Delay Budget Research Articles

A comparative analysis of the semi-persistent and dynamic scheduling schemes in NR-V2X mode 2

Over the last years, the evolution of Vehicle-to-Everything (V2X) services from basic safety-related to enhanced V2X (eV2X) applications prompted the development of the 5G New Radio (NR)-V2X technology. Standardized by the Third Generation Partnership Project (3GPP) in Release 16, NR-V2X features a distributed resource allocation mode, known as Mode 2, that allows vehicles to autonomously select their transmission resources employing a Semi-Persistent Scheduling (SPS) or a Dynamic Scheduling (DS) scheme. The SPS approach relies on the periodic reservation of resources, whereas the DS scheme is a reservation-less solution that forces the selection of new transmission resources for every generated message. 3GPP standards do not indicate under which conditions each scheduling scheme should be used. In this context, this study analyzes and compares the performance of SPS and DS under different traffic types and Packet Delay Budget (PDB) requirements. Simulation results demonstrate that the SPS scheme represents the best solution for serving fixed size periodic traffic, whereas DS is more adequate for aperiodic traffic (of fixed or variable size). The study shows that the superiority of DS over SPS becomes more evident when tighter PDB requirements are considered, and that the performance of the DS scheme is independent of the PDB. It is also demonstrated that an adaptive scheduling strategy, which allows vehicles to select the scheduling scheme that best suits the type of generated traffic, is the best solution in mixed traffic scenarios where fixed size periodic traffic and variable size aperiodic traffic sources coexist.

A Stochastic Network Calculus (SNC)-Based Model for Planning B5G uRLLC RAN Slices

Radio Access Network (RAN) slicing involves several challenges. In particular, the Mobile Network Operator (MNO) must ensure —before deploying each slice—that corresponding requirements can be met throughout its lifetime. For ultra-Reliable Low Latency Communication (uRLLC) slices, the MNO must guarantee the packet transmission delay within a delay budget with a certain probability. Most existing solutions focus on allocating dynamically radio resources to maximize the number of packets, whose transmission delay is within the delay budget. However, these solutions do not ensure the violation probability is below a target value in the long term. In this paper, we focus on slicing from a planning perspective. Specifically, we propose a Stochastic Network Calculus (SNC)-based model, which given the amount of radio resources allocated for a uRLLC slice, the target violation probability and the traffic demand distribution, provides the delay bound for such conditions. Additionally, we propose heuristics for planning uRLLC slices. Interestingly, such heuristics benefit from the proposed SNC-based model to compute the amount of radio resources to be assigned to each slice while its delay bound, given a target violation probability, is within the delay budget. We validate the SNC-based model and demonstrate the effectiveness of the heuristics.

Optimal resource scheduling algorithm for cell boundaries users in heterogenous 5G networks

Deployment of small cells over the existing cellular network is an effective solution to improve the system coverage and throughput of fifth generation (5G) mobile communication networks. The arrival of the 5G mobile networks have demonstrated the importance of advanced scheduling techniques to manage the limited frequency spectrum available while achieving 5G transmission requirements. Cellular networks of the future necessitate the formulation of efficient resource allocation schemes that mitigate the interference between the different cells. In this research work, we formulate an optimization problem for heterogenous networks (HetNets) for resource allocation to maximize the system throughput among the cell center users (CCUs) and cell edge users (CEUs). We solve the optimization problem by effective utilization of the weight factors distribution for resource allocation. A novel Utility-based Resource Scheduling Algorithm (URSA) optimizes the resource sharing among the users with better delay budget of each application. The designed URSA ameliorates fairness along with reduced cross layer interference for real and non-real time applications. Performance of the URSA has been evaluated and compared most relevant state of art algorithms using the matlab based simulators. Furthermore, simulation results validate the superiority of the proposed scheduling scheme against conventional techniques in terms of throughput, fairness, and spectral efficiency.

Scalable priority-based resource allocation scheme for M2M communication in LTE/LTE-A network

Machine-to-Machine (M2M) communication in the Long Term Evolution (LTE) network has recently grown exponentially as the volume of connected devices has increased rapidly in the last decade. M2M traffic can be understood via certain parameters in terms of packet length, packet generation frequency, delay, and data rate requirements, and it typically flows in the uplink direction. Primarily, the LTE network design is optimized for Human-to-Human (H2H) communication. As a result, designing uplink scheduling in LTE networks is fraught with difficulties which restrict the use of potential capacity. In response to the preceding methodologies, focusing on the QCI priority degrades resource utilization and cell throughput. Therefore, a scheduling mechanism needs to optimize the system performance with priority support to use LTE in M2M communication. This paper highlights existing flaws in the optimization process and proposes a scalable priority-based resource allocation scheme for M2M communication in the LTE/LTE-Advance network. The proposed scheme for resource allocation strikes a balance between resource utilization and application priority support. According to the results, the proposed scheduling algorithm outperforms the standard algorithms concerning resource sharing fairness, average resource utilization, QCI priority support, and delay budget violation.

Keterlambatan Penyerapan Anggaran: Peran Komitmen Organisasi Sebagai Moderasi

The purpose of this line of research is to analyze the effects of planning violations, implementation of violations, quality of HR, administration, and documentation of procurement against delays in the absorption of violations by being moderated by the organization. The population in this study is OPD in Indragiri Hulu Regency which consists of 33 agencies. Respondents in this study were budget management officials taken as many as 3 people per OPD. Data were collected by sending questionnaires directly to respondents in the study. Data analysis with multiple regression and Moderate Regression Analysis shows that budget planning, budget execution, quality of human resources, administration, and procurement documents affect the delay budget absorption. Organizational commitment moderates the effect of planning violations, quality of human resources, administration, and procurement documents against delays in the absorption of violations. However, organizational commitment does not moderate the effect of budget execution on delays in budget absorption.

Delay-Aware Priority Access Classification for Massive Machine-Type Communication

Massive Machine-type Communications (mMTC) is one of the principal features of the 5th Generation and beyond (5G+) mobile network services. Due to sparse but synchronous MTC nature, a large number of devices tend to access a base station simultaneously for transmitting data, leading to congestion. To accommodate a large number of simultaneous arrivals in mMTC, efficient congestion control techniques like access class barring (ACB) are incorporated in LTE-A random access. ACB introduces access delay which may not be acceptable in delay-constrained scenarios, such as, eHealth, self-driven vehicles, and smart grid applications. In such scenarios, MTC devices may be forced to drop packets that exceed their delay budget, leading to a decreased system throughput. To this end, in this paper a novel delay-aware priority access classification (DPAC) based ACB is proposed, where the MTC devices having packets with lesser leftover delay budget are given higher priority in ACB. A reinforcement learning (RL) aided framework, called DPAC-RL, is also proposed for online learning of DPAC model parameters. Simulation studies show that the proposed scheme increases successful preamble transmissions by up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$75 \%$</tex-math></inline-formula> while ensuring that the access delay is well within the delay budget.

Joint Computation Offloading and Radio Resource Allocation in MEC-Based Wireless-Powered Backscatter Communication Networks

The multi-access edge computing (MEC)-based wireless-powered backscatter communication networks (WP-BackComNets) allow wireless devices (WDs) to offload computation resources to lightweight and widely deployed MEC servers with the assistance of backscatter devices (BDs), which have substantial application prospects for the emerging Internet-of-Things applications. However, the limited battery capacity of WDs is one of the bottlenecks restricting its further development. Reducing the energy consumption and the computation burden of WDs while ensuring the quality-of-service requirements is an urgent issue. To this end, a joint computation offloading and radio resource allocation problem is formulated to minimize the total energy consumption of WDs for an MEC-based WP-BackComNet by jointly optimizing user association, the transmit power and transmission time of WDs, the computational offloading coefficient of each task, and the reflection coefficients of BDs, where the circuit power consumption of BDs, the computational capabilities of WDs, and the task execution delay budgets are considered. To handle this non-convex problem, we propose an efficient algorithm to obtain a suboptimal solution. Simulation results demonstrate that the proposed scheme can effectively decrease the energy consumption compared with the benchmarks.

Managing 5G network slicing and edge computing with the MATILDA telecom layer platform

The fifth generation (5G) of mobile networks is seen as a key enabler to support the introduction of digital technologies in multiple sectors, empowering different verticals and enabling new business models. Thanks to native capabilities of 5G networks, especially network slicing, edge computing and multi-tenancy, highly integrated telecom infrastructures with end-to-end flexibility will be realized to lower the barriers for creating 5G-ready applications that are able to satisfy business and user necessities. In this context, the MATILDA Project has established a holistic framework that unifies the development, deployment and operation for this new kind of applications. Since Edge Computing is not natively supported by 4G, in order to anticipate the technological improvements foreseen with the advent of 5G, as well as to smoothen the transition to the new technology, this paper describes the design of the end-point between the mobile and edge environments that has been integrated in the MATILDA telecom layer platform. Such end-point, designed in a Virtual Network Function (VNF), allows intercepting and forwarding data and control traffic towards external Data Networks. Instances of this VNF can be horizontally scaled according to a decision policy, which determines the minimum number of instances required for the current load. Results show that the latency ascribable to the VNF processing is sufficiently low to satisfy the delay budget for all 5G use-cases up to 10 ms and that a QCI-based decision policy allows scaling with the traffic load, while still fulfilling the performance requirements of each application.

Finding the Sweet Spot for Frame Aggregation in 802.11 WLANs

This letter proposes an algorithm for the dynamic tuning of the maximum size of aggregated frames in 802.11 WLANs. Traffic flows with opposed requirements may coexist in these networks: traditional services as web browsing or file download that need high throughput, and services with real-time requirements that need low latency. The proposed algorithm allows the network manager to find an optimal balance ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> the “sweet spot” between throughput and latency: a “delay budget” can be assigned to real-time flows, with the objective of keeping the latency as close as possible to that budget, while penalizing the throughput of traditional services as little as possible.

Holu: Power-Aware and Delay-Constrained VNF Placement and Chaining

Service function chains (SFCs) are an ordered set of virtual network functions (VNFs) which can realize a specific network service. Enabled by virtualization technologies, these VNFs are hosted on physical machines (PMs), and interconnected by network switches. In today networks, these resources are usually under-utilized and/or over-provisioned, resulting in power-inefficient deployments. To improve power-efficiency, SFCs should be deployed utilizing the minimum number of PMs and network equipment, which are not concomitant. Considering the existing PM and switch power consumption models and their resource constraints, we formulate the power-aware and delay-constrained joint VNF placement and routing (PD-VPR) problem as an Integer Linear Program (ILP). Due to the NP-completeness of the problem, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Holu</i> , a fast heuristic framework that efficiently solves the PD-VPR problem in an online manner. Specifically, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Holu</i> decomposes the PD-VPR into two sub-problems and solve them sequentially: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i)</i> a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VNF placement</i> problem that consists of mapping the VNFs to PMs using a centrality-based ranking method, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii)</i> a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">routing</i> problem that efficiently splits the delay budget between consecutive VNFs of the SFC, and finds a Delay-Constrained Least-Cost (DCLC) shortest-path through the selected PMs (hosting VNFs) using the Lagrange Relaxation based Aggregated Cost (LARAC) algorithm. Our simulation results indicate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Holu</i> outperforms the state-of-the-art algorithms in terms of total power consumption and acceptance rate by 24.7% and 31%, respectively.

Optimal 5G network slicing using machine learning and deep learning concepts

Network slicing is predetermined to hold up the diversity of emerging applications with enhanced performance and flexibility requirements in the way of splitting the physical network into numerous logical networks. Consequently, a tremendous data count has been generated with an enormous number of mobile phones due to these applications. This has made remarkable challenges and has a considerable influence on the network slicing performance. This work aims to design an efficient network slicing using a hybrid learning algorithm. Thus, we proposed a model, which involves three main phases: (a) Data collection, (b) Optimal weighted feature extraction (OWFE), and (c) Slicing classification. First, we collected the 5G network slicing dataset, which involves the attributes associated with various network devices like “user device type, duration, packet loss ratio, packet delay budget, bandwidth, delay rate, speed, jitter, and modulation type.” Next, we performed the OWFE, in which a weight function is multiplied with the attribute values to have high scale variation. We optimized this weight function by the hybridization of two meta-heuristic algorithms—glowworm swarm optimization and deer hunting optimization algorithm (DHOA)—and named the proposed model glowworm swarm-based DHOA (GS-DHOA). For the given attributes, we classified the exact network slices like “eMBB, mMTC, and URLLC” for each device by a hybrid classifier using deep belief and neural networks. The weight function of both networks is optimized by the GS-DHOA. The experiment results revealed that the proposed model could influence the provision of accurate 5G network slicing.

Policy Distillation for Real-Time Inference in Fronthaul Congestion Control

Centralized Radio Access Networks (C-RANs) are improving their cost-efficiency through packetized fronthaul networks. Such a vision requires network congestion control algorithms to deal with sub-millisecond delay budgets while optimizing link utilization and fairness. Classic congestion control algorithms have struggled to optimize these goals simultaneously in such scenarios. Therefore, many Reinforcement Learning (RL) approaches have recently been proposed to deal with such limitations. However, when considering RL policies’ deployment in the real world, many challenges exist. This paper deals with the real-time inference challenge, where a deployed policy has to output actions in microseconds. The experiments here evaluate the tradeoff of inference time and performance regarding a TD3 (Twin-delayed Deep Deterministic Policy Gradient) policy baseline and simpler Decision Tree (DT) policies extracted from TD3 via a process of policy distillation. The results indicate that DTs with a suitable depth can maintain performances similar to those of the TD3 baseline. Additionally, we show that by converting the distilled DTs to rules in C++, we can make inference-time nearly negligible, i.e., sub-microsecond time scale. The proposed method enables the use of state-of-the-art RL techniques to congestion control scenarios with tight inference-time and computational constraints.

Delay-Sensitive Network Selection and Offloading in LTE-A and Wi-Fi Heterogeneous Networks

LTE-Advanced (LTE-A) offloading is concerned about alleviating traffic congestion for the LTE-A network, which includes the core network and the radio access network (RAN). Due to the scarcity of the radio resource, offloading for the LTE-A RAN is more critical, for which an efficient way is to integrate Wi-Fi with LTE-A to form a heterogeneous RAN environment. An LTE-A UE (User Equipment) with the wi-fi interface can therefore access the Internet via an LTE-A base station of Evolved Node B (eNB) or a wi-fi Access Point (AP). In this paper, wireless network selection for UEs with delay-sensitive traffic in the heterogeneous RAN of LTE-A and wi-fi is addressed. Based on the queueing model of M/G/1, a novel network selection and offloading scheme, namely Delay-Sensitive Network Selection and Offloading (DSO), is proposed. The average system time at LTE-A eNBs and wi-fi APs calculated according to M/G/1 is used for network selection as well as offloading operations in DSO. The benefit of DSO in terms of satisfying the delay budget of UEs and load balancing is demonstrated by the simulation study.

Edge Learning with Timeliness Constraints: Challenges and Solutions

Future machine learning (ML) powered applications, such as autonomous driving and augmented reality, involve training and inference tasks with timeliness requirements and are communication- and computation-intensive, which demands the edge learning framework. The real-time requirements drive us to go beyond accuracy for ML. In this article, we introduce the concept of timely edge learning, aiming to achieve accurate training and inference while minimizing the communication and computation delay. We discuss key challenges and propose corresponding solutions from data, model, and resource management perspectives to meet the timeliness requirements. In particular, for edge training, we argue that the total training delay rather than rounds should be considered, and propose data or model compression, and joint device scheduling and resource management schemes for both centralized training and federated learning systems. For edge inference, we explore the dependency between accuracy and delay for communication and computation, and propose dynamic data compression and flexible pruning schemes. Two case studies show that the timeliness performance, including the training accuracy under a given delay budget and the completion ratio of inference tasks within deadline, are highly improved with the proposed solutions.

Cost-effective joint optimisation of BBU placement and fronthaul deployment in brown-field scenarios

In this work, one proposes a model to evaluate the optimal deployment of Centralised Radio Access Network (C-RAN) architecture elements, i.e. Base Band processing Units (BBUs) and fronthaul links, in a brown-field scenario, in which traditional base stations are already deployed and a physical network is present. The proposed optimisation framework jointly optimises BBU placement and accesses network infrastructures deployment. It clusterises the Remote Radio Heads in the scenario through a Multicommodity Flow approach and solves the minimum cost fronthaul network deployment through a Rooted Delay-Constrained Minimum Spanning Tree approach. Optical fibre and microwave links are considered as fronthaul infrastructures. The proposed optimisation framework is validated through a comparison with a theoretical output for a canonical scenario, being afterwards applied to a real scenario. A cost analysis for different scenario configurations is presented, and trade-offs and guidelines for a cost optimal deployment of C-RAN are provided. The analysis of results for the real scenario of the city of Lisbon and its surrounding areas shows that the delay budget in the fronthaul network highly impacts on capital expenditures as well as on operational ones. It is shown that a larger delay budget enables an annual cost reduction up to 72% in urban areas and 54% in rural ones.

Enhance Latency-Constrained Computation in MEC Networks Using Uplink NOMA

Non-orthogonal multiple access (NOMA) based mobile edge computing (MEC) networks can enhance delay-constrained computation. Most of the existing works focus on the energy or delay minimization, and the study on NOMA based MEC in terms of successful computation probability has been limited, which motivates this work. In particular, randomly deployed edge computing users are modeled as the homogeneous Poisson Point Process and are divided into two groups namely center group (C-group) and edge group (E-group) for uplink NOMA grouping. We propose a new hybrid offloading scheme in a NOMA-based MEC network that can operate in three different modes, namely partial offloading, complete local computation, and complete offloading. We firstly consider a NOMA grouping scenario where a user from the C-group and a user from the E-group are each given a fixed location. The probability that the computation can be completed within the given delay budget is derived and the optimal parameters, i.e., the time for offloading, the power allocation, and the offloading ratios for the two fixed users, are obtained. It reveals that the optimal offloading ratios are determined by the difference of the computational capability between the edge computing user and the MEC server, and that the locations of users have a big impact on the successful computation probability. Inspired by this, we further study three distance-dependent NOMA grouping schemes in the MEC offloading. Specifically, we provide closed-form mathematical expressions of the successful computation probability and its partial optimal solutions for these three schemes. Simulation results verify the accuracy of the analytical results and compare the performance of the proposed offloading scheme with the existing schemes. Insights on the pros and the cons of different user selection schemes are also provided.

Exploiting Delay Budget Flexibility for Efficient Group Delivery in the Internet of Things

Further accelerated by the Internet of Things (IoT) concept, various devices are being continuously introduced into diverse application scenarios. To achieve unattended updates of IoT smart object(s), there remains a challenging problem concerning how to efficiently deliver messages to specific groups of target nodes, especially considering node mobility. In this paper, the relay selection problem is investigated on the basis of directional movement with randomness (e.g., typically associated with the searching or migrating behavior of animals). Unlike numerous works tackling one-to-one communication, we focus on efficient group delivery (one-to-many). A two-level delay budget model is considered to reflect the flexibility of delay tolerance, which brings potential efficiency gains for group delivery compared with using a single budget boundary. Following the description of the system model, a combinatorial bi-objective optimization problem is formulated and solutions are proposed. Simulation results show that the greedy algorithm can achieve comparable performance to an evolutionary algorithm when the delivery satisfaction outweighs efficiency. Furthermore, we show that our proposed greedy scheme can outperform the state-of-the-art when the delivery efficiency becomes increasingly important.

Urgency-Based Fair Scheduling for LTE to Improve Packet Loss and Fairness: Design and Evaluation

Classic scheduling algorithms for the LTE uplink in the literature may cause severe packet loss, poor fairness, and inefficient resource allocation. To alleviate packet loss, delay budget rather than channel condition plays a key factor in our proposed algorithm that allows the user equipment (UE) with the least left delay budget to be scheduled first. Besides, the UEs with fewer allocated resources can be served earlier to improve fairness. For reaching efficient resource utilization, our proposed algorithm further poses a constraint in sequential resource allocation. Via extensive simulations, we successfully demonstrate that our proposed algorithm significantly outperforms the closely related algorithms in terms of packet loss ratio and fairness index at the cost of raised end-to-end delay but still within the delay bound. For our proposed algorithm, it also gains improvement in throughput due to its reduction in the packet loss ratio and bounded relaxation in the end-to-end delay.

Computing aware scheduling in mobile edge computing system

Mobile edge computing (MEC) is an emerging technology recognized as an effective solution to guarantee the Quality of Service for computation-intensive and latency-critical traffics. In MEC system, the mobile computing, network control and storage functions are deployed by the servers at the network edges (e.g., base station and access points). One of the key issue in designing the MEC system is how to allocate finite computational resources to multi-users. In contrast with previous works, in this paper we solve this issue by combining the real-time traffic classification and CPU scheduling. Specifically, a support vector machine based multi-class classifier is adopted, the parameter tunning and cross-validation are designed in the first place. Since the traffic of same class has similar delay budget and characteristics (e.g. inter-arrival time, packet length), the CPU scheduler could efficiently scheduling the traffic based on the classification results. In the second place, with the consideration of both traffic delay budget and signal baseband processing cost, a preemptive earliest deadline first (EDF) algorithm is deployed for the CPU scheduling. Furthermore, an admission control algorithm that could get rid off the domino effect of the EDF is also given. The simulation results show that, by our proposed scheduling algorithm, the classification accuracy for specific traffic class could be over 82 percent, meanwhile the throughput is much higher than the existing scheduling algorithms.

A Cross-Layer SCTP Scheme With Redundant Detection for Real-Time Transmissions in IEEE 802.11 Wireless Networks

In this paper, we propose a Cross-Layer Stream Control Transmission Protocol (CL-SCTP), which supports multi-streaming and partial reliability for real-time transmissions in IEEE 802.11 wireless networks. In the application layer, we design a Screening Overdue MEssages (SOME) algorithm to leave out transmissions of probable overdue messages. An overdue message refers to a message exceeding its delay budget before the message can be transmitted in the transport layer. Then in the MAC layer, we propose the MOnitoring REdundant frames (MORE) algorithm to detect and remove probable redundant frames. A retransmitted frame from the upper layer makes the same frame entering the MAC layer earlier a redundant frame. We discuss our scheme in detail and perform simulations for both random losses and burst losses. Simulation results show our scheme performs better than Cross-Layer Forwarding Prediction Scheduling (CL-FPS), partially reliable SCTP (PR-SCTP), and SCTP in terms of efficient delivery ratio, average goodput, average end-to-end delay, and representative video metrics including Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), and Video Quality Metric (VQM).