We propose a new approach for constructing P2P networks based on a dynamic decomposition of a continuous space into cells corresponding to processors. We demonstrate the power of these design rules by suggesting two new architectures, one for DHT (Distributed Hash Table) and the other for dynamic expander networks. The DHT network, which we call Distance Halving, allows logarithmic routing and load, while preserving constant degrees. Our second construction builds a network that is guaranteed to be an expander. The resulting topologies are simple to maintain and implement. Their simplicity makes it easy to modify and add protocols. We show it is possible to reduce the dilation and the load of the DHT with a small increase of the degree. We present a provably good protocol for relieving hot spots and a construction with high fault tolerance. Finally we show that, using our approach, it is possible to construct any family of constant degree graphs in a dynamic environment, though with worst parameters. Therefore we expect that more distributed data structures could be designed and implemented in a dynamic environment.

Abstract — This paper presents a new cooperative storage system for sensor networks geared for disconnected operation (where sensor nodes do not have a connected path to a basestation). The goal of the system is to maximize its data storage capacity by appropriately distributing storage utilization and opportunistically offloading data to external devices when possible. The system is motivated by the observation that a large

We present a scheme for evenly partitioning the key space in distributed hash tables among the participating nodes. The scheme is based on the multiple random choices paradigm [3, 19], and handles both node joins and leaves. It achieves, with high probability, a ratio of at most 4 between the loads of the most and least burdened nodes, in the face or arbitrary node arrivals and departures. Each join or leave operation incurs message cost that is, with high probability, O(log 2 n), where n is the number of nodes, and causes the relocation of keys from at most one node (for joins) or three nodes (for leaves). A version of our scheme is suitable for heterogeneous systems, where the capacities of nodes to serve keys can vary widely.

Abstract—Over the past few years, peer-to-peer (P2P) systems have rapidly grown in popularity and have become a dominant means for sharing resources. In these systems, load balancing is a key challenge because nodes are often heterogeneous. While several load-balancing schemes have been proposed in the literature, these solutions are typically ad hoc, heuristic based, and localized. In this paper, we present a general framework, HiGLOB, for global load balancing in structured P2P systems. Each node in HiGLOB has two key components: 1) a histogram manager maintains a histogram that reflects a global view of the distribution of the load in the system, and 2) a load-balancing manager that redistributes the load whenever the node becomes overloaded or underloaded. We exploit the routing metadata to partition the P2P network into nonoverlapping regions corresponding to the histogram buckets. We propose mechanisms to keep the cost of constructing and maintaining the histograms low. We further show that our scheme can control and bound the amount of load imbalance across the system. Finally, we demonstrate the effectiveness of HiGLOB by instantiating it over three existing structured P2P systems: Skip Graph, BATON, and Chord. Our experimental results indicate that our approach works well in practice. Index Terms—Peer-to-peer, framework, load balancing, histogram, DHT, overlay network. Ç

Ranged hash functions generalize hash tables to the setting where hash buckets may come and go over time, a typical case in distributed settings where hash buckets may correspond to unreliable servers or network connections. Monotone ranged hash functions are a particular class of ranged hash functions that minimize item reassignments in response to churn: changes in the set of available buckets. The canonical example of a monotone ranged hash function is the ring-based consistent hashing mechanism of Karger et al. [13]. These hash functions give a maximum load of Θ � n m log m � when n is the number of items and m is the number of buckets. The question of whether some better bound could be obtained using a more sophisticated hash function has remained open. We resolve this question by showing two lower bounds. First, the maximum load of any randomized monotone ranged hash function is Ω ( � n m ln m) when n = o(m log m). This bound covers almost all of the nontrivial case, because when n = Ω(m log m) simple random assignment matches the trivial lower bound of Ω(n/m). We give a matching (though impractical) upper bound that shows that our lower bound is tight over almost all of its range. Second, for randomized monotone ranged hash functions derived from metric spaces, there is a further trade-off between the expansion factor of the metric and the load balance, which for the special case of growth-restricted metrics gives a bound of Ω � n m log m � , asymptotically equal to that of consistent hashing. These are the first known non-trivial lower bounds for ranged hash functions. They also explain why in ten years no better ranged hash functions have arisen to replace consistent hashing.

Abstract — In the past few years, several DHT-based abstractions for peer-to-peer systems have been proposed. The main characteristic is to associate nodes (peers) with keys (objects) and to construct distributed routing structures to support an efficient location. These approaches address the load problem, and load balancing is achieved by moving the keys. However, the problem is still not properly covered. In this paper we present an analysis of structured peer-to-peer systems taking into consideration Zipf-like requests distribution. Based on our analysis, we propose a novel approach for load balancing relying on object popularity. Our approach is based on routing table reorganization in order to balance the lookup traffic load. We have implemented this approach in a Pastry-like system. The obtained results demonstrate a better balance of load, which can lead to improved scalability and performance. I.

We present a scheme for evenly partitioning the key space in distributed hash tables among the participating nodes. The scheme is based on the multiple random choices paradigm and handles both node joins and leaves. It achieves, with high probability, a ratio of at most 4 between the loads of the most and least burdened nodes, in the face or arbitrary node arrivals and departures. Each join or leave operation incurs message cost that is, with high probability, O (log² n), where n is the number of nodes, and causes the re location of keys from at most one node (for joins) or three nodes (for leaves).In this paper, A version of heterogeneous systems, where the capacities of nodes to serve keys can vary widely.

Efficient handling of multidimensional data is a challenging issue in P2P systems. DHT-based systems provide mechanisms for handling exact-match lookups that are extremely scalable. However, efficient evaluation of complex queries (such as multi-attributes range search, kNN search...) over huge volumes of multidimensional data is still an open problem in DHTs, mainly because they use hashing that destroys the spatial locality of the stored data, and also due to the high cost of nodes joins and departures. In this paper we propose a new scalable and distributed indexing structure for managing multidimensional data in dynamic P2P systems. Our approach is based on the Content Addressable Networks paradigm. The key idea is to equip each node with long links towards some distant nodes in the system such that a message moves faster to its target during routing, while the cost of maintaining the network during a nodes churn is minimized. Our system is a pure P2P overlay that is fully-decentralized and self-organizing, where no predefined limits are imposed on the sizes of the network or the routing state per node. Each node self-adjusts its routing state to cope with changes in network membership. Specifically, in a network with N nodes, each node maintains O(log N) long links. Exact-match and range queries are routed within O(log N) hops. We also provided an effective load balancing mechanism that assign a new joining node to a heavily loaded area in the key space. This mechanism guarantees a constant load imbalance factor, with an amortized cost of O(log N) messages node join compared to O(log 2 N) in other systems. We implemented a simulator and conducted experiments to study the performance of our design. Experimental results validate the full scalability and efficiency of our approach. 1.