Latency-sensitive Services Research Articles

Joint Service Caching and Computing Resource Allocation for Edge Computing-Enabled Networks

In this paper, we consider the service caching and the computing resource allocation in edge computing (EC) enabled networks. We introduce a random service caching design considering multiple types of latency sensitive services and the base stations (BSs)’ service caching storage. We then derive a successful service probability (SSP). We also formulate a SSP maximization problem subject to the service caching distribution and the computing resource allocation. Then, we show that the optimization problem is nonconvex and develop a novel algorithm to obtain the stationary point of the SSP maximization problem by adopting the parallel successive convex approximation (SCA). Moreover, to further reduce the computational complexity, we also provide a low complex algorithm that can obtain the near-optimal solution of the SSP maximization problem in high computing capability region. Finally, from numerical simulations, we show that proposed solutions achieve higher SSP than baseline schemes. Moreover, we show that the near-optimal solution achieves reliable performance in the high computing capability region. We also explore the impacts of target delays, a BSs’ service cache size, and an EC servers’ computing capability on the SSP.

Revving up VNDN: Efficient caching and forwarding by expanding content popularity perspective and mobility

Network caching in Vehicular Named Data Networks (VNDN) has the potential to support latency-sensitive services, though given the massive amount of content generated by vehicles in a VNDN, it is challenging to cache sufficiently diverse content across the network for an acceptable hit ratio. Redundant interest transmissions can negatively impact network overhead, wasting network resources when retrieving duplicate content. In the literature, a caching decision is typically based on ongoing popularity, computed from a vehicle’s experience with requests received for individual content. This leads to each vehicle caching the most popular content. Instead, we group content into different categories. All content in a popular category of vehicular service (e.g., tourist information category) is cached equally, not just the most popular content within a category (e.g., information relating to a particular museum). This increases content diversity in the cache. Further, we posit that a service requester from a particular location at a specific time would be interested in the same category of service/application as other users from the same location/time. Leveraging this observation, we consider both forthcoming spatial/temporal and ongoing popularity of vehicular services when ranking their popularity. We also address network overhead with a mobility model that captures the communication duration between vehicles. This parameter, which has been overlooked in existing literature, enables the selection of an appropriate forwarder for content requests. We use ndnSIM VNDN with a mobility trace of Luxembourg city for simulation. Compared to an approach where individual content-wise popularity is computed, our scheme shows more than 100% improvement in cache hit ratio where the number of contents in the network varied between 104 and 105. Simultaneously, we also enhanced the diversity of content caching, while considering the trade-off between cache hit ratio and network overhead.

Throughput-Aware Dynamic Task Offloading Under Resource Constant for MEC With Energy Harvesting Devices

With the explosive increase of Internet of Things (IoT) devices, an increasing number of computation-intensive applications are emerging in IoT system. However, most IoT devices are limited by size and location, equipped with low-performance CPUs and low-capacity batteries, which cannot go well with computation-intensive applications. Mobile edge computing (MEC) is considered as a promising solution to provide computation-intensive and latency-sensitive services in IoT system, but it is still challenging to improve the throughput and extend the battery life of IoT devices under communication constraints. This paper focuses on the task offloading problem for an MEC system with multiple energy harvesting (EH) devices. To accommodate the system dynamics and ensure the system stability in terms of task queue and battery level, we apply Lyapunov optimization theory, and design a computation tasks maximum offloading algorithm to maximize the system throughput. The algorithm can determine the offloading decision in real-time without knowing any statistical information about the system. We first give a series of mathematical analysis to verify the system stability and discuss the performance of the algorithm. In addition, a number of simulation experiments are conducted to present the efficiency of the algorithm.

Role-Based User Allocation Driven by Criticality in Edge Computing

Edge computing is a promising solution to enabling highly accessible resources and latency-sensitive services for nearby users. In public safety, it can provide critical support for urban crowd/hazard management services, such as real-time path planning, hazard warning, etc. In a crowd/hazard scenario, crowds can be allocated to nearby edge servers for obtaining real-time support, e.g., evacuation instructions for those who want to evacuate and crowd flow updates for those who want to rescue, etc. In such scenarios, the behaviors of different roles (like rescuers and evacuees) and the positive/negative interactions among them must be considered in user allocation for reducing injuries and fatalities. In this paper, these issues are defined as a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Role-Based Criticality</i> (RBC) model to describe the fatal risks of different roles in the crowd/hazard scenarios. Based on the model, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Role-Based User Allocation</i> (RUA) problem is formulated. To tackle this problem, we devise an optimal solution named RUA-ILP based on Integer Linear Programming. To accommodate large-scale scenarios, we propose two representative approximate approach named RUA-A and RUA-GA to ensure efficient and effectiveness user allocation respectively. They can maximize the overall role-based criticality which can reduce injuries and fatalities in crowd/hazard scenarios by theoretical proofing and extensive experiments conducted on a real-world dataset.

SAPFS: An Efficient Symmetric-Key Authentication Key Agreement Scheme With Perfect Forward Secrecy for Industrial Internet of Things

An edge-cloud Industrial Internet of Things (IIoT) can help meet the computing requirements of industrial applications, particularly in time and latency-sensitive services. Ensuring the security and privacy of (sensitive) information collected by IIoT end devices is crucial, and has an important impact on the decision-making as well as operational safety. However, these devices are energy-constrained and vulnerable to corruption. The authentication schemes suitable for this environment need to be lightweight, efficient and concise. In this paper, we propose a Symmetric-key Authentication scheme with Perfect Forward Secrecy (SAPFS) protocol, which relies on both authentication and derivation master keys. The SAPFS scheme uses only XOR operation and hash function to achieve mutual authentication, key exchange and message integrity. On the condition of irreversible hash function and indistinguishable master keys, we demonstrate that SAPFS is provably secure under the random oracle model. Finally, a comparative summary with three other competing schemes (in terms of communication cost, storage requirement and computation complexity) demonstrates its utility.

Multi-user edge service orchestration based on Deep Reinforcement Learning

The fifth generation (5G) of mobile network offers a remarkable degree of flexibility to mobile operators, enabling them to provide users with effective and tailored network services. Software Defined Networking (SDN), Network Function Virtualization (NFV), and edge computing have given the operator the opportunity to easily bring computational capacity to the edge and to support latency-sensitive services. While 5G standards have defined the technological and architectural frameworks to orchestrate services, finding effective resources management and QoS optimization policies is still an open research issue.In this paper, we propose an online orchestration methodology for a multi-user edge service. The orchestrator goal is to simultaneously maximize the QoS, and minimize the amount of resources needed.We provide a mathematical formulation to compute an optimal offline policy and derive an online approach based on a model-free Deep Reinforcement Learning (DRL) framework. As a novel feature, the DRL agent action is modeled as a parametric combinatorial problem. A tailored multi-objective reward function leads the agent towards an effective choice of parameters for such a model. Our models are built, trained and fine-tuned by exploiting real data.Extensive simulations in diverse scenarios show that our DRL online approach produces solutions with small gaps to the optimal offline ones, enabling the operator to both save resources and grant the users an adequate QoS level.

Mobility-Aware Proactive Flow Setup in Software-Defined Mobile Edge Networks

The software-defined network (SDN) enabled mobile edge network greatly facilitates network resource management and promotes many emerging applications. However, user mobility may cause the SDN controller to set flow rules frequently, introduce additional flow setup latency, cause delay jitter, and undermine latency-sensitive services. Proactive flow setup is an effective way to eliminate flow setup latency, but existing work fails to maximize the flow setup hit ratio, a metric for evaluating the quality of proactive flow setup decisions, which is critical for latency-sensitive services. In this paper, we study how to proactively set flow rules to maximize the flow setup hit ratio under limited available network resources to eliminate the flow setup latency as much as possible. Then, we formalize the proactive flow setup problem as two integer linear programming problems under two typical routing strategies, default routing and dynamic routing. Both problems are proved to be NP-hard. To tackle these two problems, we propose a linear programming-based polynomial-time approximation algorithm for the default routing case and a greedy-based heuristic algorithm for the dynamic routing case. Extensive trace-driven experimental and simulation results verify that our algorithms can improve the flow setup hit ratio by up to 30.99% compared to existing solutions.

VioLinn: Proximity-aware Edge Placementwith Dynamic and Elastic Resource Provisioning

Deciding where to handle services and tasks, as well as provisioning an adequate amount of computing resources for this handling, is a main challenge of edge computing systems. Moreover, latency-sensitive services constrain the type and location of edge devices that can provide the needed resources. When available resources are scarce there is a possibility that some resource allocation requests are denied. In this work, we propose the VioLinn system to tackle the joint problems of task placement, service placement, and edge device provisioning. Dealing with latency-sensitive services is achieved through proximity-aware algorithms that ensure the tasks are handled close to the end-user. Moreover, the concept of spare edge device is introduced to handle sudden load variations in time and space without having to continuously overprovision. Several spare device selection algorithms are proposed with different cost/performance tradeoffs. Evaluations are performed both in a Kubernetes-based testbed and using simulations and show the benefit of using spare devices for handling localized load spikes with higher quality of service (QoS) and lower computing resource usage. The study of the different algorithms shows that it is possible to achieve this increase in QoS with different tradeoffs against cost and performance.

Safe: Synergic Data Filtering for Federated Learning in Cloud-Edge Computing

With the increasing data scale in the Industrial Internet of Things, edge computing coordinated with machine learning is regarded as an effective way to raise the novel latency-sensitive services. To ensure the data privacy for frequent service access, federated learning (FL), as a privacy-preserving distributed framework, is integrated into edge computing, enabling user data invisible to the training process. However, sophisticated network attacks threaten deep learning (DL) models by data poison and malicious reasoning, making the DL-based system untrustworthy. To this end, a synergic data filtering method, named Safe, is proposed to deal with the poisoning attacks. Specifically, considering that the distributed support vector machine is at risk of being attacked due to its distribution and openness to communication, edge-cloud empowered FL framework is designed. Then, the alternating direction method of multipliers is deployed to detect attacked devices whose training processes will be interrupted. Moreover, due to the untrustworthiness of label data, the poisoned data in the attacked devices are figured out and filtered by clustering the trusted data with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -means clustering algorithm. Eventually, extensive experiment results proved that the Safe outperforms correlation methods in detection accuracy and trustworthiness.

Blockchain-Based Lightweight Multifactor Authentication for Cell-Free in Ultra-Dense 6G-Based (6-CMAS) Cellular Network

Cell-Free mMIMO is a part of technology that will be integrated with future 6G ultra-dense cellular networks to ensure unlimited wireless connectivity and ubiquitous latency-sensitive services. Cell-Free gained researchers’ interest as it offers ubiquitous communication with large bandwidth, high throughput, high data transmission, and greater signal gain. Cell-Free eliminates the idea of cell boundary in cellular communication that reduces frequent handover and inter-cell interference issues. However, the effectiveness of the current authentication protocol could become a serious issue due to the dynamic nature of Cell-Free in densely distributed, high number of users, high mobility, and frequent data exchange. Secondly, secure communication may be achieved in such a dynamic environment at the expense of high authentication overhead, high communication and computational costs. To address the above security challenges, we proposed a lightweight multifactor mutual authentication protocol for Cell-Free communication using ECC-based Deffie Hellman (ECDH). This scheme utilizes timestamping, one-way hash function, Blind-Fold Challenge scheme with public key infrastructure. The proposed cryptosystem integrates with blockchain technology using proof of staked (POS) as a consensus mechanism to ensure integrity, non-repudiation and traceability. The proposed scheme can enforce the mitigation of several major security attacks on communication links such as spoofing attacks, eavesdropping, user location privacy issues, replay attacks, denial of service attacks, and man-in-the-middle (MITM) attacks, which is one of the significant features of the scheme. Furthermore, this scheme contributes to reducing authentication, communication, and computational overheads with an average of 32.8%, 52.4% and 53.2% better performance respectively as compared baseline authentication protocols.

Research on Edge Cloud Load Balancing Strategy Based on Chaotic Hierarchical Gene Replication

Edge cloud is used to handle latency-sensitive services. However, due to the large number of concurrent requests for edge intensive tasks, the resource allocation strategy affects the stability of nodes. In addition to an adaptive resource allocation model based on chaotic hierarchical gene replication (CRPSO model), the concept of chaotic replication ratio is proposed. This study is divided into two parts. The first is to verify the algorithm verification of the simulation platform. By comparison, it is found that CRPSO reduces the CPU and bandwidth utilization by 43.7% and 62.7% on average, respectively, and the memory usage is also lower than other algorithms. Thereafter, we compared the CRPSO algorithm with the Kubernetes clustering algorithm. Experiments showed that the fitness of the CRPSO model is 33.7% higher than that of the comparison algorithm on average. The algorithm is superior to the cluster scheduling algorithm in terms of CPU utilization and memory utilization. Furthermore, the total variance of the two resources involved in this model improved significantly, reaching 69.8% on average. In addition, CRPSO also has great advantages in other aspects of CPU and memory. It is indicated that the model in this study is suitable for the scenario of edge large-scale requests.

Adaptive Request Scheduling and Service Caching for MEC-Assisted IoT Networks: An Online Learning Approach

Multiaccess edge computing (MEC) is a new paradigm to meet the demand of resource-hungry and latency-sensitive services by enabling the placement of services and execution of computing tasks at the edge of radio access networks much closer to resource-constrained devices. However, how to serve more requests while reducing service latency by exploiting limited resources (storage capacities, CPU cycles, communication bandwidth) is still a critical issue in the multidevice MEC-assisted IoT networks, since the time-varying computing demands of devices and unavailability of future information make it difficult to determine where to handle computation tasks and which services to cache. In this article, we propose a twin-timescale framework to jointly optimize adaptive request scheduling (RS) and cooperative service caching (SC) in the multidevices and MEC-assisted networks, in order to explore request dynamic, MECs heterogeneity, service difference. To accommodate the unavailability of future information and unknown system dynamics, we, respectively, formulate RS and SC as partially observable Markov decision process (POMDP) problems. Then, we propose a deep reinforcement learning (DRL)-based online algorithm to improve the service latency reduction ratio and hit rate, which do not require <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> knowledge such as service popularity. Moreover, we give the optimal CPU cycles and communication bandwidth allocations in order to further minimize the average service latency. Extensive and trace-driven simulation results demonstrate the efficacy of the proposed approach.

TERMS: Task management policies to achieve high performance for mixed workloads using surplus resources

Resource contentions and performance interferences can lead to workload performance degradation in mixed-workload deployment clusters. Previous work guarantees the resource requirements of latency-sensitive tasks and reduces performance losses to batch jobs by reclaiming surplus resources from over-provisioned tasks. While the fragmentation of resources leads to a mismatch between provisioned resources and task requirements, resulting in high operation overheads and losses of task fairness. This paper proposes TERMS, the task management policies based on task relevance, resource distribution, and task fairness to achieve efficient and low-cost task management. TERMS mainly includes three types of management policies. The task scheduling policy can schedule new tasks according to task relevance. Task selection strategies select tasks for resource provisioning and task resumption based on resource requirements and task fairness. If necessary, the node selection strategy can be used to choose befitting target nodes based on task relevance and node resource information for task migration when eliminating straggler tasks. Evaluation results show that TERMS can further improve the performance of latency-sensitive services and batch jobs, reduce management overheads, and avoid operation failures.

The Relevance of Virtual-Assisted Early Childhood Education and Occupational Psychotherapy Based on Emotional Interaction.

This paper presents an in-depth study and analysis of the relevance of early childhood education to occupational psychotherapy using a virtual-assisted affective interaction approach. Starting from the educational theory of interactive cognitive psychology, the theoretical basis for parent-child picture book education for interactive learning is explored, as well as the theoretical development after the introduction of AR technology. Firstly, the analysis of young children's emotions involves massive image processing, and the use of cloud computing architecture leads to high latency, while young children's safety is a latency-sensitive service. Secondly, face recognition accuracy based on static images is not high due to problems such as inconspicuous facial features of toddlers and low-quality kindergarten surveillance videos. In this paper, a face identity correction model based on location features is proposed and the superiority of the model is demonstrated through experiments. Finally, this paper analyzes and mines the emotional data of young children. The level of kindergarten teachers' awareness of early childhood mental health education generally showed an upward trend as their titles rose, and there were significant differences in the seven dimensions of early childhood mental health, the purpose and meaning of early childhood mental health education, implementers, targets, content, pathways, and effects; significant differences existed between teachers of different kindergarten natures, and there were significant differences in the purpose and meaning of early childhood mental health education, implementers, targets, content, pathways, effects, and mental health education for young children. Therefore, this paper proposes a face identity correction model based on position information, which considers both the correlation between pixel values in the spatial domain and the correlation between frames in the temporal domain. This paper has developed an emotion analysis system for kindergartens and put it into use in kindergartens to meet the needs of monitoring the safety of young children and evaluating early childhood education and has received good feedback from users, demonstrating the effectiveness of the system.

Joint VNF Placement and Scheduling for Latency-Sensitive Services

Next-generation 6G networks are envisioned to be a key enabler for low-latency services (e.g., extended reality, remote surgery), which cannot be potentially realized by currently deployed networks. Network function virtualization (NFV) and software-defined networking (SDN) are going to continue playing their key role as two promising technologies in 6G to realize these services due to flexibility, agility, scalability, and cost-efficiency. Although NFV and SDN bring several benefits, provisioning latency-sensitive network services (NSs) in an NFV-based infrastructure remains a challenge, as they require stringent service deadlines. To efficiently meet such stringent service deadlines, VNF placement and scheduling need to be carried out jointly. Most of the existing studies tackle these two problems separately. In this paper, we study the joint VNF placement and scheduling problem for latency-sensitive NSs. We aim at optimally determining whether to place new VNFs or to reuse the already deployed VNFs to optimize profits while guaranteeing stringent deadlines. To solve the problem, we formulate it as an integer linear programming (ILP) problem. Due to its complexity, we also propose two efficient heuristics, namely, greedy-based and Tabu search-based algorithms to solve the problem. The simulation results show that our proposed algorithms achieve higher profits than the existing benchmarks.

Federated learning based QoS-aware caching decisions in fog-enabled internet of things networks

Quality of Service (QoS) in the 6G application scenario is an important issue with the premise of the massive data transmission. Edge caching based on the fog computing network is considered as a potential solution to effectively reduce the content fetch delay for latency-sensitive services of Internet of Things (IoT) devices. Considering the time-varying scenario, the machine learning techniques could further reduce the content fetch delay by optimizing the caching decisions. In this paper, to minimize the content fetch delay and ensure the QoS of the network, a Device-to-Device (D2D) assisted fog computing network architecture is introduced, which supports federated learning and QoS-aware caching decisions based on time-varying user preferences. To release the network congestion and the risk of the user privacy leakage, federated learning, is enabled in the D2D-assisted fog computing network. Specifically, it has been observed that federated learning yields suboptimal results according to the Non-Independent Identical Distribution (Non-IID) of local users data. To address this issue, a distributed cluster-based user preference estimation algorithm is proposed to optimize the content caching placement, improve the cache hit rate, the content fetch delay and the convergence rate, which can effectively mitigate the impact of the Non-IID data set by clustering. The simulation results show that the proposed algorithm provides a considerable performance improvement with better learning results compared with the existing algorithms.

End-to-End Congestion Control to Provide Deterministic Latency Over Internet

The emerging latency-sensitive services such as smart grid and tactile internet require deterministic network performance including deterministic end-to-end latency, latency jitter and bounded packet loss rate. To empower standard Ethernet with such capability, we provide a deterministic forwarding system named DLCC with end-to-end congestion control and queue management available at common hardwares to provide bounded delay over Internet. Meanwhile, DLCC provides a per-hop delay correction scheme to minimize delay variation under dynamic scenarios. Compared with other schemes, DLCC effectively reduces the delay jitter by up to 90% and is able to quickly converge to the required end-to-end delay.

RIFL: a reliable link layer network protocol for data center communication

More and more latency-sensitive services and applications are being deployed into the data center. Performance can be limited by the high latency of the network interconnect. Because the conventional network stack is designed not only for a local area network, but also for a wide area network, it carries a great amount of redundancy that is not required in a data center network. This paper introduces the concept of a three-layer protocol stack that can fulfill the exact demands of data center network communications. The detailed design and implementation of the first layer of the stack, which we call RIFL, is presented. A novel low latency in-band hop-by-hop re-transmission protocol is proposed and adopted in RIFL, which guarantees lossless transmission in a data center environment. Experimental results show that RIFL achieves 110 ns point-to-point latency on 10 m active optical cables, at a line rate of 112 Gbps. RIFL is a multi-lane protocol with scalable throughput up to multi-hundred gigabits per second. It can be the enabler of low latency, high throughput, flexible, scalable, and lossless data center networks.

Latency and Reliability Aware Edge Computation Offloading via an Intelligent Reflecting Surface

Despite the advantages of multi-access edge computing in enabling latency-sensitive services and extending the limited computing capabilities of network devices, access communication issues are still often causing the quality of the wireless channels to be severely degraded, preventing the edge resources from being efficiently utilized. Through the deployment of low-cost passive reflecting elements, the recent studies of intelligent reflecting surfaces (IRSs) in wireless networks have shown a great potential for enhancing the quality of the wireless channels and the transmission rates. In this work, motivated by the recent findings, we study the use of an IRS-aided edge computing system for enabling low latency and high reliability computation offloading in the context of a single-user network. Specifically, we optimize the phase shift of the IRS elements along with the device&#x2019;s transmit power and offloading decision, with the objective of minimizing the device&#x2019;s energy consumption. Due to the non-convexity of the problem, we propose a customized sub-optimal solution based on the alternating optimization approach, utilizing novel successive convex approximation techniques. Numerical analysis demonstrates the energy reduction and saving in network resources provided by the optimized use of the IRS, especially for offloading services with higher reliability.

Optimal dynamic spectrum allocation-assisted latency minimization for multiuser mobile edge computing

Mobile Edge Computing (MEC) has been envisioned as an efficient solution to provide computation-intensive yet latency-sensitive services for wireless devices. In this paper, we investigate the optimal dynamic spectrum allocation-assisted multiuser computation offloading in MEC for overall latency minimization. Specifically, we first focus on a static multiuser computation offloading scenario and jointly optimize users' offloading decisions, transmission durations, and Edge Servers' (ESs) resource allocations. Owing to the nonconvexity of our joint optimization problem, we identify its layered structure and decompose it into two problems: a subproblem and a top problem. For the subproblem, we propose a bisection search-based algorithm to efficiently find the optimal users' offloading decisions and ESs’ resource allocations under a given transmission duration. Second, we use a linear search-based algorithm for solving the top problem to obtain the optimal transmission duration based on the result of the subproblem. Further, after solving the static scenario, we consider a dynamic scenario of multiuser computation offloading with time-varying channels and workload. To efficiently address this dynamic scenario, we propose a deep reinforcement learning-based online algorithm to determine the near-optimal transmission duration in a real-time manner. Numerical results are provided to validate our proposed algorithms for minimizing the overall latency in both static and dynamic offloading scenarios. We also demonstrate the advantages of our proposed algorithms compared to the conventional multiuser computation offloading schemes.