Kubernetes, the container orchestrator for cloud-deployed applications, offers automatic scaling for the application provider in order to meet the ever-changing intensity of processing demand. This auto-scaling feature can be customized with a parameter set, but those management parameters are static while incoming Web request dynamics often change, not to mention the fact that scaling decisions are inherently reactive, instead of being proactive. We set the ultimate goal of making cloud-based applications' management easier and more effective. We propose a Kubernetes scaling engine that makes the auto-scaling decisions apt for handling the actual variability of incoming requests. In this engine various machine learning forecast methods compete with each other via a short-term evaluation loop in order to always give the lead to the method that suits best the actual request dynamics. We also introduce a compact management parameter for the cloud-tenant application provider to easily set their sweet spot in the resource over-provisioning vs. SLA violation trade-off. We motivate our scaling solution with analytical modeling and evaluation of the current Kubernetes behavior. The multi-forecast scaling engine and the proposed management parameter are evaluated both in simulations and with measurements on our collected Web traces to show the improved quality of fitting provisioned resources to service demand. We find that with just a few, but fundamentally different, and competing forecast methods, our auto-scaler engine, implemented in Kubernetes, results in significantly fewer lost requests with just slightly more provisioned resources compared to the default baseline.

Edge computing is a (r)evolutionary extension of traditional cloud computing. It expands central cloud infrastructure with execution environments close to the users in terms of latency in order to enable a new generation of cloud applications. This paradigm shift has opened the door for telecommunications operators, mobile and fixed network vendors: they have joined the cloud ecosystem as essential stakeholders considerably influencing the future success of the technology. A key problem in edge computing is the optimal placement of computational units (virtual machines, containers, tasks or functions) of novel distributed applications. These components are deployed to a geographically distributed virtualized infrastructure and heterogeneous networking technologies are invoked to connect them while respecting quality requirements. The optimal hosting environment should be selected based on multiple criteria by novel scheduler algorithms which can cope with the new challenges of distributed cloud architecture where networking aspects cannot be ignored. The research community has dedicated significant efforts to this topic during recent years and a vast number of theoretical results have been published addressing different variants of the related mathematical problems. However, a comprehensive survey focusing on the technical and analytical aspects of the placement problem in various edge architectures is still missing. This survey provides a comprehensive summary and a structured taxonomy of the vast research on placement of computational entities in emerging edge infrastructures. Following the given taxonomy, the research papers are analyzed and categorized according to several dimensions, such as the capabilities of the underlying platforms, the structure of the supported services, the problem formulation, the applied mathematical methods, the objectives and constraints incorporated in the optimization problems, and the complexity of the proposed methods. We summarize the gained insights and important lessons learned, and finally, we reveal some important research gaps in the current literature.

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.

Over the last couple of years, industry operators' associations issued requirements towards an end‐to‐end management and orchestration plane for 5G networks. Consequently, standard organisations started their activities in this domain. This article provides an analysis and an architectural survey of these initiatives and of the main requirements, proposes descriptions for the key concepts of domain, resource and service slicing, end‐to‐end orchestration and a reference architecture for the end‐to‐end orchestration plane. Then, a set of currently available or under development domain orchestration frameworks are mapped to this reference architecture. These frameworks, meant to provide coordination and automated management of cloud and networking resources, network functions and services, fulfil multi‐domain (i.e. multi‐technology and multi‐operator) orchestration requirements, thus enabling the realisation of an end‐to‐end orchestration plane. Finally, based on the analysis of existing single‐domain and multi‐domain orchestration components and requirements, this paper presents a functional architecture for the end‐to‐end management and orchestration plane, paving the way to its full realisation. Copyright © 2016 The Authors. Transactions on Emerging Telecommunications Technologies Published by John Wiley & Sons Ltd.

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 paper, 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.