Latency-sensitive Applications Research Articles

FiCA: A Fixed-Point Custom Architecture FastICA for Real-Time and Latency-Sensitive Applications.

Independent Component Analysis (ICA) is a common method exploited in different biomedical signal processing applications, especially in noise removal of electroencephalography (EEG) signals. Among different existing ICA algorithms, FastICA is a popular method with less complexity, which makes it more suitable for practical implementation. However, and due to its inherent computationally intensive nature, development of a custom FastICA hardware is the best way to utilize it in high-performance real-time applications. On the other hand, development of a custom hardware in a fixed-point manner is also a complex and challenging task due to the algorithm's iterative nature. Moreover, the algorithm intrinsically suffers from some convergence problems which prevents to be practically exploited in latency-sensitive applications. In this paper, a fixed-point fully customized, scalable, and high-performance FastICA processor architecture has been presented. The proposed architecture is developed in an algorithm-aware manner to mitigate the inherent FastICA algorithmic failures. The synthesis results in a 90 nm technology show that the design proposes a computational time of 0.32 ms to perform an 8-channel ICA with a frequency of 555 MHz. The performance-related measurements prove that its normalized throughput is 10 times more, compared to the closest rival.

Design of Latency-Aware IoT Modules in Heterogeneous Fog-Cloud Computing Networks

The modern paradigm of the Internet of Things (IoT) has led to a significant increase in demand for latency-sensitive applications in Fog-based cloud computing. However, such applications cannot meet strict quality of service (QoS) requirements. The large-scale deployment of IoT requires more effective use of network infrastructure to ensure QoS when processing big data. Generally, cloud-centric IoT application deployment involves different modules running on terminal devices and cloud servers. Fog devices with different computing capabilities must process the data generated by the end device, so deploying latency-sensitive applications in a heterogeneous fog computing environment is a difficult task. In addition, when there is an inconsistent connection delay between the fog and the terminal device, the deployment of such applications becomes more complicated. In this article, we propose an algorithm that can effectively place application modules on network nodes while considering connection delay, processing power, and sensing data volume. Compared with traditional cloud computing deployment, we conducted simulations in iFogSim to confirm the effectiveness of the algorithm. The simulation results verify the effectiveness of the proposed algorithm in terms of end-to-end delay and network consumption. Therein, latency and execution time is insensitive to the number of sensors.

Machine and Deep Learning for Resource Allocation in Multi-Access Edge Computing: A Survey

With the rapid development of Internet-of-Things (IoT) devices and mobile communication technologies, Multi-access Edge Computing (MEC) has emerged as a promising paradigm to extend cloud computing and storage capabilities to the edge of cellular networks, near to IoT devices. MEC enables IoT devices with limited battery capacity and computation/storage capabilities to execute their computation-intensive and latency-sensitive applications at the edge of the networks. However, to efficiently execute these applications in MEC systems, each task must be properly offloaded and scheduled onto the MEC servers. Additionally, the MEC servers may intelligently balance and share their computing resources to satisfy the application QoS and QoE. Therefore, effective resource allocation (RA) mechanisms in MEC are vital for ensuring its foreseen advantages. Recently, Machine Learning (ML) and Deep Learning (DL) have emerged as key methods for many challenging aspects of MEC. Particularly, ML and DL play a crucial role in addressing the challenges of RA in MEC. This paper presents a comprehensive survey of ML/DL-based RA mechanisms in MEC. We first present tutorials that demonstrate the advantages of applying ML and DL in MEC. Then, we present enabling technologies for quickly running ML/DL training and inference in MEC. Afterward, we provide an in-depth survey of recent works that used ML/DL methods for RA in MEC from three aspects: (1) ML/DL-based methods for task offloading; (2) ML/DL-based methods for task scheduling; and (3) ML/DL-based methods for joint resource allocation. Finally, we discuss key challenges and future research directions of applying ML/DL for resource allocation in MEC networks.

ShareOn: Shared Resource Dynamic Container Migration Framework for Real-Time Support in Mobile Edge Clouds

Mobile Edge Cloud (MEC) technology is envisioned to play a key role in next generation mobile networks by supporting low-latency applications using geographically distributed local cloud clusters. However, MEC faces challenges of resource assignment and load balancing to support user mobility and latency-sensitive applications. Virtualized resource reallocation techniques including dynamic service migration are evolving to achieve load balance, fault tolerance and system maintenance objectives for resource constrained edge nodes. In this work, a compute and network-aware lightweight resource sharing framework with dynamic container migration, ShareOn, is proposed. The migration framework is validated using a set of heterogeneous edge cloud nodes distributed in San Francisco city, serving mobile taxicab users across that region. The end-to-end system is implemented using a container hypervisor called LXD (Linux Container Hypervisor) executing a real-time application to detect license number plates in automobiles. The system is evaluated based on key metrics associated with application quality-of-service (QoS) and network efficiency such as the average system response time and the migration cost for different combinations of load, compute resources, inter-edge cloud bandwidth, network and user latency. A detailed migration cost analysis enables evaluation of migration strategies to improve ShareOn’s performance in comparison to alternative migration techniques, achieving a gain of 15–22% in system response time for highly loaded edge cloud nodes.

A Heuristic for Load Distribution on Data Center Hierarchy: A MEC Approach

Mobile Edge Computing (MEC) extends Cloud Computing to the edge of the network, creating a hierarchy of data centers. This paradigm provides computing capacity close to final users, relieves backhaul and the leading network, and serves latency-sensitive applications. When providing computing services at different network levels, it becomes necessary to carry out a more efficient distribution of the resources that come to coexist. A random allocation of these resources can lead to a low service acceptance rate and backhaul overhead problems. Problems like these can be solved with MEC. To maximize the service acceptance and ensure a fair distribution of services in the kinds of servers guaranteeing their QoS requirements, we propose a MILP (Mixed Integer Linear Programming). The model performs an optimal allocation of applications in a two-level hierarchy of data centers: (i) MEC and (ii) cloud computing. On a large scale, the use of MILP becomes unfeasible due to the high computational cost, so we propose a heuristic based on the application profiles.We compare the proposed heuristic with two metaheuristics: (i) Genetic Algorithm and (ii) Particle Swarm Optimization. The solutions are compared in terms of service acceptance rate, largescale performance, and efficient use of available resources. Results show that the proposed heuristic reaches 91% of the optimal solution and over 140% compared to AG and PSO solutions.

From 5g to Beyond: Passive Optical Network and Multi-Access Edge Computing Integration for Latency-Sensitive Applications (Invited)

From 5g to Beyond: Passive Optical Network and Multi-Access Edge Computing Integration for Latency-Sensitive Applications (Invited)

A cost aware topology formation scheme for latency sensitive applications in edge infrastructure-as-a-service paradigm

The network topology formation in an Edge Infrastructure-as-a-Service (EIaaS) paradigm must consider the placement of Edge Computational Nodes (ECNs) so as to minimize the delay. Existing ECN placement schemes consider redundant node density, non-optimal location selection, and distance-based association, which affect the ultra-low latency requirement(s) of applications. Further, per ECN to IoT nodes association is key to efficient utilization of ECNs and delay minimization between IoT node(s) and ECN. This work proposes a Cost-aware Edge Computational Node Placement (coECNP) scheme for optimal topology formation in EIaaS paradigm with the objective of IoT nodes delay minimization. It formulates ECN placement problem as a constrained optimization problem. Each iteration in the location discovery module of coECNP identifies optimal placement location by utilizing IoT node’s density on an updated set of IoT nodes and hop-distance among previous iterations’ ECN locations and current candidate locations. As a result, it maximizes the number of IoT nodes that access ECN with minimum hop-distance, leading to end-to-end delay minimization. The assignment module of coECNP takes care of previously assigned nodes in each iteration before associating new IoT nodes to the nearest ECN to attain balanced mapping. Thus, it alleviates total delay from IoT node to respective ECN and enhances edge resource utilization to cater the application(s) near real-time execution requirement(s). The performance comparison indicates that coECNP achieves promising results by reducing IoT nodes delay by 23%–64%, 20%–66%, and 35%–73% on periodic, event-based, and query-based data traffic scenarios, respectively, under various network settings, compared to the benchmark solutions.

Resolving Multitask Competition for Constrained Resources in Dispersed Computing: A Bilateral Matching Game

With the explosive emergence of computation-intensive and latency-sensitive applications, data processing could be envisioned to perform closer to the data source. Similar to edge and fog computing, dispersed computing is considered as a complementary computing paradigm, which can excavate potential computation resources in the network to users, and serve as a supplement for sharing the computational burden when the edge is overloaded. In this article, we first make full use of idle and geographically dispersed computation resources via task offloading, contributing to conserve energy for mobile devices. Especially, a dispersed computing offloading framework concerning the interests of users and networked computation points is proposed. We further transform the initial problem into a multiobjective optimization problem subject to latency and resource constraints. To tackle such a complex problem, an energy-saving bilateral matching algorithm is designed to obtain the optimal task offloading strategy. The simulation results demonstrate that our proposed algorithm can outperform the benchmark schemes in terms of user fairness and can achieve a relatively balanced energy cost ratio. Furthermore, comparative experiments with edge computing are implemented in Amber Response and Disaster Relief scenarios, respectively, to reveal the advantages of the proposed framework.

Fair Server Selection in Edge Computing With $Q$-Value-Normalized Action-Suppressed Quadruple Q-Learning

Edge computing is a promising paradigm that brings servers closer to users, leading to lower latencies and enabling latency-sensitive applications such as cloud gaming, virtual/augmented reality, telepresence, and telecollaboration. Due to the high number of possible edge servers and incoming user requests, the optimum choice of user-server matching has become a difficult challenge, especially in the 5G era where the network can offer very low latencies. In this article, we introduce the problem of fair server selection as not only complying with an application’s latency threshold but also reducing the variance of the latency among users in the same session. Due to the dynamic and rapidly evolving nature of such an environment and the capacity limitation of the servers, we propose as solution a reinforcement learning (RL) method in the form of a quadruple Q-Learning model with action suppression, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -value normalization, and a reward function that minimizes the variance of the latency. Our evaluations in the context of a cloud gaming application show that, compared to an existing methods, our proposed method not only better meets the application’s latency threshold but is also more fair with a reduction of up to 35% in the standard deviation of the latencies experienced by users.

FedMes: Speeding Up Federated Learning With Multiple Edge Servers

We consider federated learning (FL) with multiple wireless edge servers having their own local coverage. We focus on speeding up training in this increasingly practical setup. Our key idea is to utilize the clients located in the overlapping coverage areas among adjacent edge servers (ESs); in the model-downloading stage, the clients in the overlapping areas receive multiple models from different ESs, take the average of the received models, and then update the averaged model with their local data. These clients send their updated model to multiple ESs by broadcasting, which acts as bridges for sharing the trained models between servers. Even when some ESs are given biased datasets within their coverage regions, their training processes can be assisted by adjacent servers through the clients in their overlapping regions. As a result, the proposed scheme does not require costly communications with the central cloud server (located at the higher tier of edge servers) for model synchronization, significantly reducing the overall training time compared to the conventional cloud-based FL systems. Extensive experimental results show remarkable performance gains of our scheme compared to existing methods. Our design targets latency-sensitive applications where edge-based FL is essential, e.g., when a number of connected cars/drones must cooperate (via FL) to quickly adapt to dynamically changing environments.

Deep Reinforcement Learning-Based Server Selection for Mobile Edge Computing

With Mobile Edge Computing (MEC), computational intensive applications can be offloaded to the nearby edge servers to support latency-sensitive applications on mobile devices. Different from the cloud, edge servers usually have limited resources, and then selecting which edge server to run the offloaded computation becomes an important issue. Although server selection has received considerable attention, not much work has been done to consider the limited coverage of the edge server and the frequent user movement, which introduce many dynamic changing factors affecting the workload of the edge server and making it hard to achieve long-term optimum in the edge server selection. To deal with these challenges, we model the problem of continuous server selection as a Markov Decision Process (MDP). The difficulty of this problem is that achieving long-term optimum requires future knowledge, such as user mobility, server workload, etc, which is not known a priori. We do not have such knowledge and thus cannot find the optimal policy through traditional methods. To address this problem, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Deep Reinforcement Learning (DRL)</i> based algorithm to learn the selection policy based on the observed performance of past server selections. Specifically, a Long Short-Term Memory (LSTM) based neural network is exploited to encode the historical information which helps infer future knowledge of the dynamically changing factors. Then the DRL model selects the optimal server automatically based on the extracted system states. Extensive trace-driven evaluations demonstrate that the proposed DRL-based algorithm has the lowest overall cost compared to existing solutions.

When latency matters

Several emerging classes of interactive applications are demanding for extremely low-latency to be fully unleashed, with edge computing generally regarded as a key enabler thanks to reduced delays. This paper presents the outcome of a large-scale end-to-end measurement campaign focusing on task-offloading scenarios, showing that moving the computation closer to the end-users, alone, may turn out not to be enough. Indeed, the complexity associated with modern networks, both at the access and in the core, the behavior of the protocols at different levels of the stack, as well as the orchestration platforms used in data-centers hide a set of pitfalls potentially reverting the benefits introduced by low propagation delays. In short, we highlight how ensuring good QoS to latency-sensitive applications is definitely a multi-dimensional problem, requiring to cope with a great deal of customization and cooperation to get the best from the underlying network.

Joint Computation Partitioning and Resource Allocation for Latency Sensitive Applications in Mobile Edge Clouds

The proliferation of mobile devices and ubiquitous access of the wireless network enable many new mobile applications such as augmented reality, mobile gaming and so on. As the applications are latency sensitive, researchers propose to offload the complex computations of these applications to the nearby edge cloud, in order to reduce the latency. Existing works mostly consider the problem of partitioning the computations between the mobile device and the traditional cloud that has abundant resources. The proposed approaches can not be applied in the context of mobile edge cloud, because both the resources in the mobile edge cloud and the wireless access bandwidth to the edge cloud are constrained. In this paper, we study <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">joint computation partitioning and resource allocation problem</i> for latency sensitive applications in mobile edge clouds. The problem is novel in that we combine the computation partitioning and the two-dimensional resource allocations in both the computation resources and the network bandwidth. We develop a new and efficient method, namely Multi-Dimensional Search and Adjust (MDSA), which is an offline algorithm, to solve the problem. We compare MDSA with the classic list scheduling method and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SearchAdjust</i> algorithm via comprehensive simulations. The results show that MDSA outperforms the benchmark algorithms in terms of the overall application latency. Moreover, we also design an online method, named by Cooperative Online Scheduling (COS), which can be easily deployed in practical systems. By extensive evaluations, we show that COS outperforms the benchmark methods by 25 percent on average.

Security in IoT-Driven Mobile Edge Computing: New Paradigms, Challenges, and Opportunities

With the exponential growth in the number of connected devices, in recent years there has been a paradigm shift toward mobile edge computing. As a promising edge technology, it pushes mobile computing, network control, and storage to the network edges so as to provide better support to computation-intensive Internet of Things (IoT) applications. Although it enables offloading latency-sensitive applications at the resource-limited mobile devices, decentralized architectures and diversified deployment environments bring new security and privacy challenges. This is due to the fact that, with wireless communications, the medium can be accessed by both legitimate users and adversaries. Though cloud computing has helped in substantial transformation of global business, it falls short in provisioning distributed services, namely, security of IoT systems. Thus, the ever-evolving IoT applications require robust cyber-security measures particularly at the network's edge, for widespread adoption of IoT applications. In this vein, the classic machine learning models devised during the last decade, fall short in terms of low accuracy and reduced scalability for real-time attack detection across widely dispersed edge nodes. Thus, the advances in areas of deep learning, federated learning, and transfer learning could mark the evolution of more sophisticated models that can detect cyberattacks in heterogeneous IoT-driven edge networks without human intervention. We provide a SecEdge-Learn Architecture that uses deep learning and transfer learning approaches to provided a secure MEC environment. Moreover, we utilized blockchain to store the knowledge gained from the MEC clusters and thereby realizing the transfer learning approach to utilize the knowledge for handling different attack scenarios. Finally, we discuss the industry relevance of the MEC environment.

Optimal cloud assistance policy of end-edge-cloud ecosystem for mitigating edge distributed denial of service attacks

Edge computing has become a fundamental technology for Internet of Things (IoT) applications. To provide reliable services for latency-sensitive applications, edge servers must respond to end devices within the shortest amount of time possible. Edge distributed denial-of-service (DDoS) attacks, which render edge servers unusable by legitimate IoT applications by sending heavy requests from distributed attacking sources, is a threat that leads to severe latency. To protect edge servers from DDoS attacks, a hybrid computing paradigm known as an end-edge-cloud ecosystem provides a possible solution. Cloud assistance is allowed with this architecture. Edge servers can upload their pending tasks onto a cloud center for a workload reduction when encountering a DDoS attack, similar to borrowing resources from the cloud. Nevertheless, before using the ecosystem to mitigate edge DDoS attacks, we must address the core problem that edge servers must decide when and to what extent they should upload tasks to the cloud center. In this study, we focus on the design of optimal cloud assistance policies. First, we propose an edge workload evolution model that describes how the workload of the edge servers change over time with a given cloud assistance policy. On this basis, we quantify the effectiveness of the policy by using the resulting overall latency and formulate an optimal control problem for seeking optimal policies that can minimize such latency. We then provide solutions by deriving the optimality system and discuss some properties of the optimal solutions to accelerate the problem solving. Next, we introduce a numerical iterative algorithm to seek solutions that can satisfy the optimality system. Finally, we provide several illustrative numerical examples. The results show that the optimal policies obtained can effectively mitigate edge DDoS attacks.

IoHT-enabled gliomas disease management using fog Computing computing for sustainable societies

The proliferation of sensor-based applications in healthcare has given rise to Internet of Health Things (IoHT) that improves patient safety, staff morale, and operational efficiency. Edge-fog computing has seen significant development in recent years and supports the association of various intelligent things with sensors for establishing smooth data transfer. However, it becomes challenging for edge-fog computing to tackle diverse IoHT settings such as efficient disease management, emergency response management, etc. The key limitation of existing architectures is the restricted scalability and inability to meet the demands of hierarchical computing environments for IoHT. This is because latency-sensitive applications often require large quantities of data to be measured and transferred to the data centers, which causes delay and reduced output. This research proposes a novel edge-fog computing framework for the convergence of machine learning ensemble with edge-fog computing. The proposed architecture delivers healthcare as a fog system that handles data from different sources to manage the diseases effectively. The proposed framework is used for the real-life implementation and automatic detection of gliomas diseases. Glioma is a kind of tumor, which ensues in the spinal cord and a portion of the brain. Glioma instigates in the glial cells that surround the nerve cells. The proposed edge-fog framework efficiently manages the real-time data related to gliomas. This framework is configured for specific operating modes including diverse edge-fog scenarios, different user requirements, quality of service, precision, and predictive accuracy. The proposed framework is evaluated using real-time datasets from various sources and experimentally tested with reliable datasets that disclose the effectiveness of the proposed architecture. The performance of the proposed model is evaluated in terms of power consumption, latency, accuracy, and execution time, respectively.

Overview of the challenges and solutions for 5G channel coding schemes

ABSTRACT 5G is the next generation of mobile communications networks which will use cutting-edge network technologies to deliver enhanced mobile connectivity. 5G has introduced new requirements for channel coding in its three different service classes which are enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC) and massive machine-type communications (mMTC). eMBB is expected to keep up with consumer’s insatiable demand for high mobile data rates to support data-extensive applications. mMTC will provide connectivity to a massive number of connected devices sending short data packets simultaneously to support applications such as Internet of Things (IoT). Finally, URLLC will ensure reliable low latency connectivity to support mission-critical latency-sensitive applications such as telesurgery. To address these new requirements, several new channel coding schemes are being developed. This review article provides a detailed analysis of the new channel coding challenges set out by 5G. A detailed review of existing and emerging solutions is provided. Moreover, simulation are performed to assess the performances of Low-Density Parity-Check (LDPC) codes and Polar codes used in 5G’s eMBB. Directions for future works and new solutions for 5G channel coding are also discussed.

Precise Power Capping for Latency-Sensitive Applications in Datacenter

Power capping is widely used in cloud datacenters to mitigate power over-provisioning problem, thus improve datacenter capacity and cut off their operation cost. However, inappropriate or aggressive power capping may lead to performance degradation of applications (especially latency-sensitive ones), and there are few effective methods that can accurately evaluate and control such negative impact caused by aggressive power capping. In this paper, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fine-Grained Differential Method</i> (FGD) to quantitatively analyze how inappropriate power capping degrades the performance of latency-sensitive applications. By using FGD, we can minimize the provisioned power for each server by setting a precise power budget according to application’s <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Service Level Agreement</i> (SLA). And we further propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Precise Power Capping</i> (PPCapping) which is designed to increase the datacenter capacity with a fixed power supply by means of FGD. Our research also provides an insight of precise tradeoff between applications’ SLAs and datacenter capacity. We verify FGD and PPCapping by using real world traces from Tencent’s datecenter with 25,328 servers. The experimental results show that FGD can accurately analyze the impact of power capping on the performance of latency-sensitive applications, and PPCapping can effectively increase datacenter capacity compared with the typical power provisioning strategy.

Zero-Wire

Latency-sensitive applications for the Internet of Things (IoT) often require performance guarantees that contemporary wireless networks fail to offer. Application scenarios involving real-time control of industrial machinery, robotics, or delay-sensitive actuation therefore typically still rely on cables: today's wireless networks cannot deliver messages in a sufficiently small and predictable amount of time. Drop-in wireless replacements for these cabled systems would nevertheless provide great benefit by eliminating the high cost and complexity associated with running cables in harsh industrial environments [1]. The symbolsynchronous bus, introduced in this article and embodied in a platform called Zero-Wire, is a novel wireless networking paradigm that addresses this gap. Using concurrent optical transmissions, it strives to bring low-latency deterministic networking to the wireless IoT.

A measurement study of offloading virtual network functions to the edge

The deployment of virtual network functions (VNFs) at edge servers potentially impairs the performance of latency-sensitive applications due to their computational cost. This work considers a new approach to addressing this problem that provides line rate acceleration of VNFs by employing field-programmable gate array (FPGA) equipped edge servers. This approach has been validated by practical use cases with both TCP and UDP as underlying protocol on a physical testbed environment. We examine the performance implications of executing a security VNF at an FPGA-equipped edge server. We experimentally demonstrate reduced VNF execution latency and energy consumption for a real-time video streaming application in comparison with a software-only baseline. In particular, the results show that the approach lowers VNF execution latency and power consumption at the edge by up to 44% and 76%, respectively, in our experiments while satisfying time constraints and maintaining confidentiality with high scalability. We also highlight the potential research challenges to make this approach viable in practice.