6,193 research outputs found
On-Demand Routing for Scalable Name-Based Forwarding
Information-centric Networking (ICN) is a future Internet architecture design, where application-level names are directly used to route interests to fetch a copy of the desired content/data from any location. Following the conventions of the Internet Protocol to store the pre-computed routing/forwarding state for all prefixes at the network nodes raises scalability concerns in ICN (where content name prefixes need to be stored), especially at the inter-domain level. Instead, we consider the other extreme; that is, On-Demand Routing (ODR) computation for content name prefixes as interests arrive. ODR makes use of domain-level, per-prefix routing instructions usable by all the forwarders in a domain, named Routing Information Objects (RIO). Forwarders discover and retrieve RIOs in a similar way as content and can be cached in a new data structure called Route Information Store (RIS). RIOs are handed to a routing strategy module to perform a routing decision before relaying the packets. We demonstrate through extensive simulations that ODR scales the storage of routing/forwarding information through caching and information discovery-two mechanisms inherent to the ICN design. We propose our design as an extension of the Named Data Networking (NDN) architecture and discuss all the proposed enhancements in detail
Efficient tree-based content-based routing schemes
This thesis is about routing and forwarding for inherently multicast communication such as the communication typical of information-centric networks. The notion of Information-Centric Networking (ICN) is an evolution of the Internet from the current host-centric architecture to a new architecture in which communication is based on ānamed informationā. The ambitious goal of ICN is to effectively support the exchange and use of information in an ever more connected world, with billions of devices, many of which are mobile, producing and consuming large amounts of data. ICN is intended to support scalable content distribution, mobility, and security, for such applications as video on demand and networks of sensors or the so-called Internet of Things. Many ICN architectures have emerged in the past decade, and the ICN community has made significant progress in terms of infrastructure, test-bed deployments, and application case studies. And yet, despite the impressive research effort, the fundamental problems of routing and forwarding remain open. In particular, none of the proposed architectures has developed truly scalable name-based routing schemes and efficient name-based forwarding algorithms. This is not surprising, since the problem of routing based on names, in its most general formulation, is known to be fundamentally difficult. In general, one would want to support application-defined names (as opposed to network-defined addresses) with a compact routing scheme (small routing tables) that uses optimal paths and minimizes congestion, and that admits to a fast forwarding algorithm. Furthermore, one would want to construct this routing scheme with a decentralized and incremental protocol for administrative autonomy and efficient dynamic updates. However, there are clear theoretical limits that simply make it impossible to achieve all these goals. In this thesis we explore the design space of routing and forwarding in an information-centric network. Our purpose is to develop routing schemes and forwarding algorithms that combine many desirable properties. We consider two forms of addressing, one tied to network locations, and one based on more expressive content descriptors. We then consider trees as basic routing structures, and with those we develop routing schemes that are intended to minimize path lengths and congestion, separately or together. For one of these schemes based on expressive content descriptors, we also develop a fast forwarding algorithm specialized for massively parallel architectures such as GPUs. In summary, this thesis presents two efficient and scalable routing algorithms for two different types of networks, plus one scalable forwarding algorithm. We summarize each individual contribution below: Low-congestion geographic routing for wireless networks. We develop a low-congestion, multicast routing scheme designed specifically for wireless networks. The scheme supports geographical multicast routing, meaning routing to a set of nodes addressed by their physical position. The scheme builds a geometric minimum spanning tree connecting the source to all the destinations. Then, for each edge in this tree, the scheme routes a message through a random intermediate node, chosen independently of the set of multicast requests. The intermediate node is chosen in the vicinity of the corresponding edge such that congestion is reduced without stretching routes by more than a constant factor. Multi-tree scheme for content-based routing in ICN. We develop a tree-based routing scheme designed for large-scale wired networks such as the Internet. The scheme supports two forms of addresses: application-defined content descriptors, and network-defined locators. We first show that the scheme is effective in terms of stretch and congestion on the current AS-level Internet graph even with only a few spanning trees. Then we show that our content descriptors, which consist of sets of tags and that are more expressive than the name prefixes used in mainstream ICN, aggregate well in practice under our scheme. We also explain in detail how to use descriptors and locators, together with unique content identifiers, to support the efficient transmission and sharing of information through scalable and loop-free routes. Tag-based forwarding (partial matching) algorithm on GPUs. To accompany our ICN routing scheme, we develop a fast forwarding algorithm that matches incoming packets against forwarding tables with tens of millions of entries. To achieve high performance, we develop a practical solution for the partial matching problem that lies at the heart of this forwarding scheme. This solution amounts to a massively parallel algorithm specifically designed for a hybrid CPU/GPU architecture
Scalable bloom-filter based content dissemination in community networks using information centric principles
Information-Centric Networking (ICN) is a new communication paradigm that shifts the focus from content location to content objects themselves. Users request the content by its name or some other form of identifier. Then, the network is responsible for locating the requested content and sending it to the users. Despite a large number of works on ICN in recent years, the problem of scalability of ICN systems has not been studied and addressed adequately. This is especially true when considering real-world deployments and the so-called alternative networks such as community networks. In this work, we explore the applicability of ICN principles in the challenging and unpredictable environments of community networks. In particular, we focus on stateless content dissemination based on Bloom filters (BFs). We highlight the scalability limitations of the classical single-stage BF based approach and argue that by enabling multiple BF stages would lead to performance enhancements. That is, a multi-stage BF based content dissemination mechanism could support large network topologies with heterogeneous traffic and diverse channel conditions. In addition to scalability improvements, this approach also is more secure with regard to Denial of Service attacks
MENU: multicast emulation using netlets and unicast
High-end networking applications such as Internet TV and software distribution have generated a demand for multicast protocols as an integral part of the network. This will allow such applications to support data dissemination to large groups of users in a scalable and reliable manner. Existing IP multicast protocols lack these features and also require state storage in the core of the network which is costly to implement. In this paper, we present a new multicast protocol referred to as MENU. It realises a scalable and a reliable multicast protocol model by pushing the tree building complexity to the edges of the network, thereby eliminating processing and state storage in the core of the network. The MENU protocol builds multicast support in the network using mobile agent based active network services, Netlets, and unicast addresses. The multicast delivery tree in MENU is a two level hierarchical structure where users are partitioned into client communities based on geographical proximity. Each client community in the network is treated as a single virtual destination for traffic from the server. Netlet based services referred to as hot spot delegates (HSDs) are deployed by servers at "hot spots" close to each client community. They function as virtual traffic destinations for the traffic from the server and also act as virtual source nodes for all users in the community. The source node feeds data to these distributed HSDs which in turn forward data to all downstream users through a locally constructed traffic delivery tree. It is shown through simulations that the resulting system provides an efficient means to incrementally build a source customisable secured multicast protocol which is both scalable and reliable. Furthermore, results show that MENU employs minimal processing and reduced state information in networks when compared to existing IP multicast protocols
An Experimental Investigation of Hyperbolic Routing with a Smart Forwarding Plane in NDN
Routing in NDN networks must scale in terms of forwarding table size and
routing protocol overhead. Hyperbolic routing (HR) presents a potential
solution to address the routing scalability problem, because it does not use
traditional forwarding tables or exchange routing updates upon changes in
network topologies. Although HR has the drawbacks of producing sub-optimal
routes or local minima for some destinations, these issues can be mitigated by
NDN's intelligent data forwarding plane. However, HR's viability still depends
on both the quality of the routes HR provides and the overhead incurred at the
forwarding plane due to HR's sub-optimal behavior. We designed a new forwarding
strategy called Adaptive Smoothed RTT-based Forwarding (ASF) to mitigate HR's
sub-optimal path selection. This paper describes our experimental investigation
into the packet delivery delay and overhead under HR as compared with
Named-Data Link State Routing (NLSR), which calculates shortest paths. We run
emulation experiments using various topologies with different failure
scenarios, probing intervals, and maximum number of next hops for a name
prefix. Our results show that HR's delay stretch has a median close to 1 and a
95th-percentile around or below 2, which does not grow with the network size.
HR's message overhead in dynamic topologies is nearly independent of the
network size, while NLSR's overhead grows polynomially at least. These results
suggest that HR offers a more scalable routing solution with little impact on
the optimality of routing paths
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