powerful approach to automate the design of heuristics for a number of different problems. Production scheduling is a particularly popular application area for which a number of different hyper-heuristics have been developed and are shown to be effective, efficient, easy to implement, and reusable in different shop conditions. In particular, they seem to be a promising way to tackle highly dynamic and stochastic scheduling problems, an aspect that is specifically emphasized in this survey. Despite their success and the substantial number of papers in this area, there is currently no systematic discussion of the design choices and critical issues involved in the process of developing such approaches. This paper strives to fill this gap by summarizing the state-of-the-art approaches, suggesting a taxonomy, and providing the interested researchers and practitioners with guidelines for the design of hyper-heuristics in production scheduling. This paper also identifies challenges and open questions and highlights various directions for future work. [ABSTRACT FROM PUBLISHER]

In this paper, an overview is given of the project data instances available in the literature to carry out academic research in the field of integrated project management and control. This research field aims at integrating static planning methods and risk analyses with dynamic project control methodologies using the state-of-the-art knowledge from literature and the best practices from the professional project management discipline. Various subtopics of this challenging discipline have been investigated from different angles, each time using project data available in literature, obtained from project data generators or based on a sample of empirical case studies. This paper gives an overall overview of the wide variety of project data that are available and are used in various research publications. It will be shown how the combination of artificial data and empirical data leads to improved knowledge on and deeper insights into the structure and characteristics of projects useful for academic research and professional use. While the artificial data can be best used to test novel ideas under a strict design in a controlled academic environment, empirical data can serve as the necessary validation step to translate the academic research results into practical ideas, aiming at narrowing the bridge between the theoretical knowledge and practical relevance. A summary of the available project data discussed in this paper can be downloaded from http://www.projectmanagement. ugent.be/research/data. [ABSTRACT FROM AUTHOR]

This paper considers the resource allocation problem in an orthogonal-frequency-division-multiple-access (OFDMA)-based cognitive radio (CR) network, where the CR base station adopts a full-overlay scheme to transmit both private and open information to multiple users with average delay and power constraints. A stochastic optimization problem is formulated to develop flow control and radio resource allocation to maximize the long-term system throughput of open and private information in a CR system and ensure the stability of the primary system. The corresponding optimal condition for employing full overlay is derived in the context of concurrent transmission of open and private information. An online resource allocation scheme is designed to adapt the transmission of open and private information based on monitoring the status of the primary system, as well as the channel and queue states in the CR network. The scheme is proven to be asymptotically optimal in solving the stochastic optimization problem without knowing any statistical information. Simulations are provided to verify the analytical results and efficiency of the scheme. [ABSTRACT FROM PUBLISHER]

Product collaborative design scheduling problem (PCDSP) has received increasing attention in the engineering field for its popularity in manufacturing and supply chain systems. Although many scholars have achieved many useful results in Job-shop scheduling problem (JSP), the research work of PCDSP is still lacking. Moreover, the fuzziness, dynamics and multi-objective of PCD pose a great challenge for enterprises making quick response to unplanned perturbations. Thus, in this paper, multi-objective dynamic fuzzy scheduling and its solution algorithm are presented. Emergencies cases are analyzed and dynamic scheduling procedure based on event-driven and lifecycle-driven is developed firstly. The fuzziness of PCD process is studied meanwhile fuzzy triangular number and fuzzy trapezoid number are used to express the fuzziness. Based on this, a multi-objective dynamic fuzzy scheduling model aiming to minimum the makespan and tardiness penalty is constructed. As a solution, multi-objective dynamic adaptive scheduling algorithm, based on bi-layer coding strategy and self-adaptive double point crossover and selfadaptive mutation, is proposed following closely. Finally, analytic results from a case of a wind turbine are used to illustrate the model and method proposed in this paper. With the analysis, it can provide insight into ways of improving the strategic and operational decision making for enterprises. [ABSTRACT FROM AUTHOR]

Spectrum auction is considered a suitable approach to efficiently allocate spectrum among unlicensed users. In a typical spectrum auction, secondary users (SUs) bid to buy spectrum bands from a primary owner (PO) who acts as the auctioneer. Existing spectrum auctions assume that SUs have static and known values for the channels. However, in many real world settings, the SUs do not know the exact value of channel access at first, but they learn it and adapt it over time. In this paper, we study spectrum auctions in a dynamic setting where SUs can change their valuations based on their experiences with the channel quality. We propose ADAPTIVE, a dynAmic inDex Auction for sPectrum sharing with TIme-evolving ValuEs that maximizes the social welfare of the SUs. ADAPTIVE is based on multi-armed bandit models where for each user an allocation index is independently calculated in polynomial time. Then we generalize ADAPTIVE to Multi-ADAPTIVE that auctions multiple channels at each time. We provide a sufficient condition under which Multi-ADAPTIVE achieves the maximum social welfare. Both ADAPTIVE and Multi-ADAPTIVE have some desired economic properties that are formally proven in the analysis. Also, we provide a numerical performance comparison between our proposed mechanisms and the well known static auctions, namely the Vickrey second price auction and the VCG mechanism. [ABSTRACT FROM PUBLISHER]

In this paper, we provide comments on the recent paper by Aslam et al. that proposed two low-complexity edge-based scheduling schemes for belief-propagation (BP) decoding of low-density parity-check (LDPC) codes. In their schemes, the check-node reliability threshold (${\mathcal {T}}$) is important to select the updating edges. However, it is found that the selected value of ${\mathcal {T}}$ in the above literature is not optimal. In this comment, we present a method that leads to a better choice of ${\mathcal {T}}$. [ABSTRACT FROM AUTHOR]

Network slicing is a key paradigm in 5G and is expected to be inherited in future 6G networks for the concurrent provisioning of diverse quality of service (QoS). Unfortunately, effective slicing of Radio Access Networks (RAN) is still challenging due to time-varying network situations. This paper proposes a new intelligent RAN slicing strategy with two-layered control granularity, which aims at maximizing both the long-term QoS of services and spectrum efficiency (SE) of slices. The proposed method consists of an upper-level controller to ensure the QoS performance, which enforces loose control by performing adaptive slice configuration according to the long-term dynamics of service traffic. The lower-level controller is to improve SE of slices, by tightly scheduling radio resources to users at the small time-scale. To realize the proposed RAN slicing strategy, we propose a model-free deep reinforcement learning (DRL) framework, which is a hierarchical structure that collaboratively integrating the modified deep deterministic policy gradient (DDPG) and double deep-Q-network algorithm. Specifically, the lower-level control problem is a mixed-integer stochastic optimization problem with multiple constraints. This kind of problem is hard to be directly solved by the exiting DRL algorithms, since it involves searching for the solution in a vast set of mixed-integer action space, which will induce unbearable computational complexity. Thus, we propose a novel action space reducing approach, embedding the convex optimization tools into the DDPG algorithm, to speed up the lower-level control. Furthermore, simulation results confirm the effectiveness of our proposed intelligent RAN slicing scheme. [ABSTRACT FROM AUTHOR]

Radio access network (RAN) slicing is one of the key technologies in 5G and beyond mobile networks, where multiple logical subnets, i.e., RAN slices, are allowed to run on top of the same physical infrastructure so as to provide slice-specific services. Due to the dynamic environments of wireless networks and the diverse requirements of RAN slices, inter-slice radio resource management (IS-RRM) has become a highly challenging task in RAN slicing. In this paper, we propose a novel online convex optimization (OCO) framework for IS-RRM, which directly learns the instant resource allocation from the data revealed by previous allocations, such that sophisticated modeling and parameterization can be avoided in highly complicated and dynamic wireless environments. Specifically, an online IS-RRM scheme that employs multiple expert-algorithms running in parallel is proposed to keep track of the environmental changes and adjust the resource allocation accordingly. Both theoretical analysis and simulation results show that our proposed scheme can guarantee long-term performance comparable to the optimal strategies given in hindsight. [ABSTRACT FROM AUTHOR]

Elastic Optical Networks (EONs) enable finer-grained resource allocation and tuning of transmission configurations for right-sized resource allocation. These features make EONs excellent choice for 5G transport networks supporting highly dynamic traffic with diverse Quality-of-Service (QoS) requirements. 5G network slices are expected to host applications with a dynamic nature (e.g., augmented/virtual reality broadcasting), which will result in slice resource requirement changing over time. The initial resource allocation to network slices has to be adapted to accommodate such changes without causing significant disruption to existing traffic and using minimal additional resources. In this paper, we address the problem of scaling bandwidth demand of network slices on an EON-enabled 5G transport network. In contrast to the state-of-the-art, we do not assume any specific technologies for minimizing disruption when accommodating the scaling request. Rather, we propose an Integer Linear Program (ILP) and a heuristic algorithm for accommodating scaling requests by choosing from a comprehensive set of reconfiguration actions. We carefully design a novel cost model for capturing traffic disruptions and additional resource usage by these different actions. Our extensive simulations using realistic network topologies shed light on the trade-off between additional resource usage and disruption while accommodating slice scaling requests by employing a comprehensive set of reconfiguration actions. Simulation results also show that our heuristic algorithm can find solutions that remain within 10% of ILP-based solutions, while executing several orders of magnitude faster than ILP. [ABSTRACT FROM AUTHOR]

By fully exploiting the cooperative communication capacities among mobile terminals (MTs), the MTs can adapt the offloading designs well to the practical network with dynamic features. In this paper, joint multi-user cooperative partial offloading, transmission scheduling and computation allocating is discussed for device-to-device (D2D) underlay mobile edge computing (MEC). By considering stochastic application requests, unpredictable MTs states, time-varying channel states and computation resources, a customized application offloading model, which aims to minimize the network-wide response latency and energy consumption simultaneously, is formulated. In order to solve this non-convex and non-smooth optimization problem, an online resource coordinating and allocating scheme (ORCAS) is proposed by exploiting Lyapunov optimization theory, variable substitution technique and resource provisioning priority mechanism. Both theoretical analyses and simulation results demonstrate that the proposed ORCAS can 1) drive the application response cost converge to the minimum; 2) achieve superior performance (e.g., the average network-wide response cost under ORCAS is approximately 19.14% lower than that under partial offloading directly); 3) adapt to dynamic situations in terms of stochastic user demands and channel states. [ABSTRACT FROM AUTHOR]

Jobs in modern computing clusters have highly diverse processing duration and heterogeneous resource requirements. In this paper, we consider the problem of job scheduling for a computing cluster comprised of multiple servers with heterogeneous computation resources, while taking the different resource demands of the jobs into account. Our focus is to achieve a low overall job response time for the system (which is also referred to as the job flowtime) while providing fairness between small and large jobs. Since the job flowtime minimization problem under multiple (even homogeneous) servers are known to be NP-hard, we propose an approximation algorithm to tackle the original online scheduling problem by adopting the recently-proposed notion of fractional job flowtime as a surrogate objective for minimization. For the general online job arrival case with multi-dimensional resource requirements, we apply Online Convex Optimization (OCO) techniques to design the corresponding scheduling algorithm with performance guarantees. In the single-dimensional resource setting, we show that the dynamic fit of the online version of our approximate algorithm grows only sublinearly with respect to time and derive a bound for its dynamic regret when comparing to its offline counterpart. While the baseline version of our proposed scheduling algorithm assumes the possibilities of job preemption and job migration across different servers, we show that the extent of job preemption and migration can be well controlled by augmenting the objective function with the corresponding switching costs. [ABSTRACT FROM AUTHOR]

In this paper, the problem of strategic resource management in fog networks is discussed while considering a pay-per-use model, similar to that used in cloud. Fog networks are distributed in nature, because of which resource management in these networks is an NP-hard problem. In the existing literature, the researchers focused on resource management in fog networks, while considering the network delay constraint. However, none of these works considered the effect of pricing policy while deciding on resource allocation. Hence, there is a need for pricing-based resource management in fog networks. In this work, we proposed a dynamic pricing-based resource allocation scheme, named FogPrime, for analyzing the trade-off between the service delay and the associated price. In FogPrime, we use dynamic coalition-formation game to decide the resource allocation strategy locally within a cluster. On the other hand, we use utility game to choose the fog nodes, strategically, while considering the aforementioned trade-off. Through simulation, we observed that FogPrime outperforms the existing schemes in terms of satisfaction of the involved entities – the end-user and the fog nodes. Using FogPrime, the satisfaction of the end-users and the fog nodes increases by 24.49–47.82%, respectively. Additionally, we observe that FogPrime ensures an even distribution of profit among the fog nodes and enables the end-users to pay less at most by 15.88–47.27%. [ABSTRACT FROM AUTHOR]

In this paper, we study the resource slicing problem in a dynamic multiplexing scenario of two distinct 5G services, namely Ultra-Reliable Low Latency Communications (URLLC) and enhanced Mobile BroadBand (eMBB). While eMBB services focus on high data rates, URLLC is very strict in terms of latency and reliability. In view of this, the resource slicing problem is formulated as an optimization problem that aims at maximizing the eMBB data rate subject to a URLLC reliability constraint, while considering the variance of the eMBB data rate to reduce the impact of immediately scheduled URLLC traffic on the eMBB reliability. To solve the formulated problem, an optimization-aided Deep Reinforcement Learning (DRL) based framework is proposed, including: 1) eMBB resource allocation phase, and 2) URLLC scheduling phase. In the first phase, the optimization problem is decomposed into three subproblems and then each subproblem is transformed into a convex form to obtain an approximate resource allocation solution. In the second phase, a DRL-based algorithm is proposed to intelligently distribute the incoming URLLC traffic among eMBB users. Simulation results show that our proposed approach can satisfy the stringent URLLC reliability while keeping the eMBB reliability higher than 90%. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a radio access network (RAN) slicing problem for Internet of vehicles (IoV) services with different quality of service (QoS) requirements, in which multiple logically-isolated slices are constructed on a common roadside network infrastructure. A dynamic RAN slicing framework is presented to dynamically allocate radio spectrum and computing resource, and distribute computation workloads for the slices. To obtain an optimal RAN slicing policy for accommodating the spatial-temporal dynamics of vehicle traffic density, we first formulate a constrained RAN slicing problem with the objective to minimize long-term system cost. This problem cannot be directly solved by traditional reinforcement learning (RL) algorithms due to complicated coupled constraints among decisions. Therefore, we decouple the problem into a resource allocation subproblem and a workload distribution subproblem, and propose a two-layer constrained RL algorithm, named Resource Allocation and Workload diStribution (RAWS) to solve them. Specifically, an outer layer first makes the resource allocation decision via an RL algorithm, and then an inner layer makes the workload distribution decision via an optimization subroutine. Extensive trace-driven simulations show that the RAWS effectively reduces the system cost while satisfying QoS requirements with a high probability, as compared with benchmarks. [ABSTRACT FROM AUTHOR]

With the spatial flexibility exchange across the network, mobile energy storage systems (MESSs) offer promising opportunities to elevate power distribution system resilience against emergencies. Despite the remarkable growth in integration of renewable energy sources (RESs) in power distribution systems (PDSs), most recovery and restoration strategies do not unlock the full potential in such resources due to their inherent uncertainty and stochasticity. This paper develops a novel restoration mechanism in PDSs for routing and scheduling of MESSs integrated with stochastic RESs to achieve agile system response and recovery in facing the aftermath of high-impact low-probability (HILP) incidents. The proposed integrated model is presented as a non-convex non-linear stochastic optimization formulation with joint probabilistic constraints (JPCs). The problem is equivalently reformulated to a tractable mixed-integer linear programming (MILP) model that can be solved by commercial off-the-shelf solvers. Case studies on the IEEE 33-node and 123-node test systems demonstrate the effectiveness and scalability of the proposed framework in boosting the system resilience. This is achieved via effective routing and scheduling of MESSs jointly managed with dynamic network reconfiguration in presence of stochastic RESs. [ABSTRACT FROM AUTHOR]

This paper intends to address the dynamic repair scheduling of electric power transmission systems based on look-ahead strategy approximation. The objective is to minimize system functionality loss during the restoration stage after disruptive events. A series of decisions regarding which damaged component to be repaired has to be made successively considering currently available information of repair time and its uncertainty in the future. To achieve this goal, the dynamic repair scheduling problem is represented as a stochastic Markovian decision process (MDP). To overcome the computational complexity of MDP derived from exponentially growing state space, the cost-to-go function is approximated by a look-ahead strategy based on repair importance ordering. Stage-dependent coefficients are used to balance the approximated functionality loss at different decision stages. The tradeoff between the efficiency and optimality can be achieved by adjusting the look-ahead depth and the updating policy of look-ahead strategy. The IEEE-14 and 118-bus systems were used for performance evaluation of the proposed method and comparison with various approaches. The results show that it can produce decisions close to the best-known solutions within small amount of time. [ABSTRACT FROM AUTHOR]

Due to progressive displacement of conventional power plants by wind turbines, dynamic security of large-scale wind integrated power systems is significantly compromised. In this paper we first highlight the importance of dynamic reactive power support/voltage security in large-scale wind integrated power systems with least presence of conventional power plants. Then we propose a mixed integer dynamic optimization based method for optimal dynamic reactive power allocation in large-scale wind integrated power systems. One of the important aspect of the proposed methodology is that unlike static optimal power flow based approaches, the proposed method considers detailed system dynamics and wind turbine grid code compliance while optimizing the allocation of dynamic reactive power sources. We also advocate that in large-scale wind integrated power systems, 1) better utilization of existing wind turbines especially wind farms with additional grid support functionalities like dynamic support (e.g., dynamic reactive power support, etc.) and 2) refurbishment of existing conventional central power plants to synchronous condensers could be one of the efficient, reliable and cost-effective option to address not only the issue of dynamic voltage security but also to strengthen other dynamic capabilities of the system including system inertia, etc. that are also significant challenges in large-scale wind penetrated power system. The proposed methodology is applied to the detailed model of the western Danish power system which is characterized by large-scale wind integration and least presence of central power plants. [ABSTRACT FROM AUTHOR]

This paper presents a simulation-based environment for verification of static task scheduling methodology in a time predictable system. Different types of processed tasks are distinguished and presented a unified system design methodology consisting of the selection of real time system configuration, task mapping, scheduling, and generation of a sequence of task identifiers to control the interleaved pipeline. An original Worst Case Timing Analyzer (WCTA) has been developed to automate the design process. The methodology was introduced into the original PRET (PREcision Timed) architecture recently presented (Antolak and Pulka, 2020), (Antolak and Pulka, 2021). The PRET system was implemented on a Virtex7 FPGA (Field Programmable Gate Array) platform. A dedicated verification environment is proposed that allows on-line real time system monitoring, analysis of timing parameters, and comparing the results with initial requirements and design constraints. The practical experiments presented in the paper proved the correct operation of the author’s hardware architecture. The obtained results confirmed the validity of the proposed scheduling method and the concept of calculating the execution times of tasks before they are started, which allows for optimal hardware matching to the tasks to be performed.

Real-time Ethernet (RTE) technologies are becoming increasingly popular, as they provide high bandwidth and are able to meet the requirements of industrial real-time communications. Among RTE protocols, the EtherCAT standard is suitable for motion control and closed-loop control applications, which require very short cycle times. As EtherCAT was specifically devised for periodic traffic, aperiodic real-time transmissions are far from being efficient, as they entail long cycle times. To overcome this limitation, this paper presents a general framework for priority-driven swapping (PdS)-based scheduling of aperiodic real-time messages over EtherCAT networks, which uniformly covers both dynamic and static priority and allows for very short cycle times. This paper provides a description of the PdS framework, a schedulability analysis for both static priority and dynamic priority scheduling, and simulative assessments obtained through OMNeT++ simulations. [ABSTRACT FROM PUBLISHER]

Spectrum scarcity and intercell interference (ICI) are two fundamental limiting factors in wireless cellular systems. Soft frequency reuse (SFR) has been proposed as an effective way to manage the spectrum and reduce ICI in cellular systems. In a static SFR scheme, the allocations of transmit power or subcarriers in each cell are fixed prior to system deployment. This limits the potential performance of the SFR scheme. In this paper, we propose an intercell resource-allocation algorithm, which is referred to as the adaptive SFR algorithm (ASFR), that dynamically optimizes subcarrier and power allocations for multicell wireless networks to improve system capacity. The ASFR first finds the subcarrier and power allocations in each cell by using exhaustive search and greedy descend methods and then iteratively repeats among cells until a predefined convergence criterion is satisfied. The theoretical analysis proves the convergence of the ASFR algorithm. Simulation results show that the ASFR achieves a higher system throughput and better cell edge user performance than existing frequency reuse schemes. [ABSTRACT FROM PUBLISHER]

Novel advance reservation (AR) requests, such as user data replication and grid computing, which are different from immediate reservation requests, allow certain initial-delay during setting up, as long as the resources are allocated before a preset deadline. Empowered by the optical orthogonal frequency-division multiplexing (O-OFDM) technology, the elastic optical networks (EON) support channels operating at heterogeneous line rates by allocating spectral resources in a flexible and dynamic manner. However, in a dynamic traffic scenario, dynamic path setup and teardown operation will inevitably lead to spectral fragmentation. In this paper, to effectively allocate the spectrum in a suitable duration along a better route, we introduce a notion of available time-spectrum consecutiveness (TSC) to describe the spectrum fragmentations along frequency axis and time axis. Based on TSC, three time-aware routing and spectrum assignment (RSA) algorithms are proposed. The novel algorithms retain the TSC as much as possible when establishing a lightpath and reduce the spectrum fragmentation. Simulation results indicate three proposed algorithms achieve lower blocking probability and higher spectrum efficiency. [ABSTRACT FROM PUBLISHER]

This paper explores the possibility of efficiently executing a single application using multicores simultaneously with multiple GPU accelerators under a parallel task programming paradigm. In particular, we address the challenge of extending a parallel_for template to allow its exploitation on heterogeneous architectures. Due to the asymmetry of the computing resources, we propose in this work a dynamic scheduling strategy coupled with an adaptive partitioning scheme that resizes chunks to prevent underutilization and load imbalance of CPUs and GPUs. In this paper we also address the problem of the underutilization of the CPU core where a host thread operates. To solve it, we propose two different approaches: (1) a collaborative host thread strategy, in which the host thread, instead of busy-waiting for the GPU to complete, it carries out useful chunk processing; and (2) a host thread blocking strategy combined with oversubscription, that delegates on the OS the duty of scheduling threads to available CPU cores in order to guarantee that all cores are doing useful work. Using two benchmarks we evaluate the overhead introduced by our scheduling and partitioning algorithms, finding that it is negligible. We also evaluate the efficiency of the strategies proposed finding that allowing oversubscription controlled by the OS can be beneficial under certain scenarios. [ABSTRACT FROM AUTHOR]

With the rapid development of economic globalization and green manufacturing, traditional flexible job shop scheduling has evolved into the low-carbon heterogeneous distributed flexible job shop scheduling problem (LHDFJSP). Additionally, modern smart manufacturing processes encounter complex and diverse contingencies, necessitating the ability to address dynamic events in real-world production activities. To date, there are limited studies that comprehensively address the intricate factors associated with the LHDFJSP, including workshop heterogeneity, job insertions and transfers, and considerations of low-carbon objectives. This paper establishes a multi-objective mathematical model with the goal of minimizing the total weighted tardiness and total energy consumption. To effectively solve this problem, diverse composite scheduling rules are formulated, alongside the application of a deep reinforcement learning (DRL) framework, i.e., Rainbow deep-Q network (Rainbow DQN), to learn the optimal scheduling strategy at each decision point in a dynamic environment. To verify the effectiveness of the proposed method, this paper extends the standard dataset to adapt to the LHDFJSP. Evaluation results confirm the generalization and robustness of the presented Rainbow DQN-based method.

Permutation flowshop scheduling problem (PFSP) has attracted lots of attention from both academia and industry as it finds many applications especially for today’s mass customization. The PFSP is proved NP-hard, and the dynamic uncertainties such as stochastic new order arrivals significantly increase the problem complexity and difficulty. Many enterprises often struggle to make decisions on accepting new orders and setting due dates for them due to lack of effective scheduling methods. To fill in the knowledge gap, this paper is to propose a new meta-heuristic algorithm which is based on a new enhanced destruction and construction method and a novel repair method while adopting the architecture of the iterated greedy algorithm. Statistical tests were conducted and results show that the new algorithm outperforms existing ones. [ABSTRACT FROM AUTHOR]

This paper proposes a new sequencing and scheduling algorithm for the point merge system based on mixed integer linear programming considering the uncertain flight time of aircraft. In the first stage of the proposed algorithm, for a static environment, deterministic robust solutions are determined. To consider the uncertainty, an extra buffer is introduced in the sequencing and scheduling algorithm, and the buffer size is analytically derived by generating a deterministic robust counterpart problem. In the second stage, to compensate for unforeseen situations under a dynamic environment, the static solution determined in the first stage is adjusted by using the proposed heuristic algorithm, with a sliding time window to reduce the computational load. The performances of the proposed algorithm are verified via numerical simulations based on historical data analysis. [ABSTRACT FROM AUTHOR]

In this paper, a community-based cooperative energy consumption scheme in smart grid, which alleviates energy consumption cost to customers, is proposed. The concept of community among customers in the smart grid is discussed. To form different communities among customers, a community-based game among customers is orchestrated, while considering the dynamic nature of the composition of the community. A practical scenario involving multiple customers forming a group and cooperating with one another is considered. The proposed dynamic community formation scheme always achieves an equilibrium state. Furthermore, the proposed scheme also helps to reduce peak-to-average ratio of the energy demands from the customers in different time periods. Simulation results show that the proposed cooperation-based scheme outperforms the existing schemes. It is also shown that customers can minimize their energy consumption cost by approximately 16% using the proposed scheme, compared to noncooperative approaches. [ABSTRACT FROM AUTHOR]

The advancements in renewable energy technologies direct the power sector to focus on power generation from renewable energy resources (RER) as an alternative solution for meeting the future demand. Nowadays residential buildings are becoming smarter with wide use of smart appliances, integration of information and communication technology, and in-house power generation using RER. In this paper, an intelligent residential energy management system (IREMS) for prosumers of smart residential buildings is proposed, and its benefits are demonstrated through a case study. The primary objective of IREMS is reduction in electricity bills while maintaining the power demand under the maximum demand limit subjected to the various constraints governing the operation of household loads and RER. The IREMS achieves the objective by scheduling the schedulable loads during low pricing intervals while considering the operational dynamics of nonschedulable loads and availability of RER. IREMS also manages the battery energy storage in such a way so as to reduce the power dissipated through the dump load when excess power is available from RER due to the utility-defined power export limit to grid. Further, an optimal resources sizing algorithm is used to choose the size of RER and battery storage for the effective utilization of available renewable energy. [ABSTRACT FROM PUBLISHER]

Underwater acoustic sensor networks (UW-ASNs) have been attracting more and more research interests recently due to their various promising applications. In this paper, we focus on the time division multiple access (TDMA) based media access control (MAC) protocol design for UW-ASNs in order to improve the network performance. Different from existing work, we consider a more general underwater communication scenario, where the communication network topology is modeled as a three-dimensional (3-D) scenario and the mobility of sensor nodes is also taken into consideration. We introduce a dynamic and flexible spatial reuse strategy for the TDMA protocol design, and formulate the interference scenario as a dynamic interference-free graph according to the nodes’ current position distribution and a preset interference-free threshold. To achieve high spatial reuse efficiency, we propose two interference-free graph (IG) clustering algorithms, which lead to the optimal IG-TDMA protocol and the heuristic IG-TDMA protocol, respectively. The former can achieve the optimal network throughput but may not be feasible for high traffic networks due to its high computational complexity. The latter can achieve near-optimal network performance with much lower computational complexity and thus is more practical. Simulations verify the benefits of our proposed protocols in comparison with the CSMA/CA protocol and some existing TDMA protocols with and without spatial reuse. [ABSTRACT FROM PUBLISHER]

The problem of dynamic task allocation in a distributed network of redundant robot manipulators for path-tracking with limited communications is investigated in this paper, where k fittest ones in a group of n redundant robot manipulators with n>k are allocated to execute an object tracking task. The problem is essentially challenging in view of the interplay of manipulator kinematics and the dynamic competition for activation among manipulators. To handle such an intricate problem, a distributed coordination control law is developed for the dynamic task allocation among multiple redundant robot manipulators with limited communications and with the aid of a consensus filter. In addition, a theorem and its proof are presented for guaranteeing the convergence and stability of the proposed distributed control law. Finally, an illustrative example is provided and analyzed to substantiate the efficacy of the proposed control law. [ABSTRACT FROM AUTHOR]

The recent emergence of mobile cloud computing has enabled mobile users to offload computing tasks from mobile devices to nearby cloudlets, so as to reduce energy consumption and improve application performance. In this paper, we consider the problem of maximizing the profit of the cloudlets’ managing platform that receives computing requests from mobile users and fulfils these requests by leveraging computing service of participating cloudlets. However, it is very challenging to maximize the operating profit for such a managing platform, due to unpredictable arrival of user requests, dynamic participation of mobile cloudlets, and complexity in computing resource allocations. Based on the Lyapunov optimization technique combined with the technique of weight perturbation, we introduce a new stochastic control algorithm that makes online decisions on computing request admission and dispatching, computing service purchasing, and computing resource allocation. Different from traditional techniques, this algorithm does not require any statistical knowledge of relevant system dynamics, and is efficient for implementation in practice. Theoretical analysis and simulation results have demonstrated both the profit optimality and the system stability achieved by the proposed control algorithm. [ABSTRACT FROM PUBLISHER]

Problem definition: We study optimal scheduling of customers in service systems, such as call centers. In such systems, customers typically hang up and abandon the system if their wait for service is too long. Such abandonments are detrimental for the system, and so managers typically use scheduling as a tool to mitigate it. In this paper, we study the interplay between customer impatience and scheduling decisions when managing heterogeneous customer classes. Academic/practical relevance: Call centers constitute a large industry that has a global spending of around $300 billion and employs more than 15 million people worldwide. Our work focuses on improving call center operations, which can reduce costs and improve customer satisfaction. Mathematically, customer patience is typically modeled as exponentially distributed for tractability. Our work makes inroads into relaxing this restrictive assumption to allow modeling more realistic call center situations. Methodology: We use heavy traffic-motivated asymptotic queueing machinery that provides us the traction to successfully capture and incorporate the customer impatience distribution into the scheduling problem. In our approach, the scheduling problem reduces to a diffusion control problem, which we solve to propose near-optimal scheduling policies. Results: We propose near-optimal scheduling policies that can be implemented by call centers to improve their quality-of-service metrics. One of our main results is that, for a class of parameters, we establish sufficient conditions for both the optimality and nonoptimality of threshold policies. Managerial implications: Threshold policies are widely used for scheduling. Our work provides additional insight into whether these may be suboptimal. Our work provides an easy-to-implement alternative that can reduce customer abandonments considerably; for instance, our numerical results indicate that for a system with two customer types, the abandonment rate of one class can be lowered by 30% by using our policy relative to the best threshold policy. [ABSTRACT FROM AUTHOR]

Network softwarization is a major paradigm shift, which enables programmable and flexible system operation in challenging use cases. In the fifth-generation (5G) mobile networks, the more advanced scenarios envision transfer of high-rate mission-critical traffic. Achieving end-to-end reliability of these stringent sessions requires support from multiple radio access technologies and calls for dynamic orchestration of resources across both radio access and core network segments. Emerging 5G systems can already offer network slicing, multi-connectivity, and end-to-end quality provisioning mechanisms for critical data transfers within a single software-controlled network. Whereas these individual enablers are already in active development, a holistic perspective on how to construct a unified, service-ready system as well as understand the implications of critical traffic on serving other user sessions is not yet available. Against this background, this paper first introduces a softwarized 5G architecture for end-to-end reliability of the mission-critical traffic. Then, a mathematical framework is contributed to model the process of critical session transfers in a softwarized 5G access network, and the corresponding impact on other user sessions is quantified. Finally, a prototype hardware implementation is completed to investigate the practical effects of supporting mission-critical data in a softwarized 5G core network, as well as substantiate the key system design choices. [ABSTRACT FROM AUTHOR]

With the increasing of the scale and resolution of terrains, graphic processing hardware meet the new challenges during the terrain rendering. To solve this problem, a dynamic scheduling algorithm based on the three-dimensional array and Sudoku is proposed in this paper. Mesh optimization and texture format conversion mode is utilized to reduce the terrain data size without quality reduction. Stratified block terrains can be then built corresponding to the three-dimensional array. Finally, these block terrains are loaded and unloaded dynamically based on Sudoku strategy according to the viewpoint position. Experimental results show that the efficiency of the proposed algorithm is significantly higher than six state-of-the-art algorithms. Consequently, our algorithm has the ability to load a great amount of terrain data with high performance in frame frequency, which achieves more fluid visual experience. [ABSTRACT FROM AUTHOR]

Mobile edge-cloud (MEC) aims to support low latency mobile services by bringing remote cloud services nearer to mobile users. However, in order to deal with dynamic workloads, MEC is deployed in a large number of fixed-location micro-clouds, leading to resource wastage during stable/low workload periods. Limiting the number of micro-clouds improves resource utilization and saves operational costs, but faces service performance degradations due to insufficient physical capacity during peak time from nearby micro-clouds. To efficiently support services with low latency requirement under varying workload conditions, we adopt the emerging network function virtualization (NFV)-enabled MEC, which offers new flexibility in hosting MEC services in any virtualized network node, e.g., access points, routers, etc. This flexibility overcomes the limitations imposed by fixed-location solutions, providing new freedom in terms of MEC service-hosting locations. In this paper, we address the questions on where and when to allocate resources as well as how many resources to be allocated among NFV-enabled MECs, such that both the low latency requirements of mobile services and MEC cost efficiency are achieved. We propose a dynamic resource allocation framework that consists of a fast heuristic-based incremental allocation mechanism that dynamically performs resource allocation and a reoptimization algorithm that periodically adjusts allocation to maintain a near-optimal MEC operational cost over time. We show through extensive simulations that our flexible framework always manages to allocate sufficient resources in time to guarantee continuous satisfaction of applications’ low latency requirements. At the same time, our proposal saves up to 33% of cost in comparison to existing fixed-location MEC solutions. [ABSTRACT FROM PUBLISHER]

In this paper we consider the time-varying characteristics of practical wireless networks and propose a joint dynamic rate allocation and transmission scheduling optimization scheme for scalable video multirate multicast based on opportunistic routing (OR) and network coding. With OR the decision of optimal routes for scalable video coding layered streaming is integrated into the joint optimization formulation. The network throughput is also increased by taking advantage of the broadcast nature of the wireless shared medium and by network coding operations in intermediate nodes. To maximize the overall video reception quality among all destinations the proposed scheme can jointly optimize the video reception rate the associated routes to different destinations and the time fraction scheduling of transmitter sets that are concurrently transmitting in the shared wireless medium. By using dual decomposition and primal-dual update approach we develop a cross-layer algorithm in a fully distributed manner. Simulation results demonstrate significant network multicast throughput improvement and adaptation to dynamic network changes relative to existing optimization schemes. [ABSTRACT FROM PUBLISHER]

A wireless sensor network (WSN) consists of sensor nodes which can self-organize to relay information such as measurements to a base station. To reduce latency and increase data transmission throughput, multi-channel data collection protocols have been proposed to enable simultaneous parallel transmissions between pairs of nodes within the network. However, the existing protocols require long scheduling phase, are less dynamic to network traffic changes, and/or compromise on efficiency by relying on the back-off mechanism such as carrier sense multiple access with collision avoidance (CSMA/CA). This paper proposes a fast, adaptive, and energyefficient data collection protocol in multi-channel-multi-path WSN. The protocol consists of two major phases. The first phase is the node-channel assignment that uses the graph coloring technique to resolve the issue of node overhearing and interference. The second phase is the scheduling and packet forwarding, in which a novel three-dimensional parallel iterative matching (3DPIM) algorithm is proposed to pair up sensor nodes in different time slots so as to enable collision-free multiple simultaneous data transmissions in every time slot. Simulation results show that our proposed protocol can achieve fast and energy-efficient data collection while being adaptive to the change of network traffic in WSN. [ABSTRACT FROM PUBLISHER]