20/5/2015 11:30EE, Meyer 1007


Ownership-Aware Software-Defined Backhauls in Next-Generation Cellular Networks

Francesco Malandrino

Hebrew University

Future cellular networks will be owned by multiple parties, e.g., two mobile operators, each of which controls some elements of the access and backhaul infrastructure. In this context, it is important that as much traffic as possible is processed by the same party that generates it, i.e., that the coupling between traffic and network ownership is maximized.

Software-defined backhaul networks can attain this goal; however, existing management schemes ignore ownership altogether. We fill this gap by presenting an ownership-aware network management scheme. Our simulations show that its performance is very close to the optimum, and substantially higher than the one of state-of-the-art alternatives

Bio: Francesco Malandrino earned his Ph.D. from Politecnico di Torino, Italy and is currently a Fibonacci Fellow at the Hebrew University of Jerusalem. His research interests mainly focus on wireless networks with infrastructure, especially next-generation cellular networks.

25/5/2015 11:30CS 337


Thunderstrike: EFI firmware bootkits for Apple MacBooks

Trammell Hudson

Two Sigma

In this presentation we demonstrate the installation of persistent firmware modifications into the EFI boot ROM of Apple's popular MacBooks. The bootkit can be easily installed by an evil-maid via the externally accessible Thunderbolt ports and can survive reinstallation of OSX as well as hard drive replacements. Once installed, it can prevent software attempts to remove it and could spread virally across air-gaps by infecting additional Thunderbolt devices.

Bio: Trammell Hudson works at Two Sigma Investments on security, networking and distributed computation projects. Prior to coming to New York, he worked for many years at Sandia National Labs on message passing and operating systems for Top500 parallel supercomputers. More info: http://twosigma.com and https://trmm.net/

27/5/2015 11:30EE, Meyer 861


Coded Retransmission in Wireless Networks Via Abstract MDPs

Mark Shifrin

Ben Gurion University

We consider a transmission scheme with a single transmitter and multiple receivers over a faulty broadcast channel. For each receiver, the transmitter has a unique infinite stream of packets, and its goal is to deliver them at the highest throughput possible. While such multiple-unicast models are unsolved in general, several network coding based schemes were suggested. In such schemes, the transmitter can either send an uncoded packet, or a coded packet which is a function of a few packets. Sent packets can be received by the designated receiver (with some probability) or heard and stored by other receivers. Two functional modes are considered; the first presumes that the storage time is unlimited, while in the second it is limited by a given Time To Live (TTL) parameter. We model the transmission process as an infinite horizon Markov Decision Process (MDP). Since the large state space renders exact solutions computationally impractical, we introduce policy restricted and induced MDPs with significantly reduced state space, which with properly chosen reward have equal optimal value function. We then derive a reinforcement learning algorithm, which approximates the optimal strategy and significantly improves over uncoded schemes. The algorithm adapts to the packet loss rates, unknown in advance, attains high gain over the uncoded setup and is comparable with the upper bound by Wang, derived for a much stronger coding scheme.

Bio: Mark Shifrin received his PHD from EE, Technion, in 2014. He is recipient of Kreitman postdoctoral fellowship in Ben Gurion university, where he currently pursues topics in wireless communication using stochastic control methods, at department of Communication Systems Engineering.

3/6/2015 11:30CS 337


Processing Real-time Data Streams on GPU-Based Systems

Uri Verner


Real-time stream processing of Big Data has an increasing demand in modern data centers. There, a continuous torrent of data, created from different streaming data-sources like social networks, video streams, and financial markets, is being processed and analyzed to produce valuable insights about its content, and, in some cases, their value has an expiration date. For example, in a silicon-wafer production inspection system, dozens of high-resolution cameras scan the wafer and generate high-rate streams of images, where defects as small as a pixel and even smaller are detected using image processing algorithms. The inspection is a computationally intensive task that must adhere to strict processing time limitations in order to keep up with the production line. High-end computing platforms, packed with CPUs and compute accelerators, are being used to deal with the large processing need. Optimizing such systems is a very challenging task because they are heterogeneous in both computation and communication.

In this talk, I will present my PhD research work on the problem of processing multiple data streams with hard processing-latency bounds on multi-GPU compute nodes. The talk will describe the challenges in work distribution and communication scheduling in such a system, and present new methods that achieve higher utilization of the resources, while guaranteeing hard real-time compliance. The generalization of the previously discussed problems leads to an important new class of scheduling problems where processors affect each other's speed. To demonstrate its usefulness, a problem from this class is used to develop an efficient new scheduler that minimizes the makespan of compute jobs on Intel CPUs.

Bio: Uri Verner is a PhD student at the Department of Computer Science in the Technion, under the supervision of Professors Assaf Schuster and Avi Mendelson. His research interests include theoretical and practical aspects of data processing in GPU-based systems, computation and communication scheduling, system modeling and optimization, machine learning, and parallel computing.

This talk doubles as a PhD seminar lecture.