Networks employ complex, and hence error-prone, routing control plane configurations. In many cases, the impact of errors manifests only under failures and leads to devastating effects. Thus, it is important to proactively verify control plane behavior under arbitrary link failures. State-of-the-art verifiers are either too slow or impractical to use for such verification tasks. In this paper we propose a new high level abstraction for control planes, ARC, that supports fast control plane analyses under arbitrary failures. ARC can check key invariants without generating the data plane-which is the main reason for current tools' ineffectiveness. This is possible because of the nature of verification tasks and the constrained nature of control plane designs in networks today. We develop algorithms to derive a network's ARC from its configuration files. Our evaluation over 314 networks shows that ARC computation is quick, and that ARC can verify key invariants in under 1s in most cases, which is orders-of-magnitude faster than the state-of-the-art.

Frequent episode discovery is a popular framework for temporal pattern discovery in event streams. An episode is a partially ordered set of nodes with each node associated with an event type. Currently algorithms exist for episode discovery only when the associated partial order is total order (serial episode) or trivial (parallel episode). In this paper, we propose efficient algorithms for discovering frequent episodes with unrestricted partial orders when the associated event-types are unique. These algorithms can be easily specialized to discover only serial or parallel episodes. Also, the algorithms are flexible enough to be specialized for mining in the space of certain interesting subclasses of partial orders. We point out that frequency alone is not a sufficient measure of interestingness in the context of partial order mining. We propose a new interestingness measure for episodes with unrestricted partial orders which, when used along with frequency, results in an efficient scheme of data mining. Simulations are presented to demonstrate the effectiveness of our algorithms.

Network functions virtualization (NFV) together with softwaredefined networking (SDN) has the potential to help operators satisfy tight service level agreements, accurately monitor and manipulate network traffic, and minimize operating expenses. However, in scenarios that require packet processing to be redistributed across a collection of network function (NF) instances, simultaneously achieving all three goals requires a framework that provides efficient, coordinated control of both internal NF state and network forwarding state. To this end, we design a control plane called OpenNF. We use carefully designed APIs and a clever combination of events and forwarding updates to address race conditions, bound overhead, and accommodate a variety of NFs. Our evaluation shows that OpenNF offers efficient state control without compromising flexibility, and requires modest additions to NFs.

Network functions virtualization (NFV) together with softwaredefined networking (SDN) has the potential to help operators satisfy tight service level agreements, accurately monitor and manipulate network traffic, and minimize operating expenses. However, in scenarios that require packet processing to be redistributed across a collection of network function (NF) instances, simultaneously achieving all three goals requires a framework that provides efficient, coordinated control of both internal NF state and network forwarding state. To this end, we design a control plane called OpenNF. We use carefully designed APIs and a clever combination of events and forwarding updates to address race conditions, bound overhead, and accommodate a variety of NFs. Our evaluation shows that OpenNF offers efficient state control without compromising flexibility, and requires modest additions to NFs.

Many distributed machine learning (DML) workloads are increasingly being run in shared clusters. Training in such clusters can be impeded by unexpected compute and network contention, resulting in stragglers. We present MLfabric, a contention-aware DML system that manages the performance of a DML job running in a shared cluster. The DML application hands all network communication (gradient and model transfers) to the MLfabric communication library. MLfabric then carefully orders transfers to improve convergence, opportunistically aggregates them at idle DML workers to improve resource efficiency, and replicates them to support new notions of fault tolerance, while systematically accounting for compute stragglers and network contention. We find that MLfabric achieves up to 3× speed-up in training large deep learning models in realistic dynamic cluster settings. CCS CONCEPTS • Computing methodologies → Parallel algorithms.