Dr. Yotam Harchol

Hebrew University

Advisors: Prof. Anat Bremler-Barr and Prof. David Hay

Graduation 2016

Postdoc, Berkeley, with Prof. Scott Shenker

Research Scientist at the EPFL

Current: research scientist at DFINITY

Publications

Talk
Yotam Harchol
Stanford University NetSeminar,
2018
Journal
Anat Bremler-Barr, David Hay, Yotam Harchol, Yacov Hel-Or
ACM/IEEE Transactions on Networking,
2018

We present range encoding with no expansion (RENÉ)- a novel encoding scheme for short ranges on Ternary content addressable memory (TCAM), which, unlike previous solutions, does not impose row expansion, and uses bits proportionally to the maximal range length. We provide theoretical analysis to show that our encoding is the closest to the lower bound of number of bits used. In addition, we show several applications of our technique in the field of packet classification, and also, how the same technique could be used to efficiently solve other hard problems, such as the nearest-neighbor search problem and its variants. We show that using TCAM, one could solve such problems in much higher rates than previously suggested solutions, and outperform known lower bounds in traditional memory models. We show by experiments that the translation process of RENÉ on switch hardware induces only a negligible 2.5% latency overhead. Our nearest neighbor implementation on a TCAM device provides search rates that are up to four orders of magnitude higher than previous best prior-art solutions.

Conferences & Workshops
Yotam Harchol, David Hay, and Tal Orenstein
ACM/IEEE ANCS,
2018

One of the major concerns about Network Function Virtualization (NFV) is the reduced stability of virtual network functions (VNFs), compared to dedicated hardware appliances. Stateful VNFs make recovery a complex process, where a major concern is how to handle non-determinism such as multi-threaded processing, time dependence, and randomness.

In this paper we present FTvNF — a new approach for network functions recovery with very low overhead in failure-free time. This is in contrast to previous suggestions to take snapshots of the VNF state at certain checkpoints or to store the VNF state externally. Compared with state-of-the-art approaches, our approach significantly reduces the latency overhead incurred by the network elements, both in failure-free operations and when failures occur. In addition, our approach better suits the common case of NFV service chaining, as our mechanisms are applied once per chain, thus significantly improve the performance over approaches that treat each VNF separately.

Conferences & Workshops
Anat Bremler-Barr, David Hay, Yotam Harchol
SIGCOMM,
2016

We present OpenBox — a software-defined framework for network-wide development, deployment, and management of network functions (NFs). OpenBox effectively decouples the control plane of NFs from their data plane, similarly to SDN solutions that only address the network’s forwarding plane.
OpenBox consists of three logic components. First, user-defined OpenBox applications provide NF specifications through the OpenBox north-bound API. Second, a logically-centralized OpenBox controller is able to merge logic of multiple NFs, possibly from multiple tenants, and to use a network-wide view to efficiently deploy and scale NFs across the network data plane. Finally, OpenBox instances constitute OpenBox’s data plane and are implemented either purely in software or contain specific hardware accelerators (e.g., a TCAM). In practice, different NFs carry out similar processing steps on the same packet, and our experiments indeed show a significant improvement of the network performance when using OpenBox. Moreover, OpenBox readily supports smart NF placement, NF scaling, and multi-tenancy through its controller.

Conferences & Workshops
Anat Bremler-Barr, David Hay, Yotam Harchol, Yacov Hel-Or
SPAA,
2016

We present RENE ́ — a novel encoding scheme for short ranges on Ternary content addressable memory (TCAM), which, unlike previous solutions, does not impose row expansion, and uses bits proportionally to the maximal range length. We provide theoretical analysis to show that our encoding is the closest to the lower bound of number of bits used. In addition, we show several applications of our technique in the field of packet classification, and also, how the same technique could be used to efficiently solve other hard problems such as the nearest-neighbor search problem and its variants. We show that using TCAM, one could solve such problems in much higher rates than previously suggested solutions, and outperform known lower bounds in traditional memory models. We show by experiments that the translation process of RENE ́ on switch hardware induces only a negligible 2.5% latency overhead. Our nearest neighbor implementation on a TCAM device provides search rates that are up to four orders of magnitude higher than previous best prior-art solutions.