Serverless computing is a recently introduced deployment model to provide cloud services. The autoscaling of function instances allows adapting allocated resources to workload, so as to reduce latency and improve resource usage efficiency. However, autoscaling mechanisms could be affected by undesired ‘cold starts’ events, causing latency peaks due to spawning of new instances, which can be critical in edge deployments where applications are typically sensitive to latency. In order to regulate autoscaling of functions and mitigate the latency for accessing services, which may hinder the adoption of the serverless model in edge computing, we resort to the usage of reinforcement learning. Our experimental system is based on OpenFaaS, the most popular open-source Kubernetes-based serverless platform. In this system, we introduce a Q-Learning (QL) agent to dynamically configure the Kubernetes Horizontal Pod Autoscaler (HPA). This is accomplished via a QL model state space and a reward function definition that enforce service level agreement (SLA) compliance, in terms of latency, without allocating excessive resources. The agent is trained and tested using real serverless function invocation patterns, made available by Microsoft Azure. The experimental results show the benefits provided by the proposed solution over state-of-the-art in terms of compliance to the SLA, while limiting resource consumption and service request losses.

One of the key metrics of interest for stream processing applications is “latency”, which indicates the total time it takes for the application to process and generate insights from streaming input data. For mission-critical video analytics applications like surveillance and monitoring, it is of paramount importance to report an incident as soon as it occurs so that necessary actions can be taken right away. Stream processing applications are typically developed as a chain of microser-vices and are deployed on container orchestration platforms like Kubernetes. Allocation of system resources like “cpu” and “memory” to individual application microservices has direct impact on “latency”. Kubernetes does provide ways to allocate these resources e.g. through fixed resource allocation or through vertical pod autoscaler (VPA), however there is no straight-forward way in Kubernetes to prioritize “latency” for an end-to-end application pipeline. In this paper, we present LARA, which is specifically designed to improve “latency” of stream processing application pipelines. LARA uses a regression-based technique for resource allocation to individual microservices. We implement four real-world video analytics application pipelines i.e. license plate recognition, face recognition, human attributes detection and pose detection, and show that compared to fixed allocation, LARA is able to reduce latency by up to 2.8X and is consistently better than VPA. While reducing latency, LARA is also able to deliver over 2X throughput compared to fixed allocation and is almost always better than VPA.

Modern applications are designed as an interacting set of microservices, and these applications are typically deployed on container orchestration platforms like Kubernetes. Several attractive features in Kubernetes make it a popular choice for deploying applications, and automatic scaling is one such feature. The default horizontal scaling technique in Kubernetes is the Horizontal Pod Autoscaler (HPA). It scales each microservice independently while ignoring the interactions among the microservices in an application. In this paper, we show that ignoring such interactions by HPA leads to inefficient scaling, and the optimal scaling of different microservices in the application varies as the stream content changes. To automatically adapt to variations in stream content, we present a novel system called DataX AutoScaler that leverages knowledge of the entire stream processing application pipeline to efficiently auto-scale different microservices by taking into account their complex interactions. Through experiments on real-world video analytics applications, such as face recognition and pose classification, we show that DataX AutoScaler adapts to variations in stream content and achieves up to 43% improvement in overall application performance compared to a baseline system that uses HPA.

The joined use of cloud computing and Internet of Things (IoT) led to the definition of Cloud of Things (CoT), a powerful combination which builds on the strengths of both technologies. Active research in this field provides brand new solutions or improves the ones already in place, but it is hampered by the lack of standardization and by the heterogeneity of adopted technologies. The focus of this work is on the application of a service mesh network to include IoT edge devices in a multi-cloud cluster federation. As the adoption of multi-cloud is rising day by day, the integration of IoT applications in it will surely follow. Available solutions are often restricted to specific use cases and technologies or require IoT middleware to work. The goal of this paper is to propose an architecture which can be used to achieve connectivity, interaction and data exchange between multiple IoT edge devices hosted in distinct environments, through the realisation of a network service mesh. A logical architecture is proposed, defining the components needed, discussing potential applications. An implementation is realised to demonstrate the feasibility of the system. Measurements show that with a negligible increase of computational resources and time, the benefits of a service mesh can be extended to IoT edge devices and the applications deployed on them.

When looking at cloud computing, aside from the commercial solutions like Amazon Web Services and Microsoft Azure, there are also promising open source alternatives. In this paper, our hands-on experiences are summarized with consuming, deploying and managing an on-premise Infrastructure as a Service (IaaS) cloud solution based on the open source OpenStack platform in combination with Ceph as a distributed storage solution. We introduce means on how to achieve high-availability of this small-scale on-premise cloud infrastructure solution and provide network architecture and storage recommendations. Finally, performance measurements of these network and storage solutions are presented, indicating observable throughput and latency differences between the various configurations.

Fifth generation (5G) and beyond systems require flexible and efficient monitoring platforms to guarantee optimal key performance indicators (KPIs) in various scenarios. Their applicability in Edge computing environments requires lightweight monitoring solutions. This work evaluates different candidate technologies to implement a monitoring platform for 5G and beyond systems in these environments. For monitoring data plane technologies, we evaluate different virtualization technologies, including bare metal servers, virtual machines, and orchestrated containers. We show that containers not only offer superior flexibility and deployment agility, but also allow obtaining better throughput and latency. In addition, we explore the suitability of the Function-as-a-Service (FaaS) serverless paradigm for deploying the functions used to manage the monitoring platform. This is motivated by the event oriented nature of those functions, designed to set up the monitoring infrastructure for newly created services. When the FaaS warm start mode is used, the platform gives users the perception of resources that are always available. When a cold start mode is used, containers running the application’s modules are automatically destroyed when the application is not in use. Our analysis compares both of them with the standard deployment of microservices. The experimental results show that the cold start mode produces a significant latency increase, along with potential instabilities. For this reason, its usage is not recommended despite the potential savings of computing resources. Conversely, when the warm start mode is used for executing configuration tasks of monitoring infrastructure, it can provide similar execution times to a microservice-based deployment. In addition, the FaaS approach significantly simplifies the code logic in comparison with microservices, reducing lines of code to less than 38%, thus reducing development time. Thus, FaaS in warm start mode represents the best candidate technology to implements such management functions.