We propose a simple distributed hash table called ReCord, which is a generalized version of Randomized-Chord and offers improved tradeoffs in performance and topology maintenance over existing P2P systems. ReCord is scalable and can be easily implemented as an overlay network, and offers a good tradeoff between the node degree and query latency. For instance, an @math -node ReCord with @math node degree has an expected latency of @math hops. Alternatively, it can also offer @math hops latency at a higher cost of @math node degree. Meanwhile, simulations of the dynamic behaviors of ReCord are studied.
It is difficult to say which one of above proposed DHTs is best". Each routing algorithm offers some insight on routing in overlay network. One appropriate strategy is to combine these insights and formulate an even better scheme @cite_0 .
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Dipsea is a modular architecture for building a Distributed Hash Table (DHT). A DHT is a large hash table that is cooperatively maintained by a large number of machines communicating over the Internet. Decentralization and automatic re-configuration are two key design goals for a DHT. The architecture of Dipsea consists of three layers: ID Management, Overlay Routing and Data Management. The Overlay Routing layer consists of three modules: Emulation Engine, Ring Management and Choice of Long-Distance Links. Efficient algorithms for ID Management are designed—these algorithms require few messages, require few re-assignments of existing IDs and ensure that the hash table is divided among the participating machines as evenly as possible. Ring Management ensures that participating machines establish connections among themselves, as a function of their IDs, to form a fault-tolerant ring. The Emulation Engine is responsible for “emulation” of arbitrary families of routing networks. It handles issues arising out of dynamism (arrival and departure of participating machines), scale (variation in the average number of participating machines) and physical network proximity. Choice of Long-Distance Links allows a DHT to choose any family of routing networks (deterministic or randomized) for emulation. Several deterministic and randomized routing networks are designed and analyzed. Among these are Symphony (one of the first randomized DHT routing networks), Papillon (a deterministic routing network that guarantees asymptotically optimal route lengths with greedy routing for a fixed out-degree of nodes), and Mariposa (a randomized routing network that also guarantees optimal route lengths for a given out-degree of nodes). This paper presents Virtual Ring Routing (VRR), a new network routing protocol that occupies a unique point in the design space. VRR is inspired by overlay routing algorithms in Distributed Hash Tables (DHTs) but it does not rely on an underlying network routing protocol. It is implemented directly on top of the link layer. VRR provides both raditional point-to-point network routing and DHT routing to the node responsible for a hash table key.VRR can be used with any link layer technology but this paper describes a design and several implementations of VRR that are tuned for wireless networks. We evaluate the performance of VRR using simulations and measurements from a sensor network and an 802.11a testbed. The experimental results show that VRR provides robust performance across a wide range of environments and workloads. It performs comparably to, or better than, the best wireless routing protocol in each experiment. VRR performs well because of its unique features: it does not require network flooding or trans-lation between fixed identifiers and location-dependent addresses. The equality and anonymity of peer-to-peer networks makes them vulnerable to routing denial of service attacks from misbehaving nodes. In this paper, we investigate how existing social networks can benefit P2P networks by leveraging the inherent trust associated with social links. We present a trust model that lets us compare routing algorithms for P2P networks overlaying social networks. We propose SPROUT, a DHT routing algorithm that significantly increases the probability of successful routing by using social links. Finally, we discuss further optimization and design choices for both the model and the routing algorithm. In the past few years, more and more researchers have paid close attention to the emerging field of delay tolerant networks (DTNs), in which network often partitions and end-to-end paths do not exist nearly all the time. To cope with these challenges, most routing protocols employ the "store-carry-forward" strategy to transmit messages. However, the difficulty of this strategy is how to choose the best relay node and determine the best time to forward messages. Fortunately, social relations among nodes can be used to address these problems. In this paper, we present a comprehensive survey of recent social-aware routing protocols, which offer an insight into how to utilize social relationships to design efficient and applicable routing algorithms in DTNs. First, we review the major practical applications of DTNs. Then, we focus on understanding social ties between nodes and investigating some design-related issues of social-based routing approaches, e.g., the ways to obtain social relations among nodes, the metrics and approaches to identify the characteristics of social ties, the strategies to optimize social-aware routing protocols, and the suitable mobility traces to evaluate these protocols. We also create a taxonomy for social-aware routing protocols according to the sources of social relations. Finally, we outline several open issues and research challenges. The various proposed DHT routing algorithms embody several different underlying routing geometries. These geometries include hypercubes, rings, tree-like structures, and butterfly networks. In this paper we focus on how these basic geometric approaches affect the resilience and proximity properties of DHTs. One factor that distinguishes these geometries is the degree of flexibility they provide in the selection of neighbors and routes. Flexibility is an important factor in achieving good static resilience and effective proximity neighbor and route selection. Our basic finding is that, despite our initial preference for more complex geometries, the ring geometry allows the greatest flexibility, and hence achieves the best resilience and proximity performance. We study a fundamental tradeoff issue in designing a distributed hash table (DHT) in peer-to-peer (P2P) networks: the size of the routing table versus the network diameter. Observing that existing DHT schemes have either 1) a routing table size and network diameter both of O(log sub 2 n), or 2) a routing table of size d and network diameter of O(n sup 1 d ), S. (2001) asked whether this represents the best asymptotic "state-efficiency" tradeoffs. We show that some straightforward routing algorithms achieve better asymptotic tradeoffs. However, such algorithms all cause severe congestion on certain network nodes, which is undesirable in a P2P network. We rigorously define the notion of "congestion" and conjecture that the above tradeoffs are asymptotically optimal for a congestion-free network. The answer to this conjecture is negative in the strict sense. However, it becomes positive if the routing algorithm is required to eliminate congestion in a "natural" way by being uniform. We also prove that the tradeoffs are asymptotically optimal for uniform algorithms. Furthermore, for uniform algorithms, we find that the routing table size of O(log sub 2 n) is a magic threshold point that separates two different "state-efficiency" regions. Our third result is to study the exact (instead of asymptotic) optimal tradeoffs for uniform algorithms. We propose a new routing algorithm that reduces the routing table size and the network diameter of Chord both by 21.4 without introducing any other protocol overhead, based on a novel number-theory technique. Our final result is to present Ulysses, a congestion-free nonuniform algorithm that achieves a better asymptotic "state-efficiency" tradeoff than existing schemes in the probabilistic sense, even under dynamic node joins leaves. The objective of this paper is to introduce a model to guide the analysis of the impact of churn in P2P networks. Using this model, a variety of node membership scenarios is created. These scenarios are used to capture and analyze the performance trends of chord, a distributed hash table (DHT) based resource lookup protocol for peer-to-peer overlay networks. The performance study focuses both on the performance of routing and content retrieval. This study also identifies the limitations of various churn-alleviating mechanisms, frequently proposed in the literature. The study highlights the importance of the content nature and access pattern on the performance of P2P, DHT-based overlay networks. The results show that the type of content being accessed and the way the content is accessed has a significant impact on the performance of P2P networks A number of distributed hash table (DHT)-based protocols have been proposed to address the issue of scalability in peer-to-peer networks. In this paper, we present Ulysses, a peer-to-peer network based on the butterfly topology that achieves the theoretical lower bound of log n log log n on network diameter when the average routing table size at nodes is no more than log n. Compared to existing DHT-based schemes with similar routing table size, Ulysses reduces the network diameter by a factor of log log n, which is 2–4 for typical configurations. This translates into the same amount of reduction on query latency and average traffic per link node. In addition, Ulysses maintains the same level of robustness in terms of routing in the face of faults and recovering from graceful ungraceful joins and departures, as provided by existing DHT-based schemes. The performance of the protocol has been evaluated using both analysis and simulation. Copyright © 2004 AEI We consider the problem of finding the jointly optimal end-to-end communication rates, routing, power allocation and transmission scheduling for wireless networks. In particular, we focus on finding the resource allocation that achieves fair end-to-end communication rates. Using realistic models of several rate and power adaption schemes, we show how this cross-layer optimization problem can be formulated as a nonlinear mathematical program. We develop a specialized solution method, based on a nonlinear column generation technique, and prove that it converges to the globally optimal solution. We present computational results from a large set of networks and discuss the insight that can be gained about the influence of power control, spatial reuse, routing strategies and variable transmission rates on network performance. In recent years new insights and algorithms have been obtained for the classical, deterministic vehicle routing problem as well as for natural stochastic and dynamic variations of it. These new developments are based on theoretical analysis, combine probabilistic and combinatorial modeling, and lead to new algorithms that produce near-optimal solutions, and a deeper understanding of uncertainty issues in vehicle routing. In this paper, we survey these new developments with an emphasis on the insights gained and on the algorithms proposed. In recent years, highly effective hierarchical and goal-directed speed-up techniques for routing in large road networks have been developed. This article makes a systematic study of combinations of such techniques. These combinations turn out to give the best results in many scenarios, including graphs for unit disk graphs, grid networks, and time-expanded timetables. Besides these quantitative results, we obtain general insights for successful combinations. Distributed hash table (DHT) systems are an important class of peer-to-peer routing infrastructures. They enable scalable wide-area storage and retrieval of information, and will support the rapid development of a wide variety of Internet-scale applications ranging from naming systems and file systems to application-layer multicast. DHT systems essentially build an overlay network, but a path on the overlay between any two nodes can be significantly different from the unicast path between those two nodes on the underlying network. As such, the lookup latency in these systems can be quite high and can adversely impact the performance of applications built on top of such systems.In this paper, we discuss a random sampling technique that incrementally improves lookup latency in DHT systems. Our sampling can be implemented using information gleaned from lookups traversing the overlay network. For this reason, we call our approach lookup-parasitic random sampling (LPRS). LPRS is fast, incurs little network overhead, and requires relatively few modifications to existing DHT systems.For idealized versions of DHT systems like Chord, Tapestry and Pastry, we analytically prove that LPRS can result in lookup latencies proportional to the average unicast latency of the network, provided the underlying physical topology has a power-law latency expansion. We then validate this analysis by implementing LPRS in the Chord simulator. Our simulations reveal that LPRS-Chord exhibits a qualitatively better latency scaling behavior relative to unmodified Chord.Finally, we provide evidence which suggests that the Internet router-level topology resembles power-law latency expansion. This finding implies that LPRS has significant practical applicability as a general latency reduction technique for many DHT systems. This finding is also of independent interest since it might inform the design of latency-sensitive topology models for the Internet. Most standard cluster interconnect technologies are flexible with respect to network topology. This has spawned a substantial amount of research on topology-agnostic routing algorithms, which make no assumption about the network structure, thus providing the flexibility needed to route on irregular networks. Actually, such an irregularity should be often interpreted as minor modifications of some regular interconnection pattern, such as those induced by faults. In fact, topology-agnostic routing algorithms are also becoming increasingly useful for networks on chip (NoCs), where faults may make the preferred 2D mesh topology irregular. Existing topology-agnostic routing algorithms were developed for varying purposes, giving them different and not always comparable properties. Details are scattered among many papers, each with distinct conditions, making comparison difficult. This paper presents a comprehensive overview of the known topology-agnostic routing algorithms. We classify these algorithms by their most important properties, and evaluate them consistently. This provides significant insight into the algorithms and their appropriateness for different on- and off-chip environments. We present Tapestry, a peer-to-peer overlay routing infrastructure offering efficient, scalable, location-independent routing of messages directly to nearby copies of an object or service using only localized resources. Tapestry supports a generic decentralized object location and routing applications programming interface using a self-repairing, soft-state-based routing layer. The paper presents the Tapestry architecture, algorithms, and implementation. It explores the behavior of a Tapestry deployment on PlanetLab, a global testbed of approximately 100 machines. Experimental results show that Tapestry exhibits stable behavior and performance as an overlay, despite the instability of the underlying network layers. Several widely distributed applications have been implemented on Tapestry, illustrating its utility as a deployment infrastructure. Motivated by the increasing availability of vehicle trajectory data, we propose learn-to-route, a comprehensive trajectory-based routing solution. Specifically, we first construct a graph-like structure from trajectories as the routing infrastructure. Second, we enable trajectory-based routing given an arbitrary (source, destination) pair. In the first step, given a road network and a collection of trajectories, we propose a trajectory-based clustering method that identifies regions in a road network. If a pair of regions are connected by trajectories, we maintain the paths used by these trajectories and learn a routing preference for travel between the regions. As trajectories are skewed and sparse, and although the introduction of regions serves to consolidate the sparse data, many region pairs are not connected by trajectories. We thus transfer routing preferences from region pairs with sufficient trajectories to such region pairs and then use the transferred preferences to identify paths between the regions. In the second step, we exploit the above graph-like structure to achieve a comprehensive trajectory-based routing solution. Empirical studies with two substantial trajectory data sets offer insight into the proposed solution, indicating that it is practical. A comparison with a leading routing service offers evidence that the paper's proposal is able to enhance routing quality. This paper uses big data and machine learning for the real-time management of Internet scale quality-of-service (QoS) route optimisation with an overlay network. Based on the collection of data sampled every 2 min over a large number of source–destinations pairs, we show that intercontinental Internet protocol (IP) paths are far from optimal with respect to QoS metrics such as end-to-end round-trip delay. We, therefore, develop a machine learning-based scheme that exploits large scale data collected from communicating node pairs in a multihop overlay network that uses IP between the overlay nodes, and selects paths that provide substantially better QoS than IP. Inspired from cognitive packet network protocol, it uses random neural networks with reinforcement learning based on the massive data that is collected, to select intermediate overlay hops. The routing scheme is illustrated on a 20-node intercontinental overlay network that collects some @math measurements per week, and makes scalable distributed routing decisions. Experimental results show that this approach improves QoS significantly and efficiently. Releasing private data to the future is a challenging problem. Making private data accessible at a future point in time requires mechanisms to keep data secure and undiscovered so that protected data is not available prior to the legitimate release time and the data appears automatically at the expected release time. In this paper, we develop new mechanisms to support self-emerging data storage that securely hide keys of encrypted data in a Distributed Hash Table (DHT) network that makes the encryption keys automatically appear at the predetermined release time so that the protected encrypted private data can be decrypted at the release time. We show that a straight-forward approach of privately storing keys in a DHT is prone to a number of attacks that could either make the hidden data appear before the prescribed release time (release-ahead attack) or destroy the hidden data altogether (drop attack). We develop a suite of self-emerging key routing mechanisms for securely storing and routing encryption keys in the DHT. We show that the proposed scheme is resilient to both release-ahead attack and drop attack as well as to attacks that arise due to traditional churn issues in DHT networks. Our experimental evaluation demonstrates the performance of the proposed schemes in terms of attack resilience and churn resilience. Different uses of a road network call for the consideration of different travel costs: in route planning, travel time and distance are typically considered, and green house gas (GHG) emissions are increasingly being considered. Further, travel costs such as travel time and GHG emissions are time-dependent and uncertain. To support such uses, we propose techniques that enable the construction of a multi-cost, time-dependent, uncertain graph (MTUG) model of a road network based on GPS data from vehicles that traversed the road network. Based on the MTUG, we define stochastic skyline routes that consider multiple costs and time-dependent uncertainty, and we propose efficient algorithms to retrieve stochastic skyline routes for a given source-destination pair and a start time. Empirical studies with three road networks in Denmark and a substantial GPS data set offer insight into the design properties of the MTUG and the efficiency of the stochastic skyline routing algorithms. During recent years, Distributed Hash Tables (DHTs) have been extensively studied through simulation and analysis. However, due to their limited deployment, it has not been possible to observe the behavior of a widely-deployed DHT in practice. Recently, the popular eMule file-sharing software incorporated a Kademlia-based DHT, called Kad, which currently has around one million simultaneous users. In this paper, we empirically study the performance of the key DHT operation, lookup, over Kad. First, we analytically derive the benefits of different ways to increase the richness of routing tables in Kademlia-based DHTs. Second, we empirically characterize two aspects of the accuracy of routing tables in Kad, namely completeness and freshness, and characterize their impact on Kad’s lookup performance. Finally, we investigate how the efficiency and consistency of lookup in Kad can be improved by performing parallel lookup and maintaining multiple replicas, respectively. Our results pinpoint the best operating point for the degree of lookup parallelism and the degree of replication for Kad. Distributed Hash Tables (DHTs) are very efficient distributed systems for routing, but at the same time vulnerable to disruptive nodes. Designers of such systems want them used in open networks, where an adversary can perform a sybil attack by introducing a large number of corrupt nodes in the network, considerably degrading its performance. We introduce a routing strategy that alleviates some of the effects of such an attack by making sure that lookups are performed using a diverse set of nodes. This ensures that at least some of the nodes queried are good, and hence the search makes forward progress. This strategy makes use of latent social information present in the introduction graph of the network.