Online learning on the programmable dataplane

This thesis makes the case for managing computer networks with datadriven methods automated statistical inference and control based on measurement data and runtime observations—and argues for their tight integration with programmable dataplane hardware to make management decisions faster and from more precise data. Optimisation, defence, and measurement of networked infrastructure are each challenging tasks in their own right, which are currently dominated by the use of hand-crafted heuristic methods. These become harder to reason about and deploy as networks scale in rates and number of forwarding elements, but their design requires expert knowledge and care around unexpected protocol interactions. This makes tailored, per-deployment or -workload solutions infeasible to develop. Recent advances in machine learning offer capable function approximation and closed-loop control which suit many of these tasks. New, programmable dataplane hardware enables more agility in the network— runtime reprogrammability, precise traffic measurement, and low latency on-path processing. The synthesis of these two developments allows complex decisions to be made on previously unusable state, and made quicker by offloading inference to the network. To justify this argument, I advance the state of the art in data-driven defence of networks, novel dataplane-friendly online reinforcement learning algorithms, and in-network data reduction to allow classification of switchscale data. Each requires co-design aware of the network, and of the failure modes of systems and carried traffic. To make online learning possible in the dataplane, I use fixed-point arithmetic and modify classical (non-neural) approaches to take advantage of the SmartNIC compute model and make use of rich device local state. I show that data-driven solutions still require great care to correctly design, but with the right domain expertise they can improve on pathological cases in DDoS defence, such as protecting legitimate UDP traffic. In-network aggregation to histograms is shown to enable accurate classification from fine temporal effects, and allows hosts to scale such classification to far larger flow counts and traffic volume. Moving reinforcement learning to the dataplane is shown to offer substantial benefits to stateaction latency and online learning throughput versus host machines; allowing policies to react faster to fine-grained network events. The dataplane environment is key in making reactive online learning feasible—to port further algorithms and learnt functions, I collate and analyse the strengths of current and future hardware designs, as well as individual algorithms

The design of efficient and secure P2PSIP systems

Doktorgradsavhandling i informasjons- og kommunikasjonsteknologi, Universitetet i Agder, Grimstad, 201

Inter-socket Victim Cacheing for Platform Power Reduction

Abstract — On a multi-socket architecture with load below peak, as is often the case in a server installation, it is common to consolidate load onto fewer sockets to save processor power. However, this can increase main memory power consumption due to the decreased total cache space. This paper describes inter-socket victim cacheing, a technique that enables such a system to do both load consolidation and cache aggregation at the same time. It uses the last level cache of an idle processor in a connected socket as a victim cache, holding evicted data from the active processor. This enables expensive main memory accesses to be replaced by cheaper cache hits. This work examines both static and dynamic victim cache management policies. Energy savings is as high as 32.5%, and averages 5.8%. I

Distributed Concurrent Persistent Languages: An Experimental Design and Implementation

A universal persistent object store is a logical space of persistent objects whose localities span over machines reachable over networks. It provides a conceptual framework in which, on one hand, the distribution of data is transparent to application programmers and, on the other, store semantics of conventional languages is preserved. This means the manipulation of persistent objects on remote machines is both syntactically and semantically the same as in the case of local data. Consequently, many aspects of distributed programming in which computation tasks cooperate over different processors and different stores can be addressed within the confines of persistent programming. The work reported in this thesis is a logical generalization of the notion of persistence in the context of distribution. The concept of a universal persistent store is founded upon a universal addressing mechanism which augments existing addressing mechanisms. The universal addressing mechanism is realized based upon remote pointers which although containing more locality information than ordinary pointers, do not require architectural changes. Moreover, these remote pointers are transparent to the programmers. A language, Distributed PS-algol, is designed to experiment with this idea. The novel features of the language include: lightweight processes with a flavour of distribution, mutexes as the store-based synchronization primitive, and a remote procedure call mechanism as the message-based interprocess communication mechanism. Furthermore, the advantages of shared store programming and network architecture are obtained with the introduction of the programming concept of locality in an unobtrusive manner. A characteristic of the underlying addressing mechanism is that data are never copied to satisfy remote demands except where efficiency can be attained without compromising the semantics of data. A remote store operation model is described to effect remote updates. It is argued that such a choice is the most natural given that remote store operations resemble remote procedure calls