Real-time Communications (RTC) services, including multiparty conferencing, live streaming, and cloud-gaming, rely on a large-scale media plane infrastructure that provides real-time audio/video processing to clients. Unfortunately, offthe- shelf RTC services are not elastically scalable. As a result, operators must provision media servers to meet peak demand, resulting in resource under-utilization and high cost. Given that today microservice orchestrators like Kubernetes allow web-services to scale transparently and econimically, this paper looks at applying the same approach to scale large-scale RTC services. We find that this is challenging for two reasons: (a) the default network dataplane underlying Kubernetes does not meet the compelling traffic management, performance and real-time requirements of RTC; and (b) current autoscaling policies are ill-suited to RTC. We address these challenges by designing a RTC-specific service mesh that pushes media traffic processing into the OS kernel and designing new RTC-specific Kubernetes autoscaling policies. Our evaluation on a functional VoIP test-bed shows that this combination allows to deploy elatically scalable RTC services with 100× lower-jitter and 700× lower RTT than the current state-of-the art.

Novel telecommunication systems build on a cloudified architecture running softwarized network services as disaggregated virtual network functions (VNFs) on commercial off-the-shelf (COTS) hardware to improve costs and flexibility. Given the stringent processing deadlines of modern applications, these systems are critically dependent on a closed-loop control algorithm to orchestrate the execution of the disaggregated components. At the moment, however, the formal model for implementing such real-time control loops is mostly missing. In this paper, we introduce a new real-time VNF execution environment that runs entirely on COTS hardware. First, we define a comprehensive formal model that enables us to reason about packet processing delays across disaggregated VNF processing chains analytically. Then we integrate the model into a gradient-optimization control algorithm to provide optimal scheduling for real-time infocommunication services in a programmable way. We present experimental evidence that our model gives a proper delay estimation on a real software switch. We evaluate our control algorithm on multiple representative use cases using a software switch simulator. Our results show the algorithm drives the system to a real-time capable state in just a few control periods even in case of complex services.

Network Functions (NFs) now process a significant fraction of Internet traffic. Software-based NF Virtualization (NFV) promised to enable rapid development of new NFs by vendors and leverage the power and economics of commodity computing infrastructure for NF deployment. To date, no cloud NFV systems achieve NF chaining, isolation, SLO-adherence, and scaling together with existing cloud computing infrastructure and abstractions, all while achieving generality, speed, and ease of deployment. These properties are taken for granted in other cloud contexts but unavailable for NF processing.

For decades, Internet protocols have been specified using natural language. Given the ambiguity inherent in such text, it is not surprising that protocol implementations have long exhibited bugs. In this paper, we apply natural language processing (NLP) to effect semi-automated generation of protocol implementations from specification text. Our system, Sage, can uncover ambiguous or under-specified sentences in specifications; once these are clarified by the author of the protocol specification, Sage can generate protocol code automatically.

The killer features of the next-generation 5G mobile standard, including mobile edge computing and network slicing, will be very difficult to support with traditional fixed-function network appliances. Rather, the 5G core will depend on programmable switches, which allow packet processing functionality to be reconfigured on the fly in order to deploy virtualized network functions and service chains instantaneously. With 5G on the close horizon, it has become crucial to identify the price for programmability in the software data plane, considering the expected complexity and scale of the next-generation mobile core. In this paper, we report on a multi-year data-plane scalability study we have conducted for a large mobile vendor. Our results paint a rather pessimistic picture on the current landscape of the programmable software data plane. We find that the prominent programmable switches either do not provide all the features necessary to implement 5G telco pipelines efficiently or struggle to meet the scale, and the performance operators have come to expect from conventional fixed-function appliances. The only exception, ESwitch, remains proprietary. We call for further work on data-plane scalability and sketch some directions for future research.

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.