Resources in a cloud can be identified using identifiers based on random numbers. When using a distributed hash table to resolve such identifiers to network locations, the straightforward approach is to store the network location directly in the hash table entry associated with an identifier. When a mobile host contains a large number of resources, this requires that all of the associated hash table entries must be updated when its network address changes. We propose an alternative approach where we store a host identifier in the entry associated with a resource identifier and the actual network address of the host in a separate host entry. This can drastically reduce the time required for updating the distributed hash table when a mobile host changes its network address. We also investigate under which circumstances our approach should or should not be used. We evaluate and confirm the usefulness of our approach with experiments run on top of OpenDHT.
Ballintijn al argue that resource naming should be decoupled from resource identification @cite_7 . Resources are named with human-friendly names, which are based on DNS @cite_9 , while identification is done with object handles, which are globally unique identifiers that need not contain network locations. They use DNS to resolve human-friendly names to object handles and a location service to resolve object handles to network locations. The location service uses a hierarchical architecture for resolving object handles. This two-level approach allows the naming of resources without worrying about replication or migration and the identification of resources without worrying about naming policies.
@cite_9 @cite_7
2096392987 2083158002 1505083828 2047197963
To fill the gap between what uniform resource names (URNs) provide and what humans need, we propose a new kind of uniform resource identifier (URI) called human-friendly names (HFNs). In this article, we present the design for a scalable HFN-to-URL (uniform resource locator) resolution mechanism that makes use of the Domain Name System (DNS) and the Globe location service to name and locate resources. This new URI proposes to improve both scalability and usability in naming replicated resources on the Web. Name services are critical for mapping logical resource names to physical resources in large-scale distributed systems. The Domain Name System (DNS) used on the Internet, however, is slow, vulnerable to denial of service attacks, and does not support fast updates. These problems stem fundamentally from the structure of the legacy DNS.This paper describes the design and implementation of the Cooperative Domain Name System (CoDoNS), a novel name service, which provides high lookup performance through proactive caching, resilience to denial of service attacks through automatic load-balancing, and fast propagation of updates. CoDoNS derives its scalability, decentralization, self-organization, and failure resilience from peer-to-peer overlays, while it achieves high performance using the Beehive replication framework. Cryptographic delegation, instead of host-based physical delegation, limits potential malfeasance by namespace operators and creates a competitive market for namespace management. Backwards compatibility with existing protocols and wire formats enables CoDoNS to serve as a backup for legacy DNS, as well as a complete replacement. Performance measurements from a real-life deployment of the system in PlanetLab shows that CoDoNS provides fast lookups, automatically reconfigures around faults without manual involvement and thwarts distributed denial of service attacks by promptly redistributing load across nodes. This thesis describes a novel statistical named-entity (i.e. “proper name”) recognition system known as “MENE” (Maximum Entropy Named Entity). Named entity (N.E.) recognition is a form of information extraction in which we seek to classify every word in a document as being a person-name, organization, location, date, time, monetary value, percentage, or “none of the above”. The task has particular significance for Internet search engines, machine translation, the automatic indexing of documents, and as a foundation for work on more complex information extraction tasks. Two of the most significant problems facing the constructor of a named entity system are the questions of portability and system performance. A practical N.E. system will need to be ported frequently to new bodies of text and even to new languages. The challenge is to build a system which can be ported with minimal expense (in particular minimal programming by a computational linguist) while maintaining a high degree of accuracy in the new domains or languages. MENE attempts to address these issues through the use of maximum entropy probabilistic modeling. It utilizes a very flexible object-based architecture which allows it to make use of a broad range of knowledge sources in making its tagging decisions. In the DARPA-sponsored MUC-7 named entity evaluation, the system displayed an accuracy rate which was well-above the median, demonstrating that it can achieve the performance goal. In addition, we demonstrate that the system can be used as a post-processing tool to enhance the output of a hand-coded named entity recognizer through experiments in which MENE improved on the performance of N.E. systems from three different sites. Furthermore, when all three external recognizers are combined under MENE, we are able to achieve very strong results which, in some cases, appear to be competitive with human performance. Finally, we demonstrate the trans-lingual portability of the system. We ported the system to two Japanese-language named entity tasks, one of which involved a new named entity category, “artifact”. Our results on these tasks were competitive with the best systems built by native Japanese speakers despite the fact that the author speaks no Japanese. We consider the problem of resolving duplicates in a database of places, where a place is defined as any entity that has a name and a physical location. When other auxiliary attributes like phone and full address are not available, deduplication based solely on names and approximate location becomes an exceptionally challenging problem that requires both domain knowledge as well an local geographical knowledge. For example, the pairs "Newpark Mall Gap Outlet" and "Newpark Mall Sears Outlet" have a high string similarity, but determining that they are different requires the domain knowledge that they represent two different store names in the same mall. Similarly, in most parts of the world, a local business called "Central Park Cafe" might simply be referred to by "Central Park", except in New York, where the keyword "Cafe" in the name becomes important to differentiate it from the famous park in the city. In this paper, we present a language model that can encapsulate both domain knowledge as well as local geographical knowledge. We also present unsupervised techniques that can learn such a model from a database of places. Finally, we present deduplication techniques based on such a model, and we demonstrate, using real datasets, that our techniques are much more effective than simple TF-IDF based models in resolving duplicates. Our techniques are used in production at Facebook for deduplicating the Places database.