Operating server components beyond their voltage and power design limit (i.e., overclocking) enables improving performance and lowering cost for cloud workloads. However, overclocking can significantly degrade component lifetime, increase power draw, and cause power capping events, eventually diminishing the performance benefits. In this paper, we characterize the impact of overclocking on cloud workloads by studying their profiles from production deployments. Based on the characterization insights, we propose SmartOClock, the first distributed overclocking management platform specifically designed for cloud environments. SmartOClock is a workload-aware scheme that relies on power predictions to heterogeneously distribute the power budgets across its servers based on their needs and then enforce budget compliance locally, per-server, in a decentralized manner. SmartOClock reduces the tail latency by 9%, application cost by 30% and total energy consumption by 10% for latencysensitive microservices on a 36-server deployment. Simulation analysis using production traces show that SmartOClock reduces the number of power capping events by up to 95% while increasing the overclocking success rate by up to 62%. We also describe lessons from building a first-of-its-kind overclockable cluster in Microsoft Azure for production experiments.

Cloud providers typically implement abstractions for network virtualization on the server, within the operating system that hosts the tenant virtual machines or containers. Despite being flexible and convenient, this approach has fundamental problems: incompatibility with bare-metal support, unnecessary performance overhead, and susceptibility to hypervisor breakouts. To solve these, we propose to offload the implementation of network-virtualization abstractions to the top-of-rack switch (ToR). To show that this is feasible and beneficial, we present VNToR, a ToR that takes over the implementation of the security-group abstraction. Our prototype combines commodity switching hardware with a custom software stack and is integrated in OpenStack Neutron. We show that VNToR can store tens of thousands of access rules, adapts to traffic-pattern changes in less than a millisecond, and significantly outperforms the state of the art.

Software bugs are inevitable in software-defined networking control software, and troubleshooting is a tedious, time-consuming task. In this paper we discuss how to improve control software troubleshooting by presenting a technique for automatically identifying a minimal sequence of inputs responsible for triggering a given bug, without making assumptions about the language or instrumentation of the software under test. We apply our technique to five open source SDN control platforms---Floodlight, NOX, POX, Pyretic, ONOS---and illustrate how the minimal causal sequences our system found aided the troubleshooting process.

The distributed systems research community has developed many provably correct algorithms and abstractions that are in wide use. However, practical implementations of distributed systems often contain many bugs, and practitioners spend much of their time troubleshooting these bugs. In this paper we present an algorithm, retrospective causal inference, to ease the burden of troubleshooting. We end by enumerating several open research problems related to the troubleshooting process.