Latency-critical Applications Research Articles

Evaluation of ICT for Networked Control Systems of Latency-Critical Applications in Production

This paper evaluates information and communication technologies (ICT) for networked control systems (NCS) of latency-critical applications in production. Latency-critical applications in production (e.g. closed-loop control or motion control) are focused when optimizing production. However, data capturing of complex latency-critical applications is a challenge. Wired solutions hamper its flexibility. Wireless technologies, however, might lack capability to process data with required latencies and reliability.First, a morphology to characterize latency-critical applications is presented. Second, current ICT (e.g. 5G, LTE, WiFi) are typified. Third, ICT are clustered regarding their process and business potential to control latency-critical applications in production. Finally, the approach is validated with the use cases motion control, factory automation and automated guided vehicles.

A Labeled Architecture for Low-Entropy Clouds: Theory, Practice, and Lessons

Resource efficiency and quality of service (QoS) are both long-pursuit goals for cloud providers over the last decade. However, hardly any cloud platform can exactly achieve them perfectly even until today. Improving resource efficiency or resource utilization often could cause complicated resource contention between colocated cloud applications on different resources, spanning from the underlying hardware to the software stack, leading to unexpected performance degradation. The low-entropy cloud proposes a new software-hardware codesigned technology stack to holistically curb performance interference from the bottom up and obtain both high resource efficiency and high quality of application performance. In this paper, we introduce a new computer architecture for the low-entropy cloud stack, called labeled von Neumann architecture (LvNA), which incorporates a set of label-powered control mechanisms to enable shared components and resources on chip to differentiate, isolate, and prioritize user-defined application requests when competing for hardware resource. With the power of these mechanisms, LvNA was able to protect the performance of certain applications, such as latency-critical applications, from disorderly resource contention while improving resource utilization. We further build and tapeout Beihai, a 1.2 GHz 8-core RISC-V processor based on the LvNA architecture. The evaluation results show that Beihai could drastically reduce the performance degradation caused by memory bandwidth contention from 82.8% to 0.4%. When improving the CPU utilization over 70%, Beihai could reduce the 99th tail latency of Redis from 115 ms to 18.1 ms. Furthermore, Beihai can realize hardware virtualization, which boots up two unmodified virtual machines concurrently without the intervention of any software hypervisor.

ALI: Anonymous Lightweight Inter-Vehicle Broadcast Authentication with Encryption

Wireless broadcast transmission enables Inter-vehicle or Vehicle-to-Vehicle (V2V) communication among nearby vehicles. This communication supports latency-critical applications for improved safety and maybe optimized traffic. However, V2V communication is vulnerable to cyber attacks involving message manipulation. Mechanisms are required to ensure both authenticity and integrity of broadcast data, while maintaining drivers privacy against surveillance. Considering the limited computational resources of vehicles and the possibility of high traffic density scenarios, authentication processes should have low computational overhead. Prior research has produced multiple authentication protocol proposals based on digital signatures, hash functions, or Message Authentication Codes (MACs). To date, there is no computationally efficient secure broadcast authentication scheme tolerable by the vehicles resource-constrained On-Board Units (OBUs) for latency-critical applications in heavy traffic conditions. This paper provides a new secure, efficient, and privacy-preserving scheme proposing Anonymous Lightweight Inter-vehicle (ALI) broadcast authentication with encryption. ALI provides a high level of anonymity by combining a message authentication scheme with beacon encryption. The cryptographic overhead for V2V communication in the ALI scheme is only 149 bytes, and can handle authentication of approximately 700 broadcast messages every 100 milliseconds. This demonstrates the suitability of the ALI scheme in heavy traffic scenarios. We show the security and efficiency of our proposal by conducting security proof and performance analysis.

A Stackelberg Game-Based Dynamic Resource Allocation in Edge Federated 5G Network

Mobile Edge Computing (MEC) plays key role for providing fast-response and high interactivity in the emerging 5G network. Edge service providers (ESP) are responsible for serving edge users or IoT devices running latency-critical applications. An MEC server provides faster response to ESPs but has limited computation resources, hence, it can be overloaded due to extensive resource demand. Thus, federation of multiple MEC servers offers an opportunity for dynamic resource allocation in a distributed manner. The federation objective is to maximize the usage of underutilized edge resources and reduction of service provision time simultaneously. As all the ESPs and MECs act autonomously, it is quite impossible for all the individuals to achieve optimal behavior simultaneously. In this paper, we develop a Stackelberg Game ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$SBG$ </tex-math></inline-formula> ) based dynamic resource allocation method to reach the expected performance. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$SBG$ </tex-math></inline-formula> analyzes the pricing of MECs and the resource-purchasing problem of ESPs. We also develop a many-to-many matching algorithm for resource sharing among the MECs and a one-to-many matching algorithm for that between an MEC server and ESPs. The results from an extensive performance evaluation demonstrate effectiveness of the proposed system in increasing utilities for MECs and ESPs, reducing the turnaround time of application tasks, and ensuring fair resource distribution compared to state-of-the-art works.

Analysis of the capabilities of MPLS technology for managing traffic in communication networks

To support a growing number of users and multiple classes of applications with different performance requirements and characteristics, service providers have been forced to adapt to new technologies. To improve traffic management and Internet service quality, the Internet Engineering Task Force (IETF) proposed MPLS technology to support several classes of latency-critical applications. Traditional IP networks use a hop-by-pop principle for transmitting traffic. This leads to aggregation of heterogeneous traffic on links in different parts of the network, which causes considerable possible growth of congestion and leaves the network with both unbalanced use of resources and link failure in congested parts. This raises the need for traffic engineering to ensure bandwidth guarantees and efficient use of network resources. To overcome these problems, the IETF has proposed a new data transmission mechanism, which is MPLS (Multi protocol label switching), in accordance with the current requirements. The application of MPLS (Multi protocol label switching) technology in modern communication networks is defined by the author as a research task. The report discusses the prospects of MPLS as a universal technology that supports several protocols. The features of construction of virtual private networks (VPN) on MPLS are considered, and how traffic engineering in MPLS takes into account the use of resources, which makes the development of routes based on separate streams or different streams between the same endpoints more effective.

A low-latency real-time PAM-4 receiver enabled by deep-parallel technique

High-speed photonic networks using digital signal processing (DSP) techniques are flourishing nowadays to meet the high-bandwidth requirements of modern bandwidth-thirsty applications in a cost-effective manner. However, the additional latency introduced by DSP is hindering the latency-critical applications. In this paper, a FPGA-based real-time low-latency four-level pulse amplitude modulation (PAM-4) receiver including digital adaptive equalization (DAE) is designed and implemented by using a latency-reducing parallel architecture. The DSP-introduced latency in the receiver end is analyzed in detail. As for DAE parallel implementation, a novel re-allocation scheme is proposed to cope with the issue of the dependency of the output on the successive input samples, and a look-ahead computation technique is introduced to improve the adaptive update efficiency. A real-time PAM-4 receiver is demonstrated in an experimental fiber link with 2.5 Gbit/s data rate for the performance evaluation. Compared with offline processing with MATLAB, the BER performance has little deterioration at 7% FEC limit of 1 × 10−3. With the help of the proposed deep-parallel technique, the DSP-introduced latency is reduced to 0.4μs on average, which better meets the requirements of latency-sensitive user cases in 5G networks. Furthermore, the real-time PAM-4 receiver could be flexibly reconfigured for various scenarios with low-latency requirements, and the latency-efficient parallel technique as well as the latency analysis method can also be extended to high-speed hardware implementation for data rates up to 100 Gbit/s or more.

Latency Reduction in Vehicular Sensing Applications by Dynamic 5G User Plane Function Allocation with Session Continuity.

Vehicle automation is driving the integration of advanced sensors and new applications that demand high-quality information, such as collaborative sensing for enhanced situational awareness. In this work, we considered a vehicular sensing scenario supported by 5G communications, in which vehicle sensor data need to be sent to edge computing resources with stringent latency constraints. To ensure low latency with the resources available, we propose an optimization framework that deploys User Plane Functions (UPFs) dynamically at the edge to minimize the number of network hops between the vehicles and them. The proposed framework relies on a practical Software-Defined-Networking (SDN)-based mechanism that allows seamless re-assignment of vehicles to UPFs while maintaining session and service continuity. We propose and evaluate different UPF allocation algorithms that reduce communications latency compared to static, random, and centralized deployment baselines. Our results demonstrated that the dynamic allocation of UPFs can support latency-critical applications that would be unfeasible otherwise.

Blockchain-enabled fog resource access and granting

Fog computing is a new computing paradigm for meeting ubiquitous massive access and latency-critical applications by moving the processing capability closer to end users. The geographical distribution/floating features with potential autonomy requirements introduce new challenges to the traditional methodology of network access control. In this paper, a blockchain-enabled fog resource access and granting solution is proposed to tackle the unique requirements brought by fog computing. The smart contract concept is introduced to enable dynamic, and automatic credential generation and delivery for an independent offer of fog resources. A per-transaction negotiation mechanism supports the fog resource provider to dynamically publish an offer and facilitates the choice of the preferred resource by the end user. Decentralized authentication and authorization relieve the processing pressure brought by massive access and single-point failure. Our solution can be extended and used in multi-access and especially multi-carrier scenarios in which centralized authorities are absent.

Latency reduction for narrowband URLLC networks: a performance evaluation

Fifth-generation (5G) cellular mobile networks are expected to support mission-critical low latency applications in addition to mobile broadband services, where fourth-generation (4G) cellular networks are unable to support Ultra-Reliable Low Latency Communication (URLLC). However, it might be interesting to understand which latency requirements can be met with both 4G and 5G networks. In this paper, we discuss (1) the components contributing to the latency of cellular networks and (2) evaluate control-plane and user-plane latencies for current-generation narrowband cellular networks and point out the potential improvements to reduce the latency of these networks, (3) present, implement and evaluate latency reduction techniques for latency-critical applications. The two elements we detected, namely the short transmission time interval and the semi-persistent scheduling are very promising as they allow to shorten the delay to processing received information both into the control and data planes. We then analyze the potential of latency reduction techniques for URLLC applications. To this end, we develop these techniques into the long term evolution (LTE) module of ns-3 simulator and then evaluate the performance of the proposed techniques into two different application fields: industrial automation and intelligent transportation systems. Our detailed evaluation results from simulations indicate that LTE can satisfy the low-latency requirements for a large choice of use cases in each field.

Opportunistic Deployment of Distributed Edge Clouds for Latency-Critical Applications

The growing number of latency-critical applications are posing novel challenges for network operators, cloud/hosting companies, and application providers. Edge Computing is the strongest candidate for providing low-latency responses, but it is not yet clear what edge infrastructures will be like. This paper introduces a new platform for enabling an edge infrastructure according to a disaggregated distributed cloud architecture and an opportunistic model based on bare-metal providers. Results from a multi-server online gaming application deployed in a real geo-distributed edge infrastructure show the feasibility, performance and cost efficiency of the solution.

Semantic Coordination on the Edge of Heterogeneous Ultra Dense Networks

Ultra Dense Networks with flexible heterogeneous technologies (HetUDN) for the Radio Access Network (RAN) are prospective candidate architectures that promise to bring the required network flexibility and to support the transition to 5G networks. In the same time, Multi-access Edge Computing (MEC) is a recent paradigm shift that pushes the main focal point of mobile systems from centralized Mobile Cloud Computing (MCC) towards the network edge. Network softwarization enables the operation of new computational elements such as base stations and access points at the MEC, whereas edge computing, network control and storage promise to enable computation-intensive and latency-critical applications for resource constrained mobile devices. In this paper, we employ a novel disaggregated 5G architecture and apply a semantic-based coordination approach from the network edge. We consider semantics as the key enabling principle for the implementation of spectrum coordination schemes addressing the coexistence of heterogeneous technologies at the edge of 5G HetUDN wireless networks. A Semantic-based Framework for dynamic Spectrum Coordination (SFSC) deployed according to the MEC approach is proposed dealing with agile complex system resource management. Practical experimental evidence evaluated on a networking testbed is provided, proving that a semantic MEC middleware is capable of delivering novel advanced computation communication paradigms as needed for 5G networks.

5G as an enabler for cloud-based machine tool control

The new cellular network standard 5G is characterized by greater bandwidth, lower latencies, higher reliabilities, and a higher number of recipients per cell compared to industrial 4G or wireless networks. This makes 5G a promising technology in latency-critical applications in manufacturing, such as machine tool control systems. Today, machine tools are mostly controlled with on-site industrial PCs, which require space, are often heterogeneous, and have limited processing power. 5G potentially enables to migrate the machine control system to external computing resources. This paper aims at elaborating resulting potentials by conceptualizing and outlining an architecture that integrates the requirements and capabilities of 5G and a machine tool control system.

A Deep Neural Network-Based Multi-Label Classifier for SLA Violation Prediction in a Latency Sensitive NFV Application

Recent advancements in the domain of Network Function Virtualization (NFV), and rollout of next-generation networks have necessitated the requirement for the upkeep of latency-critical application architectures in future networks and communications. While Cloud service providers recognize the evolving mission-critical requirements in latency sensitive verticals such as autonomous driving, multimedia, gaming, telecommunications, and virtual reality, there is a wide gap to bridge the Quality of Service (QoS) constraints for the end-user experience. Most latency-critical services are over-provisioned on all fronts to offer reliability, which is inefficient towards scalability in the long run. To address this, we propose a strategy to model frequent violations on the application level as a multi-output target to enable more complex decision-making in the management of virtualised communication networks. In this work, we utilize data from a real-world deployment to configure and draft a realistic set of Service Level Objectives (SLOs) for a voice based NFV application, and develop a deep neural network based multi-label classification methodology to identify and predict multiple categories of SLO breaches associated with an application state. With this, we aim to gain granular SLA and SLO violation insights, enabling us to study and mitigate their impact and inform precision in drafting proactive scaling policies. We further compare the performance against a set of multi-label compatible machine learning classifiers, and address class imbalance in a multi-label setup. We perform a comprehensive evaluation to assess the performance on example-based, label-based and ranking-based measures, and demonstrate the suitability of deep learning in such a use-case.

A Two-stage Strategy to Optimize Energy Consumption for Latency-critical Workload under QoS Constraint

Data centers afford huge energy costs. Saving energy while providing efficient quality of service (Qos) is the goal pursued by data centers. But this is a challenging issue. To ensure the Qos of latency-critical applications, data centers always schedule processors to run at higher frequencies. The continuous high frequency operation will cause great energy waste. Modern processors are equipped with dynamic voltage and frequency scaling (DVFS) technology, which allows the processor to run at every frequency levels it supports, so we focus on how to use DVFS to trade-off between energy and Qos. In this paper, we propose a two-stage strategy based on DVFS to dynamically scaling the CPU frequency during latency-critical workload execution, aimed at minimizing the energy consumption for latency-critical workload which is under the Qos constraint. The two-stage strategy includes a static stage and dynamic stage, which are worked together to determine the optimal frequency for running workload. The static stage uses a well designed heuristic algorithm to determine the frequency-load matches under Qos constraint, while the dynamic stage leverages a threshold method to determine whether to adjust the pre-set frequency. We evaluate the two-stage strategy in terms of Qos and energy saving on the cloudsuite benchmark, and compares the two metrics with the-state-of art Ondemand. Results show that our strategy is superior to Ondemand for energy saving, improving more than 13%.

QoE-Based Mobility-Aware Collaborative Video Streaming on the Edge of 5G

Today's Internet traffic is dominated by video streaming applications transmitted through wireless/cellular interfaces of mobile devices. Although ultrahigh-definition videos are now easily transmitted through mobile devices, video quality level that users perceive is generally lower than expected due to distance-based high latency between sources and end-users. Mobile edge computing (MEC) paradigm is expected to address this issue and provide users with higher perceived quality of experience (QoE) for latency-critical applications, deploying MEC servers at edges. However, due to capacity concerns on MEC servers, a more comprehensive approach is needed to meet users’ expectations applying all possible operations over the resources such as caching, prefetching, and task offloading policies depending on the data repetition or memory/CPU utilization. To address these issues, this article proposes a novel collaborative QoE-based mobility-aware video streaming scheme deployed at MEC servers. Throughout the article, we demonstrate how the proposed scheme can be implemented so as to preserve the desired QoE level per user during entire video sessions. Performance of the proposed scheme has been investigated by extensive simulations. In comparison to existing schemes, the results illustrate that high efficiency is achieved through collaboration among MEC servers, utilizing explicit window size adaptation, collaborative prefetching, and handover among the edges.

Computation Offloading in Hierarchical Multi-Access Edge Computing Based on Contract Theory and Bayesian Matching Game

Multi-access edge computing (MEC) has emerged as a promising paradigm because of its good performance for computation-intensive and latency-critical applications. However, the enormous computing requests from computation service subscribers (CSSs) still cannot be satisfied by pre-existing edge computation nodes (ECNs). To fully utilize the advantage of the MEC network, a hierarchical computation offloading framework is developed under network virtualization (NV) scenario. Accordingly, a two-step sequential process is designed to stimulate the proposed framework. In the first step, an incentive mechanism is proposed in which more temporary ECNs can be motivated by MEC operator and then join the MEC network. Without perfect ECN information, the optimal contract items (the ECN's CPU contribution and reward) between the MEC operator and ECNs can be achieved by taking account of individual rationality (IR) and incentive compatible (IC) constraints. After acquiring the ECNs’ CPU contributions, the computing resource allocation problem between the ECNs and CSSs is then considered in the second step. Since the CSSs have private information, a Bayesian matching game with externality is leveraged to model the problem. Whereas, the conventional resident-oriented Gale-Shapley (RGS) algorithm cannot ensure the stability. Hence, an iterative matching algorithm that can always converge to stable results is developed. Finally, simulation results demonstrate that our proposed two-step sequential decision process can significantly improve social welfare considering the practical scenarios, with reasonable computational complexity.

Optical transport for Industry 4.0 [Invited

Industry 4.0 represents a new industrial revolution that will dramatically change the landscape in many sectors, including manufacturing and logistics. Robotics, machine intelligence, and new forms of connectivity are key ingredients of this paradigm. 5G mobile networks are expected to play a crucial role, supporting a lower cost per transported bit in air and a lower latency compared with 4G. 5G radio ensures different performance levels in very heterogeneous coverage situations, including a mix of indoor and outdoor contexts. Mobile network evolution requires new transport networks to address the new challenging requirements: increasing transmission capacity, compatibility with latency-critical applications, significantly reduced cost with respect to conventional metro network segments, lower energy consumption, and, in some cases, switching capabilities. Optical communications and networking will play a key role in these new transport scenarios, where tailored transmission techniques and network architectures are needed. This paper discusses the requirements and challenges that Industry 4.0 scenarios pose to optical communications and networking architectures. Performance of optical transmission schemes, tailored to support these new radio access networks, are detailed and benchmarked. A network test bed, focused on transport for vertical use cases, is described. Experimental results demonstrate the compliance of the proposed optical transport network with a latency-critical cloud robotics application, which presents industry-grade connectivity needs.

Virtualized EAST–WEST PON architecture supporting low-latency communication for mobile functional split based on multiaccess edge computing

Ultralow latency end-to-end communication with high reliability is one of the most important requirements in 5G networks to support latency-critical applications. A recent approach toward this target is to deploy edge computing nodes with networking capabilities, known as multiaccess edge computing (MEC), which can greatly reduce the service end-to-end latency. However, the use of MEC nodes poses radical changes to the access network architecture. This requires movement from the classical point-to-multipoint (or point-to-point) structure, used to deliver residential broadband and cloud radio access network (Cloud-RAN) services, to a mesh architecture that can fully embed the MEC nodes with all other end points (i.e., mobile cells, fixed residential, businesses). In this paper, we propose what we believe to be a novel passive optical network (PON)-based mobile fronthaul transport architecture based on PON virtualization that allows EAST–WEST communication along with traditional NORTH–SOUTH communication. The architecture enables the end points of a PON tree, where usually optical networking units (ONUs) are located, to also host MEC nodes by deploying an edge optical line terminal (OLT) capable of communicating directly with adjacent ONUs, by reflecting wavelength signals from the splitter nodes. We experimentally show that signal backscattering due to the reflection at the splitter does not affect the system performance. In addition, using protocol level simulations, we show how this architecture can maintain low latency ( ≈ 100 µ s ) in varying mobile traffic conditions by offloading ONUs (i.e., where remote units of Cloud-RAN cells are located) to other edge OLTs through dynamic formation of virtual PON slices. Furthermore, our results show how an efficient migration strategy for ONUs can be chosen depending on the traffic load, different functional split configurations, and the PON capacity.

Resource Management and Scheduling in Distributed Stream Processing Systems

Stream processing is an emerging paradigm to handle data streams upon arrival, powering latency-critical application such as fraud detection, algorithmic trading, and health surveillance. Though there are a variety of Distributed Stream Processing Systems (DSPSs) that facilitate the development of streaming applications, resource management and task scheduling is not automatically handled by the DSPS middleware and requires a laborious process to tune toward specific deployment targets. As the advent of cloud computing has supported renting resources on-demand, it is of great interest to review the research progress of hosting streaming systems in clouds under certain Service Level Agreements (SLA) and cost constraints. In this article, we introduce the hierarchical structure of streaming systems, define the scope of the resource management problem, and present a comprehensive taxonomy in this context covering critical research topics such as resource provisioning, operator parallelisation, and task scheduling. The literature is then reviewed following the taxonomy structure, facilitating a deeper understanding of the research landscape through classification and comparison of existing works. Finally, we discuss the open issues and future research directions toward realising an automatic, SLA-aware resource management framework.

An energy-efficient power management for heterogeneous servers in data centers

Power management for heterogeneous servers has been playing a key role in improving energy efficiency in data centers. Running latency-critical web services on such scenario is still challenging due to the overheads of task transition between such servers. In this paper, we present a runtime power management system, Montgolfier, which is built on a latency-aware feedback control mechanism. It consolidates wimpy and brawny servers into composite nodes performing latency-critical applications to improve overall energy efficiency while ensuring QoS. The key idea behind Montgolfier is to mitigate the negative effect of server switches by dynamic load prediction and to determine thin-provisioned configurations in fine-grain manner within servers for daily fluctuating loads. Our evaluation results show that Montgolfier reduces energy consumption by up to 34.9% without violating any QoS constraints.