Backscatter from the Data Plane --- Threats to Stability and Security in Information-Centric Networking

Information-centric networking proposals attract much attention in the ongoing search for a future communication paradigm of the Internet. Replacing the host-to-host connectivity by a data-oriented publish/subscribe service eases content distribution and authentication by concept, while eliminating threats from unwanted traffic at an end host as are common in today's Internet. However, current approaches to content routing heavily rely on data-driven protocol events and thereby introduce a strong coupling of the control to the data plane in the underlying routing infrastructure. In this paper, threats to the stability and security of the content distribution system are analyzed in theory and practical experiments. We derive relations between state resources and the performance of routers and demonstrate how this coupling can be misused in practice. We discuss new attack vectors present in its current state of development, as well as possibilities and limitations to mitigate them.Comment: 15 page

ULTRA-FAST AND MEMORY-EFFICIENT LOOKUPS FOR CLOUD, NETWORKED SYSTEMS, AND MASSIVE DATA MANAGEMENT

Systems that process big data (e.g., high-traffic networks and large-scale storage) prefer data structures and algorithms with small memory and fast processing speed. Efficient and fast algorithms play an essential role in system design, despite the improvement of hardware. This dissertation is organized around a novel algorithm called Othello Hashing. Othello Hashing supports ultra-fast and memory-efficient key-value lookup, and it fits the requirements of the core algorithms of many large-scale systems and big data applications. Using Othello hashing, combined with domain expertise in cloud, computer networks, big data, and bioinformatics, I developed the following applications that resolve several major challenges in the area. Concise: Forwarding Information Base. A Forwarding Information Base is a data structure used by the data plane of a forwarding device to determine the proper forwarding actions for packets. The polymorphic property of Othello Hashing the separation of its query and control functionalities, which is a perfect match to the programmable networks such as Software Defined Networks. Using Othello Hashing, we built a fast and scalable FIB named \textit{Concise}. Extensive evaluation results on three different platforms show that Concise outperforms other FIB designs. SDLB: Cloud Load Balancer. In a cloud network, the layer-4 load balancer servers is a device that acts as a reverse proxy and distributes network or application traffic across a number of servers. We built a software load balancer with Othello Hashing techniques named SDLB. SDLB is able to accomplish two functionalities of the SDLB using one Othello query: to find the designated server for packets of ongoing sessions and to distribute new or session-free packets. MetaOthello: Taxonomic Classification of Metagenomic Sequences. Metagenomic read classification is a critical step in the identification and quantification of microbial species sampled by high-throughput sequencing. Due to the growing popularity of metagenomic data in both basic science and clinical applications, as well as the increasing volume of data being generated, efficient and accurate algorithms are in high demand. We built a system to support efficient classification of taxonomic sequences using its k-mer signatures. SeqOthello: RNA-seq Sequence Search Engine. Advances in the study of functional genomics produced a vast supply of RNA-seq datasets. However, how to quickly query and extract information from sequencing resources remains a challenging problem and has been the bottleneck for the broader dissemination of sequencing efforts. The challenge resides in both the sheer volume of the data and its nature of unstructured representation. Using the Othello Hashing techniques, we built the SeqOthello sequence search engine. SeqOthello is a reference-free, alignment-free, and parameter-free sequence search system that supports arbitrary sequence query against large collections of RNA-seq experiments, which enables large-scale integrative studies using sequence-level data

Power and Memory Efficient Hashing Schemes for Some Network Applications

Hash tables (HTs) are used to implement various lookup schemes and they need to be efficient in terms of speed, space utilization, and power consumptions. For IP lookup, the hashing schemes are attractive due to their deterministic O(1) lookup performance and low power consumptions, in contrast to the TCAM and Trie based approaches. As the size of IP lookup table grows exponentially, scalable lookup performance is highly desirable. For next generation high-speed routers, this is a vital requirement when IP lookup remains in the critical data path and demands a predictable throughput. However, recently proposed hash schemes, like a Bloomier filter HT and a Fast HT (FHT) suffer from a number of flaws, including setup failures, update overheads, duplicate keys, and pointer overheads. In this dissertation, four novel hashing schemes and their architectures are proposed to address the above concerns by using pipelined Bloom filters and a Fingerprint filter which are designed for a memory-efficient approximate match. For IP lookups, two new hash schemes such as a Hierarchically Indexed Hash Table (HIHT) and Fingerprint-based Hash Table (FPHT) are introduced to achieve a a perfect match is assured without pointer overhead. Further, two hash mechanisms are also proposed to provide memory and power efficient lookup for packet processing applications. Among four proposed schemes, the HIHT and the FPHT schemes are evaluated for their performance and compared with TCAM and Trie based IP lookup schemes. Various sizes of IP lookup tables are considered to demonstrate scalability in terms of speed, memory use, and power consumptions. While an FPHT uses less memory than an HIHT, an FPHT-based IP lookup scheme reduces power consumption by a factor of 51 and requires 1.8 times memory compared to TCAM-based and trie-based IP lookup schemes, respectively. In dissertation, a multi-tiered packet classifier has been proposed that saves at most 3.2 times power compared to the existing parallel packet classifier. Intrinsic hashing schemes lack of high throughput, unlike partitioned Ternary Content Addressable Memory (TCAM)-based scheme that are capable of parallel lookups despite large power consumption. A hybrid CAM (HCAM) architecture has been introduced. Simulation results indicate HCAM to achieve the same throughput as contemporary schemes while it uses 2.8 times less memory and 3.6 times less power compared to the contemporary schemes

Full-speed scalability of the pDomus platform for DHTs

Domus is an architecture for Distributed Hash Tables (DHTs) tailored to a shared-all cluster environment. Domus DHTs build on a (dynamic) set of cluster nodes; each node may perform routing and/or storage tasks, for one or more DHTs, as a function of the node base (static) resources and of its (dynamic) state. Domus DHTs also benefit from a rich set of user-level attributes and operations. pDomus is a prototype of Domus that creates an environment where to evaluate the architecture concepts and features. In this paper, we present a set of experiments conduced to obtain figures of merit on the scalability of a specific DHT operation, with several lookup methods and storage technologies. The evaluation also involves a comparison with a database and a P2P-oriented DHT platform. The results are promising, and a motivation for further work.PRODEP III (grant 5.3/N/199.006/00)SAPIENS (grant 41739/CHS/2001