FROM EQUATION TO EQUATIONS – Antiy Labs | The Next Generation Anti-Virus Engine Innovator

Source: http://www.antiy.net/p/from-equation-to-equations/

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Sincronia: Near-Optimal Network Design for Coflows

We present Sincronia, a near-optimal network design for coflows that can be implemented on top on any transport layer (for flows) that supports priority scheduling. Sincronia achieves this using a key technical result — we show that given a “right” ordering of coflows, any per-flow rate allocation mechanism achieves average coflow completion time within 4X of the optimal as long as (co)flows are prioritized with respect to the ordering.

Sincronia uses a simple greedy mechanism to periodically order all unfinished coflows; each host sets priorities for its flows using corresponding coflow order and offloads the flow scheduling and rate allocation to the underlying priority-enabled transport layer. We evaluate Sincronia over a real testbed comprising 16-servers and commodity switches, and using simulations across a variety of workloads. Evaluation results suggest that Sincronia not only admits a practical, near-optimal design but also improves upon state-of-the-art network designs for coflows (sometimes by as much as 8X).

Source: http://delivery.acm.org/10.1145/3240000/3230569/p16-agarwal.pdf?ip=108.51.128.31&id=3230569&acc=OPEN&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E6D218144511F3437&__acm__=1533991235_320a71237c29327489e1ed7bb1e12a6a

B4 and After: Managing Hierarchy, Partitioning, and Asymmetry for Availability and Scale in Google’s Software-Defined WAN

Private WANs are increasingly important to the operation of
enterprises, telecoms, and cloud providers. For example, B4,
Google’s private software-defined WAN, is larger and growing
faster than our connectivity to the public Internet. In this
paper, we present the five-year evolution of B4. We describe
the techniques we employed to incrementally move from
offering best-effort content-copy services to carrier-grade
availability, while concurrently scaling B4 to accommodate
100x more traffic. Our key challenge is balancing the tension
introduced by hierarchy required for scalability, the partitioning
required for availability, and the capacity asymmetry
inherent to the construction and operation of any large-scale
network. We discuss our approach to managing this tension:
i) we design a custom hierarchical network topology for both
horizontal and vertical software scaling, ii) we manage inherent
capacity asymmetry in hierarchical topologies using
a novel traffic engineering algorithm without packet encapsulation,
and iii) we re-architect switch forwarding rules
via two-stage matching/hashing to deal with asymmetric
network failures at scale.

Source: http://delivery.acm.org/10.1145/3240000/3230545/p74-hong.pdf?ip=108.51.128.31&id=3230545&acc=OPEN&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E6D218144511F3437&__acm__=1533991178_bf46d05c3ddf7924ca5de25921085a50

Causal Inference Book | Miguel Hernan | Harvard T.H. Chan School of Public Health

My colleague Jamie Robins and I are working on a book that provides a cohesive presentation of concepts of, and methods for, causal inference. Much of this material is currently scattered across journals in several disciplines or confined to technical articles. We expect that the book will be of interest to anyone interested in causal inference, e.g., epidemiologists, statisticians, psychologists, economists, sociologists, political scientists, computer scientists… The book is divided in 3 parts of increasing difficulty: causal inference without models, causal inference with models, and causal inference from complex longitudinal data.

Source: https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/