Queue Backlog Research Articles

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.

Wireless Powered Mobile Edge Computing: Dynamic Resource Allocation and Throughput Maximization

Wireless powered mobile edge computing (WP-MEC) has been widely studied as a promising technology to liberate wireless terminals from the computation-intensive and energy-consuming tasks. This article considers a WP-MEC system consisting of multiple base stations (BSs) and mobile devices (MDs), where the MDs offload tasks to the BSs for computational resources and the BSs charge the MDs using wireless power transfer (WPT). In practice, each BS and MD are equipped with a task buffer with limited size and a battery with limited capacity. First, we develop a time slotted WP-MEC system with task and energy queuing dynamics to study long-term system performance under time-varying fading channels and stochastic task and energy arrivals. Second, we propose a dynamic throughput maximum (DTM) algorithm based on perturbed Lyapunov optimization to maximize the system throughput under task and energy queue stability constraints, by optimizing the allocation of communication, computation, and energy resources. For the DTM algorithm, we characterize a throughput-backlog trade-off of [ <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(1/V)$</tex-math></inline-formula> , <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(V)$</tex-math></inline-formula> ] to indicate that the system throughput goes up as the queue backlog increases, where <inline-formula><tex-math notation="LaTeX">$V$</tex-math></inline-formula> is a control parameter between the system throughput and the queue backlog. However, we find that, as <inline-formula><tex-math notation="LaTeX">$V$</tex-math></inline-formula> goes large, the system throughput can be pushed arbitrarily close to the optimum at the cost of linearly increasing queue backlog (i.e., <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(V)$</tex-math></inline-formula> ). To reduce the cost, we further develop an improved dynamic throughput maximum (IDTM) algorithm, and verify that the IDTM algorithm can achieve a trade-off of [ <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(1/V)$</tex-math></inline-formula> , <inline-formula><tex-math notation="LaTeX">$\mathcal {O}((\log (V))^2)$</tex-math></inline-formula> ] between the system throughput and the queue backlog. The simulation results demonstrate that IDTM retains close system throughput to DTM with only <inline-formula><tex-math notation="LaTeX">$\mathcal {O}((\log (V))^2)$</tex-math></inline-formula> queue backlog.

Congestion Control and Traffic Differentiation for Heterogeneous 6TiSCH Networks in IIoT.

The Routing Protocol for Low power and lossy networks (RPL) has been introduced as the de-facto routing protocol for the Industrial Internet of Things (IIoT). In heavy load scenarios, particular parent nodes are likely prone to congestion, which in turn degrades the network performance, in terms of packet delivery and delay. Moreover, there is no explicit strategy in RPL to prioritize the transmission of different traffic types in heterogeneous 6TiSCH networks, each according to its criticality. In this paper, we address the aforementioned issues by introducing a congestion control and service differentiation strategies to support heterogeneous 6TiSCH networks in IIoT applications. First, we introduce a congestion control mechanism to achieve load balancing under heavy traffic scenarios. The congestion is detected through monitoring and sharing the status of the queue backlog among neighbor nodes. We define a new routing metric that considers the queue occupancy when selecting the new parent node in congestion situations. In addition, we design a multi-queue model to provide prioritized data transmission for critical data over the non-critical ones. Each traffic type is placed in a separate queue and scheduled for transmission based on the assigned queue priority, where critical data are always transmitted first. The performance of the proposed work is evaluated through extensive simulations and compared with existing work to demonstrate its effectiveness. The results show that our proposal achieves improved packet delivery and low queue losses under heavy load scenarios, as well as improved delay performance of critical traffic.

Combining Contract Theory and Lyapunov Optimization for Content Sharing With Edge Caching and Device-to-Device Communications

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.

Cyber Threat Impact Analysis to Air Traffic Flows Through Dynamic Queue Networks

Air traffic control (ATC) increasingly depends on information and communication technology to manage traffic flow through highly congested and increasingly interdependent airspace regions. Although these systems are critical to ensuring the efficiency and safety of our airspace, they are also increasingly vulnerable to cyber threats that could potentially lead to reduction in capacity and/or reorganization of traffic flows. In this article, we model various cyber threats to ATC systems and analyze how these attacks could impact the flow of aircraft through the airspace. To perform this analysis, we consider a model for wide-area air traffic based on a dynamic queuing network model. Then we introduce three different attacks (Route Denial of Service, Route Selection Tampering, and Sector Denial of Service) to the ATC system and explore how these attacks manipulate the sector flows by evaluating the queue backlogs for each sector’s outflows. Furthermore, we explore graph-level vulnerability metrics to identify the sectors that are most vulnerable to various flow manipulations and compare them to case-study simulations of the various attacks. The results suggest that Route Denial of Service attacks have a significant impact on the target sector and lead to the largest degradation to the overall air traffic flows. Furthermore, the impact of Sector Denial of Service attack impacts are primarily confined to the target sector, whereas Route Selection Tampering impacts are mostly confined to certain aircraft.

Joint utility optimization for wireless sensor networks with energy harvesting and cooperation

In wireless sensor networks (WSNs), the limited battery capacity restricts the lifetime of the sensor nodes and thus degrades the system performance. The energy harvesting and cooperation techniques are promising solutions to prolonging the battery life, by collecting energy from ambient environment and exchanging energy among sensor nodes. This paper studies the joint utility maximization problem for WSNs in consideration of energy harvesting and cooperation. We first derive an upper bound on the Lyapunov drift for the network stability, and then formulate the optimization as a stochastic optimization problem. Furthermore, we propose an energy harvesting and energy transfer, data transmission, power control, routing and scheduling (EDPR) online algorithm by combining Lyapunov optimization technique with drift-plus-penalty method and perturbation technique. It contributes to optimal utility in a distributed manner along with a balanced trade-off between network utility and queue backlog, with no need for any statistical information about dynamic systems and no concern of curse of dimensionality under large queue backlog. Simulation results also show the practicality of the proposed algorithm in real implementation since data transmission has a linear relationship with battery life.

Channel congestion control model based on improved asynchronous back-pressure routing algorithm in wireless distributed networks

Due to the continuous increase of the network bandwidth, the traditional coarse-grained congestion control mechanisms and flow scheduling schemes are difficult to provide satisfying performance. Therefore, a distributed algorithm based on an improved asynchronous back-pressure routing algorithm is proposed for joint channel congestion control, routing and power allocation in this paper. Considering the application scenario of wireless distributed networks with node power constraints and independent buffers for traffic flow, this paper studies joint congestion control, routing and power allocation when channel state information is known. In order to improve the defect of Newton method, an algorithm with second-order convergence performance is designed and matrix decomposition method is used to realize the distributed updating of traffic source rate, link rate and link power in network nodes and links so as to maximize network utility. Compared with the known existing algorithms, our proposed algorithm has faster convergence speed, which improves the network utility and energy utility by optimizing power allocation, and it can control the queue backlog at a very low level.

Queue-Aware Beam Assignment and Rate Control for Time-Varying mm-Wave-Based Full-Duplex Small Cell Networks

This letter focuses on the resource management problem for uplink (UL) of millimeter wave based full-duplex small cell networks with consideration of user equipments’ (UEs’) queue backlog and time-varying channels. The original problem aims to maximize UL UEs’ long-term quality of experience (QoE) under queue stability constraint. To solve the intractability caused by the long-term functions, the considered problem is converted into a series of beam assignment (BA) and rate control (RC) problems in each time slot by leveraging Lyapunov method. Furthermore, novel closed-form solutions (CFSs) for BA and RC policies are derived by considering beam usage constraints; whereas an iterative algorithm is proposed based on the CFSs. Simulation results verify the superiority of the proposed algorithm based on its performance on system delay and QoE.

Towards Cost-Efficient Edge Intelligent Computing With Elastic Deployment of Container-Based Microservices

With the tremendous growth of the Internet of Things (IoT), big data, and artificial intelligence (AI), the edge computing-based service paradigm has been introduced to meet the increasing demand of applications. To provide efficient computing services at the network edge, the algorithms and applications are generally deployed based on the container-based microservice strategy, which significantly impacts the system efficiency and QoS. Considering the fundamental system uncertainties, including the dynamic workload and service rate, we investigate how to minimize the long-term system cost through the elastic microservice deployment in this paper. To this end, we formulate the container-based microservice deployment as a stochastic optimization problem to minimize the system cost while maintaining the system QoS and stability. We develop a cost-aware elastic microservice deployment algorithm to solve the formulated problem, which balances the tradeoff between system cost and QoS. Our algorithm makes the real-time decisions based on current queue backlogs and system states without predicting the future knowledge. Finally, we conduct the theoretical analysis and extensive simulations based on data traces from the ResNet-50 model-based visual recognition application. The results demonstrate that our algorithm outperforms the baseline strategies with respect to the system cost, queue backlogs, and the number of Pod replicas.

Eco-Friendly Powering and Delay-Aware Task Scheduling in Geo-Distributed Edge-Cloud System: A Two-Timescale Framework

Power consumption and task latency are two crucial issues in edge-cloud computing. This paper mainly aims to promote the use of clean power in geo-distributed data centers (DCs) in a deregulated electricity market where customers are allowed to buy power from multiple suppliers, combined with the guarantee of task latency. To alleviate the conflict between frequent switches of servers and the uncertainty of task arrivals in DCs, this paper proposes a two-timescale framework consisting of the long-term capacity planning of geo-distributed DCs and the real-time task dispatching from edge gateways (EGs) to DCs. First, DCs make long-term plans on the number of active servers aiming at the eco-friendly and delay-aware power cost minimization, which is formulated as problem P. Specifically, we introduce a convex pollution indicator function (PIF) to measure the pollution cost of the various types of powers sold by different suppliers, which can encourage the use of cleaner power and improve power savings. Second, in each sub-slot, each EG separately optimizes its individual mixed strategies of task dispatching to DCs with the knowledge of the planned capacities and the real-time queue backlogs of DCs, where a Lyapunov optimization framework is applied. Finally, we give the corresponding distributed algorithm design. Simulation results reveal that our method can realize the trade-off between the power cost and the delay cost of requests, and improve the clean power usage by up to 50%-60% of the total power usage in DCs. Additionally, comparisons with other schemes show that our approach can provide more stable guarantees of task latency in different situations of workload density, which benefits from the diverse-timescale optimizations of capacities of DCs and task routing from EGs to DCs.

Packet Dropping Minimization in Energy Harvesting-Based Wireless Sensor Network With Linear Topology

In this paper, we optimize the performance of an energy harvesting wireless sensor network (EH-WSN) with linear physical topology. We present an optimization framework to minimize the summation of packet dropping probability subject to the data queue stability and quality of monitoring constraints of the nodes. The optimization is performed using joint optimal power allocation and routing. We formulate the problem as a stochastic optimization problem. Then, by transforming it into a standard time average optimization problem, we apply the Lyapunov drift plus penalty theorem to obtain an objective value which O(1/V) is within vicinity of the optimal value, where V > 0 is a tradeoff factor between the achievable V objective value and queue backlog size. The results confirm the effectiveness of the proposed Lyapunovbased algorithm in minimizing the sum packet dropping probability under the mentioned constraints.

BBRp: Improving TCP BBR Performance Over WLAN

This paper shows the inefficiency of TCP BBR in exploiting the Wi-Fi bandwidth. This limitation of BBR has been observed with both IEEE 802.11n and IEEE 802.11ac, where the mechanism of frame aggregation is used to boost the throughput of data transmission. In the last years, many TCP variants have been introduced to limit the bufferbloat phenomena and bound the latency through a reduction of the queue backlog injection rate. However, this mechanism impacts on the Wi-Fi frame aggregation logic, impeding TCP congestion controls to reach the full throughput potential of a Wi-Fi interface. While this problem can be solved with TCP Cubic by allowing the sender node to enqueue more packets, for TCP BBR the fix is not the same, as it has a customized pacing algorithm. With this contribution we propose BBRp, a new BBR version that allows for fine-tuning the congestion control pace, achieving between four and six times more throughput over IEEE 802.11n and IEEE 802.11ac channels, at the cost of an increased latency that is however always less than the latency obtainable with loss-based TCP congestion controls.

Delay-Sensitive Energy-Harvesting Wireless Sensors: Optimal Scheduling, Structural Properties, and Approximation Analysis

We consider an energy harvesting sensor transmitting latency-sensitive data over a fading channel. We aim to find the optimal transmission scheduling policy that minimizes the packet queuing delay given the available harvested energy. We formulate the problem as a Markov decision process (MDP) over a state-space spanned by the transmitter's buffer, battery, and channel states, and analyze the structural properties of the resulting optimal value function, which quantifies the long-run performance of the optimal scheduling policy. We show that the optimal value function (i) is non-decreasing and has increasing differences in the queue backlog; (ii) is non-increasing and has increasing differences in the battery state; and (iii) is submodular in the buffer and battery states. Taking advantage of these structural properties, we derive an approximate value iteration algorithm that provides a controllable tradeoff between approximation accuracy, computational complexity, and memory, and we prove that it converges to a near-optimal value function and policy. Our numerical results confirm these properties and demonstrate that the resulting scheduling policies outperform a greedy policy in terms of queuing delay, buffer overflows, energy efficiency, and sensor outages.

Toward Optimal Resource Scheduling for Internet of Things Under Imperfect CSI

The Internet of Things (IoT) increases the number of connected devices and supports the ever-growing complexity of applications. Owing to the constrained physical size, the IoT devices can significantly enhance the computational capacity by offloading computation-intensive tasks to the resource-rich edge servers deployed at the base station (BS) via wireless networks. However, how to achieve optimal resource scheduling remains a challenge due to stochastic task arrivals, time-varying wireless channels, and imperfect estimation of channel state information (CSI). In this article, by virtue of the Lyapunov optimization technique, we propose the toward optimal resource scheduling algorithm under imperfect CSI (TORS) to optimize resource scheduling in an IoT environment. A convex transmit power and subchannel allocation problem in TORS is formulated. This problem is then solved via the Lagrangian dual decomposition method. We derive analytical bounds for the time-averaged system throughput and queue backlog. We show that TORS can arbitrarily approach the optimal system throughput by simply tuning an introduced control parameter $\beta $ without prior knowledge of stochastic task arrivals and the CSI of wireless channels. Extensive simulation results confirm the theoretical analysis on the performance of TORS.

Switching control analysis and design in queue networks

This paper deals with the switching traffic control of such communication networks that can be modeled using interconnected queue systems. A general method is proposed to analyze the stability and to compute the expected upper bounds of the queue backlogs and recovery times in controlled queue networks which apply switching control algorithms. It is also discussed, how the state switching control algorithms can be designed for network traffic control applications by applying the principle of lexicographic optimization. This optimization method ensures a unified approach to design different types of switching traffic controllers. The delay control problem in networked control systems or known data traffic control methods, as the additive increase/multiplicative decrease algorithm, can be treated in the proposed analysis and design approach. Experimental measurements are also presented to show the effectiveness of the switching data traffic controllers in delay-critical wireless telerobotic applications.

Game Theory and Lyapunov Optimization for Cloud-Based Content Delivery Networks With Device-to-Device and UAV-Enabled Caching

The paper considers a cloud-based Content Delivery Network (CDN) with Device-to-Device (D2D) and Unmanned Aerial Vehicle (UAV) enabled caching. The network is managed by the operator that offers content transfer services to the set of Content Providers (CPs). A subscriber of any CP can receive its content either by first, cellular content transfer via a terrestrial Base Station (BS) or a UAV, or by second, D2D content transfer from another subscriber of this CP. Each CP aims to maximize its expected payoff defined as a reciprocal of the weighted sum of the expected content transfer cost and delay for its subscribers. The operator's objective is to maximize its long-term average revenue and stabilize the queuing system which represents the content transfer services. To model the interactions between the operator and the CPs, a novel framework is proposed that combines: first, cooperative game that enables the CPs to form coalitions in which all subscribers can exchange content via D2D links, thereby reducing content transfer costs and delays; second, Lyapunov optimization based dynamic channel and UAVs' activity allocation policy of the operator. Through analytical and numerical evaluations, it is proven that if the operator and each CP are rational, the network reaches a state where all the CPs are in the stable coalitional structure, whereas, the dynamic policy of the operator is optimal with the trade-off in the total queue backlog.

Dynamic Power Allocation and User Scheduling for Power-Efficient and Delay-Constrained Multiple Access Networks

In this paper, we propose a joint dynamic power control and user pairing algorithm for power-efficient and low-latency hybrid multiple access systems. In a hybrid multiple access system, user pairing determines whether the transmitter should serve a certain user by orthogonal multiple access (OMA) or non-orthogonal multiple access (NOMA). The proposed optimization framework minimizes the long-term time-average transmit power expenditure while reducing the queueing delay and satisfying time-average data rate requirements. The proposed technique observes channel and queue state information and adjusts queue backlogs to avoid an excessive queueing delay by appropriate user pairing and power allocation. Further, user scheduling for determining the activation of a given user link as well as flexible use of resources are captured in the proposed algorithm. Data-intensive simulation results show that the proposed scheme guarantees an end-to-end delay smaller than 1 ms with high power-efficiency and high reliability, based on the short frame structure designed for ultra-reliable low-latency communications (URLLC).

Green Data-Collection From Geo-Distributed IoT Networks Through Low-Earth-Orbit Satellites

As a critical supplementary to terrestrial communication networks, low-Earth-orbit (LEO) satellite-based communication networks have been gaining growing attention in recent years. In this paper, we focus on data collection from geo-distributed Internet-of-Things (IoT) networks via LEO satellites. Normally, the power supply in IoT data-gathering gateways is a bottleneck resource that constrains the overall amount of data upload. Thus, the challenge is how to collect the data from IoT gateways through LEO satellites under time-varying uplinks in an energy-efficient way. To address this problem, we first formulate a novel optimization problem, and then propose an online algorithm based on Lyapunov optimization theory to aid green data-upload for geo-distributed IoT networks. The proposed approach is to jointly maximize the overall amount of data uploaded and minimize the energy consumption, while maintaining the queue stability even without the knowledge of arrival data at IoT gateways. We finally evaluate the performance of the proposed algorithm through simulations using both real-world and synthetic data traces. Simulation results demonstrate that the proposed approach can achieve high efficiency on energy consumption and significantly reduce queue backlogs compared with an offline formulation and a greedy “Big-Backlog-First” algorithm.

Predictive Location Aware Online Admission and Selection Control in Participatory Sensing

Participatory sensing is a crowdsourcing-based framework, where the platform executes the sensing requests with the help of many common peoples’ handheld devices (typically smartphones). In this paper, we mainly address the online sensing request admission and smartphone selection problem to maximize the profit of the platform, taking into account the queue backlog, and the location of sensing requests and smartphones. First, we formulate this problem as a discrete time model and design a location aware online admission and selection control algorithm (LAAS) based on the Lyapunov optimization technique. The LAAS algorithm only depends on the currently available information and makes all the control decisions independently and simultaneously. Next, we utilize the recent advancement of the accurate prediction of smartphones’ mobility and sensing request arrival information in the next few time slots and develop a predictive location aware admission and selection control algorithm (PLAAS). We further design a greedy predictive location aware admission and selection control algorithm (GPLAAS) to achieve the online implementation of PLAAS approximately and iteratively. Theoretical analysis shows that under any control parameter V > 0, both LAAS and PLAAS algorithm can achieve O (1/ V )-optimal average profit, while the sensing request backlog is bounded by O ( V ). Extensive numerical results based on both synthetic and real trace show that LAAS outperforms the Greedy algorithm and Random algorithm and GPLAAS improves the profit-backlog tradeoff over LAAS.

MMedia: An Efficient Transmission Policy for Multimedia Applications using Mobile Cloud Computing

Background &amp; Objective: Multimedia aggregates various types of media such as audio, video, images, animations, etc., to form a rich media content which produces an everlasting effect in the minds of the people. Methods: In order to process multimedia applications using mobile devices, we encounter a big challenge as these devices have limited resources and power. To address these limitations, in this work, we have proposed an efficient approach named as mMedia, wherein multimedia applications will utilize the multi cloud environment using Mobile Cloud Computing (MCC), for faster processing. The proposed approach selects the best available network. The authors have also considered using the Lyapunov optimization technique for efficient transmission between the mobile device and the cloud. Results: The simulation results indicate that mMedia can be useful for various multimedia applications by considering the energy delay tradeoff decision. Conclusion: The results have been compared alongside the base algorithm SALSA on the basis of different parameters like time average queue backlog, delay and time average utility and indicate that the mMedia outperforms in all the aspects.