Mixed Reality (MR) has launched from science fiction but its entrance in reality can reshape our society. By combining the physical and virtual worlds, it provides novel ways of immersive interactions and experiences and by these means enables a new generation of applications. The most exciting and challenging ones support collaborative, multi-user operation in large geographical scale, require real-time environment comprehension and high visual fidelity. The success or failure is definitely impacted by the capabilities and performance limits of edge cloud platforms and 5G/6G networks providing the offloading features for CPU/GPU intensive MR functions. In addition, the desired quality of user experience calls for further mechanisms at the application level hiding the consequences of varying network characteristics. In this paper, we propose a novel edge cloud based architecture for future remote-rendered MR applications supporting low-latency immersive interactions. Our contribution is threefold. First, the system architecture is presented focusing on the remote rendering, 3D simulation and environment detection control loops. Second, we highlight the main features of our proof-of-concept prototype and our dedicated application, namely the Mixed Reality version of a Rocket League inspired game. Third, the concepts are validated via experiments in a Beyond 5G infrastructure where we analyze the operation and latency characteristics of the overall system. In addition, the quality of the user experience is also evaluated via real-life experiments conducted as part of a student competition. The results show that the latency and jitter characteristics of the most sensitive render loop can be managed efficiently together by a network-level control (slice priorities) and an application-level (dynamic jitter buffer) mechanism.

–Shifting from cloud to edge computing offers the advantage of being closer to the user, which improves latency and helps meet performance-related Service Level Agreement (SLA) requirements. However, the limited resources at the edge necessitate efficient resource scaling to handle fluctuating user demand. To maximize resource utilization, microservices architecture is favored over traditional monolithic approaches, allowing independent scaling of components so that only those needing extra resources are adjusted. Yet, standard reactive scaling methods may struggle to cope with unpredictable user traffic, leading to potential SLA violations. This underscores the need for proactive scaling solutions, where machine learning can play a key role in meeting diverse SLA requirements. In this work, we address these challenges by introducing a machine learning (ML) based proactive scaling framework for microservices in the edge. Our contribution is threefold, first we analyze several ML algorithms, identifying those that can be effectively applied in scaling. Second, we design and implement a scaling system that is capable of collecting metrics at multiple levels and making scaling decisions using ML models to ensure that the application meets the requirements specified in the SLA. Third, the system's efficiency is analyzed by measurements executed in a real environment, where we scale our test microservices-based application. Results show that the system can outperform the Kubernetes' Horizontal Pod Autoscaler in terms of SLA awareness without significant additional resource allocation, making it suitable for the edge.

Monitoring is essential in cloud environments, ensuring performance, efficient resource use, and application oversight. In microservices, it aids debugging and issue resolution, but its cost and resource impact must be minimized, especially in edge-cloud scenarios. In this paper, we propose the Adaptive Monitoring Controller (AdaMC) as a novel adaptive sampling approach for monitoring systems managing dynamic microservices and operated in edge-cloud environments. Our contribution is threefold. First, we present the problem of scrape frequency adaption for microservices. Second, we propose the AdaMC framework as a practical solution to the problem. Third, we evaluate AdaMC using real-world cloud scaling patterns, and different scenarios and compare the results with those of Prometheus monitoring tool. Our tests confirm that this dynamic adjustment can lead to significant resource savings and provide frequent monitoring depending on the behavior of the microservices.

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.

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.

Ongoing research and industrial exploitation of SDN and NFV technologies promise higher flexibility on network automation and infrastructure optimization. Choosing the location of Virtual Network Functions is a central problem in the automation and optimization of the software-defined, virtualization-based next generation of networks such as 5G and beyond. Network services provided for autonomous vehicles, factory automation, e-health and cloud robotics often require strict delay bounds and reliability constraints influenced by the location of its composing Virtual Network Functions. Robots, vehicles and other end-devices provide significant capabilities such as actuators, sensors and local computation which are essential for some services. Moreover, these devices are continuously on the move and might lose network connection or run out of battery, which further challenge service delivery in this dynamic environment. This work tackles the mobility, and battery restrictions; as well as the temporal aspects and conflicting traits of reliable, low latency service deployment over a volatile network, where mobile compute nodes act as an extension of the cloud and edge computing infrastructure. The problem is formulated as a cost-minimizing Virtual Network Function placement optimization and an efficient heuristic is proposed. The algorithms are extensively evaluated from various aspects by simulation on detailed real-world scenarios.

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.

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.

Kubernetes, the prevalent container orchestrator for cloud-deployed web applications, offers an automatic scaling feature for the application provider in order to meet the ever-changing amount of demand from its clients. This auto-scaling service, however, requires a seemingly difficult parameter set to be customized by the application provider, and 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. Therefore we set the ultimate goal of making cloud-based web 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 AI-based 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, as soon as possible. We also introduce a compact management parameter for the cloud-tenant application provider in order to easily set their sweet spot in the resource over-provisioning vs. SLA violation trade-off. 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 competing forecast methods, our auto-scaling engine, implemented in Kubernetes, results in significantly less lost requests with slightly more provisioned resources compared to the default baseline.

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.

Efficient and highly available packet classification is fundamental for various security primitives. In this paper, we evaluate whether the de facto Tuple Space Search (TSS) packet classification algorithm used in popular software networking stacks such as the Open vSwitch is robust against low-rate denial-of-service attacks. We present the Tuple Space Explosion (TSE) attack that exploits the fundamental space/time complexity of the TSS algorithm.

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.

Future services and applications, such as Tactile Internet, coordinated remote driving or wireless controlled exoskeletons, pose serious challenges on the underlying networks and IT platforms in terms of reliability, latency, or capacity, just to mention a few. Towards those services, virtualization is a key enabler from both technological and economic aspects which significantly reshaped the IT and networking ecosystem. On the one hand, cloud computing and the services based on that are evident results of last years' efforts; on the other hand, networking is in the middle of a momentous revolution and important changes mainly driven by Network Function Virtualization (NFV) and Software Defined Networking (SDN). In order to enable carrier grade network services with strict QoS requirements, we need a novel data plane supporting high performance and flexible, fine granular programmability and control. As the network functions (implemented by virtual machines or containers) use the same hardware resources (cpu, memory) as the components responsible for networking, we need a low-level resource orchestrator which is capable of jointly controlling these resources. In this paper, we propose a novel resource orchestrator (RO) for a data plane making use of open source components such as, Docker, DPDK and OVS. Our goal is threefold. First, we propose a novel data plane resource model which is capable of abstracting several hardware architectures. Second, we provide an adapter module which can automatically discover the underlying hardware and build the model on-the-fly. Third, we design and implement a novel RO building on the aforementioned components and a publicly available Service Graph embedding engine. As a proof of the concept, two software switches (OVS, ERFS) are adapted and different hardware platforms are evaluated

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.

IT and telco providers are preparing for the era of 5G; in terms of technology, the driving force is virtualization, both for computing and networking. The 5G services will be superior than today's online services not only in technological aspects, but also from an economic and business perspective: fast service creation, effective utilization of resources, dynamic adaption to actual demand are all direct benefits of the virtualized infrastructure. In this paper we study the economic interactions between 5G resource providers and customers: we formalize how resources should be priced and selected for being booked. In particular we show that usage-based pricing is an income-maximizing scheme for providers, and we derive the problem the customers need to solve for cost-optimizing service deployment.

Flexible service delivery is a key requirement for 5G network architectures. This includes the support for collaborative service delivery by multiple operators, when an individual operator lacks the geographical footprint or the available network, compute or storage resources to provide the requested service to its customer. Network Function Virtualisation is a key enabler of such service delivery, as network functions (VNFs) can be outsourced to other operators. Owing to the (partial lack of) contractual relationships and co-opetition in the ecosystem, the privacy of user data, operator policy and even VNF code could be compromised. In this paper, we present a case for privacy in a VNF-enabled collaborative service delivery architecture. Specifically, we show the promise of homomorphic encryption (HE) in this context and its performance limitations through a proof of concept implementation of an image transcoder network function. Furthermore, inspired by application-specific encryption techniques, we propose a way forward for private, payload-intensive VNFs.

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.

Many networking visioners agree that 5G will be much more than the incremental improvement, in terms of data rate, of 4G. Besides the mobile networks, 5G will fundamentally influence the core infrastructure as well. In our vision the realization of the challenging promises of 5G (e.g. extremely fast, low-overhead, low-delay access of mostly cloudified services and content) will require the massive use of multipathing equipped with low overhead transport solutions tailored to fast, reliable and secure data retrieval from cloud architectures. In this demo we present a prototype architecture supporting such services by making use of automatically configured multipath service chains implementing network coding based transport solutions over off-the-shelf software defined networking (SDN) components.

Multipath TCP is an experimental transport protocol with remarkable recent past and non-negligible future potential. It has been standardized recently, however the evaluation studies focus only on a limited set of isolated use-cases and a comprehensive analysis or a feasible path of Internet-wide adoption is still missing. This is mostly because in the current networking practice it is unusual to configure multiple paths between the endpoints of a connection. Therefore, conducting and precisely controlling multipath experiments over the real “internet” is a challenging task for some experimenters and impossible for others. In this paper, we invoke SDN technology to make this control possible and exploit large-scale internet testbeds to conduct end-to-end MPTCP experiments. More specifically, we establish a special purpose control and measurement framework on top of two distinct internet testbeds. First, using the OpenFlow support of GÉANT, we build a testbed enabling measurements with real traffic. Second, we design and establish a publicly available large-scale multipath capable measurement framework on top of PlanetLab Europe and show the challenges of such a system. Furthermore, we present measurements results with MPTCP in both testbeds to get insight into its behavior in such not well explored environment.

Mininet is a great prototyping tool which combines existing SDN-related software components (e.g., Open vSwitch, OpenFlow controllers, network namespaces, cgroups) into a framework, which can automatically set up and configure customized OpenFlow testbeds scaling up to hundreds of nodes. Standing on the shoulders of Mininet, we implement a similar prototyping system called ESCAPE, which can be used to develop and test various components of the service chaining architecture. Our framework incorporates Click for implementing Virtual Network Functions (VNF), NETCONF for managing Click-based VNFs and POX for taking care of traffic steering. We also add our extensible Orchestrator module, which can accommodate mapping algorithms from abstract service descriptions to deployed and running service chains.

Multipath TCP is an experimental transport protocol with remarkable recent past and non-negligible future potential. However the lack of available large-scale testbeds and publicly accessible multiple paths grossly prohibits the adoption of the technology. Here, we demonstrate a large-scale multipath playground deployed on PlanetLab Europe, which can be used either by experimenters and researchers to test and verify their multipath-related ideas (e.g. enhancing congestion control, fairness or even the arrangement of multiple paths) and also by early adopters to enhance their Internet connection even if single-homed.

The limited capabilities of the switches renders the implementation of unorthodox routing and forwarding mechanisms as a hard task in OpenFlow. Our high level goal is therefore to inspect the possibilities of slightly smartening up the OpenFlow switches. As a first step in this direction we demonstrate (with Bloom filters, greedy routing and network coding) that a very limited computational capability enables us to natively support experimental technologies while preserving performance. We distribute the demos in source files and as a ready-to-experiment VM image to promote further improvements and evaluations.

The development and evaluation of Vehicular Ad Hoc Networks (VANET) and their applications is usually based on coupled simulation environments combining microscopic traffic models and packet level network simulations. However, it is difficult or rather impossible to build simulation scenarios where all the protocols, possible situations and various traffic conditions are properly modeled. This can be attributed to the common lack of information and fine grained control of traffic patterns, missing protocol implementations, and the performance problems of such interlinked simulators. Therefore, simplifications are usually applied on all levels of development and modeling. The novelty of this paper can be found in the effort to propose a framework integrating novel statistical information propagation and higher level communication protocol models into an overall hybrid simulator. The proposed simulation framework can be applied for protocol design and system analysis when it is difficult to build complex interlinked simulation scenarios. In our simulation framework, different models from different levels of operation are integrated. More exactly, this includes a macroscopic traffic model, an information propagation VANET model and discrete event-driven protocol models implemented in the MatLab/Simulink environment. The simulator is validated through various input parameters and scenarios. The results show good performance and interesting aspects of the hybrid simulator; thus, our framework provides a promising tool for development and evaluation of VANET protocols and applications.