Stochastic Network Optimization Research Articles

6G Goal-Oriented Communications: How to Coexist with Legacy Systems?

6G will connect heterogeneous intelligent agents to make them natively operate complex cooperative tasks. When connecting intelligence, two main research questions arise to identify how artificial intelligence and machine learning models behave depending on (i) their input data quality, affected by errors induced by interference and additive noise during wireless communication; (ii) their contextual effectiveness and resilience to interpret and exploit the meaning behind the data. Both questions are within the realm of semantic and goal-oriented communications. With this paper, we investigate how to effectively share communication spectrum resources between a legacy communication system (i.e., data-oriented) and a new goal-oriented edge intelligence one. Specifically, we address the scenario of an enhanced Mobile Broadband (eMBB) service, i.e., a user uploading a video stream to a radio access point, interfering with an edge inference system, in which a user uploads images to a Mobile Edge Host that runs a classification task. Our objective is to achieve, through cooperation, the highest eMBB service data rate, subject to a targeted goal effectiveness of the edge inference service, namely the probability of confident inference on time. We first formalize a general definition of a goal in the context of wireless communications. This includes the goal effectiveness, (i.e., the goal achievability rate, or the probability of achieving the goal), as well as goal cost (i.e., the network resource consumption needed to achieve the goal with target effectiveness). We argue and show, through numerical evaluations, that communication reliability and goal effectiveness are not straightforwardly linked. Then, after a performance evaluation aiming to clarify the difference between communication performance and goal effectiveness, a long-term optimization problem is formulated and solved via Lyapunov stochastic network optimization tools to guarantee the desired target performance. Finally, our numerical results assess the advantages of the proposed optimization and the superiority of the goal-oriented strategy against baseline 5G-compliant legacy approaches, under both stationary and non-stationary communication (and computation) environments.

Correction to “A Queue-Stabilizing Framework for Networked Multi-Robot Exploration” [Apr 21 2091-2098

Section III-F in [1] made formal claims about the limiting behavior of the controller (as time and space grow to infinity) whose proof relied on a stochastic network optimization theorem which may not apply. Specifically, we cannot make the claim that the optimal value of $\overline{Y}$ achieved by a one-step local controller and the constraints on time-averages can both be achieved arbitrarily closely by the controller given by (16). This is because to prove this claim and demonstrate that this controller stabilizes all queues as $t \rightarrow \infty$, the theorem (see drift-plus-penalty method [2]) requires that at each time slot there exist a decision that causes the size of the virtual queues to decrease, or remain constant, in expectation. This requirement is not satisfied as the robot may reach a state where all possible actions increase the queues sizes. Note however that the claim that our controller will achieve a bounded queuing delay still holds in the more realistic context of finite time and/or space, and the performance gains of the proposed controller remain valid in practice.

Two-Timescale Mobile User Association and Hybrid Generator On/Off Control for Green Cellular Networks With Energy Storage

In this paper, we study the energy-efficient (EE) cost minimization problem of green cellular networks, where base stations (BSs) are jointly powered by energy storages, i.e., batteries, external power grid with random electricity price, and local hybrid generators considering independent on/off strategies in long period and turn-on/off cost. The power consumption of each BS is correlated with the number of associated mobile users (MUs) and their random mobility. We formulate a stochastic optimization problem and decompose it into two subproblems with limited optimality gap. We solve the first MU association subproblem by minimizing the price-weighted power consumption of BSs under low-complexity matching algorithm. For the second energy control subproblem, we propose a novel two-timescale approach based on stochastic network optimization to tackle with the time-coupling turn-on/off cost and battery dynamic constraints. We derive the long-term time-averaged turn-on/off cost minimized by our approach including its upper bound and approximate expression. Lastly, extensive simulation results verify our theoretical performance analyses and show that our proposed algorithm can outperform the other three online benchmark algorithms.

Deep neural network based UAV deployment and dynamic power control for 6G-Envisioned intelligent warehouse logistics system

The intelligent warehouse logistics system (IWLS) is an essential component in the emerging industry 5.0. To well assist the IWLS, advanced network architecture and control policies with the adaptive capability to the variation of traffic should be specially designed. In this paper, we consider an air-and-ground cooperative wireless network that enables dynamic coverage to support the flexible scheduling of the automatic guided vehicles (AGVs) in the IWLS. Jointly considering the dynamic deployment of unmanned aerial vehicles (UAVs) as well as the adaptive power control of AGVs, we formulate a two time-scale network control problem to minimize the transmission power consumption of all AGVs under their individual rate requirement. On large time scales, we first propose a particle swarm optimization based algorithm (PSOA) to obtain the deployment position of the ABS. Then, using the results of the PSOA as training data, we design a deep neural network (DNN) framework aimed at reducing the computational time of the PSOA. On small time scales, we devise an online power control algorithm (OPCA) by using some of stochastic network optimization methods. With current channel conditions, the OPCA can generate the real-time power control policy and ensure the long-term rate requirement. Numerical simulations indicate that the DNN framework enhances the coverage performance of the network only consuming a few milliseconds of computation time. Incorporated with the OPCA, the total transmission power of the AGVs is significantly reduced.

Dynamic power control for time-critical networking with heterogeneous traffic

Future wireless networks will be characterized by heterogeneous traffic requirements. Examples can be low-latency or minimum-througput requirements. Therefore, the network has to adjust to different needs. Usually, users with low-latency requirements have to deliver their demand within a specific time frame, i.e., before a deadline, and they coexist with throughput oriented users. In addition, mobile devices have a limited-power budget and therefore, a power-efficient scheduling scheme is required by the network. In this work, we cast a stochastic network optimization problem for minimizing the packet drop rate while guaranteeing a minimum throughput and taking into account the limited-power capabilities of the users. We apply tools from Lyapunov optimization theory in order to provide an algorithm, named Dynamic Power Control (DPC) algorithm, that solves the formulated problem in real time. It is proved that the DPC algorithm gives a solution arbitrarily close to the optimal one. Simulation results show that our algorithm outperforms the baseline Largest-Debt-First (LDF) algorithm for short deadlines and multiple users.

Joint Switch–Controller Association and Control Devolution for SDN Systems: An Integrated Online Perspective of Control and Learning

In software-defined networking (SDN) systems, it is a common practice to adopt a multi-controller design and control devolution techniques to improve the performance of the control plane. However, in such systems the decision-making for joint switch-controller association and control devolution often involves various uncertainties, e.g., the temporal variations of controller accessibility, and computation and communication costs of switches. In practice, statistics of such uncertainties are unattainable and need to be learned in an online fashion, calling for an integrated design of learning and control. In this article, we formulate a stochastic network optimization problem that aims to minimize time-average system costs and ensure queue stability. By transforming the problem into a combinatorial multi-armed bandit problem with long-term stability constraints, we adopt bandit learning methods and optimal control techniques to handle the exploration-exploitation tradeoff and long-term stability constraints, respectively. Through an integrated design of online learning and online control, we propose an effective Learning-Aided Switch-Controller Association and Control Devolution ( LASAC ) scheme. Our theoretical analysis and simulation results show that LASAC achieves a tunable tradeoff between queue stability and system cost reduction with a sublinear time-averaged regret bound over a finite time horizon.

Predictive Switch-Controller Association and Control Devolution for SDN Systems

For software-defined networking (SDN) systems, to enhance the scalability and reliability of control plane, existing solutions adopt either multi-controller design with static switch-controller association, or static control devolution by delegating certain request processing back to switches. Such solutions can fall short in face of temporal variations of request traffics, incurring considerable local computation costs on switches and their communication costs to controllers. So far, it still remains an open problem to develop a joint online scheme that conducts dynamic switch-controller association and dynamic control devolution. In addition, the fundamental benefits of predictive scheduling to SDN systems still remain unexplored. In this paper, we identify the non-trivial trade-off in such a joint design and formulate a stochastic network optimization problem which aims to minimize time-averaged total system costs and ensure long-term queue stability. By exploiting the unique problem structure, we devise a predictive online switch-controller association and control devolution (POSCAD) scheme, which solves the problem through a series of online distributed decision making. Theoretical analysis shows that without prediction, POSCAD can achieve near-optimal total system costs a tunable trade-off for queue stability. With prediction, POSCAD can achieve even better performance with shorter latencies. We conduct extensive simulations to evaluate POSCAD. Notably, with mild-value of future information, POSCAD incurs a significant reduction in request latencies, even when faced with prediction errors.

Online User-AP Association With Predictive Scheduling in Wireless Caching Networks

For wireless caching networks, the scheme design for content delivery is non-trivial in the face of the following tradeoff. On one hand, to optimize overall throughput, users can associate their nearby APs with great channel capacities; however, this may lead to unstable queue backlogs on APs and prolong request delays. On the other hand, to ensure queue stability, some users may have to associate APs with inferior channel states, which would incur throughput loss. Moreover, for such systems, how to conduct predictive scheduling to reduce delays and the fundamental limits of its benefits remain unexplored. In this paper, we formulate the problem of online user-AP association and resource allocation for content delivery with predictive scheduling under a fixed content placement as a stochastic network optimization problem. By exploiting its unique structure, we transform the problem into a series of modular maximization sub-problems with matroid constraints. Then we devise PUARA, a Predictive User-AP Association and Resource Allocation scheme which achieves a provably near-optimal throughput with queue stability. Our theoretical analysis and simulation results show that PUARA can not only perform a tunable control between throughput maximization and queue stability but also incur a notable delay reduction with predicted information.

POTUS: Predictive Online Tuple Scheduling for Data Stream Processing Systems

Most online service providers deploy their own data stream processing systems in the cloud to conduct large-scale and real-time data analytics. However, such systems, e.g., Apache Heron, often adopt naive scheduling schemes to distribute data streams (in the units of tuples) among processing instances, which may result in workload imbalance and system disruption. Hence, there still exists a mismatch between the temporal variations of data streams and such inflexible scheduling scheme designs. Besides, the fundamental limits of benefits of predictive scheduling to data stream processing systems remain unexplored. In this article, we focus on the problem of tuple scheduling with predictive service in Apache Heron. With a careful choice in the granularity of system modeling and decision making, we formulate the problem as a stochastic network optimization problem and propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">POTUS</i> , an online predictive scheduling scheme that aims to minimize the response time of data stream processing by steering data streams in a distributed fashion. Theoretical analysis and simulation results show that POTUS achieves an ultra-low response time with a stability guarantee. Moreover, POTUS only requires mild-value of future information to effectively reduce the response time, even with mis-prediction.

Dynamic Scheduling Algorithm for Delay-Sensitive Vehicular Safety Applications in Cellular Network

The vehicular safety applications disseminate the burst messages during an emergency scenario, but effort to reduce delay of communication are hampered by wireless access technology. As conventional VANET (Vehicular ad-hoc network) connected intermittently, the LTE-based framework has been established for the vehicular communication environment. However, resource allocation which affected by many factors, such as power, PRB (physical resource block), channel quality, are challenging to guarantee the safety services QoS in LTE downlink for OFDM. In order to solve the problem of safety message dissemination in LTE vehicular network, we proposed a delay-aware control policy by leveraging cross-layer approach to maximize the system throughput. First, we model the resource allocation problem using the queuing theory based on the MISO. Second, the method casts the problem of throughput and latency for dynamic communication system into a stochastic network optimization problem, and then makes tradeoffs between them by Lyapunov optimization technique. Finally, we use the improved the branch and bound algorithm to search optimal solution in system capacity region for these decomposed subproblems. The simulation results show that our algorithm can guarantee the delay while maximum system throughput

PORA: Predictive Offloading and Resource Allocation in Dynamic Fog Computing Systems

In multi-tiered fog computing systems, to accelerate the processing of computation-intensive tasks for real-time IoT applications, resource-limited IoT devices can offload part of their workloads to nearby fog nodes, whereafter such workloads may be offloaded to upper-tier fog nodes with greater computation capacities. Such hierarchical offloading, though promising to shorten processing latencies, may also induce excessive power consumptions and latencies for wireless transmissions. With the temporal variation of various system dynamics, such a trade-off makes it rather challenging to conduct effective and online offloading decision making. Meanwhile, the fundamental benefits of predictive offloading to fog computing systems still remain unexplored. In this paper, we focus on the problem of dynamic offloading and resource allocation with traffic prediction in multi-tiered fog computing systems. By formulating the problem as a stochastic network optimization problem, we aim to minimize the time-average power consumptions with stability guarantee for all queues in the system. We exploit unique problem structures and propose PORA, an efficient and distributed predictive offloading and resource allocation scheme for multi-tiered fog computing systems. Our theoretical analysis and simulation results show that PORA incurs near-optimal power consumptions with queue stability guarantee. Furthermore, PORA requires only mild-value of predictive information to achieve a notable latency reduction, even with prediction errors.

Stochastic Profit Maximization of Service Provider in Millimeter-Wave High-Speed Railway Networks

We study the problem of profit maximization for service providers using a newly emerged business model in millimeter-wave (mmWave) high-speed railway (HSR) networks. In such business model, a service provider purchases data transmission resource from a network operator with a low price. Then, mobile users only need to pay for requested services provided by the service provider. To optimize the profit of service provider, the profit analysis and optimization framework tailored toward the new business model and mmWave HSR networks is developed. This framework consists of three main components including admission control, transmission control, and resource purchasing control. To obtain optimal policies for these components, a stochastic profit optimization problem is formulated while taking into account differentiated service quality requirements, wireless transmission capacity, and network system stability. Then, the formulated problem is solved by using stochastic network optimization theory and stochastic profit maximization mechanism (SPMM) is established. An extensive performance evaluation of SPMM is conducted in order to demonstrate its superiority.

Learning-Aided Stochastic Network Optimization With State Prediction

We investigate the problem of stochastic network optimization in the presence of state prediction and non-stationarity. Based on a novel state prediction model featured with a distribution-accuracy curve, we develop the predictive learning-aided control ( PLC ) algorithm, which jointly utilizes historic and predicted network state information for decision making. PLC is an online algorithm that consists of three key components, namely, sequential distribution estimation and change detection, dual learning, and online queue-based control. We show that for stationary networks, PLC achieves a near-optimal utility-delay tradeoff. For non-stationary networks, PLC obtains an utility-backlog tradeoff for distributions that last longer than a time proportional to the square of the prediction error, which is smaller than that needed by backpressure (BP) for achieving the same utility performance. Moreover, PLC detects distribution change $O(w)$ slots faster with high probability ( $w$ is the prediction size) and achieves a convergence time faster than that under BP. Our results demonstrate that state prediction helps: 1) achieve faster detection and convergence and 2) obtain better utility-delay tradeoffs. They also quantify the benefits of prediction in four important performance metrics, i.e., utility (efficiency), delay (quality-of-service), detection (robustness), and convergence (adaptability) and provide new insight for joint prediction, learning, and optimization in stochastic networks

Asynchronous Incremental Stochastic Dual Descent Algorithm for Network Resource Allocation

Stochastic network optimization problems entail finding resource allocation policies that are optimum on an average but must be designed in an online fashion. Such problems are ubiquitous in communication networks, where resources such as energy and bandwidth are divided among nodes to satisfy certain long-term objectives. This paper proposes an asynchronous incremental dual decent resource allocation algorithm that utilizes delayed stochastic {gradients} for carrying out its updates. The proposed algorithm is well-suited to heterogeneous networks as it allows the computationally-challenged or energy-starved nodes to, at times, postpone the updates. The asymptotic analysis of the proposed algorithm is carried out, establishing dual convergence under both, constant and diminishing step sizes. It is also shown that with constant step size, the proposed resource allocation policy is asymptotically near-optimal. An application involving multi-cell coordinated beamforming is detailed, demonstrating the usefulness of the proposed algorithm.

New advances and applications in deterministic and stochastic network optimization

New advances and applications in deterministic and stochastic network optimization

Joint Admission Control and Resource Allocation in Edge Computing for Internet of Things

The IoT is a novel platform for making objects more intelligent by connecting to the Internet. However, mass connections, big data processing, and huge power consumption restrict the development of IoT. In order to address these challenges, this article proposes a novel ECIoT architecture. To further enhance the system performance, radio resource and computational resource management in ECIoT are also investigated. According to the characteristics of the ECIoT, we mainly focus on admission control, computational resource allocation, and power control. To improve the performance of ECIoT, cross-layer dynamic stochastic network optimization is studied to maximize the system utility, based on the Lyapunov stochastic optimization approach. Evaluation results are provided which demonstrate that the proposed resource allocation scheme can improve throughput, reduce end-to-end delay, and also achieve an average throughput and delay trade-off. Finally, the future research topics of resource management in ECIoT are discussed.

Energy-Efficient Resource Allocation in Buffer-Aided Wireless Relay Networks

In this paper, we study energy-efficient resource allocation in the downlink of buffer-aided wireless relay networks. We aim at maximizing the system average energy efficiency while maintaining the queue stability at both the base station (BS) and relays. We formulate the resource allocation design as a novel stochastic network optimization problem and based on the well-known Lyapunov drift-plus-penalty policy and the system constraints, we transform it to an instantaneous non-convex optimization problem to be solved in each time slot. We analyze the instantaneous utility function and propose a novel algorithm to find its optimum point. Based on that, we present an effective distributed strategy to get the globally optimal solution for channel and power allocation. Furthermore, we show that the proposed algorithm can be used as a building block for energy-efficient resource allocation in conventional relay networks, where the relays do not have buffering capability, but the BS queues need to be stabilized. Using extensive simulations, we show that the proposed algorithm is able to provide higher energy efficiency compared with the existing algorithms, while keeping the system queues stable.

Minimizing Secrecy Outage Probability in Multiuser Wireless Systems With Stochastic Traffic

We first extend the definition of the secrecy outage probability to wireless systems with adaptive transmission rates and secrecy rates. Then, we consider a scheduling problem in the aforementioned system, jointly considering the reliability, security, and stability, where the scheduler tries to allocate wireless resources to the legitimate users, stabilize the system, and minimize the secrecy outage probability. A stochastic network optimization framework is used to decompose the problem, and an online algorithm is proposed. We further consider a related problem, discuss the optimal solution, and show that the proposed algorithm cannot lead to optimal solution in some scenarios. By comparing the offline algorithm with our first algorithm, we further propose a second refined online algorithm, which is an optimal one. Extensive simulations are conducted to show the impact of the information arrival rate and the channel conditions on the system secrecy outage probability. These observations provide important insights and guidelines for the design and resource management of future wireless networks using secure communication technologies.

Stochastic Averaging for Constrained Optimization With Application to Online Resource Allocation

Existing approaches to resource allocation for nowadays stochastic networks are challenged to meet fast convergence and tolerable delay requirements. The present paper leverages online learning advances to facilitate stochastic resource allocation tasks. By recognizing the central role of Lagrange multipliers, the underlying constrained optimization problem is formulated as a machine learning task involving both training and operational modes, with the goal of learning the sought multipliers in a fast and efficient manner. To this end, an order-optimal offline learning approach is developed first for batch training, and it is then generalized to the online setting with a procedure termed learn-and-adapt. The novel resource allocation protocol permeates benefits of stochastic approximation and statistical learning to obtain low-complexity online updates with learning errors close to the statistical accuracy limits, while still preserving adaptation performance, which in the stochastic network optimization context guarantees queue stability. Analysis and simulated tests demonstrate that the proposed data-driven approach improves the delay and convergence performance of existing resource allocation schemes.

Optimize the Server Provisioning and Request Dispatching in Distributed Memory Cache Services

The distributed cache system contains a group of servers caching different contents based on consistent hashing. The dynamic provisioning of servers helps to improve the system efficiency, which leads to a reduction of energy cost. We first measure the cache hit rate, request batching effect and cache warm-up time of the system through experiments, considering that they can affect the system performance and efficiency. Then we formulate a stochastic network optimization problem, which aims at achieving objectives on the queue stability, energy cost and cache hit rate simultaneously, through the dynamic control of server activeness and request dispatching. The problem is transformed into a minimization problem in each time slot, which is further addressed through the proposed efficient online algorithm based on dynamic programming. Moreover, we improve the scheme with several practical considerations in the scheme implementation. Finally, the proposed algorithm and the improvements are evaluated through extensive experiments.