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The explosive growth of mobile devices promotes the prosperity of novel mobile applications, which can be realized by service offloading with the assistance of edge computing servers. However, due to limited computation and storage capabilities of a single server, long service latency hinders the continuous development of service offloading in mobile networks. By supporting multi-server cooperation, Pervasive Edge Computing (PEC) is promising to enable service migration in highly dynamic mobile networks. With the objective of maximizing the system utility, we formulate the optimization problem by jointly considering the constraints of server storage capability and service execution latency. To enable dynamic service placement, we first utilize Lyapunov optimization method to decompose the long-term optimization problem into a series of instant optimization problems. Then, a sample average approximation-based stochastic algorithm is proposed to approximate the future expected system utility. Afterwards, a distributed Markov approximation algorithm is utilized to determine the service placement configurations. Through theoretical analysis, the time complexity of our proposed algorithm is linear to the number of users, and the backlog queue of PEC servers is stable. Performance evaluations are conducted based on both synthetic and real trace-driven scenarios, with numerical results demonstrating the effectiveness of our proposed algorithm from various aspects. [ABSTRACT FROM AUTHOR]

The maritime communication network is assembled by emergent network technologies. However, the adverse maritime environment impedes the efficiency of resources allocation in maritime communication network. Here we show a joint sleeping scheduling and opportunistic transmission scheme in delay-tolerant maritime wireless communication networks based on software defined networking (SDN) to find a better tradeoff between the energy consumption and the delay. Specifically, an energy-limited delay tolerant networking (DTN) node deployed in the ocean receives/transmits data from/to vessels within its communication range. To further save the energy, a long-term energy minimization problem is formulated with sleeping scheduling and opportunistic transmission. After that, a multi-objective minimization problem of energy and delay is first modeled by Lyapunov optimization (LO), which then is solved by convex optimization. Both mathematical analyses and simulation results demonstrate how the maritime communication network allocates satisfactorily with the proposed allocation scheme. [ABSTRACT FROM AUTHOR]

Fog computing system is able to facilitate computation-intensive applications and emerges as one of the promising technology for realizing the Internet of Things (IoT). By offloading the computational tasks to the fog node (FN) at the network edge, both the service latency and energy consumption can be improved, which is significant for industrial IoT applications. However, the dynamics of computational resource usages in the FN, the radio environment and the energy in the battery of IoT devices make the offloading mechanism design become challenging. Therefore, in this article, we propose a dynamic optimization scheme for the IoT fog computing system with multiple mobile devices (MDs), where the radio and computational resources, and offloading decisions, can be dynamically coordinated and allocated with the variation of radio resources and computation demands. Specifically, with the objective to minimize the system cost related to latency, energy consumption, and weights of MDs, we propose a joint computation offloading and radio resource allocation algorithm based on Lyapunov optimization. Through minimizing the derived upper bound of the Lyapunov drift-plus-penalty function, we divide the main problem into several subproblems at each time slot and address them accordingly. Through performance evaluation, the effectiveness of the proposed scheme can be verified. [ABSTRACT FROM AUTHOR]

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Task offloading strategy optimization in mobile edge computing (MEC) has always been a hot issue. However, the mobility of a user equipment (UE) seriously affects the UE's cost and performance. This paper proposes three mobility types depending on whether the mobility characteristic of a UE is known, and formulates an energy minimization problem and a latency minimization problem to optimize the cost and performance, respectively. We first develop greedy strategy based task offloading algorithms for UEs according to their mobility characteristics. However, accurately obtaining the mobility characteristics of the UEs over a long time in practice is a huge challenge, especially in a highly random environment like the MEC. To address the issue, we use a Lyapunov optimization method to develop the algorithms that do not require any prior knowledge of the mobility characteristics to minimize the long-term energy and latency of UEs. Experimental results show that the greedy strategy based algorithms can optimize the cost and performance of UEs by using their mobility characteristics, and perform better than the Lyapunov optimization based algorithms in a short-term. However, the Lyapunov optimization based algorithms perform better than the greedy strategy based algorithms over a long-term. • Three greedy strategy based task offloading algorithms are developed by using the short-term mobility characteristics of user equipments to optimize their cost and performance. • A Lyapunov optimization based task offloading algorithm that does not require any prior knowledge of the mobility characteristics is proposed, which addresses the challenge of acquiring the long-term mobility characteristics. • Experiments are conducted to demonstrate the impact of mobility characteristics on the cost and performance, and to compare the effectiveness of the algorithms in the short- and long-term. [ABSTRACT FROM AUTHOR]

Recently, Internet of Vehicles (IoV) has become one of the most active research fields in both academic and industry, which exploits resources of vehicles and Road Side Units (RSUs) to execute various vehicular applications. Due to the increasing number of vehicles and the asymmetrical distribution of traffic flows, it is essential for the network operator to design intelligent offloading strategies to improve network performance and provide high-quality services for users. However, the lack of global information and the time-variety of IoVs make it challenging to perform effective offloading and caching decisions under long-term energy constraints of RSUs. Since Artificial Intelligence (AI) and machine learning can greatly enhance the intelligence and the performance of IoVs, we push AI inspired computing, caching and communication resources to the proximity of smart vehicles, which jointly enable RSU peer offloading, vehicle-to-RSU offloading and content caching in the IoV framework. A Mix Integer Non-Linear Programming (MINLP) problem is formulated to minimize total network delay, consisting of communication delay, computation delay, network congestion delay and content downloading delay of all users. Then, we develop an online multi-decision making scheme (named OMEN) by leveraging Lyapunov optimization method to solve the formulated problem, and prove that OMEN achieves near-optimal performance. Leveraging strong cognition of AI, we put forward an imitation learning enabled branch-and-bound solution in edge intelligent IoVs to speed up the problem solving process with few training samples. Experimental results based on real-world traffic data demonstrate that our proposed method outperforms other methods from various aspects. [ABSTRACT FROM AUTHOR]

Delay-sensitive tasks account for an increasing proportion of all tasks on the Internet of Things (IoT). How to solve such problems has become a hot research topic. Delay-sensitive tasks scenarios include intelligent vehicles, unmanned aerial vehicles, industrial IoT, intelligent transportation, etc. More and more scenarios have delay requirements for tasks and simply reducing the delay of tasks is not enough. However, speeding up the processing speed of a task means increasing energy consumption, so we try to find a way to complete tasks on time with the lowest energy consumption. Hence, we propose a heuristic particle swarm optimization (PSO) algorithm based on a Lyapunov framework (LPSO). Since task duration and queue stability are guaranteed, a balance is achieved between the computational energy consumption of the IoT nodes, the transmission energy consumption and the fog node computing energy consumption, so that tasks can be completed with minimum energy consumption. Compared with the original PSO algorithm and the greedy algorithm, the performance of our LPSO algorithm is significantly improved.

MEC servers (MESs) support multiple queues to accommodate the delay requirements of tasks offloaded from end devices or transferred from other MESs. The service time assigned to each queue trades off the queue backlog and energy consumption. Because multiple queues share the computational resources of a MES, optimally scheduling the service time among them is important, reducing the energy consumption of a MES and ensuring the delay requirement of each queue. To achieve a balance between these metrics, we propose an online service-time allocation method that minimizes the average energy consumption and satisfies the average queue backlog constraint. We employ the Lyapunov optimization framework to transform the time-averaged optimization problem into a per-time-slot optimization problem and devise an online service-time allocation method whose time complexity is linear to the number of queues. This method determines the service time for each queue at the beginning of each time slot using the observed queue length and expected workload. We adopt a long short-term memory (LSTM) deep learning model to predict the workload that will be imposed on each queue during a time slot. Using simulation studies, we verify that the proposed method strikes a better balance between energy consumption and queuing delay than conventional methods.

In this article, we propose a cross-layer resource management mechanism for an indoor multiuser visible light communication (VLC) access network. The physical layer builds upon DC-biased orthogonal frequency division multiple access. Channel state information at the physical layer serves as a performance indicator for better data rate opportunity while queue state information at medium access control layer provides the information about packet urgency. A packet based traffic arrival model is considered such that incoming packets with random inter arrival times join a separate queue dedicated to each user if the admission control accepts them, otherwise packets are dropped. We formulate and solve a stochastic cross-layer optimization problem to optimize the network resources under the constraints of queue stability and power to maximize the average system throughput. By using the Lyapunov optimization technique, we convert the long term time-average optimization problem into series of single time slot online problem. Then, we decompose the problem into independent sub problems and propose packet admission, resource (access point and subcarrier) allocation and power control solutions. We present simulation results to demonstrate the superiority of proposed scheme over existing solutions in the literature. [ABSTRACT FROM AUTHOR]

In next-generation wireless networks, providing secure transmission and delay guarantees are two critical goals. However, either of them requires a concession on the transmission rate. In this article, we consider a visible light network consisting of multiple access points and multiple users. Our first objective is to mathematically evaluate the achievable rate under constraints on delay and security. The second objective is to provide a cell formation with customized statistical delay and security guarantees for each user. First, we propose a user-centric design called secure cell formation, in which artificial noise is considered, and flexible user scheduling is determined. Then, based on the effective capacity theory, we derive the statistical-delay-constrained secrecy rate and formulate the cell formation problem as a stochastic optimization problem (OP). Further, based on the Lyapunov optimization theory, we transform the stochastic OP into a series of evolutionary per-slot drift-plus-penalty OPs. Finally, a modified particle swarm optimization algorithm and an interference graph-based user-centric scheduling algorithm are proposed to solve the OPs. We obtain a dynamic independent set of scheduled users as well as secure cell formation parameters. Simulation results show that the proposed algorithm can achieve a better delay-constrained secrecy rate than the existing cell formation approaches. [ABSTRACT FROM AUTHOR]

We consider low run-time complexity power management in distribution grids with renewable energy sources (RESs) and batteries, where forecasts are unavailable. We propose iterative Lyapunov Real-time Control (iLypRC), a fast online algorithm, which does not need forecasts, for the real-time battery control. iLypRC is designed via Lyapunov optimization and applies either to follow a previously computed dispatch plan or to optimize the monetary cost, while it satisfies look-ahead state-of-energy constraints. iLypRC requires only bounds on the uncertain quantities for each real-time interval. iLypRC efficiently accounts for grid losses, battery efficiency, and grid constraints via iterative linearizations of the power flow equations. We compute a theoretical upper bound on the difference between the cost of iLypRC and the cost of an oracle. Finally, numerical evaluations (using real data) on a 4-bus distribution grid and a 34-bus real Swiss grid show that iLypRC achieves a cost very close to that of a model predictive control (MPC) with a good forecast, but iLypRC needs no forecast and has much lower run-time complexity. Also, when the MPC forecast is inaccurate, iLypRC outperforms MPC. [ABSTRACT FROM AUTHOR]

Vehicles are becoming connected entities, and with the advent of online gaming, on demand streaming and assisted driving services, are expected to turn into data hubs with abundant computing needs. In this article, we show the value of estimating vehicular mobility as 5G users move across radio cells, and of using such estimates in combination with an online algorithm that assesses when and where the computing services (virtual machines, VM) that are run on the mobile edge nodes are to be migrated to ensure service continuity at the vehicles. This problem is tackled via a Lyapunov-based approach, which is here solved in closed form, leading to a low-complexity and distributed algorithm, whose performance is numerically assessed in a real-life scenario, featuring thousands of vehicles and densely deployed 5G base stations. Our numerical results demonstrate a reduction of more than 50% in the energy expenditure with respect to previous strategies (full migration). Also, our scheme self-adapts to meet any given risk target, which is posed as an optimization constraint and represents the probability that the computing service is interrupted during a handover. Through it, we can effectively control the trade-off between seamless computation and energy consumption when migrating VMs. [ABSTRACT FROM AUTHOR]

Mobile edge computing is a new computing paradigm, which pushes cloud computing capabilities away from the centralized cloud to the network edge to satisfy the increasing amounts of low-latency tasks. However, challenges such as service interruption caused by user mobility occur. In order to address this problem, in this article, we first propose a multiple service placement algorithm, which initializes the placement of each service according to the user's initial location and their service requests. Furthermore, we build a network model and propose a based on Lyapunov optimization method with long-term cost constraints. Considering the importance of user mobility, we use the Kalman filter to correct the user's location to improve the success rate of communication between the device and the server. Compared with the traditional scheme, extensive simulation results show that the dynamic service migration strategy can effectively improve the service efficiency of mobile edge computing in the user's mobile scene, reduce the delay of requesting terminal nodes, and reduce the service interruption caused by frequent user movement. [ABSTRACT FROM AUTHOR]

By offloading tasks from the mobile device (MD) to its nearby deployed access points (APs), each of which is connected to one server for task processing, computation offloading can strike a balance between MD’s task execution delay and energy consumption in mobile edge computing (MEC) systems. Considering communication and computation dynamics in MEC systems, we aim to design online computation offloading mechanisms in this paper to minimize the time average expected task execution delay under the constraint of average energy consumption. Firstly, with known current channel gains between the MD and APs as well as available computing capability at MEC servers, we leverage the Lyapunov optimization framework to make an optimal one-slot decision on MD’s transmit power allocation and MEC server selection. On this basis, we then consider a more realistic scenario, where it is difficult to capture current available computing capability at MEC servers, and combine the multi-armed bandit framework for an online learning based MEC server selection algorithm. Finally, through theoretical analyses and extensive simulations, we demonstrate the near-optimality and feasibility of our proposed algorithms, and present that our proposed algorithms fully explore the interplay between communication and computation with enriched user experience and reduced energy consumption. [ABSTRACT FROM AUTHOR]

As there is an increasing trend to deploy geographically distributed (geo-distributed) cloud datacenters (DCs), the scheduling of data-oriented tasks in such cloud DC systems becomes an appealing research topic. Specifically, it is challenging to achieve the distributed online scheduling that can handle the tasks' acceptance, data-transfers, and processing jointly and efficiently. In this paper, by considering the store-and-forward and anycast schemes, we formulate an optimization problem to maximize the time-average profit from serving data-oriented tasks in a cloud DC system and then leverage the Lyapunov optimization techniques to propose an efficient scheduling algorithm, i.e., GlobalAny. We also extend the proposed algorithm by designing a data-transfer acceleration scheme to reduce the data-transfer latency. Extensive simulations verify that our algorithms can maximize the time-average profit in a distributed online manner. The results also indicate that GlobalAny and GlobalAny_Ext (i.e., GlobalAny with data-transfer acceleration) outperform several existing algorithms in terms of both time-average profit and computation time.

The rapidly increasing amounts of mobile traffic have brought new challenges on the limited spectrum resource. LTE on the unlicensed band, namely, LTE-U, under the carrier aggregation technology has been considered as a potential solution to handle the increasing demand for high data rate that could provide a higher transmission data rate, spectrum efficiency, as well as seamless mobile user experience. To ensure the fairness access between LTE-U BSs in unlicensed bands, in this paper, we consider the multiple LTE-U base stations (BSs) in the same interference region sharing unlicensed bands. We first analyze the hybrid full-duplex/half-duplex transmission mode of LTE-U BSs based on the network status. Although there is throughput gain from the full-duplex transmission, it cannot always provide a superior performance than the half-duplex transmission mode due to the influence of the self-interference. In addition, we further extensively investigate the opportunistic multiple LTE-U BSs access scheme on the unlicensed band based on the channel bidding mechanism to guarantee the opportunistic harmonious sharing on unlicensed bands among LTE-U BSs. Third, a bidding-based dual-band resource allocation scheme is proposed with dynamic hybrid transmission mode to achieve the maximum network utility while guaranteeing the packet delay constraint and minimizing the packet drop rate under the Lyapunov optimization. Finally, various simulation results demonstrate that the algorithm we proposed could achieve a considerable performance improvement with respect to the schemes in the literature.

With the development of network function virtualization technology, virtual network functions (VNFs) can be chained to provide different services for users in fifth generation access networks. Under the condition of a large number of various user service requests reach the network in a short time, dynamic placement of VNFs is essential to achieve efficient physical resource allocation, while ensuring network stability. Unlike queue models in current works, we design a dynamic scheduling model of two-stage queue to perceive the status of current queue backlog and schedule the queue dynamically. In particular, we formulate a VNF placement cost minimization problem, taking the network stability into account. Owing to that the problem belongs to an NP-hard problem, we transform it into the minimization of the upper bound of the drift-plus-penalty function by the Lyapunov optimization technique. Then, we decompose the reformulated problem into a service function chain (SFC) scheduling problem and an SFC mapping problem, corresponding to our designed dynamic scheduling model of two-stage queue. For the abovementioned two problems, we present a queue-aware dynamic placement of virtual network functions algorithm. It includes a genetic algorithm-based heuristic SFC scheduling algorithm (first stage) and a genetic algorithm-based heuristic SFC mapping algorithm (second stage). Simulation results demonstrate that our placement algorithm can guarantee less placement cost while significantly enhancing network stability, compared with the existing algorithm.

The increasing use of Internet of Things (IoTs) has brought more advantages in supplying power to electric vehicles (EVs). With the help of IoTs, EVs can be charged more easily by mobile charging stations (MCSs) compared with the fixed charging stations (FCSs). However, previous works in the management of power supply in FCSs have not been properly applied in MCSs, e.g., dynamic of EV users' arrival and variable power supply from MCSs in IoTs. In this paper, we study how to manage MCSs' supply power in IoTs under the condition that MCSs supply multiple kinds of power. First, considering the randomness of power supply and dynamic of EV users' arrival, we develop the dynamic framework of power supply and the economic model. Then, aiming to maximize the long-term average profits of MCSs, a stochastic optimization problem is formulated to decide the optimal strategy of power management. Based on the Lyapunov optimization theory, a Lyapunov-based online distributed algorithm is proposed to obtain the optimal solutions. Meanwhile, the performance of our proposed algorithm is analyzed and simulation results validate the effectiveness of our proposal.

In this paper, by jointly considering resource reuse and allocation, we formulate a stochastic optimization programming to investigate the spectral efficiency maximization problem in dynamic wireless self-backhaul small cell networks, where random and finite-traffic loads are considered while keeping the system stable. Then, by leveraging the fractional programming theory and the Lyapunov optimization technique, an extremely simple but optimal queue-aware dynamic resource reuse and joint allocation algorithm (QDRRJAA) is proposed to solve the formulation. In the QDRRJAA, at the beginning of each time slot, the system will exploit the current queue states of all users and channel state information first to calculate four resource scheduling priority matrixes. Then, the category of each resource block (RB), i.e., either an access-only RB, a backhaul-only RB or a common RB can be determined. The theoretical analysis and simulation results reveal that the proposed algorithm can flexibly strike a balance between spectral efficiency and delay by simply adjusting an introduced control parameter and also reduce the required resources for packet transmission. In particular, no iteration and optimization tools are required in the QDRRJAA, which paves the way for practical applications.

Supporting ultra-reliable and low-latency communications (URLLC) is crucial for vehicular traffic safety and other mission-critical applications. In this paper, a novel proximity and quality-of-service-aware resource allocation framework for vehicle-to-vehicle (V2V) communication is proposed. The proposed scheme incorporates the physical proximity and traffic demands of vehicles to minimize the total transmission power over the allocated resource blocks (RBs) under reliability and queuing latency constraints. A Lyapunov framework is used to decompose the power minimization problem into two interrelated sub-problems: RB allocation and power optimization. To minimize the overhead introduced by frequent information exchange between the vehicles and the roadside unit (RSU), the resource allocation problem is solved in a semi-distributed fashion. First, a novel RSU-assisted virtual clustering mechanism is proposed to group vehicles into disjoint zones based on mutual interference. Second, a per-zone matching game is proposed to allocate RBs to each vehicle user equipment (VUE) based on vehicles' traffic demands and their latency and reliability requirements. In the formulated one-to-many matching game, VUE pairs and RBs rank one another using preference relations that capture both the queue dynamics and interference. To solve this game, a semi-decentralized algorithm is proposed using which the VUEs and RBs can reach a stable matching. Finally, a latency-and reliability-aware power allocation solution is proposed for each VUE pair over the assigned subset of RBs. Simulation results for a Manhattan model show that the proposed scheme outperforms the state-of-art baseline and reaches up to 45% reduction in the queuing latency and 94% improvement in reliability.

Edge computing is a promising technology that offers data analysis and computing for Internet of Things (IoT) services at the network edge. It has the potential to significantly reduce the latency and improve the reliability of IoT services by allowing computation workloads and local data generated by IoT devices to be offloaded to edge nodes. This paper aims to develop algorithms for efficient provision of both job assignment and resource allocation for edge computing networks. The main objective is to minimize the long-term average of the response time delay subject to constraints on computation resources and power consumption. We apply a drift-plus-penalty based Lyapunov optimization approach to convert the original problem into an upper bound optimization problem. We then relax the latter to a convex optimization problem. Finally, a distributed algorithm based on branch-and-bound approach is provided and the gap between the distributed algorithm solution and the optimal solution of the original problem is theoretically analyzed. Numerical results based on extensive experiments have demonstrated that our distributed algorithm can achieve the required performance of edge computing that supports IoT systems, under static traffic conditions as well as under dynamic environments with time-varying traffic. [ABSTRACT FROM AUTHOR]

Large-scale multi-antenna systems can effectively improve data transmission reliability and throughput for smart grid. However, the massive number of antennas and radio frequency (RF) chains also result in high complexity and energy cost. In this paper, we develop a new performance benchmark named energy economic efficiency for measuring the time-average throughput per energy cost. Then, we investigate how to maximize long-term energy economic efficiency via the joint optimization of communication and energy resource allocation. The formulated joint optimization problem is NP-hard because it not only involves long-term nonlinear optimization objective and constraints, but also involves both integer and continuous optimization variables. Next, we propose an online joint antenna selection and power control algorithm by combining nonlinear fractional programming, Lyapunov optimization, and bisection method. The proposed algorithm can achieve bounded performance deviation from the optimum performance without requiring the prior knowledge of future channel state information (CSI), energy arrival, and electricity price. Finally, a comprehensive theoretical analysis is provided, and the proposed algorithm is verified through simulations under various system configurations. [ABSTRACT FROM AUTHOR]

The energy crisis and increasing concerns about the environment in modern society have fostered the development of the integrated energy system (IES) in smart grid. The energy hub (EH) is a minimum construction of the IES. The chief purpose of this paper is to propose a Lyapunov optimization-based energy management strategy for the energy hub with an energy router. First, this paper describes a configuration of the EH with an energy router and sets the basic operating principles of the energy router. Second, a method is proposed to analyze the energy balance equations of the EH. Furthermore, the problem of energy management is formulated considering the constraints of energy balance equations, energy storage, flexible loads, and other constraints of operation. Finally, energy storage and flexible electric loads are modeled as stochastic processes, and three virtual queues are constructed to relax these constraints. The Lyapunov drift-plus-penalty function is applied to formulate a relaxed form of the energy management problem. Case studies of a single EH and two-connected EHs are performed to test the effectiveness of the proposed strategy, and the results are compared with a greedy algorithm. [ABSTRACT FROM AUTHOR]

With the rapid development of intelligent transportation system (ITS), increasing the throughput of all kinds of users in the mobile communication network is a pressing problem. We consider there are two types of users, i.e. vehicular users (VUs) and static users (SUs), in a vehicular communication network. Since the channel characteristics of VUs and SUs are different, designing a multi-user multiplexing transmission strategy to provide an efficient and reliable transmission for two types of users is quite challenging. We propose a dynamic cross-layer transmission strategy with joint admission control, user association and resource allocation to increase the total throughput of VUs and SUs while satisfying the different quality of service (QoS) requirements. First, we formulate a throughput utility maximization problem subject to the QoS and maximum transmit power constraints. Second, with the help of Lyapunov optimization theory, the problem can be decomposed into three subproblems including traffic admission control, user association and resource allocation. Third, a novel robust resource allocation scheme with discrete-rate hierarchical modulation (HM) is designed for the reliable multiplexing transmission of two types of users. In addition, an optimal algorithm and a low complexity suboptimal algorithm are presented to solve the user association subproblem. For two types of users, comparing with uniform modulation schemes, the proposed transmission strategy with HM scheme can improve the throughput by more than 15%. [ABSTRACT FROM AUTHOR]

Modern video players employ complex algorithms to adapt the bitrate of the video that is shown to the user. Bitrate adaptation requires a tradeoff between reducing the probability that the video freezes (rebuffers) and enhancing the quality of the video. A bitrate that is too high leads to frequent rebuffering, while a bitrate that is too low leads to poor video quality. Video providers segment videos into short segments and encode each segment at multiple bitrates. The video player adaptively chooses the bitrate of each segment to download, possibly choosing different bitrates for successive segments. We formulate bitrate adaptation as a utility-maximization problem and devise an online control algorithm called BOLA that uses Lyapunov optimization to minimize rebuffering and maximize video quality. We prove that BOLA achieves a time-average utility that is within an additive term $O(1/V)$ of the optimal value, for a control parameter V related to the video buffer size. Further, unlike prior work, BOLA does not require prediction of available network bandwidth. We empirically validate BOLA in a simulated network environment using a collection of network traces. We show that BOLA achieves near-optimal utility and in many cases significantly higher utility than current state-of-the-art algorithms. Our work has immediate impact on real-world video players and for the evolving DASH standard for video transmission. We also implemented an updated version of BOLA that is now part of the standard reference player dash.js and is used in production by several video providers such as Akamai, BBC, CBS, and Orange. [ABSTRACT FROM AUTHOR]

To satisfy the rapid growth of data service demands on the limited spectrum resources, wireless network virtualization (WNV) has been proposed as a promising technology in the future wireless networks. Meanwhile, Device-to-Device (D2D) communication, as an underlay to cellular networks, is also a proposed solution to promote the spectrum efficiency. However, it is a challenge to design an appropriate virtual resource slicing strategy among different wireless service providers in the two-tier LTE-D2D networks. In this paper, considering dynamic traffic arrivals and time-varying channel conditions, the virtualized resource management in the multi-service coexisting LTE-D2D networks is formulated into a multi-objective stochastic optimization problem. Leveraging on weighted Tchebycheff approach and Lyapunov optimization technique, the multi-objective stochastic optimization problem can be transformed into a delay-aware optimization problem, which is a mixed-integer nonlinear programming (MINLP) problem and can be transformed into a convex one by a series of reformulation. A distributed algorithm based on the alternating direction method of multipliers (ADMM) is proposed to obtain a close-to-optimal solution with a lower computational complexity. Finally, the simulation results show that the proposed scheme can achieve a performance-delay tradeoff related to the control factor $V$ with at most ${0.2}{\%}$ performance loss compared with the optimal solution. [ABSTRACT FROM AUTHOR]

The performance of existing coded caching schemes is sensitive to the worst channel quality, a problem which is exacerbated when communicating over fading channels. In this paper, we address this limitation in the following manner: in short-term, we allow transmissions to subsets of users with good channel quality, avoiding users with fades, while in long-term we ensure fairness among users. Our online scheme combines (i) the classical decentralized coded caching scheme with (ii) joint scheduling and power control for the fading broadcast channel, as well as (iii) congestion control for ensuring the optimal long-term average performance. We prove that our online delivery scheme maximizes the alpha-fair utility among all schemes restricted to decentralized placement. By tuning the value of alpha, the proposed scheme can achieve different operating points on the average delivery rate region and tune performance according to an operator’s choice. We demonstrate via simulations that our scheme outperforms two baseline schemes: (a) standard coded caching with multicast transmission, limited by the worst channel user yet exploiting the global caching gain; (b) opportunistic scheduling with unicast transmissions exploiting the fading diversity but limited to local caching gain. [ABSTRACT FROM AUTHOR]

Ultra-low latency communication for mobile intelligent machines, such as autonomous vehicles and robots, is a central technology in Internet of Things (IoT) to achieve system reliability. Proactive network association and communication has been suggested to achieve ultra-low latency under the assistance of mobile edge computing. Highly dynamic and stochastic nature of IoT mobile machines suggests applying machine learning methodology to effectively enhance the proactive network association. In this paper, an online proactive network association is proposed for this distributed computing and networking scenario, in order to minimize the average task delay subject to time-average energy consumption. We first formulate an event-triggered delay model for mobility-aware anticipatory network association mechanism that takes future possible handovers into account. Based on the Markov decision processes (MDP) and Lyapunov optimization, a two-stage online decision algorithm for proactive network association is innovated for individual mobile machine without the statistical knowledge of random events that may lack of enough prior data. Theoretical analysis proves that the delay performance of proposed algorithm attains asymptotic optimality within the bounded deviation. Furthermore, an asynchronous online distributed association decision algorithm based on the nonlinear problem transformation is proposed to support more general scenarios of multi-machine event-triggered associations. Simulations verify the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

Network slicing, as a key 5G enabling technology, is promising to support with more flexibility, agility, and intelligence towards the provisioned services and infrastructure management. Fulfilling these tasks is challenging, as nowadays networks are increasingly heterogeneous, dynamic and large-dimensioned. This contradicts the dominant network slicing solutions that only customize immediate performance over one snapshot of the system in the literature. Instead, this paper first presents a two-stage slicing optimization model with time-averaged metrics to safeguard the network slicing in the dynamical networks, where prior environmental knowledge is absent but can be partially observed at runtime. Directly solving an off-line solution to this problem is intractable since the future system realizations are unknown before decisions. Therefore, we propose a learning augmented optimization approach with deep learning and Lyapunov stability theories. This enables the system to learn a safe slicing solution from both historical records and run-time observations. We prove that the proposed solution is always feasible and nearly optimal, up to a constant additive factor. Finally, we demonstrate up to $2.6\times $ improvement in the simulation when compared with three state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

As a novel sensing paradigm, crowdsensing has gained great attention due to large-scale user participation, low cost and wide data source, replacing traditional sensor based sensing in intelligent transportation, environmental monitoring, urban public management, etc. In crowdsensing, however, user privacy leakage is a common but fatal problem, where the participants in crowdsensing might not provide their data if their sensing data expose their personal private information or even lead to malicious attacks. Additionally, it is still challenging for the platform to consider the randomness of sensing task arrival, the dynamic participation of participants and the complexity of task allocation. To this end, an online control mechanism is presented to maximize the profit of platform while guaranteeing system stability and providing personalized location privacy protection. By exploiting Lyapunov optimization theory, we transform the optimization problem into a queue stability problem, decomposing it into three subproblems further. Through rigorous theoretical analysis, we prove that our time-averaged profit is approximately optimal. We also carry out extensive simulations to verify the superiority of our proposed mechanism. [ABSTRACT FROM AUTHOR]

We all have encountered unsatisfactory video viewing experience with mobile devices in hot spot areas where a crowd of people are demanding video streaming simultaneously. One possible solution to relieve the problem is to use the unmanned aerial vehicle (UAV) as a relay to improve the quality-of-experience (QoE). In this paper, we focus on the allocation of bandwidth and transmission power for multiuser video streaming in UAV relay networks. Our goal is to maximize the total long-term QoE of users, which are determined by the freezing time and the bit rate of video. We employ the Lyapunov optimization theory to transform the original problem with long-term average form into the drift-plus-penalty problem, and propose a decentralized allocation scheme using alternative ascending clock auction to derive the optimal solution. Simulations are conducted to demonstrate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

This article elucidates a real-time energy management strategy for a smart residential apartment building having nonidentical occupants at the dwelling units (DUs). The aim of the present article is to design a distributed energy management algorithm, which can optimize the real-time demand of the entire building against abruptly updated rooftop solar generation and real-time price (RTP) of energy. The proposed energy management strategy differentiates among the DUs by considering a new parameter named load criticality level, which is defined as the value imposed by the DU residents to their power consumption. The optimization portfolio is developed as a novel bilevel, stochastic, multiobjective optimization problem where the maximization of utility of the consumed power is considered simultaneously with the cost minimization. To this end, a virtual energy trading platform is designed in this article between central building management system and the DUs, where they interact with each other by following the directives of single-leader multifollower Stackelberg game. The solution strategy is proposed as a Lyapunov optimization, which needs only the current values of the uncertain parameters, such as load variation, renewable generation, and energy price, and do not require any knowledge about their probabilistic variation, to eliminate the complexities regarding time average stochastic equations. Strenuous simulation on real-time data of four DUs, it is proved that the proposed framework can track the abrupt change in RTP and solar generation efficiently. Comparing with two benchmark methods viz. centralized process and greedy algorithm, the superiority of the designed energy management portfolio is established. [ABSTRACT FROM AUTHOR]

The paper proposes a novel framework based on the contract theory and Lyapunov optimization for content sharing in a wireless content delivery network (CDN) with edge caching and device-to-device (D2D) communications. The network is partitioned into a set of clusters. In a cluster, users can share contents via D2D links in coordination with the cluster head. Upon receiving the content request from any user in its cluster, the cluster head either delivers the content itself or forwards the request to another node, i.e., a base station (BS) or another user in the cluster. The content access at the BS and in each cluster is modeled as a queuing system, where arrivals represent the content requests directed to respective nodes. The objective is to assign content delivery nodes to stabilize all queues while minimizing the time-averaged network cost given incomplete information about content sharing costs of the users and unknown distribution of the network state defined by users’ locations and their cached/requested content. The proposed framework allows the users to truthfully reveal their content sharing expenditures, minimize the time-averaged network cost and stabilize the queuing system representing the CDN. Based on this framework, a distributed content access and delivery algorithm where the node assignments are made by every cluster head independently is developed. It is shown that the algorithm converges to the optimal policy with the trade-off in total queue backlog and achieves a superior performance compared with some other D2D content sharing policies. [ABSTRACT FROM AUTHOR]

In this paper, we consider a mission-critical control system, where an unstable dynamic plant is monitored by multiple distributed IoT sensors over a wireless communication network with shared common spectrum. To reduce the complexity of Kalman filtering, we consider a constant gain filter at the remote controller. We propose a decentralized dynamic scheduling and information fusion of the IoT sensors to stabilize the unstable dynamic plant. The proposed scheme has a $\textit {state-driven}$ multiple access structure, where a large state estimation MSE (high $\textit {transmission urgency}$) and good wireless channel conditions (good $\textit {transmission opportunities}$) promote the active mode of the sensors. Using the Lyapunov techniques, we provide the closed-form sufficient condition for stability and closed-form characterizations on the trade-off between the state estimation MSE and average power consumption of the sensors. We also propose a design guideline for the constant filter gain via minimizing the state estimation MSE. The proposed scheme is also compared with various representative literature and we show that significant performance gains can be achieved. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is a promising computing model and has gained remarkable popularity, as it deploys the resources (e.g., computation, network, and storage) to the evolved NodeB (eNB) to provide enormous benefits such as low delay and energy consumption. More and more enterprises construct their edge computing platforms to store multimedia contents (i.e., video, audio, photos, and text data) for the user equipment (UE). However, both the eNB and UEs will experience serious security attacks when transmitting or receiving multimedia data via the wireless network. Existing MEC studies mainly focus on task offloading and performance improvement without considering the enterprise multimedia security problem. This paper proposes a security and performance-aware resource allocation (Spara) algorithm for enterprise multimedia in MEC environment. More specifically, we first build the architecture of enterprise multimedia security for sending the data requests to UEs, which mainly consists of computing and bandwidth resource allocation. Then, we formulate the stochastic data transmission problem to minimize the delay and energy consumption of UEs subject to the security guarantee. To achieve this goal, two queues, namely front-end queue and back-end queue, are used for each UE, and the Lyapunov optimization technique is applied to determine how to allocate the computing and bandwidth resources. Rigorous theoretical analysis shows that Spara algorithm meets the [O(1/V), O(V)] energy-delay tradeoff. Extensive simulation experiments validate this analysis result and the effectiveness of Spara algorithm. [ABSTRACT FROM AUTHOR]

The explosive growth of user demands for mobile data puts forward higher requirements for existing network architecture. In this case, many novel technologies, such as edge computing, ultra-dense network (UND), are recently proposed to alleviate the network burden of cellular macro base stations (MBSs). However, their performance is limited by dynamic user requests and uncertain user mobility since they highly rely on the fixed infrastructure. Along with the advent of autonomous driving era, autonomous vehicles possess strong computing and communication ability, which provide a new idea to resolve the limitation of infrastructure based network architecture. In this paper, we exploit the performance of a vehicular caching scheme where the role of moving vehicles is changed from service consumers in traditional networks to service providers and service consumers. As such, a prediction based vehicular caching scheme is proposed. Specifically, an optimization problem is firstly formulated by exploring the relationship of caching vehicles and mobile users, in order to optimize the network energy efficiency. The nonconvex optimization problem is solved by decomposing it into a nonlinear programming problem. By applying the Lyapunov method and autoregressive neural network (ANN), an online caching decision algorithm is finally proposed to make caching decisions. Extensive simulations are conducted to evaluate the caching scheme in different scenarios. Results show that the vehicular caching scheme can obviously improve network energy efficiency with different requests, but the increment is reduced under the scenario of intensive requests. The comparison between online and offline caching also shows the necessity of online caching decision making due to its benefit in resource utilization where system gain is increased from 8.4% to 59.24%. [ABSTRACT FROM AUTHOR]

The problem of adaptive multi-user scalable layered video transmission is considered in energy harvesting (EH) aided wireless communication systems. With the goal of improving the quality of video services while providing fairness amongst the users despite the random nature of both energy harvesting and the channel quality, we formulate our Scalable Video Coding (SVC) design as a Constrained Utility Function Maximization (CUFM) problem. The proportional fairness and playback smoothness of our design is guaranteed by maximizing the log-sum of the users’ video qualities, while satisfying the battery fullness constraint and video layer (quality) fluctuation constraint. By invoking the classical Lyapunov drift based optimization technique, we further decompose the CUFM problem into two parallel subproblems, i.e., a dynamic transmission power allocation problem and a dynamic layer selection problem. By solving these two subproblems, we derive a joint power allocation and video layer selection strategy for multi-user SVC video transmission. The theoretical performance bound of the proposed solution is also presented. Numerical simulations are conducted with real H.264 SVC video traces and the experimental results demonstrate the reduced playback interruption rate and layer switching rate compared to a heuristic algorithm ProNTO. The results also illustrate a tradeoff between the system's utility function and the playback smoothness experienced by the users. [ABSTRACT FROM AUTHOR]

Vehicle-to-everything (V2X) communication is a key enabler that connects vehicles to neighboring vehicles, infrastructure and pedestrians. In the past few years, multimedia services have seen an enormous growth and it is expected to increase as more devices will utilize infotainment services in the future i.e. vehicular devices. Therefore, it is important to focus on user centric measures i.e. quality-of-experience (QoE) such as video quality (resolution) and fluctuations therein. In this paper, a novel joint video quality selection and resource allocation technique is proposed for increasing the QoE of vehicular devices. The proposed approach exploits the queuing dynamics and channel states of vehicular devices, to maximize the QoE while ensuring seamless video playback at the end users with high probability. The network wide QoE maximization problem is decoupled into two subparts. First, a network slicing based clustering algorithm is applied to partition the vehicles into multiple logical networks. Secondly, vehicle scheduling and quality selection is formulated as a stochastic optimization problem which is solved using the Lyapunov drift plus penalty method. Numerical results show that the proposed algorithm ensures high video quality experience compared to the baseline. Simulation results also show that the proposed technique achieves low latency and high-reliability communication. [ABSTRACT FROM AUTHOR]

Immersive virtual reality (VR) applications require ultra-high data rate and low-latency for smooth operation. Hence in this paper, aiming to improve VR experience in multi-user VR wireless video streaming, a deep-learning aided scheme for maximizing the quality of the delivered video chunks with low-latency is proposed. Therein the correlations in the predicted field of view (FoV) and locations of viewers watching 360° HD VR videos are capitalized on to realize a proactive FoV-centric millimeter wave (mmWave) physical-layer multicast transmission. The problem is cast as a frame quality maximization problem subject to tight latency constraints and network stability. The problem is then decoupled into an HD frame request admission and scheduling subproblems and a matching theory game is formulated to solve the scheduling subproblem by associating requests from clusters of users to mmWave small cell base stations (SBSs) for their unicast/multicast transmission. Furthermore, for realistic modeling and simulation purposes, a real VR head-tracking dataset and a deep recurrent neural network (DRNN) based on gated recurrent units (GRUs) are leveraged. Extensive simulation results show how the content-reuse for clusters of users with highly overlapping FoVs brought in by multicasting reduces the VR frame delay in 12%. This reduction is further boosted by proactiveness that cuts by half the average delays of both reactive unicast and multicast baselines while preserving HD delivery rates above 98%. Finally, enforcing tight latency bounds shortens the delay-tail as evinced by 13% lower delays in the 99th percentile. [ABSTRACT FROM AUTHOR]

Utilizing the intelligence at the network edge, edge computing paradigm emerges to provide time-sensitive computing services for Internet of Things. In this paper, we investigate sustainable computation offloading in an edge-computing system that consists of energy harvesting-enabled mobile devices (MDs) and a dispatcher. The dispatcher collects computation tasks generated by IoT devices with limited computation power, and offloads them to resourceful MDs in exchange for rewards. We propose an online Rewards-optimal Auction (RoA) to optimize the long-term sum-of-rewards for processing offloaded tasks, meanwhile adapting to the highly dynamic energy harvesting (EH) process and computation task arrivals. RoA is designed based on Lyapunov optimization and Vickrey-Clarke-Groves auction, the operation of which does not require a prior knowledge of the energy harvesting, task arrivals, or wireless channel statistics. Our analytical results confirm the optimality of tasks assignment. Furthermore, simulation results validate the analytical analysis, and verify the efficacy of the proposed RoA. [ABSTRACT FROM AUTHOR]

Mobile video traffic has experienced exponential growth in recent years due to increasing prevalence of mobile devices and continuous advancement of wireless communications. However, network fluctuations often occur when transmitting video data, which greatly impact the quality of experience (QoE) of mobile users. Buffering technique has been widely adopted to mitigate the effect of network fluctuations on QoE for mobile users by prefetching certain video data to mobile devices in advance. However, mobile users often abort video watching before the videos are fully watched and such early departure behavior can result in considerable wastage of buffered video data. In this paper, we study buffer-aware resource allocation for multiuser mobile video streaming system by jointly considering video users early departure behavior and wireless channel fluctuation property. We formulate a stochastic optimization problem in which the objective is to reduce both the amount of wasted buffered data and the video re-buffering time. The problem is then transformed into a series of deterministic per-slot problems by using Lyapunov optimization theory. We propose an online buffer-aware resource allocation algorithm to solve the per-slot optimization problems. After proving the convexity of the per-slot optimization problem, we further design a fast-convergent iterative algorithm based on Alternating Direction Method of Multipliers (ADMM) to perform each of the per-slot resource allocations. Simulation results show that our proposed algorithm has very low running time, near optimal performance, and is able to achieve a good trade-off between QoE improvement and data wastage reduction. [ABSTRACT FROM AUTHOR]

In this paper, we explore the effect of dual connectivity (DC) on delay-aware resource control with statistical Quality of Service (QoS) provisioning in heterogeneous aeronautical networks. Firstly, the Software Defined Networking (SDN) controller is introduced to support the DC for aircraft by centralized management of different base stations. In light of the delay analysis of user traffic, the delay-aware resource control of SDN controller is established as a multi-objective stochastic optimization problem to guarantee the statistical QoS provisioning. Secondly, the multi-objective stochastic optimization problem is transformed into a weighted single-objective optimization and relaxed by the Lagrangian relaxation. Then, the Lyapunov optimization theory is utilized to solve the relaxed stochastic optimization for optimal resource control. Finally, the performance of resource control under DC mode is verified in two aeronautical scenarios. [ABSTRACT FROM AUTHOR]

The limited information processing capability and battery life of mobile devices is becoming a bottleneck in delivering more advanced and high-quality services to the customers. To address this problem, the recently advocated mobile-edge computing (MEC) architecture is promising, where the essential idea is to bring the computation resource to the network edge and allow users to wirelessly offload resource demanding computation tasks to the nearby MEC servers for potentially faster execution and lower battery consumption. Nonetheless, the existing understanding of the privacy aspect of MEC is still far from complete. In this work, a user presence inference attack that invades user privacy by exploiting the feature tasks offloaded from users is identified for MEC. Existing privacy-preserving techniques developed for other applications cannot be applied to defeat this attack in MEC, as they may disrupt the optimal task offloading scheduling and cause severe degradation in user experience. With this consideration, a novel privacy-preserving and cost-efficient (PEACE) task offloading scheme that can preserve user privacy while still ensure the best possible user experience is developed in this work based on the generic Lyapunov optimization framework. The effectiveness of the proposed scheme is validated through both analysis and simulations. [ABSTRACT FROM AUTHOR]

Driven by the explosion transmission and computation requirement in 5G vehicular networks, mobile edge computing (MEC) attracts more attention than centralized cloud computing. The advantage of MEC is to provide a large amount of computation and storage resources to the edge of networks so as to offload computation-intensive and delay-sensitive applications from vehicle terminals. However, according to the mobility of vehicle terminals and the time varying traffic load, the optimal task offloading decisions is crucial. In this paper, we consider the uplink transmission from vehicles to road side units in the vehicular network. A dynamic task offloading decision for flexible subtasks is proposed to minimize the utility, which includes energy consumption and packet drop rate. Furthermore, a computation resource allocation scheme is introduced to allocate the computation resources of MEC server due to the differences in the computation intensity and the transmission queue of each vehicle. Consequently, a Lyapunov-based dynamic offloading decision algorithm is proposed, which combines the dynamic task offloading decision and computation resource allocation, to minimize the utility function while ensuring the stability of the queue. Finally, simulation results demonstrate that the proposed algorithm could achieve a significant improvement in the utility of vehicular networks compared with comparison algorithms. [ABSTRACT FROM AUTHOR]

A fundamental problem in fog computing networks is how to schedule the deadline-aware offloaded tasks that directly arrive from the end-users and via other fog nodes. The computational resource allocation becomes more challenging when the tasks demand different delay-deadlines. In this letter, we aim to propose a scheduling strategy to maximize the number of the completed tasks within their respective deadlines while making the network strongly stable. We exploit Lyapunov drift-plus-penalty function on the queue length to schedule the tasks in the queues. Subsequently, the scheduling policy decides the amount of task to be offloaded to the underloaded fog nodes to fully utilize the computational resources offered by all fog nodes in the network. Our simulation results reveal that the proposed strategy outperforms the baseline schemes, especially when those tasks have distinct delay-deadlines. [ABSTRACT FROM AUTHOR]

Crowdsensing paradigm facilitates a wide range of data collection, where great efforts have been made to address its fundamental issues of matching workers to their assigned tasks and processing the collected data. In this paper, we reexamine these issues by considering the spatio-temporal worker mobility and task arrivals, which more fit the actual situation. Specifically, we study the location-aware and location diversity based dynamic crowdsensing system, where workers move over time and tasks arrive stochastically. We first exploit offline crowdsensing by proposing a combinatorial algorithm, for efficiently distributing tasks to workers. After that, we mainly study the online crowdsensing, and further consider an indispensable aspect of worker's fair allocation. Apart from the stochastic characteristics and discontinuous coverage, the non-linear expectation is incurred as a new challenge concerning fairness issue. Based on Lyapunov optimization with perturbation parameters, we propose online control policy to overcome those challenges. Hereby, we can maintain system stability and achieve a time average sensing utility arbitrarily close to the optimum. Finally, we propose an optimization framework to aggregate the sensing data which can estimate worker expertise and task truth simultaneously. Performance evaluations on real and synthetic data set validate the proposed algorithm, where 80 percent gain of fairness is achieved at the expense of 12 percent loss of sensing value on average. [ABSTRACT FROM AUTHOR]

The utility optimization problem of radio access network-slicing aided mobile systems is formulated considering both throughput and delay demands from a mobile network operator's perspective, whilst relying on stochastic geometry and Lyapunov optimization. Then a joint virtual resource optimization algorithm is proposed to dynamically allocate both virtual spectral and the power resources. Our numerical results support both the high-throughput slice and the low-delay slice and quantify the associated throughput vs. delay trade-off. Moreover, we compare the proposed algorithm with the benchmark one, which ensures better utility. [ABSTRACT FROM AUTHOR]