Serverless computing is a novel cloud computing paradigm enabling flexible, cost-efficient and fine-granular development of cloud-native applications, although without any guarantees on scheduling or execution times. Thus, various high-level solutions have been proposed in recent years to find proper configurations of individual FaaS functions, while considering user-given QoS requirements. However, the ever-increasing complexity of invocation patterns among stateless functions, externalized management of intermediate states, and diverse public cloud resources pose new challenges to the composition of highly data-intensive serverless applications. In this paper, we fill this gap by proposing novel algorithms based on the emerging function fusion technique, along with the related cost/performance models of composite functions supporting implicit instance parallelization and internal state propagation. We prove the NP-completeness of the underlying latency-constrained tree partitioning problem, and design a bicriteria approximation scheme and a greedy heuristic to derive cost-efficient deployment configurations in polynomial time. With the help of extensive simulations using synthetic call graphs generated from public cloud traces, we demonstrate the applicability and superior runtime performance of our proposed methods compared to state-of-the-art solutions. In addition, we showcase that further cost-reduction of up to 3–6 % can be achieved compared to the optimal partitioning with the allowance of tolerable latency violations.

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.

Augmented Reality (AR) applications can reshape our society enabling novel ways of interactions and immersive experiences in many fields. However, multi-user and collaborative AR applications pose several challenges. The expected user experience requires accurate position and orientation information for each device and precise synchronization of the respective coordinate systems in real-time. Unlike mobile phones or AR glasses running on battery with constrained resource capacity, cloud and edge platforms can provide the computing power for the core functions under the hood. In this paper, we propose a novel edge cloud based platform for multi-user AR applications realizing an essential coordination service among the users. The latency critical, computation intensive Simultaneous Localization And Mapping (SLAM) function is offloaded from the device to the edge cloud infrastructure. Our solution is built on open-source SLAM libraries and the Robot Operating System (ROS). Our contribution is threefold. First, we propose an extensible, edge cloud based AR architecture. Second, we develop a proof-of-concept prototype supporting multiple devices. Third, a dedicated measurement methodology is described and the overall performance of the system is evaluated via real experiments.

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.

Nowadays, online applications are moving to the cloud, and for delay-sensitive ones, the cloud is being extended with edge/fog domains. Emerging cloud platforms that tightly integrate compute and network resources enable novel services, such as versatile IoT (Internet of Things), augmented reality or Tactile Internet applications. Virtual infrastructure managers (VIMs), network controllers and upper-level orchestrators are in charge of managing these distributed resources. A key and challenging task of these orchestrators is to find the proper placement for software components of the services. As the basic variant of the related theoretical problem (Virtual Network Embedding) is known to be NP-hard, heuristic solutions and approximations can be addressed. In this paper, we propose two architecture options together with proof-of-concept prototypes and corresponding embedding algorithms, which enable the provisioning of delay-sensitive IoT applications. On the one hand, we extend the VIM itself with network-awareness, typically not available in today's VIMs. On the other hand, we propose a multi-layer orchestration system where an orchestrator is added on top of VIMs and network controllers to integrate different resource domains. We argue that the large-scale performance and feasibility of the proposals can only be evaluated with complete prototypes, including all relevant components. Therefore, we implemented fully-fledged solutions and conducted large-scale experiments to reveal the scalability characteristics of both approaches. We found that our VIM extension can be a valid option for single-provider setups encompassing even 100 edge domains (Points of Presence equipped with multiple servers) and serving a few hundreds of customers. Whereas, our multi-layer orchestration system showed better scaling characteristics in a wider range of scenarios at the cost of a more complex control plane including additional entities and novel APIs (Application Programming Interfaces).

Envisioned 5G applications and services, such as Tactile Internet, Industry 4.0 use-cases, remote control of drone swarms, pose serious challenges to the underlying networks and cloud platforms. On the one hand, evolved cloud infrastructures provide the IT basis for future applications. On the other hand, networking is in the middle of a momentous revolution and important changes are mainly driven by Network Function Virtualization (NFV) and Software Defined Networking (SDN). A diverse set of cloud and network resources, controlled by different technologies and owned by cooperating or competing providers, should be coordinated and orchestrated in a novel way in order to enable future applications and fulfill application level requirements. In this paper, we propose a novel cross domain orchestration system which provides wholesale XaaS (Anything as a Service) services over multiple administrative and technology domains. Our goal is threefold. First, we design a novel orchestration system exploiting a powerful information model and propose a versatile embedding algorithm with advanced capabilities as a key enabler. The main features of the architecture include i) efficient and multi-purpose service embedding algorithms which can be implemented based on graph models, ii) inherent multidomain support, iii) programmable aggregation of different resources, iv) information hiding together with flexible delegation of certain requirements enabling multi-operator use-cases, and v) support for legacy technologies. Second, we present our proof-of-concept prototype implementing the proposed system. Third, we establish a dedicated test environment spanning across multiple European sites encompassing sandbox environments from both operators and the academia in order to evaluate the operation of the system. Dedicated experiments confirm the feasibility and good scalability of the whole framework.

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.

Supporting end-to-end network slices and services across operators has become an important use case of study for 5G networks as can be seen by 5G use cases published in 3GPP, ETSI as well as NGMN. This paper presents the in- depth architecture, implementation and experiment on a multi-domain orchestration framework that is ab le to deploy such multi-operator service as well as monitor the service for SLA compliance. Our implemented architecture allows operators to abstract their sensitive details while exposing the relevant amount of information to support inter-operator slice creation. Our experiment shows that the implemented framework is capable of creating services across operators while fulfilling the respective service requirements.

Cloud networks provide various services on top of virtualized compute and storage resources. The flexible operation and optimal usage of the underlying infrastructure are realized by resource orchestration methods and virtualization techniques developed during the recent years. In contrast, service deployment and service provisioning in carrier networks have several limitations in terms of flexibility, scalability or optimal resource usage as the built-in mechanisms are strongly coupled to the physical topology and special purpose hardware elements. Network Function Virtualization (NFV) opens the door between cloud and carrier networks by providing software-based telecommunication services which can run in virtualized environment on general purpose hardwares. Our main goal is to unify software and network resources in a common framework. In this paper, we propose a novel architecture supporting automated, dynamic service creation based on a fine-granular service chaining model, SDN and cloud virtualization techniques. First, we introduce the architecture with the main components. Second, the most important benefits are highlighted and compared to other state-of-the-art approaches. Finally, preliminary experiences with our proof-of-concept prototypes are presented.

End-to-end service delivery often includes transparently inserted Network Functions (NFs) in the path. Flexible service chaining will require dynamic instantiation of both NFs and traffic forwarding overlays. Virtualization techniques in compute and networking, like cloud and Software Defined Networking (SDN), promise such flexibility for service providers. However, patching together existing cloud and network control mechanisms necessarily puts one over the above, e.g., OpenDaylight under an OpenStack controller. We designed and implemented a joint cloud and network resource virtualization and programming API. In this demonstration, we show that our abstraction is capable for flexible service chaining control over any technology domains.