Serverless computing and the function as a service model are new paradigms enabling the fine granular, bottomup construction of cloud-native applications. It can significantly reduce operating costs while shifting the management tasks from developers and application providers towards the cloud operators. But these benefits are provided at the cost of less control over the underlying infrastructure and the application performance, including the end-to-end latency. However, grouping of functions into deployable serverless software artifacts remains still under our control, which has a considerable impact on performance and operation costs. In this paper, we propose fast and efficient algorithms that can partition an application’s functions into separate deployment artifacts in a cost-optimal way while meeting user-defined average end-to-end latency bounds. Moreover, our approach supports the dynamic redesign and reconfiguration of the current deployment setup in response to changes in monitored metrics. Our main contribution is threefold. First, we establish the relevant theoretical models capturing the behavior of the serverless ecosystem and we define the main problem. In addition, the concept of the integrated application management is introduced. Second, we propose novel algorithms providing optimal solutions for different variants of the core problem and the complexity of the methods are analyzed. Third, we demonstrate the applicability and the benefits of our solution by evaluating different deployment scenarios of a realistic use case in Amazon’s public cloud environment.

The constant growth of the number of Internet of Things devices drives a huge increase in data that needs to be analyzed, at times in real time. Multiple platforms are available for delivering such data to analytics engines that can perform various operations on the data with low processing latency. These platforms can find their home in cloud native environments where high availability and scaling to the actual workload can be easily achieved. While the deployment environment is elastic, clusters still need to be adequately dimensioned to accommodate the components of the platforms even under high load.In this paper, we provide an analysis in this regard: we discuss key performance indicators of the popular Kafka message bus and the related Kafka Streams processing engine. Namely, we analyze latency, throughput, CPU and memory resource footprint aspects of these services under varying load and processing tasks that appear in Internet of Things applications. We find subsecond processing latency and linear but heavily task-dependent scaling behavior in the other performance indicators’ case.

At the dawn of autonomous driving, vehicular communications and coordination become more vital than ever. Fast information gathering, processing and sharing creates the basis of safety and efficiency, the main promises of conceding the control of vehicles from humans to machines. In this paper we propose to deploy an information gathering and distributing system that aims exactly at minimizing the latency of delivering the essential information to the end clients. We specifically tackle the crowd-sourced maintenance of high definition maps, i.e., road maps with extremely high accuracy and environmental fidelity containing dynamic information about the traffic as well, via a federated analysis scheme, and by broadcasting those maps through a 5G network. The system is designed for minimizing the latency of information delivery: analytical models based on queuing theory and optimization are proposed, and a wide range of system parameters are evaluated in numerical simulations. We find that the latency of delivering timely high quality information to end clients can be reduced with careful dimensioning of the system. According to our measurements, high-speed 5G data connection is a must, as we reach the optimal latency by building map segments with 1km in diameter via Gb/s uplink speeds in densely populated central metropolitan settings.

Vehicular coordination and communication tasks are crucial aspects of enabling autonomous driving, guaranteeing safety and efficiency. In our present work, we explore methods for collecting and distributing information among participants by employing collaboratively-built high-definition maps that contain fine-grained contextual data. We leverage a hierarchical federated learning structure and anticipatory onboarding of the maps through a mobility-aware content caching scheme and minimize the delay of data delivery in both subsystems. We provide analytical models built on queuing theory and integer linear programming and evaluate essential system parameters in an emulation testbed. Based on our results, we conclude that we can significantly reduce the delay in delivering timely information to vehicular clients by introducing intermediary layers in the federated learning structure and by pre-loading current map tiles corresponding to vehicle paths.

Serverless computing has recently been presented as an effective technology for handling short-lived compute tasks in the cloud. It has the potential of becoming an attractive option also in the context of edge computing where resource-aware deployment, constrained by both limited edge computing resources and experienced latency, plays a vital role. In this paper, we present and experimentally validate a framework that oversees serverless applications in an edge computing scenario. It completely automates serverless application deployment and provides hitless dynamic migration of application compute tasks between a pair of edge nodes, paving the way for handling significantly more complex cases. The framework relies on an integrated deployment, monitoring and offloading infrastructure that enhances AWS IoT Greengrass features and performance. Our implementation provides two separate options for relocating compute tasks by steering application traffic towards the most suitable node. One builds on an on-the-fly application component reconfiguration, while the other selects the suitable node through P4 in-network processing of resource metrics emitted by the nodes. Our experimental demonstration evaluates the migration performance using a latency-sensitive application decomposed to serverless functions. Results reveal extremely fast dynamic reconfiguration and traffic rerouting operations. The used methods avoid congestion peaks at the edge and show no end-to-end latency increase upon migration between the nodes.

Nowadays cloud-based applications and Internet of Things use-cases are becoming more and more common in the field of IT benefiting from the virtually limitless resources and microservice-based deployment options available in the cloud. Observability in such environments is key for tracing application execution to detect possible malfunctions and anomalies. Collecting logs can greatly help in this regard, however, a high volume of logging data can add huge costs for the maintenance of the infrastructure gathering monitoring data. In order to increase the profitability of the application, monitoring-related infrastructure needs to have the lowest cost possible while still being able to fully serve the application’s monitoring needs. In this work, we investigate this aspect and provide an evaluation of the resource footprint of one of the most prominent log collection services, Elastic Stack, from the perspective of its write path.

Cloud native programming and serverless architectures provide a novel way of software development and operation. A new generation of applications can be realized with features never seen before while the burden on developers and operators will be reduced significantly. However, latency sensitive applications, such as various distributed IoT services, generally do not fit in well with the new concepts and today's platforms. In this article, we adapt the cloud native approach and related operating techniques for latency sensitive IoT applications operated on public serverless platforms. We argue that solely adding cloud resources to the edge is not enough and other mechanisms and operation layers are required to achieve the desired level of quality. Our contribution is threefold. First, we propose a novel system on top of a public serverless edge cloud platform, which can dynamically optimize and deploy the microservice-based software layout based on live performance measurements. We add two control loops and the corresponding mechanisms which are responsible for the online reoptimization at different timescales. The first one addresses the steady-state operation, while the second one provides fast latency control by directly reconfiguring the serverless runtime environments. Second, we apply our general concepts to one of today's most widely used and versatile public cloud platforms, namely, Amazon's AWS, and its edge extension for IoT applications, called Greengrass. Third, we characterize the main operation phases and evaluate the overall performance of the system. We analyze the performance characteristics of the two control loops and investigate different implementation options.

The serverless technology, introduced for data center operation, represents an attractive technology for latency-sensitive applications operated at the edge, enabling a resource-aware deployment accounting for limited edge computing resources or end-to-end network congestion to the cloud. This paper presents and validates a framework for automated deployment and dynamic reconfiguration of serverless functions at either the edge or cloud. The framework relies on extensive telemetry data retrieved from both the computing and packet-optical network infrastructure and operates on diverse Amazon Web Services technologies, including Greengrass on the edge. Experimental demonstration with a latency-sensitive serverless application is then provided, showing fast dynamic reconfiguration capabilities, e.g., enabling even zero outage time under certain conditions.

Microservices, serverless architectures, cloud native programming are novel paradigms and techniques which could significantly reduce the burden on both developers and operators of future services. Several types of applications fit in well with the new concepts easing the life of different stakeholders while enabling cloud-grade service deployments. However, latency sensitive applications with strict delay constraints between different components pose additional challenges on the platforms. In order to gain benefit from recent cloud technologies for latency sensitive applications as well, a comprehensive performance analysis of available platforms and relevant components is a crucial first step. In this paper, we address one of the most widely used and versatile cloud platforms, namely Amazon Web Services (AWS), and reveal the delay characteristics of key components and services which impact the overall performance of latency sensitive applications. Our contribution is threefold. First, we define a detailed measurement methodology for CaaS/FaaS (Container/Function as a Service) platforms, specifically for AWS. Second, we provide a comprehensive analysis of AWS components focusing on delay characteristics. Third, we attempt to adjust a drone control application to the platform and investigate the performance on today's system.

Recent cloud technologies enable a diverse set of novel applications with capabilities never seen before. Cloud native programming, microservices, serverless architectures are novel paradigms reducing the burden on both software developers and operators while enabling cloud-grade service deployments. Several types of applications fit in well with the new concepts, however, latency sensitive applications with strict delay constraints pose additional challenges on the platforms. Can we run these applications on today's public cloud platforms making use of the brand new tools and techniques? In this paper, we try to answer this question by addressing one of the most widely used and versatile public cloud platforms, namely Amazon's AWS, and we propose a novel mechanism to optimize the software "layout" based on dynamic performance measurements. Our contribution is threefold. First, we define a combined performance and cost model on CaaS/FaaS (Container/Function as a Service) platforms, specifically for AWS, based on a comprehensive performance analysis, and we also provide an application model capturing the performance requirements. Second, we formulate an optimization problem which minimizes the deployment costs on AWS while meeting the latency constraints. A polynomial algorithm finding the optimal solution is also given. Third, we evaluate the model and the algorithm for different scenarios and investigate the performance on today's system.

Safe and efficient Human-Robot Collaboration (HRC) is an essential feature of future Industry 4.0 production systems which requires sophisticated collision avoidance mechanisms with intense computation need. Digital twins provide a novel way to test the impact of different control decisions in a simulated virtual environment even in parallel. In addition, Virtual/Augmented Reality (VR/AR) applications can revolutionize future industry environments. Each component requires extreme computational power which can be provided by cloud platforms but at the cost of higher delay and jitter. Moreover, clouds bring a versatile set of novel techniques easing the life of both developers and operators. Can these applications be realized and operated on today's systems? In this demonstration, we give answers to this question via real experiments.

This experience paper describes the process of leveraging the NFV orchestration platform built in the EU FP7 project UNIFY to deploy a dynamic network service exemplified by an elastic router. Elasticity is realized by scaling dataplane resources as a function of traffic load. To achieve this, the service includes a custom scaling logic and monitoring capabilities. An automated monitoring framework not only triggers elastic scaling, but also a troubleshooting process which detects and analyzes anomalies, pro-actively aiding both dev and ops personnel. Such a DevOps-inspired approach enables a shorter update cycle to the running service. We highlight multiple learnings yielded throughout the prototype realization, focussing on the functional areas of service decomposition and scaling; programmable monitoring; and automated troubleshooting. Such practical insights will contribute to solving challenges such as agile deployment and efficient resource usage in future NFV platforms.

Next generation network services will be realized by NFV-based microservices to enable greater dynamics in deployment and operations. Here, we present a demonstrator that realizes this concept using the NFV platform built in the EU FP7 project UNIFY. Using the example of an Elastic Router service, we show automated deployment and configuration of service components as well as corresponding monitoring components facilitating automated scaling of the entire service. We also demonstrate automatic execution of troubleshooting and debugging actions. Operations of the service are inspired by DevOps principles, enabling quick detection of operational conditions and fast corrective actions. This demo conveys essential insights on how the life-cycle of an NFV-based network service may be realized in future NFV platforms.

SDN opens a new chapter in network troubleshooting as besides misconfigurations and firmware/hardware errors, software bugs can occur all over the SDN stack. As an answer to this challenge the networking community developed a wealth of piecemeal SDN troubleshooting tools aiming to track down misconfigurations or bugs of a specific nature (e.g. in a given SDN layer). In this demonstration we present EPOXIDE, an Emacs based modular framework, which can effectively combine existing network and software troubleshooting tools in a single platform and defines a possible way of integrated SDN troubleshooting.