A5034926658- Software-Defined Networks and 5G
- Cloud Computing and Resource Management
- Interconnection Networks and Systems
- Advanced Optical Network Technologies
- Network Security and Intrusion Detection
- Internet Traffic Analysis and Secure E-voting
- Parallel Computing and Optimization Techniques
- Caching and Content Delivery
- Software System Performance and Reliability
- Network Traffic and Congestion Control
- Peer-to-Peer Network Technologies
- IoT and Edge/Fog Computing
- Distributed systems and fault tolerance
- Mobile Agent-Based Network Management
- Distributed and Parallel Computing Systems
- Advanced Memory and Neural Computing
- Spam and Phishing Detection
- Smart Grid Security and Resilience
- Software Testing and Debugging Techniques
- Blockchain Technology Applications and Security
- Stock Market Forecasting Methods
- Ethics and Social Impacts of AI
- Embedded Systems Design Techniques
- Advanced Malware Detection Techniques
- Network Packet Processing and Optimization
University of Oxford
2020-2025
Science Oxford
2023-2024
University of Cambridge
2013-2019
Tel Aviv University
2009-2013
The demand-led growth of datacenter networks has meant that many constituent technologies are beyond the research community's budget. NetFPGA SUME is an FPGA-based PCI Express board with I/O capabilities for 100 Gbps operation as a network interface card, multiport switch, firewall, or test and measurement environment. provides accessible development environment both reuses existing codebases enables new designs
With the recent wave of progress in artificial intelligence (AI) has come a growing awareness large-scale impacts AI systems, and recognition that existing regulations norms industry academia are insufficient to ensure responsible development. In order for developers earn trust from system users, customers, civil society, governments, other stakeholders they building responsibly, will need make verifiable claims which can be held accountable. Those outside given organization also effective...
The geographical location of Internet IP addresses is important for academic research, commercial and homeland security applications. Thus, both databases tools are available mapping to geographic locations. Evaluating the accuracy these services complex since obtaining diverse large scale ground truth very hard. In this work we evaluate using an algorithm that groups PoPs, based on structure delay. This way able group close 100,000 world wide into known share a geo-location with high...
Machine learning is currently driving a technological and societal revolution. While programmable switches have been proven to be useful for in-network computing, machine within had little success so far. Not using network devices has high toll, given the known power efficiency performance benefits of processing network. In this paper, we explore potential use commodity classification, by mapping trained models match-action pipelines. We introduce IIsy, software hardware based prototype our...
P4 has emerged as the de facto standard language for describing how network packets should be processed, and is becoming widely used by owners, systems developers, researchers in classroom. The goal of work presented here to make it easier engineers, students learn program using P4, build prototypes running on real hardware. Our target NetFPGA SUME platform, a 4x10 Gb/s PCIe card designed use universities teaching research. Until now, users have needed an HDL such Verilog or VHDL, making off...
Programmable network hardware can run services traditionally deployed on servers, resulting in orders-of-magnitude improvements performance. Yet, despite these performance improvements, operators remain skeptical of in-network computing. The conventional wisdom is that the operational costs from increased power consumption outweigh any benefits. Unless computing justify its costs, it will be disregarded as yet another academic exercise.
In this paper, we explore how a programmable forwarding plane offered by new breed of network switches might naturally accelerate consensus protocols, specifically focusing on Paxos. The performance protocols has long been concern. By implementing Paxos in the plane, are able to significantly increase throughput and reduce latency. Our P4-based implementation running an ASIC isolation can process over 2.5 billion messages per second, four orders magnitude improvement widely-used software...
Machine learning is widely used to solve networking challenges, ranging from traffic classification and anomaly detection network configuration. However, machine also requires significant processing often increases the load on both networks servers. The introduction of in-network computing, enabled by programmable devices, has allowed run applications within network, providing higher throughput lower latency. Soon after, solutions started emerge, enabling functionality itself. This survey...
The soaring use of machine learning leads to increasing processing demands. As data volume keeps growing, providing classification services with good performance, high throughput, low latency, and minimal equipment overheads becomes a challenge. Offloading tasks network switches can be scalable solution this problem, throughput latency. However, devices are resource constrained, lack support for functionality. In paper, we introduce IIsy -a novel mapping tool models off-the-shelf switches....
Despite network monitoring and testing being critical for computer networks, current solutions are both extremely expensive inflexible. Into this lacuna we launch the Open Source Network Tester, a fully open source traffic generator capture system. Our prototype implementation on NetFPGA-10G supports 4 × 10 Gb/s generation across all packet sizes, is supported up to 2 10Gb/s with naïve host software. system provides methods scaling coordinating multiple generator/capture systems, 6.25 ns...
Using programmable network devices to aid in-network machine learning has been the focus of significant research. However, most research was a limited scope, providing proof concept or describing closed-source algorithm. To date, no general solution provided for mapping algorithms devices. In this paper, we present Planter, an open-source, modular framework trained models Planter supports wide range models, multiple targets and can be easily extended. The evaluation compares different...
The rat race between user-generated data and data-processing systems is currently won by data. increased use of machine learning leads to further increase in processing requirements, while volume keeps growing. To win the race, needs be applied as it goes through network. In-network classification can reduce load on servers, response time scalability. In this paper, we introduce IIsy, implementing models a hybrid fashion using off-the-shelf network devices. IIsy targets three main challenges...
In-network machine learning inference provides high throughput and low latency. It is ideally located within the network, power efficient, improves applications' performance. Despite its advantages, bar to in-network research high, requiring significant expertise in programmable data planes, addition knowledge of application area. Existing solutions are mostly one-time efforts, hard reproduce, change, or port across platforms. In this paper, we present Planter: a modular efficient...
Modern high-speed networks have evolved from relatively static to highly adaptive facilitating dynamic reconfiguration. This evolution has influenced all levels of network design and management, introducing increased programmability configuration flexibility. influence extended the lowest level physical hardware interfaces highest management by software. A key representative this is emergence software-defined networking (SDN). In paper, we review current state art in reconfigurable systems,...
Data classification within the network brings significant benefits in reaction time, servers offload and power efficiency. Still, only very simple models were mapped to network. In-network will not be useful unless we manage map complex machine learning devices. We present Planter, an algorithm that maps a variety of ensemble models, such as XGBoost Random Forest, programmable switches. By overlapping trees coded tables, Planter manages switches with high accuracy low resource overhead.
The range of application artificial intelligence (AI) is vast, as the potential for harm. Growing awareness risks from AI systems has spurred action to address those risks, while eroding confidence in and organizations that develop them. A 2019 study found over 80 published adopted "AI ethics principles'', more have joined since. But principles often leave a gap between "what" "how" trustworthy development. Such gaps enabled questionable or ethically dubious behavior, which casts doubts on...
In-network computing offers an appealing scalability trajectory for network services, as application performance scales with devices. Despite its potential, in-network may not be suitable all applications, due to paradigm assumptions and network-device limitations. As users' Internet demands keep growing, any limitations on the of services such DNS limits end-to-end experience. In this paper we present P4DNS, solution, exploring span implementing a realistic service within device using P4....
In-network computing provides significant performance benefits, load reduction, and power savings. Still, an in-network service's functionality is strictly limited to a single hardware device. Research has focused on enabling on-device functionality, with consideration distributed computing. This paper explores the applicability of We present DINC, framework computing, generating deployment strategies, overcoming resource constraints providing guarantees across network. It uses...
The widespread use of IoT devices has unveiled overlooked security risks. With the advent ultrareliable low-latency communications (URLLCs) in 5G, fast threat defense is critical to minimize damage from attacks. gateways, equipped with wireless/wired interfaces, serve as vital frontline against emerging threats on edge. However, current gateways struggle dynamic traffic and have limited capabilities attacks changing patterns. In-network computing offers machine learning (ML)-based attack...
In-network computing accelerates applications natively running on the host by executing them within network devices. While in-network offers significant performance improvements, its limitations and design trade-offs have not been explored. To usefully efficiently run network, we first need to understand implications of their design. In this work introduce LaKe, a Layered Key-Value Store design, as an application. LaKe is scalable enabling exploration decisions effect throughput, latency...