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 3x speed-up in training large deep learning models in realistic dynamic cluster settings.

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.

Network functions virtualization (NFV) together with software-defined 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.

In this paper, we present a Bayesian framework for multilabel classification using compressed sensing. The key idea in compressed sensing for multilabel classification is to first project the label vector to a lower dimensional space using a random transformation and then learn regression functions over these projections. Our approach considers both of these components in a single probabilistic model, thereby jointly optimizing over compression as well as learning tasks. We then derive an efficient variational inference scheme that provides joint posterior distribution over all the unobserved labels. The two key benefits of the model are that a) it can naturally handle datasets that have missing labels and b) it can also measure uncertainty in prediction. The uncertainty estimate provided by the model naturally allows for active learning paradigms where an oracle provides information about labels that promise to be maximally informative for the prediction task. Our experiments show significant boost over prior methods in terms of prediction performance over benchmark datasets, both in the fully labeled and the missing labels case. Finally, we also highlight various useful active learning scenarios that are enabled by the probabilistic model.