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Key/value stores which are built on structured overlay networks often lack support for atomic transactions and strong data consistency among replicas. This is unfortunate, because consistency guarantees and transactions would allow a wide range of additional application domains to benefit from the inherent scalability and fault-tolerance of DHTs. The Scalaris key/value store supports strong data consistency and atomic transactions. It uses an enhanced Paxos Commit protocol with only four communication steps rather than six. This improvement was possible by exploiting information from the replica distribution in the DHT. Scalaris enables implementation of more reliable and scalable infrastructure for collaborative Web services that require strong consistency and atomic changes across multiple items.
Transactional DHT Algorithms
(2009)
We present a framework for transactional data access on data stored in a DHT. It allows to atomically read and write items and to run distributed transactions consisting of a sequence of read and write operations on the items. Items are symmetrically replicated in order to achieve durability of data stored in the SON. To provide availability of items despite the unavailability of some replicas, operations on items are quorum-based. They make progress as long as a majority of replicas can be accessed. Our framework processes transactions optimistically with an atomic commit protocol that is based on Paxos atomic commit. We present algorithms for the whole framework with an event based notation. Additionally we discuss the problem of lookup inconsistencies and its implications on the one-copy serializability property of the transaction processing in our framework.
We present a middleware to store multidimensional data sets on Internet-scale distributed systems and to efficiently perform range queries on them. Our structured overlay network \emph{SONAR (Structured Overlay Network with Arbitrary Range queries)} puts keys which are adjacent in the key space on logically adjacent nodes in the overlay and is thereby able to process multidimensional range queries with a single logarithmic data lookup and local forwarding. The specified ranges may have arbitrary shapes like rectangles, circles, spheres or polygons. Empirical results demonstrate the routing performance of SONAR on several data sets, ranging from real-world data to artificially constructed worst case distributions. We study the quality of SONAR's routing information which is based on local knowledge only and measure the indegree of the overlay nodes to find potential hot spots in the routing process. We show that SONAR's routing table is self-adjusting, even under extreme situations, keeping always a maximum of $\lceil \log N \rceil$ routing entries.
\newcommand{\chordsharp}{Chord$^\##$} Data lookup is a fundamental problem in peer-to-peer systems: Given a key, find the node that stores the associated object. Chord and other P2P algorithms use distributed hash tables (DHTs) to distribute the keys and nodes evenly across a logical ring. Using an efficient routing strategy, DHTs provide a routing performance of $O (\log N)$ in networks of $N$ nodes. While the routing performance has been shown to be optimal, the uniform key distribution makes it impossible for DHTs to support range queries. For range queries, consecutive keys must be stored on lo gically neighboring nodes. In this paper, we present an enhancement of Chord that eliminates the hash function while keeping the same routing performance. The resulting algorithm, named \chordsharp{}, provides a richer function ality while maintaining the same complexity. In addition to Chord, \chordsharp{} adapts to load imbalance.
The performance of heuristic search algorithms depends crucially on the effectiveness of the heuristic. A pattern database (PDB) is a powerful heuristic in the form of a pre-computed lookup table. Larger PDBs provide better bounds and thus allow more cut-offs in the search process. Today, the largest PDB for the 24-puzzle is a 6-6-6-6 PDB with a size of 486 MB.
We created 8-8-8, 9-8-7 and 9-9-6 PDBs that are three orders of magnitude larger (up to 1.4 TB) than the 6-6-6-6 PDB. We show how to compute such large PDBs and we present statistical and empirical data on their efficiency. The largest single PDB gives on average an 8-fold improvement over the 6-6-6-6 PDB. Combining several large
PDBs gives on average an 12-fold improvement.