Delay-aware Resource Allocation Research Articles

Delay-Aware resource allocation to increase efficiency over LTE network for M2M communications in a noisy channel

Currently, the predominant source of Internet traffic stems from interpersonal user interactions, commonly referred to as user-to-user or H.2.H communications, facilitated through computer systems or mobile devices. The Internet of Things (IoT) is a transformative concept that has gained prominence over the past few years. It represents a paradigm shift from isolated systems to a vast interconnected network of devices that are capable of collecting environmental data, processing it, and making informed decisions based on the processed information. Therefore, it is imperative to have an effective resource scheduler in place for the management of an Long-Term Evolution (L.T.E) network system that encompasses both Machine-to-Machine (M.2.M) devices and Human-to-Human (H.2.H) users. In order to develop analytical formulas for assessing the performance of the suggested programs in respect to average waiting time, average system delay, and average number in the system for M.2.M and H.2.H users, this study uses the M / G / 1 priority queue model. It is possible to provide M.2.M consumers with good Quality of Service (QoS) while maintaining the targeted QoS levels for H.2.H users, according to the analysis of the collected data. The relaxing method is employed to cater to the needs of users in the M.2.M queue. To provide QoS for delay-sensitive traffic generated by H.2.H users, a classification scheme is employed to categorize H.2.H traffic into two primary classes. The findings show that the proposed systems have provided 87.34% improvement in machine-to-machine (M.2.M) performance while managing 92.26% QoS for H.2.H services.

AI-federated novel delay-aware link-scheduling for Industry 4.0 applications in IoT networks

Purpose With the advent of AI-federated technologies, it is feasible to perform complex tasks in industrial Internet of Things (IIoT) environment by enhancing throughput of the network and by reducing the latency of transmitted data. The communications in IIoT and Industry 4.0 requires handshaking of multiple technologies for supporting heterogeneous networks and diverse protocols. IIoT applications may gather and analyse sensor data, allowing operators to monitor and manage production systems, resulting in considerable performance gains in automated processes. All IIoT applications are responsible for generating a vast set of data based on diverse characteristics. To obtain an optimum throughput in an IIoT environment requires efficiently processing of IIoT applications over communication channels. Because computing resources in the IIoT are limited, equitable resource allocation with the least amount of delay is the need of the IIoT applications. Although some existing scheduling strategies address delay concerns, faster transmission of data and optimal throughput should also be addressed along with the handling of transmission delay. Hence, this study aims to focus on a fair mechanism to handle throughput, transmission delay and faster transmission of data. The proposed work provides a link-scheduling algorithm termed as delay-aware resource allocation that allocates computing resources to computational-sensitive tasks by reducing overall latency and by increasing the overall throughput of the network. First of all, a multi-hop delay model is developed with multistep delay prediction using AI-federated neural network long–short-term memory (LSTM), which serves as a foundation for future design. Then, link-scheduling algorithm is designed for data routing in an efficient manner. The extensive experimental results reveal that the average end-to-end delay by considering processing, propagation, queueing and transmission delays is minimized with the proposed strategy. Experiments show that advances in machine learning have led to developing a smart, collaborative link scheduling algorithm for fairness-driven resource allocation with minimal delay and optimal throughput. The prediction performance of AI-federated LSTM is compared with the existing approaches and it outperforms over other techniques by achieving 98.2% accuracy. Design/methodology/approach With an increase of IoT devices, the demand for more IoT gateways has increased, which increases the cost of network infrastructure. As a result, the proposed system uses low-cost intermediate gateways in this study. Each gateway may use a different communication technology for data transmission within an IoT network. As a result, gateways are heterogeneous, with hardware support limited to the technologies associated with the wireless sensor networks. Data communication fairness at each gateway is achieved in an IoT network by considering dynamic IoT traffic and link-scheduling problems to achieve effective resource allocation in an IoT network. The two-phased solution is provided to solve these problems for improved data communication in heterogeneous networks achieving fairness. In the first phase, traffic is predicted using the LSTM network model to predict the dynamic traffic. In the second phase, efficient link selection per technology and link scheduling are achieved based on predicted load, the distance between gateways, link capacity and time required as per different technologies supported such as Bluetooth, Wi-Fi and Zigbee. It enhances data transmission fairness for all gateways, resulting in more data transmission achieving maximum throughput. Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation. Findings Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation. It also shows that AI- and IoT-federated devices can communicate seamlessly over IoT networks in Industry 4.0. Originality/value The concept is a part of the original research work and can be adopted by Industry 4.0 for easy and seamless connectivity of AI and IoT-federated devices.

Delay-Aware Resource Allocation in Fog-Assisted IoT Networks Through Reinforcement Learning

Fog nodes in the vicinity of IoT devices are promising to provision low-latency services by offloading tasks from IoT devices to them. Mobile IoT is composed by mobile IoT devices, such as vehicles, wearable devices, and smartphones. Owing to the time-varying channel conditions, traffic loads, and computing loads, it is challenging to improve the Quality of Service (QoS) of mobile IoT devices. As task delay consists of both the transmission delay and computing delay, we investigate the resource allocation (i.e., including both radio resource and computation resource) in both the wireless channel and fog node to minimize the delay of all tasks while their QoS constraints are satisfied. We formulate the resource allocation problem into an integer nonlinear problem, where both the radio resource and computation resource are taken into account. As IoT tasks are dynamic, the resource allocation for different tasks are coupled with each other and the future information is impractical to be obtained. Therefore, we design an online reinforcement learning algorithm to make the suboptimal decision in real time based on the system’s experience replay data. The performance of the designed algorithm has been demonstrated by extensive simulation results.

Delay-Based Resource Allocation With Fairness Guarantee and Minimal Loss for eMBB in 5G Heterogeneous Networks

Heterogeneity is defined as one of the core merits in the 5th Generation (5G) mobile systems. Such a trait is prominent in modern 5G traffic scenarios, i.e., enhanced Mobile Broadband (eMBB), where coarse Quality of Service (QoS) profiles need to be satisfied over the shared transmission channel. For that, Radio Resource Management (RRM) became a challenging problem that should be figured out based on the traffic scenario. Extensive efforts dwelled in the literature attempt to solve the resource allocation problem in the downlink channel by maximizing the gain for a certain aspect in the QoS profile. This, however, poses a challenge for 5G-related traffic scenarios such that multi-dimension QoS shall be attainable. Therefore, in this paper, a Delay-aware Resource Allocation for Guaranteed Fairness and minimal Loss (DRAGFL) is proposed to enhance the QoS for Real-Time (RT) services in eMBB 5G Heterogeneous Networks. The ultimate objective of DRAGFL is to maintain a steadily low latency for delay-sensitive flows without restraining the service fairness and the data loss in the overload states. DRAGFL enhances the efficiency of the MAC layer scheduler throughout three phases. Firstly, generating delay-derived weight matrix of the resources blocks to radio bearers from eMBB different classes. Then, greedy-based flows prioritization for Resource Blocks (RBs) assignment. Finally, the data rate of the RT and Non RT (NRT) flows is further emphasized through a channel-aware principle that is invoked conditioning to the remaining radio resources and available data to be scheduled in the interval. The performance evaluation using system-level simulations reveals prominent gains of DRAGFL in sustaining a steadily low end-to-end delay in the high congestion states. Moreover, it maintains ideal states of service fairness with low data loss for different traffic loads to indicate a promising MAC scheduler for the developing scenarios of 5G.

Delay Aware Resource Allocation With Radio Remote Head Cooperation in User-Centric C-RAN

High spectral efficiency and low latency are required to provide ubiquitous communication for the emerging applications in 5G wireless communication networks. In this letter, we propose a novel framework that considers these requirements simultaneously by integrating the notion of effective capacity (EC) into orthogonal frequency division multiple access (OFDMA) cloud-radio access networks (C-RAN) where the users select the distributed radio remote heads (RRHs) based on their specific delay requirements to transmit over different subcarriers cooperatively. Consequently, an optimization problem is defined to maximize the EC under the average peak power constraint and the delay requirements. The problem is combinatorial and non-convex and an algorithm based on the duality and alternating optimization algorithms is proposed, which is efficiently computed with good accuracy. Simulation and analytical results demonstrate that the proposed solution has a near-optimal performance and there is a trade-off between delay and spectral efficiency. Moreover, the cooperation between RRHs can considerably improve the system throughput.

CoDeC: A Cost-Effective and Delay-Aware SFC Deployment

Service Function Chain (SFC) provides an end-to-end service by processing traffic flow through a series of Virtual Network Functions (VNFs) in a specific order. Satisfying user’s demands (e.g., end-to-end delay) on one hand and minimizing the cost of SFC deployment in terms of energy and resource on the other hand, introduces VNFs placement as a crucial issue that is receiving significant attention by researchers. To address this problem and boost the performance of SFC, different techniques such as Network Function (NF) distribution, NF parallelism and optimal resource allocation have been utilized. Applying these mechanisms imposes other costs which must be taken into account by network providers. In this paper, we introduce CoDeC as a Cost-effective and Delay-aware resource allocation approach. By having user defined end-to-end threshold and using aforementioned mechanisms, CoDeC tries to place the requested VNFs with the minimum cost of deployment, distribution, parallelism and energy. Therefore, we formulate the addressed problem in form of Mixed Integer linear Programming (MILP) model. We then show that the problem is NP-complete and suffers from high time complexity in large-scale scenarios. Thus, a heuristic algorithm is introduced to determine a near-optimal solution in a reasonable amount of time. Our simulation results show that CoDeC achieves better performance in term of cost and acceptance rate compared to using each mechanism individually.

Delay-Aware Resource Allocation for 5G Wireless Networks With Wireless Power Transfer

For 5G wireless networks, low latency and high energy efficiency are two of the most critical key performance indicators. In this work, we propose a framework to consider these requirements simultaneously by applying the notions of effective capacity, effective energy efficiency, and simultaneous wireless information and power transfer (SWIPT). At first, we define the effective capacity and the effective energy efficiency of SWIPT in multiuser orthogonal frequency division multiple access systems by considering two practical schemes of SWIPT: time switching (TS) and power splitting (PS). Afterwards, we formulate two resource allocation problems to maximize the effective capacity and effective energy efficiency, respectively, subject to three constraints: minimum harvested power, average sum transmit power, and delay quality of service. Numerical results illustrate that there is a fundamental tradeoff between harvested power and the performance in terms of effective capacity and effective energy efficiency. Moreover, the efficiency of PS scheme over TS greatly depends on the minimum required harvested power and the number of users.

F-DARA Algorithm For Time Slot Allocation In Internet of Things

In multi-user wireless video transmission over Internet of Things, to ensure that all video senders have sufficient transmission opportunity before their deadline expire is a challenging and key issue. Thus fuzzy based delay aware resource allocation (F-DARA) algorithm is proposed in this paper. This algorithm solves the problem without assuming detail packet level knowledge. Instead, transmission delay deadlines of each sender’s video packets are converted into linearly decreasing weight distribution within the considered time period. Non-stationary slot allocation method is a unique characteristic of F-DARA algorithm which depends on the allocation of previous slots. This is different from all existing slot allocation policies such as round-robin or rate-adaptive round-robin policies, which are stationary because the allocation of the current slot does not depend on the allocation of previous slots.

Delay-aware cross-layer design for device-to-device communications in future cellular systems

Device-to-device (D2D) communications allow direct communications between nodes without transmitting data via the base stations in cellular systems, which could bring significant performance improvement. Since most applications are delay-sensitive, it is very important to consider delay performance in addition to physical layer throughput for D2D communications. To improve delay performance it is necessary to dynamically control the radio resource in a cross-layer way according to both the channel fading information and the queue length information. The former allows an observation of good transmission opportunity and the latter provides the urgency of data flows. However, the resource control with delay constraints involves stochastic optimization, which is very challenging. In this article we first summarize various approaches to solve the delay-aware resource allocation problems for D2D communications. We propose a low complexity practical solution by exploiting the interference filtering property of CSMA-like MAC protocols in the D2D system. Based on the solution structure, we further discuss the implementation issues based on LTE-Advanced systems and evaluate the associated performance and complexity. Finally we discuss the choice of MAC parameters for the overall D2D system performance.

Non-Stationary Resource Allocation Policies for Delay-Constrained Video Streaming: Application to Video over Internet-of-Things-Enabled Networks

Due to the high bandwidth requirements and stringent delay constraints of multi-user wireless video transmission applications, ensuring that all video senders have sufficient transmission opportunities to use before their delay deadlines expire is a longstanding research problem. We propose a novel solution that addresses this problem without assuming detailed packet-level knowledge, which is unavailable at resource allocation time (i.e. prior to the actual compression and transmission). Instead, we translate the transmission delay deadlines of each sender's video packets into a monotonically-decreasing weight distribution within the considered time horizon. Higher weights are assigned to the slots that have higher probability for deadline-abiding delivery. Given the sets of weights of the senders' video streams, we propose the low-complexity Delay-Aware Resource Allocation (DARA) approach to compute the optimal slot allocation policy that maximizes the deadline-abiding delivery of all senders. A unique characteristic of the DARA approach is that it yields a non-stationary slot allocation policy that depends on the allocation of previous slots. This is in contrast with all existing slot allocation policies such as round-robin or rate-adaptive round-robin policies, which are stationary because the allocation of the current slot does not depend on the allocation of previous slots. We prove that the DARA approach is optimal for weight distributions that are exponentially decreasing in time. We further implement our framework for real-time video streaming in wireless personal area networks that are gaining significant traction within the new Internet-of-Things (IoT) paradigm. For multiple surveillance videos encoded with H.264/AVC and streamed via the 6tisch framework that simulates the IoT-oriented IEEE 802.15.4e TSCH medium access control, our solution is shown to be the only one that ensures all video bitstreams are delivered with acceptable quality in a deadline-abiding manner.

A Survey on Delay-Aware Resource Control for Wireless Systems—Large Deviation Theory, Stochastic Lyapunov Drift, and Distributed Stochastic Learning

In this tutorial paper, a comprehensive survey is given on several major systematic approaches in dealing with delay-aware control problems, namely the equivalent rate constraint approach, the Lyapunov stability drift approach and the approximate Markov Decision Process (MDP) approach using stochastic learning. These approaches essentially embrace most of the existing literature regarding delay-aware resource control in wireless systems. They have their relative pros and cons in terms of performance, complexity and implementation issues. For each of the approaches, the problem setup, the general solution and the design methodology are discussed. Applications of these approaches to delay-aware resource allocation are illustrated with examples in single-hop wireless networks. Furthermore, recent results regarding delay-aware multi-hop routing designs in general multi-hop networks are elaborated. Finally, the delay performance of the various approaches are compared through simulations using an example of the uplink OFDMA systems.