Geoffrey Lefebvre

StEERING: A software-defined networking for inline service chaining

Network operators are faced with the challenge of deploying and managing middleboxes (also called inline services) such as firewalls within their broadband access, datacenter or enterprise networks. Due to the lack of available protocols to route traffic through middleboxes, operators still rely on error-prone and complex low-level configurations to coerce traffic through the desired set of middleboxes. Built upon the recent software-defined networking (SDN) architecture and OpenFlow protocol, this paper proposes StEERING, short for SDN inlinE sERvices and forwardlNG. It is a scalable framework for dynamically routing traffic through any sequence of middleboxes. With simple centralized configuration, StEERING can explicitly steer different types of flows through the desired set of middleboxes, scaling at the level of per-subscriber and per-application policies. With its capability to support flexible routing, we further propose an algorithm to select the best locations for placing services, such that the performance is optimized. Overall, StEERING allows network operators to monetize their middlebox deployment in new ways by allowing subscribers flexibly to select available network services.

Execution mining

Operating systems represent large pieces of complex software that are carefully tested and broadly deployed. Despite this, developers frequently have little more than their source code to understand how they behave. This static representation of a system results in limited insight into execution dynamics, such as what code is important, how data flows through a system, or how threads interact with one another. We describe Tralfamadore, a system that preserves complete traces of machine execution as an artifact that can be queried and analyzed with a library of simple, reusable operators, making it easy to develop and run new dynamic analyses. We demonstrate the benefits of this approach with several example applications, including a novel unified source and execution browser.

Retroactive aspects: programming in the past

We present a novel approach to the problem of dynamic program analysis: writing analysis code directly into the program source, but evaluating it against a recording of the original programs execution. This approach allows developers to reason about their program in the familiar context of its actual source, and take full advantage of program semantics, data structures, and library functionality for understanding execution. It also gives them the advantage of hindsight, letting them easily analyze unexpected behavior after it has occurred. Our position is that writing offline analysis as retroactive aspects provides a unifying approach that developers will find natural and powerful.

Separating durability and availability in self-managed storage

Building reliable data storage from unreliable components presents many challenges and is of particular interest for peer-to-peer storage systems. Recent work has examined the trade-offs associated with ensuring data availability in such systems. Reliability, however, is more than just availability. In fact, the durability of data is typically of more paramount concern. While users are likely to tolerate occasional disconnection from their data (they will likely have no choice in the matter), they demand a much stronger guarantee that their data is never permanently lost due to failure. To deliver strong durability guarantees efficiently, however, requires decoupling durability from availability. This paper describes the design of a data redundancy scheme that guarantees durability independently from availability. We provide a formula for determining the rate of redundancy repair when durability is the only concern and show that availability requires much more frequent repair. We simulate modified versions of the Total Recall block store that incorporate our design. Our results show that we can deliver durability more cheaply than availability, reducing network overhead by between 50% and 97%.