scholarly journals Dynamic Prefix Tree for Service Discovery within Large Scale Grids

2006 ◽  
Author(s):  
E. Caron ◽  
F. Desprez ◽  
C. Tedeschi
Keyword(s):  
Service Discovery ◽  
Large Scale ◽  
Prefix Tree

Peer-to-Peer Service Discovery for Grid Computing

2012 ◽  
pp. 232-259
Author(s):  
Eddy Caron ◽  
Frédéric Desprez ◽  
Franck Petit ◽  
Cédric Tedeschi
Keyword(s):  
Fault Tolerance ◽  
Load Balancing ◽  
Service Discovery ◽  
Large Scale ◽  
Dynamic Networks ◽  
Peer To Peer ◽  
Indexing System ◽  
Key Points ◽  
Computing Platforms ◽  
Traditional Approaches

Within distributed computing platforms, some computing abilities (or services) are offered to clients. To build dynamic applications using such services as basic blocks, a critical prerequisite is to discover those services. Traditional approaches to the service discovery problem have historically relied upon centralized solutions, unable to scale well in large unreliable platforms. In this chapter, we will first give an overview of the state of the art of service discovery solutions based on peer-to-peer (P2P) technologies that allow such a functionality to remain efficient at large scale. We then focus on one of these approaches: the Distributed Lexicographic Placement Table (DLPT) architecture, that provide particular mechanisms for load balancing and fault-tolerance. This solution centers around three key points. First, it calls upon an indexing system structured as a prefix tree, allowing multi-attribute range queries. Second, it allows the mapping of such structures onto heterogeneous and dynamic networks and proposes some load balancing heuristics for it. Third, as our target platform is dynamic and unreliable, we describe its powerful fault-tolerance mechanisms, based on self-stabilization. Finally, we present the software prototype of this architecture and its early experiments.

Runtime Service Discovery for Grid Applications

2009 ◽  
pp. 212-234 ◽  
Author(s):  
James Dooley ◽  
Andrea Zisman ◽  
George Spanoudakis
Keyword(s):  
Social Networks ◽  
Service Discovery ◽  
Large Scale ◽  
Virtual Organisations ◽  
Small World ◽  
Resource Discovery ◽  
Local Network ◽  
Network Node ◽  
Grid Resource ◽  
New Generation

A Virtual Organisation in large-scale distributed systems is a set of individuals and/or institutions with some common purposes or interests that need to share their resources to further their objectives, which is similar to a human community in social networks that consists of people have common interests or goals. Due to the similarity between social networks and Grids, the concepts in social science (e.g. small world phenomenon) can be adopted for the design of new generation Grid systems. This chapter presents a Small World Architecture for Effective Virtual Organisations (SWEVO) for Grid resource discovery in Virtual Organisations, which enables Virtual Organisations working in a more collaborative manner to support decision makers. In SWEVO, Virtual Organisations are connected by a small number of interorganisational links. Not every local network node needs to be connected to remote Virtual Organisations, but every network node can efficiently find connections to specific Virtual Organisations.

scholarly journals Towards a Protocol for the Attachment of Metadata to Grid Service Descriptions and Its Use in Semantic Discovery

2004 ◽  
Vol 12 (4) ◽  
pp. 201-211 ◽  
Author(s):  
Simon Miles ◽  
Juri Papay ◽  
Terry Payne ◽  
Michael Luck ◽  
Luc Moreau
Keyword(s):  
Distributed Systems ◽  
Service Discovery ◽  
Large Scale ◽  
Grid Service ◽  
Semantic Grid ◽  
Essential Component ◽  
Open Distributed Systems ◽  
Semantically Enhanced ◽  
Semantic Service ◽  
Semantic Discovery

Service discovery in large scale, open distributed systems is difficult because of the need to filter out services suitable to the task at hand from a potentially huge pool of possibilities. Semantic descriptions have been advocated as the key to expressive service discovery, but the most commonly used service descriptions and registry protocols do not support such descriptions in a general manner. In this paper, we present a protocol, its implementation and an api for registering semantic service descriptions and other task/user-specific metadata, and for discovering services according to these. Our approach is based on a mechanism for attaching structured and unstructured metadata, which we show to be applicable to multiple registry technologies. The result is an extremely flexible service registry that can be the basis of a sophisticated semantically-enhanced service discovery engine, an essential component of a Semantic Grid.

A COLLABORATIVE APPROACH TO CONSTRUCTION OF LARGE SCALE DISTRIBUTED REASONING SYSTEMS

2011 ◽  
Vol 20 (06) ◽  
pp. 1083-1106 ◽  
Author(s):  
ATE PENDERS ◽  
GREGOR PAVLIN ◽  
MICHIEL KAMERMANS
Keyword(s):  
Collaborative Design ◽  
Service Discovery ◽  
Large Scale ◽  
Collaborative Approach ◽  
Complex Situation ◽  
Distributed Reasoning ◽  
Heterogeneous Services ◽  
Efficient Construction ◽  
Service Oriented ◽  
Definition Of

This paper introduces a new collaborative approach to construction of large scale service oriented systems supporting distributed reasoning. In particular, we assume systems in which complex situation assessment is carried out through composition of heterogeneous services, each specialized for a particular type of analysis. Different services are composed automatically by using service discovery and negotiation. One of the major challenges in such settings is efficient definition of a large number of different types of services. The presented solution supports efficient definition of services by using a combination of light weight service ontologies, efficient construction procedures and tools. In particular, machine-understandable descriptions of heterogeneous services with well defined syntax and semantics can be created by multiple designers, without complex coordination of collaborative design processes and without any knowledge of formal ontologies.

Deep Learning-Based Service Discovery for Business Process Re-Engineering in the Era of Big Data

2020 ◽  
Vol 1 (1) ◽  
pp. 1-22
Author(s):  
Bo Jiang ◽  
Junwu Chen ◽  
Ye Wang ◽  
Liping Zhao ◽  
Pengxiang Liu
Keyword(s):  
Deep Learning ◽  
Business Process ◽  
Service Discovery ◽  
Large Scale ◽  
Business Processes ◽  
Feature Fusion ◽  
The Internet ◽  
Business Services ◽  
Business Goals ◽  
Effective Manner

In recent years, business process re-engineering has played an important role in the development of large-scale web-based applications. To re-engineer business processes, business services are developed and coordinated by reusing a set of open APIs and services on the internet. Yet, the number of services on the internet has grown drastically, making it difficult for them to be discovered to support the changing business goals. One major challenge is therefore to search for a suitable service that matches a specific business goal from a large number of available services in an efficient and effective manner. To address this challenge, this paper proposes a deep learning approach for massive service discovery. The approach, thus called MassRAFF, employs a combination of the recurrent attention and feature fusion methods. This paper first presents the MassRAFF approach and then reports on an experiment for evaluating this approach. The experimental results show that the MassRAFF approach has performed reasonably well and has potential to be improved further in future work.

Peer-to-Peer Service Discovery for Grid Computing

2010 ◽  
pp. 285-312
Author(s):  
Eddy Caron ◽  
Frédéric Desprez ◽  
Franck Petit ◽  
Cédric Tedeschi
Keyword(s):  
Fault Tolerance ◽  
Load Balancing ◽  
Service Discovery ◽  
Large Scale ◽  
Dynamic Networks ◽  
Peer To Peer ◽  
Indexing System ◽  
Key Points ◽  
Computing Platforms ◽  
Traditional Approaches

Within distributed computing platforms, some computing abilities (or services) are offered to clients. To build dynamic applications using such services as basic blocks, a critical prerequisite is to discover those services. Traditional approaches to the service discovery problem have historically relied upon centralized solutions, unable to scale well in large unreliable platforms. In this chapter, we will first give an overview of the state of the art of service discovery solutions based on peer-to-peer (P2P) technologies that allow such a functionality to remain efficient at large scale. We then focus on one of these approaches: the Distributed Lexicographic Placement Table (DLPT) architecture, that provide particular mechanisms for load balancing and fault-tolerance. This solution centers around three key points. First, it calls upon an indexing system structured as a prefix tree, allowing multi-attribute range queries. Second, it allows the mapping of such structures onto heterogeneous and dynamic networks and proposes some load balancing heuristics for it. Third, as our target platform is dynamic and unreliable, we describe its powerful fault-tolerance mechanisms, based on self-stabilization. Finally, we present the software prototype of this architecture and its early experiments.

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