In an ideal world, all research papers would be runnable: simply click to replicate all results, using the same setup as the authors. One approach to enable runnable network systems papers is Container-Based Emulation (CBE), where an environment of virtual hosts, switches, and links runs on a modern multicore server, using real application and kernel code with software-emulated network elements. CBE combines many of the best features of software simulators and hardware testbeds, but its performance fidelity is unproven.In this paper, we put CBE to the test, using our prototype, Mininet-HiFi, to reproduce key results from published network experiments such as DCTCP, Hedera, and router buffer sizing. We report lessons learned from a graduate networking class at Stanford, where 37 students used our platform to replicate 16 published results of their own choosing. Our experiences suggest that CBE makes research results easier to reproduce and build upon.

Network performance monitoring today is restricted by existing switch support for measurement, forcing operators to rely heavily on endpoints with poor visibility into the network core. Switch vendors have added progressively more monitoring features to switches, but the current trajectory of adding specific features is unsustainable given the ever-changing demands of network operators. Instead, we ask what switch hardware primitives are required to support an expressive language of network performance questions. We believe that the resulting switch hardware design could address a wide variety of current and future performance monitoring needs. We present a performance query language, Marple, modeled on familiar functional constructs like map, filter, groupby, and zip. Marple is backed by a new programmable key-value store primitive on switch hardware. The key-value store performs flexible aggregations at line rate (e.g., a moving average of queueing latencies per flow), and scales to millions of keys. We present a Marple compiler that targets a P4-programmable software switch and a simulator for highspeed programmable switches. Marple can express switch queries that could previously run only on end hosts, while Marple queries only occupy a modest fraction of a switch's hardware resources.

The behavior of a Software-Defined Network is controlled by programs, which like all software, will have bugs -but this programmatic control also enables new ways to debug networks. This paper introduces ndb, a prototype network debugger inspired by gdb, which implements two primitives useful for debugging an SDN: breakpoints and packet backtraces. We show how ndb modifies forwarding state and logs packet digests to rebuild the sequence of events leading to an errant packet, providing SDN programmers and operators with a valuable tool for tracking down the root cause of a bug.

Today's networks are maintained by "masters of complexity": network admins who have accumulated the wisdom to troubleshoot complex problems, despite a limiting toolset. This position paper advocates a more structured troubleshooting approach that leverages architectural layering in SoftwareDefined Networks (SDNs). In all networks, high-level intent (policy) must correctly map to low-level forwarding behavior (hardware configuration). In SDNs, intent is explicitly expressed, forwarding semantics are explicitly defined, and each architectural layer fully specifies the behavior of the network. Building on these observations, we show how recently-developed troubleshooting tools fit into a coherent workflow that detects mistranslations between layers to precisely localize sources of errant control logic. Our goals are to explain the overall picture, show how the pieces fit together to enable a systematic workflow, and highlight the questions that remain. Once this workflow is realized, network admins can formally verify that their network is operating correctly, automatically troubleshoot bugs, and systematically track down their root cause -freeing admins to fix problems, rather than diagnose their symptoms.

We present ExplainIt!, a declarative, unsupervised rootcause analysis engine that uses time series monitoring data from large complex systems such as data centres. ExplainIt! empowers operators to succinctly specify a large number of causal hypotheses to search for causes of interesting events. ExplainIt! then ranks these hypotheses, reducing the number of causal dependencies from hundreds of thousands to a handful for human understanding. We show how a declarative language, such as SQL, can be effective in declaratively enumerating hypotheses that probe the structure of an unknown probabilistic graphical causal model of the underlying system. Our thesis is that databases are in a unique position to enable users to rapidly explore the possible causal mechanisms in data collected from diverse sources. We empirically demonstrate how ExplainIt! had helped us resolve over 30 performance issues in a commercial product since late 2014, of which we discuss a few cases in detail.