Programmable data planes allow for sophisticated applications that give operators the power to customize the functionality of their networks. Deploying these applications, however, often requires tedious and burdensome optimization of their layout and design, in which programmers must manually write, compile, and test an implementation, adjust the design, and repeat. In this paper we present Parasol, a framework that allows programmers to define general, parameterized network algorithms and automatically optimize their various parameters. The parameters of a Parasol program can represent a wide variety of implementation decisions, and may be optimized for arbitrary, high-level objectives defined by the programmer. Furthermore, optimization may be tailored to particular environments by providing a representative sample of traffic. We show how we implement the Parasol framework, which consists of a sketching language for writing parameterized programs, and a simulation-based optimizer for testing different parameter settings. We evaluate Parasol by implementing a suite of ten data-plane applications, and find that Parasol produces a solution with comparable performance to hand-optimized P4 code within a two-hour time budget.

The P4 language enables a range of new network applications. However, it is still far from easy to implement and optimize P4 programs for PISA hardware. Programmers must engage in a tedious "trial and error" process wherein they write their program (guessing it will fit within the hardware) and then check by compiling it. If it fails, they repeat the process. In this paper, we argue that programmers should define elastic data structures that stretch automatically to make use of available switch resources. We present P4All, an extension of P4 that supports elastic switch programming. Elastic data structures also make P4All modules reusable across different applications and hardware targets, where resource needs and constraints may vary.Our design is oriented around use of symbolic primitives (integers that may take on a range of possible values at compile time), arrays, and loops. We show how to use these primitive mechanisms to build a range of reusable libraries such as hash tables, Bloom filters, sketches, and key-value stores. We also explain the important role that elasticity plays in modular programming, and we allow programmers to declare utility functions that control the relative share of data-plane resources apportioned to each module.

For several years researchers have used the term "network orchestration" as a metaphor. In this paper, we make the metaphor reality; we describe a novel approach to network orchestration that leverages sounds to augment or replace various network management operations. We test our Music-Defined Networking approach with both a real and a virtual network testbed, on several mechanisms and applications: from datacenter server fan failure detection to authentication, from load balancing to explicit congestion notification and detection of heavy hitter flows. Our approach can be used with and without a Software-Defined Network controller. Despite its limitations, we believe that sound-based network management has potential to be further explored as an effective and inexpensive out-of-band orchestration technique.

One of the main goals of mobile edge computing is to support new generation latency-sensitive networked applications. To manage such demanding applications, a fine-grained control of end-to-end paths is imperative. End-to-end delay estimation and forecast techniques were essential traffic engineering tools even before the mobile edge computing paradigm pushed the cloud closer to the end user. In this paper, we model the path selection problem for edge traffic engineering using a risk minimization technique inspired by portfolio theory in economics, and we use machine learning to estimate the risk of a path. In particular, using real latency time series measurements, collected with and without the GENI testbed, we compare four short-horizon latency estimation techniques, commonly used by the finance community to estimate prices of volatile financial instruments. Our initial results suggest that a Bayesian Network approach may lead to good latency estimation performance and open a few research questions that we are currently exploring.

Traffic engineering at network edges is challenging given the latency-sensitive nature of all applications that need to be supported. End-to-end delay estimation and forecasts were essential traffic engineering tools even before the mobile edge computing paradigm pushed the cloud closer to the end user. In this paper, we model the path selection problem for edge traffic engineering using a risk minimization technique inspired by portfolio theory in economics, and we use machine learning to estimate path selection risks. In particular, using real latency time series measurements, both existing and collected with and without the GENI testbed, we compare four short-horizon latency estimation techniques, commonly used by the finance community to estimate prices of volatile financial instruments. Our results suggest that a Bayesian Network approach may lead to good latency (peak) estimation performance, as long as there are dependencies among the time series path latency measurements.