Optimal Packet Scheduling Research Articles

Advancing 6G-IoT networks: Willow catkin packet transmission scheduling with AI and bayesian game-theoretic approach-based resource allocation.

The rapid expansion of mobile broadband networks and the proliferation of Internet of Things (IoT) applications have substantially increased data transmission and processing demands. However, the application domains of IoT-enabled models often face resource limitations, requiring rapid responses, low latency, and large bandwidth, surpassing their inherent capabilities. To address these challenges, we propose a fishnet approach-based packet scheduling and resource allocation system, termed Fishnet-6G, to optimize network resource allocation in the proposed 6G networks. Initially, we constructed a Sierpinski Triangle-based network in a 6G-IoT environment, enhancing device connectivity. We utilize the Quantum Density Peak Clustering (QDPC) algorithm to perform clustering for IoT devices, establishing Cluster Head (CH) and Substitute CH (SUB CH) based on actual metrics. Furthermore, traffic prediction is achieved through two processes, grouping, and fair queue status, using the Improved Deep Deterministic Policy Gradient (IMPDDPG) algorithm with a variable sampling rate, resulting in well-organized packet scheduling. Subsequently, we perform optimal packet scheduling by employing the Willow Catkin Optimization (WCO) algorithm, and the scheduled packets are managed within a Fishing Net Topology to reduce energy consumption and system complexity. Finally, we allocate the scheduled packets to the desired resource blocks using the Bayesian Game-Theoretic Approach (BGTA). The proposed approach is implemented using Network Simulator-3.26, and the performance of the Fishnet-6G model is evaluated based on time, transmission rate, energy efficiency, average throughput, latency, and Packet loss rate. Numerical analysis demonstrates that Fishnet-6G outperforms existing approaches across these metrics, showcasing its effectiveness in addressing the challenges of 6G-IoT networks.

Internet of Drones: Improving Multipath TCP over WiFi with Federated Multi-Armed Bandits for Limitless Connectivity

We consider multipath TCP (MPTCP) flows over the data networking dynamics of IEEE 802.11ay for drone surveillance of areas using high-definition video streaming. Mobility-induced handoffs are critical in IEEE 802.11ay (because of the smaller coverage of mmWaves), which adversely affects the performance of such data streaming flows. As a result of the enhanced 802.11ay network events and features (triggered by beamforming, channel bonding, MIMO, mobility-induced handoffs, channel sharing, retransmissions, etc.), the time taken for packets to travel end-to-end in 802.11ay are inherently time-varying. Several fundamental assumptions inherent in stochastic TCP models, including Poisson arrivals of packets, Gaussian process, and parameter certainty, are challenged by the improved data traffic dynamics over IEEE 802.11ay networks. The MPTCP model’s state estimation differs largely from the actual network values. We develop a new data-driven stochastic framework to address current deficiencies of MPTCP models and design a foundational architecture for intelligent multipath scheduling (at the transport layer) considering lower layer (hybrid) beamforming. At the heart of our cross-layer architecture is an intelligent learning agent for actuating and interfacing, which learns from experience optimal packet cloning, scheduling, aggregation, and beamforming using successful features of multi-armed bandits and federated learning. We demonstrate that the proposed framework can estimate and optimize jointly (explore–exploit) and is more practicable for designing the next generation of low-delay and robust MPTCP models.

The Optimal Packets Scheduling for Buffer-Aid Energy Harvesting RSUs in Cooperative Vehicle Infrastructure System

In this paper, we investigate the optimal packet scheduling for RSU-to-vehicle downlink communication in Cooperative Vehicle Infrastructure System (CVIS). The RSU is powered by a capacity-limited battery storing harvested energy with reliable energy source as backup, which meets requirement of renewable energy and ensures sustainability of communication system. We aim to find the optimal packet scheduling policy that minimizes the data queuing delay under a given the reliable power constraint and downlink availability rate. According to probabilistic scheduling policy, we adopt data buffer queue and harvesting energy queue as the state-space, and determine the transition action by the joint state of packet arrivals and channel state to establish a two-dimensional Markov chain. Based on this, the optimization problem is formulated. By solving the problem and analysing its solution, we derive the optimal transmission parameters and the optimal scheduling policy which turns out to be threshold-based. Simulations are demonstrated to verify the accuracy of the theoretical derivation results.

Collision-free optimal packet scheduling algorithm for multi-hop wireless IoT networks

One of the key challenges for the IoT (Internet of Things) evolution is the network connectivity provision to a large number of resource-limited low-cost IoT devices. To meet this challenge, various efforts have been made in today’s wireless network technologies, such as LTE eMTC, 5G NB-IoT, IEEE 802.11ah, BLE, etc. Meanwhile, most of these technologies consider multi-hop wireless relaying to extend the wireless coverage and to enhance the network transmission capacity. Despite the benefits, however, the usage of multi-hop relaying has been limited in the real world, because of the performance degradation caused by the self-interference between adjacent wireless links. In this paper, we tackle the problem of alleviating the performance degradation in the multi-hop wireless network for IoT devices. In particular, we target low cost IoT devices that are equipped with inexpensive wireless links such as WiFi, IEEE 802.15.4, and Bluetooth. Our approach is to avoid self-interference by controlling the transmission timing at each hop. More specifically, our design goal is to minimize the data delivery delay for fast IoT sensing by preventing packet collisions (i.e., collision-free transmission). Since the retransmission at the IoT devices is eliminated, buffering at the intermediate nodes is not necessary so that no extra storage is consumed at the IoT devices to store the data being relayed. We present an algorithm to produce the packet transmission schedule at each hop. We prove that our algorithm is optimal in terms of data delivery delay (i.e., minimal delay is guaranteed) while collision between transmissions is completely avoided.

Fair-Hierarchical Scheduling for Diversified Services in Space, Air and Ground for 6G-Dense Internet of Things

The 6G space-air-ground integrated network can achieve global full-area three-dimensional coverage and ultra-wide-area broadband access capabilities anytime and anywhere. Through the integration of satellite networks and terrestrial networks, it can provide a better user experience and has become the core development direction of 6G networks. Inspired by the potential of service differentiation ability of 6G IEEE 802.11ax protocols (Wi-Fi 6), a Data-driven Fair-Hierarchical Scheduling (DFHS) is proposed in this paper to schedule packets from diversified applications in dense Internet of Things (IoT) networks, which allows data streams to fairly share the spectrum and at the same time satisfy delay requirements. Our DFHS is divided into the outer-layer and inner-layer schedulers. First, diversified packets of IoT services are classified into categories according to delay requirement and transmission frequency. Then the outer-layer scheduler assigns classified packets to different Access Categories (AC) with differentiated channel competition capabilities. Next, the inner-layer scheduler optimally schedules the AC queues according to the packet access response ratio. Particle Swarm Optimization algorithm is further introduced to solve the optimal packet scheduling problem. Numerical results demonstrate that the DFHS outperforms the classical FCFS, the latest EARS and Tian's schemes in terms of service missing ratio, queueing delay and fair index.

KM-based efficient algorithms for optimal packet scheduling problem in celluar/infostation integrated networks

Request models in previous works on the Packet Scheduling (PS) problem in cellular/infostation integrated networks usually have some restrictions which make them less practical in some situations. In this paper, we investigate the packet scheduling problem with our new request model, which is general enough to include previous request models as special cases. We first propose a Greedy algorithm for the PS problem and analyze its approximation ratio. We then transform our PS problem to the mini-slot mini-request version PS problem (MSMR-PS problem) by using the concept of mini-slot and mini-request. Next, by embedding problem information into bipartite graphs, we investigate the relationships between the MSMR-PS problem and the corresponding maximum matching (MM) problem on these graphs. Based on these relations, we propose efficient algorithms to obtain quasi-optimal solutions to the original PS problem by using the Kuhn–Munkres (KM) algorithm to solve the MM problems on the bipartite graphs. These algorithms include two offline algorithms (named as KMUpper and KMLower) and one online algorithm named KMOnline. KMUpper and KMLower return upper and lower bounds for optimal profit of the PS problem, respectively. Simulation results show that our algorithms are more preferable than the other existing algorithms like FIFO, exponential capacity algorithm.

Automating selection of optimal packet scheduling during VоIP-traffic transmission

The usage of various packet scheduling disciplines in computer networking devices as a mechanism to ensure the quality of service is described. Stages for selection of necessary parameters values of packet scheduling during VoIP-traffic transmission in computer networks are defined. VoIP-traffic was set as a research object because there are strict requirements of VoIP-applications to the network transmission parameters. With the aid of training and experimental simulation system the numerous experiments for parameters selection of the most common packet scheduling disciplines were carried out (FIFO, WFQ, non-preemptive priority queueing). The example that illustrates the ability to adjust the weighting coefficients of WFQ packet scheduling discipline is presented. Approximate analytical dependences are obtained and they will significantly reduce system administrators’ efforts to assess and modify the parameters of packet scheduling in network devices. A method of automating selection of the optimal packet scheduling discipline is formulated.

Quality-Driven Cross-Layer Design for H.264/AVC Video Transmission over OFDMA System

Video applications over wireless network have attracted more and more attention in recent years. However, due to the unique characteristics of video, limited available resources and the time-varying wireless channel state, video transmission over wireless network is still a challenging task. In this paper, a novel cross-layer design is proposed to maximize the overall received video quality with the limited network resource and the stringent deadline constraints over OFDMA network. Considering the information extracted from the Application layer, Media Access Control layer and Physical layer, we formulate the packet scheduling, subcarrier assignment and power allocation into a mathematical model. By employing Lagrange dual decomposition, asymptotically optimal packet scheduling and resource allocation strategy can be obtained. To reduce the computation complexity, we further develop a suboptimal solution which performs packet scheduling and subcarrier assignment jointly but power optimization independently. Finally, we validate our proposed algorithms in a multi-user scenario where all the video sequences are pre-encoded by H.264/AVC encoder. Experimental results demonstrate that our proposed algorithms can indeed improve the received video quality significantly compared with other existing algorithms.

Optimal packet scheduling in a multiple access channel with energy harvesting transmitters

In this paper, we investigate the optimal packet scheduling problem in a two-user multiple access communication system, where the transmitters are able to harvest energy from the nature. Under a deterministic system setting, we assume that the energy harvesting times and harvested energy amounts are known before the transmission starts. For the packet arrivals, we assume that packets have already arrived and are ready to be transmitted at the transmitter before the transmission starts. Our goal is to minimize the time by which all packets from both users are delivered to the destination through controlling the transmission powers and transmission rates of both users. We first develop a generalized iterative backward waterfilling algorithm to characterize the maximum departure region of the transmitters for any given deadline T. Then, based on the sequence of maximum departure regions at energy arrival instants, we decompose the transmission completion time minimization problem into convex optimization problems and solve the overall problem efficiently.

Optimal packet scheduling for energy harvesting sources on time varying wireless channels

In this paper, we consider a source node that operates over a time varying channel with energy harvesting capability. The goal of the source is to maximize the average number of successfully delivered packets per time slot. The source is able to choose whether to transmit a packet or defer the transmission in each time slot. The decision which is chosen by the source depends on the channel information available and the length of the energy queue. We formulate the problem of finding the optimal policy as a Markovian decision problem. We show some properties of the value function that represents the discounted number of successfully delivered packets per time slot. We prove that the optimal policy is a threshold type policy depending on the state of the channel and the length of the energy queue. We also derive an upper bound for the average number of packets per time slots successfully received by the destination. We show using numerical results that this bound is a tight bound on the performance of the optimal policy. And we consider the case of time varying channel but without channel state information (CSI). Then, we study the impact of channel time varying nature and the availability of CSI. In this case, we show that the optimal policy is a greedy policy. The performance of this greedy policy is also calculated.

Optimal Packet Scheduling in an Energy Harvesting Communication System

We consider the optimal packet scheduling problem in a single-user energy harvesting wireless communication system. In this system, both the data packets and the harvested energy are modeled to arrive at the source node randomly. Our goal is to adaptively change the transmission rate according to the traffic load and available energy, such that the time by which all packets are delivered is minimized. Under a deterministic system setting, we assume that the energy harvesting times and harvested energy amounts are known before the transmission starts. For the data traffic arrivals, we consider two different scenarios. In the first scenario, we assume that all bits have arrived and are ready at the transmitter before the transmission starts. In the second scenario, we consider the case where packets arrive during the transmissions, with known arrival times and sizes. We develop optimal off-line scheduling policies which minimize the time by which all packets are delivered to the destination, under causality constraints on both data and energy arrivals.

Optimal Packet Scheduling on an Energy Harvesting Broadcast Link

The minimization of transmission completion time for a given number of bits per user in an energy harvesting communication system, where energy harvesting instants are known in an offline manner is considered. An achievable rate region with structural properties satisfied by the 2-user AWGN Broadcast Channel capacity region is assumed. It is shown that even though all data are available at the beginning, a non-negative amount of energy from each energy harvest is deferred for later use such that the transmit power starts at its lowest value and rises as time progresses. The optimal scheduler ends the transmission to both users at the same time. Exploiting the special structure in the problem, the iterative offline algorithm, FlowRight, from earlier literature, is adapted and proved to solve this problem. The solution has polynomial complexity in the number of harvests used, and is observed to converge quickly on numerical examples.

Packet scheduling with playout adaptation for scalable video delivery over wireless networks

In this paper, we propose a playout deadline-aware packet scheduling for scalable video delivery over wireless networks. We develop a novel playout adaptation algorithm to reduce playback interruptions by jointly considering the active playout buffer status and adaptive playout rate. We also propose a packet priority analysis method based on the layer information of Scalable Video Coding (SVC). Based on the priority of the video packet and the adaptive playout-deadline, an optimal packet scheduling algorithm is proposed. Packets are selected for transmission to minimize the quality degradation caused as well as to reduce the playout latency. We also adopt a benchmark for the packet priority analysis by calculating the distortion impact of each packet with the consideration of the packet dependency in SVC. When compared with the state-of-the-art algorithms as well as the benchmark, our proposed scheduling algorithm shows a good trade-off between the video quality and the playout latency.

Optimal Packet Scheduling in WDM Optical Switches With Output Buffer and Limited Wavelength Conversion

All-optical packet switching is a promising candidate for future high-speed switching. However, due to the absence of optical random access memory, the traditional Virtual Output Queue (VOQ) based input-queued switches are difficult to implement in the optical domain. In this paper we consider output-buffered optical packet switches. We focus on packet scheduling in an output-buffered optical packet switch with limited-range wavelength conversion, aiming at maximizing throughput and minimizing average queuing delay simultaneously. We show that this problem can be converted to a minimum cost maximum network flow problem. To cope with the high complexity of general network flow algorithms, we present an algorithm that can efficiently find an optimal schedule in O(min{NW,BW}) time, where <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> is the switch size, W is the number of wavelength channels per fiber and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</i> is the length of the longest FDL at the output of the switch. The complexity of the new algorithm asymptotically matches the lower bound of the scheduling problem. We also conduct extensive simulations to test the performance of the proposed scheduling algorithm under different traffic models.

Optimal scheduling in high-speed downlink packet access networks

We present an analytic model and a methodology to determine the optimal packet scheduling policy in a High-Speed Downlink Packet Access (HSDPA) system. The optimal policy is the one that maximizes cell throughput while maintaining a level of fairness between the users in the cell. A discrete stochastic dynamic programming model for the HSDPA downlink scheduler is presented. Value iteration is then used to solve for the optimal scheduling policy. We use a FSMC (Finite State Markov Channel) to model the HSDPA downlink channel. A near-optimal heuristic scheduling policy is developed. Simulation is used to study the performance of the resulting heuristic policy and compare it to the computed optimal policy. The results show that the performance of the heuristic policy is very close to that of the optimal policy. The heuristic policy has much less computational complexity, which makes it easy to deploy, with only slight reduction in performance compared to the optimal policy.

Application of Hardware Architecture of Genetic Algorithm for Optimal Packet Scheduling

In Dense Wavelength Division Multiplexing (DWDM) technologies, the optimal packet scheduling issue is commonly encountered in multiple channel networks. NP-hard problems deal with finding a way to rearrange packets from multiple channels into a finite and rare channel. Genetic Algorithm (GA) is one of the most efficient ways to solve these issues. We hope to find a better solution to our task through the GA characteristics of multiprocessor searching and survival of the fittest. Therefore, a modified and achievable hardware architecture of GA is presented in this paper. This architecture can increase both the speed of packet scheduling and the efficiency of DWDM in Optical Communication Networks.

An on-line learning algorithm for energy efficient delay constrained scheduling over a fading channel

In this paper, we consider the problem of energy efficient scheduling under average delay constraint for a single user fading channel. We propose a new approach for on-line implementation of the optimal packet scheduling algorithm. This approach is based on reformulating the value iteration equation by introducing a virtual state called post-decision state. The resultant value iteration equation becomes amenable to online implementation based on stochastic approximation. This approach has an advantage that an explicit knowledge of the probability distribution of the channel state as well as the arrivals is not required for the implementation. We prove that the on-line algorithm indeed converges to the optimal policy.

Optimal uplink packet scheduling for CDMA non-real-time data

The problem of optimal uplink packet scheduling for non-real-time data in CDMA systems is investigated and a load-based optimisation model is presented. In contrast to existing models, the proposed model takes the actual load of each mobile user into consideration and adopts an achievable throughput in the objective function. The proposed model also optimises the resource utilisation. Numerical results show that the proposed model can achieve a higher throughput and a higher resource utilisation.

Optimal and suboptimal packet scheduling over correlated time varying flat fading channels

We address the issue of optimal packet scheduling over correlated fading channels which trades off between minimization of three goals: average transmission power, average delay and average packet dropping probability. We show that the problem forms a weakly communicating Markov decision process and formulate the problem as both unconstrained and constrained problem. Relative value iteration (RVI) algorithm is used to find optimal deterministic policy for unconstrained problem, while optimal randomized policy for constrained problem is obtained using linear programming (LP) technique. Whereas with RVI only a finite number of scheduling policies can be obtained over the feasible delay region, LP can produce policies for all feasible delays with a fixed dropping probability and is computationally faster than the RVI. We show the structure of optimal deterministic policy in terms of the channel and buffer state and form a simple log functional suboptimal scheduler that approximately follows the optimal structure. Performance results are given for both constant and bursty Poisson arrivals, and the proposed suboptimal scheduler is compared with the optimal and channel threshold scheduler. Our suboptimal scheduler performs close to the optimal scheduler for every feasible delay and is robust to different channel parameters, number of actions and incoming traffic distributions.

Optimal packet scheduling algorithms for token-bucket based rate control

In this paper, we consider a scenario in which the source has been offered QoS guarantees subject to token-bucket regulation. The rate of the source should be controlled such that it conforms to the token-bucket regulation, and also the distortion obtained is the minimum. We have developed an optimal scheduling algorithm for offline (like pre-recorded video) sources with convex distortion function and which can not tolerate any delay. This optimal offline algorithm has been extended for the real-time online source by predicting the number of packets that the source may send in future. The performance of the online scheduler is not substantially degraded as compared to that of the optimal offline scheduler. A sub-optimal offline algorithm has also been developed to reduce the computational complexity and it is shown to perform very well. We later consider the case where the source can tolerate a fixed amount of delay and derive optimal offline algorithm for such traffic source.