Delay Tolerance Research Articles

A market-based real-time algorithm for congestion alleviation incorporating EV demand response in active distribution networks

The dynamic charging behavior of electric vehicles (EVs) is causing frequent line-overloading problems and serious power security issues. Controlled and smart charging mechanisms for EVs incorporating demand response (DR) may provide significant operational flexibility to the grid operators and reduce charging costs for EV users. However, controlling EV charging may cause inconvenience to individual EV users. A market-based mechanism is proposed in this research work that allows EV users and households to participate in the network congestion alleviation DR program online while maintaining the power quality. Consumers submit their charging requirements (e.g., charging power and charging deadlines) and load curtailment tolerances to the aggregator. The aggregator is an entity assumed to have a long-term contract with the distribution system operator (DSO) and regularly receives network congestion information on behalf of retail energy users. Lyapunov optimization (LO) framework is used to reschedule the EVs, and household loads using DC optimal power flow (DCOPF) to get the dynamic congestion cost signal (CCS). The developed strategy is tested on a modified IEEE 33-bus radial active distribution network (ADN). Simulation results illustrate that the designed algorithm is promising in mitigating network congestion by ensuring significantly fewer violations of network constraints (i.e., line limits) both in terms of capacity and frequency. It also results in less energy cost as compared to other benchmark algorithms, e.g., greedy algorithm, and provides a guarantee of meeting EV charging, and flexible household loads’ (FHL) delay tolerance constraints. The average curtailment ratio of FHL and the service delay for EV-charging requests are converged to the user-defined limits, i.e., 0.25 for curtailment ratio tolerance and 10 times-lots for EV service delay. Unlike other counterparts, the developed algorithm is especially suitable for real-time applications as the per-slot average computational cost is negligible, i.e., 0.55 s.

Accurate consensus-based distributed secondary control with tolerance of communication delays for DC microgrids

Distributed secondary control is widely used in DC microgrids (MGs), which can achieve average voltage recovery and accurate power/current sharing among the distributed generators (DGs). In the distributed control architecture, a consensus-based average observer is usually desired to estimate the global average of the output voltage. However, the inevitable communication delay between DGs will cause the output average voltage to deviate from the rated. To tackle this problem, a novel dynamic average consensus observer based on bias compensation has been proposed in this paper, which can accurately converge to the average of the variables being tracked with tolerance of the bounded communication delay while getting rid of the harsh requirement for identical initial value of conventional distributed observer. Based on it, the proposed distributed secondary controller can ensure that the average output voltage converges to the rated without steady-state deviation and that the output currents are proportionally shared accurately. Instead of the conventional voltage and current dual secondary controller, the proposed secondary controller estimates the average of the voltage-current coupling variables and achieves the average voltage recovery and accurate current sharing simultaneously. Moreover, the communication delay margin is determined quantitatively. Finally, the performance of the proposed controller and the efficacy of the calculated communication delay margin is verified by real-time hardware-in-loop (HIL) tests.

Fairness resource allocation based on blockchain for secure communication in integrated IoT

The emerging Space-Air-Ground, Artificial intelligence, blockchain and Vehicle-to-everything technology in Integrated Internet of Things (IoT) enables vehicles to communicate with other vehicles and roadside units (RSU), which improves communication efficiency and driving safety. Due to the rich data in the Integrated IoT, it is easy for illegal persons to steal data or deceive users. While reasonably allocating resources to complete the transmission task, it is still a challenge to ensure the communication security of vehicle users in Integrated IoT. In this paper, we study the cost of vehicle users transmitting tasks by Vehicle to Infrastructure and Vehicle to Vehicle (V2V) in Integrated IoT. In order to protect the information security, we establish the identity authentication and matching model to obtain a better channel environment of Integrated IoT. Moreover, we consider dynamic pricing based on bandwidth resource occupancy to ensure user experience and server load in Integrated IoT. Under the constraints of task tolerable delay, we fix the bandwidth on the V2V side and decouple the task decomposition and bandwidth allocation in Integrated IoT. Then, we propose algorithms Blockchain-based Vehicle Identity Authentication (BVIA) and Delay Weight Fairness Bandwidth Allocation (DWFBA). In this way, vehicle users and RSUs entering the Integrated IoT system are authenticated and matched with devices with good trust value, and the fairness of users resource allocation is guaranteed. Simulation results show that BVIA algorithm can reduce communication overhead, and DWFBA algorithm can control user costs and effectively reduce the number of task failures in Integrated IoT.

Spatio-temporal load migration potential of data centers: Evaluation and application

The wide distribution of data centers and the delay tolerance of computing tasks endow data center loads with adjustable characteristics in both temporal and spatial dimensions. Due to the characteristics of abundant spatiotemporal flexibility, data centers can participate in the optimization of power system operation and regulation. To quantify this flexibility, this paper proposes a spatiotemporal load migration potential evaluation model. Meanwhile, a data center energy management strategy is combined with the characteristics of spatiotemporal load migration, which deeply exploits the migration potential through the spatiotemporal redistribution of delay-tolerant tasks, as well as server ON/OFF scheduling and CPU operating frequency scaling across different spatial locations. A case study demonstrates that adopting the proposed approach considering an energy management strategy can effectively improve the load migration potential of data centers. The migration characteristics of data centers have great application prospects in reducing carbon emissions and enhancing operational flexibility.

Implementing skill‐based treatment within a classroom setting for an adolescent with autism

AbstractResearch exists to support the efficacy of the Interview‐Informed Synthesized Contingency Analysis (IISCA) and skill‐based treatment (SBT) to address problem behavior within a clinical setting. However, limited research is available to support their use in a classroom setting and especially as a tool to avoid the display of problem behavior altogether. In the current study, the IISCA, informed by an open‐ended interview, was conducted with a single participant in the classroom after a prolonged absence from school due to COVID‐19. Results of the IISCA were used to determine the multiple contingencies that were likely to evoke problem behavior upon re‐introduction of academic task demands. A skill‐based treatment was then developed based on these contingencies, which involved teaching the participant functional communication, delay tolerance, and following academic instruction in relevant contexts. After 25 treatment sessions, the participant had acquired and maintained all of these skills, including the ability to functionally communicate, tolerate removal of preferred items, and follow educational instructions within the typical classroom routine. This study shows the applicability of the IISCA and SBT when implemented within the classroom setting and when used to reintroduce educational instruction to an individual with autism after a prolonged break from school.

Towards an Optimal Cloud-Based Resource Management Framework for Next-Generation Internet with Multi-Slice Capabilities

With the advent of 5G networks, the demand for improved mobile broadband, massive machine-type communication, and ultra-reliable, low-latency communication has surged, enabling a wide array of new applications. A key enabling technology in 5G networks is network slicing, which allows the creation of multiple virtual networks to support various use cases on a unified physical network. However, the limited availability of radio resources in the 5G cloud-Radio Access Network (C-RAN) and the ever-increasing data traffic volume necessitate efficient resource allocation algorithms to ensure quality of service (QoS) for each network slice. This paper proposes an Adaptive Slice Allocation (ASA) mechanism for the 5G C-RAN, designed to dynamically allocate resources and adapt to changing network conditions and traffic delay tolerances. The ASA system incorporates slice admission control and dynamic resource allocation to maximize network resource efficiency while meeting the QoS requirements of each slice. Through extensive simulations, we evaluate the ASA system’s performance in terms of resource consumption, average waiting time, and total blocking probability. Comparative analysis with a popular static slice allocation (SSA) approach demonstrates the superiority of the ASA system in achieving a balanced utilization of system resources, maintaining slice isolation, and provisioning QoS. The results highlight the effectiveness of the proposed ASA mechanism in optimizing future internet connectivity within the context of 5G C-RAN, paving the way for enhanced network performance and improved user experiences.

Handover optimisation for high-capacity low-latency 5G NR mmWave communication

In millimetre wave (mmWave) networks, due to the limited communication range and to the narrow antenna beams, traditional handover techniques might not fully comply with the requirements of high-capacity Ultra Reliable Low Latency Communications (URLLC) applications, such as autonomous vehicles. In this paper, we propose an optimal solution that sets performance boundaries for handover mechanisms. We compare the Fifth Generation (5G) handover mechanism performance with the proposed optimal solution in terms of number of handovers, ping-pong effect ratio, and delay. Our assessment indicates that the 5G handover can be improved to meet the novel applications’ tight constraints, evidenced by a ping-pong effect average reduction of 61%, a 5% increase in average capacity, and at least 17% and 15% increases in the number of messages received before delay expiration for 1 ms and 3 ms delay tolerance, respectively.

Ebublio: Edge-Assisted Multiuser 360° Video Streaming

As one of the most important manifestations of virtual reality (VR), 3600 panoramic videos in recent years have experienced booming development due to the desire for immersive and interactive experiences. Compared to traditional videos, 3600 videos are featured with uncertain user field of view (FoV), more sensitive delay tolerance, and much higher bandwidth requirement, bringing unprecedented challenges to 3600 video streaming. Meanwhile, the development of 5G and mobile edge computing starts to pave the way for high-bandwidth low-latency video streaming. Some preliminary works focus on either individual FoV prediction or multi-user QoE oriented cache strategy design, while how to design a holistic solution toward optimizing the overall user QoE with considerations over fairness and long-term system cost remains a non-trivial problem. In this paper, we propose Ebublio, a novel intelligent edge caching framework to address the aforementioned challenges in 3600 video streaming. Ebublio consists of a collaborative FoV prediction (CFP) module and a long-term tile caching optimization (LTO) module to jointly optimize the long-term user QoE and system cost. The former module integrates the features of video content, user trajectory, and other users records for combined prediction. The latter one employs the Lyapunov framework and a subgradient optimization approach towards the optimal caching replacement policy. Our trace-driven evaluation demonstrates the superiority of our framework, with about 42% improvement in FoV prediction, and 36% improvement in QoE at similar traffic consumption.

Defending edge computing based metaverse AI against adversarial attacks

The metaverse, as an impressive technology, can achieve outstanding performance in many fields. With the development of deep neural networks (DNNs), more and more metaverse artificial intelligence (AI) applications are emerging, which have low tolerance for delay. The edge computing based metaverse AI can overcome this problem. However, these AI applications bring new challenges of high reliability, especially DNNs are subject to the security risk of adversarial attacks that generate small and imperceptible noise and cause classifier to make predictions with a high error probability. To meet the reliability requirements of AI applications, in this paper, we propose a security mechanism against adversarial attacks in edge computing based metaverse AI applications. This mechanism utilizes a modified ResNet model that defends against adversarial attacks. We deploy it on the edge cloud to preprocess the data uploaded by metaverse AI applications. In order to achieve a better model performance, we use multiple residual network blocks to build this neural network model on the basis of autoencoder. To assist the model to produce defensively high-quality images, we utilize several convolution layers with various step sizes to process multi-scale images and design to use multiple types of samples with different properties together for the model training. We evaluate our method with experiments on MNIST, CIFAR-10, and CIFAR-100 datasets, and against five attacks such as FGSM and DeepFool attacks. We show that our method achieves 96% defence success rate in MNIST and significantly outperforms existing denoising methods in terms of residual network block, multi-scale image, and training methods.

Availability-aware multi-component V2X application placement

Vehicle-to-everything (V2X) applications are an integral part of the realization of autonomous and connected vehicles. Due to the large amount of data generated by the vehicles, the stringent delay requirements, and the sheer size of V2X applications, these applications are decomposed into several components that need to be placed in close proximity to requesting vehicles. Mobile-edge Computing (MEC) has been envisioned as a promising solution for vehicular task offloading. Despite the desirable properties of MEC, which can potentially satisfy the delay requirements, edge servers have limited computational capability and have a high cost. They also have availability concerns that should be taken into account. In this regard, Application Service Providers (ASPs) strive for a solution that can both satisfy the delay requirements of V2X applications and cut their expenses, which is reflected by the utilization of edge resources. To that end, this work formulates a Binary Linear Program (BLP) that minimizes the ratio of the experienced to the tolerable delay threshold and the utilization of edge resources under resource and V2X applications' availability constraints. To investigate the trade-off between these two objectives, they are parametrized by a coefficient that reflects their relative priorities. Additionally, the delay and the resource objectives are studied in light of different V2X applications' availability constraints. The experimental results show the effectiveness of our approach. On average, the delays of all V2X applications are satisfied with an acceptable level of edge server utilization.

DRL-Based Backbone SDN Control Methods in UAV-Assisted Networks for Computational Resource Efficiency

The limited coverage extension of mobile edge computing (MEC) necessitates exploring cooperation with unmanned aerial vehicles (UAV) to leverage advanced features for future computation-intensive and mission-critical applications. Moreover, the workflow for task offloading in software-defined networking (SDN)-enabled 5G is significant to tackle in UAV-MEC networks. In this paper, deep reinforcement learning (DRL) SDN control methods for improving computing resources are proposed. DRL-based SDN controller, termed DRL-SDNC, allocates computational resources, bandwidth, and storage based on task requirements, upper-bound tolerable delays, and network conditions, using the UAV system architecture for task exchange between MECs. DRL-SDNC configures rule installation based on state observations and agent evaluation indicators, such as network congestion, user equipment computational capabilities, and energy efficiency. This paper also proposes the training deep network architecture for the DRL-SDNC, enabling interactive and autonomous policy enforcement. The agent learns from the UAV-MEC environment through experience gathering and updates its parameters using optimization methods. DRL-SDNC collaboratively adjusts hyperparameters and network architecture to enhance learning efficiency. Compared with baseline schemes, simulation results demonstrate the effectiveness of the proposed approach in optimizing resource efficiency and achieving satisfied quality of service for efficient utilization of computing and communication resources in UAV-assisted networking environments.

LPB‐SPP: Solving the internet of things service placement problem using the learner performance‐based behavior algorithm

AbstractNowadays, fog computing has joined cloud computing as an emerging computing paradigm to provide resources at the network edge. Fog servers process data associated with Internet of Things (IoT) devices independently of cloud computing, thus saving bandwidth, resource reservations, and storage for real‐time applications with lower latency. Besides, cloud computing supports the integration of edge and cloud resources and facilitates the placement of IoT applications at the network edge. Recent researchers focus on how to deploy IoT services as components of IoT applications on fog computing units, where the loss of resources, energy, and bandwidth are minimized. This problem, known as the IoT service placement problem (SPP), is NP‐hard, and meta‐heuristic models are popular to address it. Each IoT service has its own requirements in terms of latency sensitivity, processor, memory, and storage. Meanwhile, fog computing units are heterogeneous and have limited resource capacities. Therefore, SPP should be addressed by considering the features of fog environment, tolerable delay, and network bandwidth. We formulate SPP as a multi‐objective optimization problem with the perspective of throughput, service cost, resource utilization, energy consumption, and service latency. To solve this problem, the learner performance‐based behavior (LPB) algorithm is presented as a meta‐heuristic model that originates from the MAPE‐K autonomous planning model. The proposed approach, LPB‐SPP, considers resource consumption distribution and service deployment prioritization, and also uses the concepts of elitism and balanced resource consumption to improve the placement process. The validation of LPB‐SPP has been done using different performance metrics and the results have been compared against state‐of‐the‐art algorithms. Simulations show that LPB‐SPP performs better in most comparisons.

Sustainable Next-Generation Network Design using Social Aware and Delay Tolerant Approach

With the ever-growing popularity of smartphones, new services are emerging where the local and positioning aspects become more important. Additionally, new routing algorithms are being developed based on various networking technology such as Delay Tolerance Networks (DTN), Deviceto- Device communication, Opportunistic Network, etc. DTN is categorized as stable and unstable, a continuous path between the source and the destination node. Here, communication is done by carrying the message through the intermediate relay node on the store and carry forward paradigm. In this work, a Sustainable Energy Delay Tolerance Approach (SEDA) considers nodes mobility patterns for energy-efficient data transmission. This routing model is based on the appropriate usage of node information along with node mobility and its contacts for data dissemination. Next step is applying some techniques to predict the most likely position for next encounter. We will design and implement a social relation-aware routing relation-awareness the social relation as a part of the routing matrix. We assume that most of the movement of nodes are included in various numbers of social contacts; therefore, there is a higher probability of message delivery to the destination. Simulation results show that the proposed routing protocol improves the delivery ratio by reducing network size and distance, which also impacts energy saving.

Federated Multiagent Actor–Critic Learning Task Offloading in Intelligent Logistics

Intelligent logistics empowered by artificial intelligence (AI) has become an inevitable trend in the development of modern logistics, thus a convenient and efficient logistics system has attracted widespread attention. However, how to use AI to execute computation-intensive applications on resource-constrained logistics vehicles still faces enormous challenges. For the dependent applications in intelligent logistics, this paper investigates a task dynamic offloading strategy for logistics vehicles-edge collaboration with multiple dependent tasks, considering the inter-task dependency, to guarantee the quality of service (QoS) requirements of logistics vehicles. Firstly, the dependent application ARCore is modeled and transformed into a model with a linear execution sequence. Then, based on this task model, the joint task offloading and resource allocation problem is formulated. The goal is to minimize the weighted sum cost of the execution delay and energy consumption while guaranteeing the delay tolerance and computing resource constraints of the tasks. Furthermore, we propose a federated logistics vehicles-edge collaborative computation (FECC) offloading framework to solve the optimization problem, which only requires each agent to share its model parameters without sharing local training data, thereby reducing the computation complexity and signaling overhead of the multi-agent training process. Numerical results show that the proposed strategy has significant advantages in terms of total system cost compared to the baseline strategy.

LMI-Based robust controller for load frequency control of three area interconnected system

The purpose of this research is to create a robust predictive load frequency control (RPLFC) for power systems with unpredictable parameters and temporal delays in communication networks. The study looks closely at the communication of delay constraints across the various control regions as well as the relationship between delay restrictions and control gains. In addition, the usage of delay restrictions as a new performance index to steer controller design is discussed, along with the regulation of the controller for a trade-off between delay tolerance and dynamic reaction, and opting for the upper bound of the sampling period of a discrete realization of the controller, and the upper bound of the fault counter of the communication channel. All of these topics are brought up in the context of determining how to steer controller design. To determine whether or not the proposed technique is useful, simulation studies have all been outfitted with PID-type controllers and have been run for both single-area and three-area LFC schemes. MATLAB/SIMULINK served as the platform upon which the simulations were carried out.

Research on 5G Network Slicing Strategy for Urban Complex Environment

Focusing on the layout of the 5G mobile communication base station in the city center, we design a 5G city network slicing strategy for the three typical application scenarios with enhanced mobile broadband (eMBB), ultrareliable low-latency communications (URLLC), and massive machine type communications (mMTC). The strategy considers multiple important network performance indicators, including user guaranteed bandwidth, maximum bandwidth limit, QoS (quality of service), link delay tolerance, and slicing throughput. The slicing strategy can greatly increase the connections of base station clients and the utilization of network resources, and effectively reduce block radio and handover radio. The simulation experiments adopt the 5G base station dataset of a coastal city layout in Zhejiang province. Our tests show that the 5G network slicing strategy has certain advantages in network transmission performance in urban complex environment. The research can provide an effective reference for 5G infrastructure construction in other cities.

Mars: Near-Optimal Throughput with Shallow Buffers in Reconfigurable Datacenter Networks

The performance of large-scale computing systems often critically depends on high-performance communication networks. Dynamically reconfigurable topologies, e.g., based on optical circuit switches, are emerging as an innovative new technology to deal with the explosive growth of datacenter traffic. Specifically, periodic reconfigurable datacenter networks (RDCNs) such as RotorNet (SIGCOMM 2017), Opera (NSDI 2020) and Sirius (SIGCOMM 2020) have been shown to provide high throughput, by emulating a complete graph through fast periodic circuit switch scheduling. However, to achieve such a high throughput, existing reconfigurable network designs pay a high price: in terms of potentially high delays, but also, as we show as a first contribution in this paper, in terms of the high buffer requirements. In particular, we show that under buffer constraints, emulating the high-throughput complete graph is infeasible at scale, and we uncover a spectrum of unvisited and attractive alternative RDCNs, which emulate regular graphs, but with lower node degree than the complete graph. We present Mars, a periodic reconfigurable topology which emulates a d-regular graph with near-optimal throughput. In particular, we systematically analyze how the degree~d can be optimized for throughput given the available buffer and delay tolerance of the datacenter. We further show empirically that Mars achieves higher throughput compared to existing systems when buffer sizes are bounded.

Delay and Price Differentiation in Cloud Computing: A Service Model, Supporting Architectures, and Performance

Many cloud service providers (CSPs) offer an on-demand service with a small delay. Motivated by the reality of cloud ecosystems, we study non-interruptible services and consider a differentiated service model to complement the existing market by offering multiple service level agreements (SLAs) to satisfy users with different delay tolerance. The model itself is incentive compatible by construction. Two typical architectures are considered to fulfill SLAs: (i) non-preemptive priority queues and (ii) multiple independent groups of servers. We leverage queueing theory to establish guidelines for the resultant market: (a) Under the first architecture, the service model can only improve the revenue marginally over the pure on-demand service model and (b) under the second architecture, we give a closed-form expression of the revenue improvement when a CSP offers two SLAs and derive a condition under which the market is viable. Additionally, under the second architecture, we give an exhaustive search procedure to find the optimal SLA delays and prices when a CSP generally offers multiple SLAs. Numerical results show that the achieved revenue improvement can be significant even if two SLAs are offered. Our results can help CSPs design optimal delay-differentiated services and choose appropriate serving architectures.

Delay tolerance for stable hybrid stochastic differential equations with Lévy noise based on Razumikhin technique

Delay tolerance for stable hybrid stochastic differential equations with Lévy noise based on Razumikhin technique

Game-Based Task Offloading and Resource Allocation for Vehicular Edge Computing With Edge-Edge Cooperation

Vehicular Edge Computing (VEC) enables task offloading from vehicles to the edge servers deployed on Road Side Units (RSUs), thus enhancing the task processing performance of the vehicles. However, in a multi-RSU VEC scenario, the uneven geographical distribution of the vehicles naturally causes the load imbalance among the edge servers and leads to the overload and performance degradation problems of the edge servers in hot areas. To this end, in this paper, we propose a joint task offloading and resource allocation for VEC with edge-edge cooperation, in which the tasks offloaded to a high-load edge server can be further offloaded to the other low-load edge servers. Our objective is to minimize the total task processing delay of all the vehicles while guaranteeing the task processing delay tolerance and the holding time of each vehicle. An M/M/1 queue is used to model the task queuing and task computing processes on each RSU. An exact potential game is adopted to model the competition process for the task offloading among the RSUs. A two-stage iterative algorithm is designed to decompose the optimization problem into two stages and solve them iteratively. We analyze the computational complexity of the algorithm and conduct extensive simulations by varying different crucial parameters. The superiority of our scheme is demonstrated in comparison with 3 other reference schemes.