Resource Discovery Mechanism Research Articles

Grid Resource Discovery Algorithm of the Multi-Layer Overlay Network Model Based on Distance

The resource discovery mechanism is a hot topic issue in grid environments and It has great impact on the efficiency of the resource sharing and cooperative computing. Based on the domain and resource routing nodes, a grid resource discovery model of multilayer overlay network was given in this paper. And on this basis, using a linear combination of the block distance and chessboard distance instead of Euclidean distance, grid resource discovery algorithm based on distance was proposed. Experiment shows that the algorithm has low cost, fast response and can obtain better success rate of lookup, as well as the effectiveness of the resource discovery algorithm.

Dual Heap Overlay for Range Queries for Handling Hardware Resources

Grid resource discovery involves resolving various types of queries including exact match and range queries. Our recent effort resulted in Dual Heap Overlay Resource Discovery Protocol for a Mobile Grid (DEEPG) to resolve exact match queries. DEEPG attempted to reduce the search set for query resolution by rejecting nodes which do not satisfy the sum of the attributes specified in the query. In this paper, we propose DEEPG-R, a Dual Heap Overlay Resource Discovery Protocol for Range Queries in Grid. Distributed Hash Table (DHT) based resource discovery mechanism is common in the field and fails to handle few challenges. Hence, DEEPG-R employs the non-DHT based overlays of DEEPG and is tuned to handle range queries. The focus of the extended protocol is on reducing the number of comparisons further by comparing the resource attributes with the lower and upper bounds specified in the range query. Simulation results prove that the proposed protocol is efficient with reduced search time complexities and low maintenance overheads.

Decentralized resource discovery mechanisms for distributed computing in peer-to-peer environments

Resource discovery is an important part of distributed computing and resource sharing systems, like grids and utility computing. Because of the increasing importance of decentralized and peer-to-peer environments, characterized by high dynamism and churn, a number of resource discovery mechanisms, mainly based on peer-to-peer techniques, have been presented recently. We present and classify them according to criteria like their topology and the degree of achievement of various common requirements of great importance for the targeted environments, as well as compare their reported performance. These classifications intend to provide an intuitive vision of the strengths and weaknesses of each system.

Resource Discovery Algorithm Based on Multilayer Overlay Network

In this article, firstly the principles of r esource discovery mechanism and the techniques involved in Multi-Layer Overlay Network (MLON) based grid resource discovery mechanism are introduced; then, using Euclidean space distance and Top-K technique, the resource matching strategy and the transformation of queries in multidimensional attribute space are discussed in detail, and a resource discovery algorithm MLON-RSA (MLON-based Resource discovery Algorithm) is proposed and theoretically analyzed; at last, the feasibility, high efficiency, and high user satisfaction of the proposed algorithm are demonstrated through simulation tests.

PERFORMANCE IMPROVEMENT IN CLOUD COMPUTING USING RESOURCE CLUSTERING

Cloud computing is a computing paradigm in which the various tasks are assigned to a combination of connections, software and services that can be acce ssed over the network. The computing resources and services can be efficiently delivered and utilized, making the vision of computing utility realizable. In various applications, execution of services with mo re number of tasks has to perform with minimum intertask communication. The applications are more likely to exhibit different patterns and levels and the distributed resources organize into various topolog ies for information and query dissemination. In a distributed system the resource discovery is a sign ificant process for finding appropriate nodes. The earlier resource discovery mechanism in cloud system relies on the recent observations. In this study, resourc e usage distribution for a group of nodes with identi cal resource usages patterns are identified and kep t as a cluster and is named as resource clustering approac h. The resource clustering approach is modeled usin g CloudSim, a toolkit for modeling and simulating clo ud computing environments and the evaluation improves the performance of the system in the usage of the resources. Results show that resource clust ers are able to provide high accuracy for resource disc overy.

Mobile digcovery: discovering and interacting with the world through the Internet of things

The application of Internet-enabled devices in the real world for the development of Smart Cities, environmental monitoring, bus tracking, and parking requires scalability, extensibility, and integration of emerging resources to reach a suitable ecosystem for data acquisition and interaction with citizens. Internet of things needs to offer efficient support for global communications and access to services and information. It needs to enable homogeneous and seamless machine-to-machine communication for different solutions and applications. This work presents an homogeneous and suitable mechanism for global resource discovery, device access for deployed smart objects in different scenarios, and sensors and devices from end users (participative sensing). The integration of legacy and sensors already available from smart buildings and smart objects is presented. For this purpose, a resolution infrastructure called "digcovery" is defined for maximizing efficiency and sustainability of deployments. Digcovery architecture offers the framework to allow users to register/include their own sensors into a common infrastructure and access/discover the available resources through mobile digcovery. Mobile digcovery exploits the context-awareness, geo-location, and identification technologies available in mobile platforms such as smartphones to discover, interact, and access the resources through its ElasticSearch engine.

A Resource Discovery Mechanism on Cloud Computing System

Cloud Computing is an efficient way to resolve the resource sharing and cooperative work in distributed environments. Considering the disadvantages of traditional Scheduling method and the characteristics of Cloud Computing System, a resource discovery mechanism has been designed and implemented. Firstly, a calculation method based on each join points’ loading has been proposed, providing the Formula of method. Secondly, a comprehensive service resource distribution method has been designed and implemented in consideration of respective service resource counts, each join points’ performance and current loading distribution. Finally, the result of the experiment indicates that the scheduling system can improve the efficiency of dispatching service resource and the utilization ratio of distributed service resource on Cloud Computing System.

A constraints-based resource discovery model for multi-provider cloud environments

Abstract Abstract Increasingly infrastructure providers are supplying the cloud marketplace with storage and on-demand compute resources to host cloud applications. From an application user’s point of view, it is desirable to identify the most appropriate set of available resources on which to execute an application. Resource choice can be complex and may involve comparing available hardware specifications, operating systems, value-added services (such as network configuration or data replication) and operating costs (such as hosting cost and data throughput). Providers’ cost models often change and new commodity cost models (such as spot pricing) can offer significant savings. In this paper, a software abstraction layer is used to discover the most appropriate infrastructure resources for a given application, by applying a two-phase constraints-based approach to a multi-provider cloud environment. In the first phase, a set of possible infrastructure resources is identified for the application. In the second phase, a suitable heuristic is used to select the most appropriate resources from the initial set. For some applications a cost-based heuristic may be most appropriate; for others a performance-based heuristic may be of greater relevance. A financial services application and a high performance computing application are used to illustrate the execution of the proposed resource discovery mechanism. The experimental results show that the proposed model can dynamically select appropriate resouces for an application’s requirements.

The POWDER protocol as infrastructure to serving and compressing semantic data

The POWDER protocol is a Semantic Web technology that takes advantage of natural groupings of URIs to annotate all the resources in a regular expression-delineated sub-space of the URI space. POWDER is a mechanism for accreditation, trustmarking and resource discovery, emphasising the publishing of attributed metadata by third parties and trusted authorities. Demonstrating its versatility, it has also been deployed in unforeseen use cases, such as repository compression. In this paper, we present the POWDER protocol, explain its position in the Semantic Web architecture, expose and discuss current implementations and use cases and future directions.

Research on a Resource Discovery Mechanism in Cloud Computing Environment Based on Analysis of Scientific Materials

Cloud computing technology is an efficient way to resolve the resource sharing and cooperative work in distributed environments. The resource discovery mechanism is a key problem in Cloud computing. In order to implement the quick discovery of service resource and the balanced distribution of resource on the Cloud computing system, a resource discovery mechanism in Cloud computing environment has been designed and implemented. Firstly, a Formula based on each node's loading has been proposed. Secondly, a comprehensive service distribution method has been designed in consideration about each node’s performance and current loading distribution. Finally, the result of the experiment indicates that the discovery mechanism can improve the efficiency of the resource and the utilization ratio of the service resource.

UDDI Based Support System for Collaborative Product Development

Aimed at the puzzle of heterogeneous system integration for collaborative product development, this paper proposed a support system for product collaborative development based on UDDI. In the paper, it explored the resource discovery and description mechanism based on UDDI, and discussed the service integration mechanism based on the extended unified description, discovery and integrated protocol. The research adopted the technologies such as SOA, Web service and so on, and finally it made a typical application example, and verified the practicability and effectiveness of the pattern and method mentioned above. And it can promote the rapidity share and high efficiency utilization of product collaborative development software system.

Research of Grid Resource Discovery Mechanism Based on P2P

To solve the Grid resource discovery framework problem, this paper proposes the resource discovery mechanism based on Peer-to-Peer (P2P). The mechanism introduces the concept of super-node (SN) and virtual organization (VO), which combines both the advantage of decentralized and centralized resource discovery, and makes up a layered resource management model: SN manages user and storages resource information by MDS(Meta Data Service) mechanism in the VO; a P2P virtual level is composed of a large amount of peer service (PS) among VOs, which utilizes the querying and transmitting policy based on historical information and control the diffusion algorithm in resource discovery mechanism. And the simulation shows that the algorithm can find the most suitable resources and improve resource discovery efficiency.

A Survey of Resource Discovery Approaches in Distributed Computing Environment

the increasing need for networked applications and distributed resource sharing, there is a strong incentive for an open large-scale service infrastructure operating over multi- domain and multi-technology networks. Distributed computing such as peer-to-peer networks, promise a distributed computing infrastructure that can provide globally available network resources. The network size and complexity continue to increase dynamically. Huge number and heterogeneous nature of computing resources make the resource management a significantly challenging task. Discovering resources in a Grid environment is complex due to the heterogeneous nature and dynamic availability of resources and are owned by different individuals and organizations, each having their own resource management policies and different access and cost models. The motivation behind this paper is to explore the various resource discovery mechanisms that have been designed and implemented with a variety of algorithms and approaches. KeywordsManagement, Resource Discovery, Gnutella, Agents.

An Efficient Resource Discovery Methodology for HPGRID Systems

An efficient resource discovery mechanism is one of the fundamental requirements for grid computing systems, as it aids in resource management and scheduling of applications. Resource discovery activity involves searching for the appropriate resource types that match the user’s application requirements. Classical approaches to Grid resource discovery are either centralized or hierarchical, and it becomes inefficient when the scale of Grid systems increases rapidly. On the other hand, the Peer-to-Peer (P2P) paradigm emerged as a successful model as it achieves scalability in distributed systems. Grid system using P2P technology can improve the central control of the traditional grid and restricts single point of failure. In this paper, we propose a new approach based on P2P techniques for resource discovery in grids using Hypercubic P2P Grid (HPGRID) topology connecting the grid nodes. A scalable, fault-tolerant, self-configuring search algorithm is proposed as Parameterized HPGRID algorithm, using isomorphic partitioning scheme. By design, the algorithm improves the probability of reaching all the working nodes in the system, even in the presence of non-alive nodes (inaccessible, crashed or nodes loaded by heavy traffic). The scheme can adapt to a complex, heterogeneous and dynamic resources of the grid environment, and has a better scalability

A Self-Organized Overlay Network Management Mechanism for Heterogeneous Environments

The technologies of Cloud Computing and NGN are now growing a paradigm shift where various services are provided to business users over the network. In conjunction with this movement, many studies are active to realize a ubiquitous computing environment in which a huge number of individual users can share their computing resources on the Internet, such as personal computers (PCs), game consoles, sensors and so on. To realize an effective resource discovery mechanism for such an environment, this paper presents an adaptive overlay network that enables a self-organizing resource management system to efficiently adapt to a heterogeneous environment. The proposed mechanism is composed of two functions. One is to adjust the number of logical links of a resource, which forward search queries so that less-useful query flooding can be reduced. The other is to connect resources so as to decrease the communication latency on the physical network rather than the number of query hops on an overlay network. To further improve the discovery efficiency, this paper integrates these functions into a self-organizing resource management system, SORMS, which has been proposed in our previous work. The simulation results indicate that the proposed mechanism can increase the number of discovered resources by 60% without decreasing the discovery efficiency, and can reduce the total communication traffic by 80% compared with the original SORMS. This performance improvement is obtained by efficient control of logical links in a large scale network.

Resource discovery in a Grid system: Directing requests to trustworthy virtual organizations based on global trust values

Abstract This paper studies the resource discovery problem in a Grid system, in which global trust values play a crucial role. The proposed mechanism suggests that routers and resources comprise virtual organizations (VOs) within a Grid system, where a router controls locally a number of resources in each virtual organization. Global trust values are assigned to the system’s VOs. These trust values show whether a VO and subsequently its local resources are trustworthy or not. Our primary goal is to discover the appropriate resource for a specific request and then effectively direct this request to a trustworthy VO that controls locally the appropriate resource. Furthermore, the trust-aware resource discovery mechanism also manages the cases of dynamic changes in the trustworthiness of VOs. For instance, VOs that in the past were untrustworthy could now be trustworthy. The proposed mechanism is capable of detecting these dynamic changes, so that the directing of requests occurs in an up-to-date way. Finally, this paper presents the performance evaluation of the proposed trust-aware resource discovery mechanism by providing a number of simulation tests in Grid systems of different sizes.

Resource Bundles: Using Aggregation for Statistical Large-Scale Resource Discovery and Management

Resource discovery is an important process for finding suitable nodes that satisfy application requirements in large loosely coupled distributed systems. Besides internode heterogeneity, many of these systems also show a high degree of intranode dynamism, so that selecting nodes based only on their recently observed resource capacities can lead to poor deployment decisions resulting in application failures or migration overheads. However, most existing resource discovery mechanisms rely mainly on recent observations to achieve scalability in large systems. In this paper, we propose the notion of a resource bundle-a representative resource usage distribution for a group of nodes with similar resource usage patterns-that employs two complementary techniques to overcome the limitations of existing techniques: resource usage histograms to provide statistical guarantees for resource capacities and clustering-based resource aggregation to achieve scalability. Using trace-driven simulations and data analysis of a month-long PlanetLab trace, we show that resource bundles are able to provide high accuracy for statistical resource discovery, while achieving high scalability. We also show that resource bundles are ideally suited for identifying group-level characteristics (e.g., hot spots, total group capacity). To automatically parameterize the bundling algorithm, we present an adaptive algorithm that can detect online fluctuations in resource heterogeneity.

Enabling efficient information discovery in a self-structured grid

One of the key success factors enabling the deployment of large scale grid systems is the existence of efficient resource discovery mechanisms. Accordingly, the main issues to be addressed by such a grid information system are those of scalability and minimal network overhead. In this respect, we propose a solution based on proactive information caching supported by a self-structured overlay topology. The proposed approach features a fully distributed ant-inspired self-organized overlay construction that maintains a bounded diameter overlay, and a selective flooding-based discovery algorithm that exploits local caches to reduce the number of visited nodes. To improve the caching scheme while retaining minimal bandwidth consumption, cache contents are periodically exchanged between neighboring nodes using an epidemic replication mechanism that is based on a gossiping algorithm, thus allowing nodes to have a more general view of the network and its resources. Extensive experimentation provides evidence that the average number of hops required to efficiently locate resources is limited and that our framework performs well with respect to hit rate and network overhead.

An S2P-based resource discovery mechanism for tuple-based pervasive systems

A critical issue in pervasive systems is the efficient discovery of resources in dynamic environments. Existing resource discovery mechanisms are inefficient since they broadcast resource request messages to all peers. They are also inflexible and hard to use. By imitating human behavioural patterns in social interactions known as S2P networks we present a more efficient discovery mechanism for tuple-based pervasive systems that only communicates to social peers in search of resources. The choice of tuple-based model yields more flexibility and ease of use. The result of S2P style search for a resource requested by a peer is consistently cached in the local tuple space of that peer for later use. Exemplar scenarios were run on an implementation of the mechanism in Linda in a mobile environment (LIME) on different platforms. The results showed significant improvements in resource discovery and access times.

Performance evaluation of a discovery and scheduling protocol for multihop ad hoc mobile grids

Abstract Despite the many research efforts addressing the integration of mobile nodes into grids, only a few of them have considered the establishment of mobile grids over wireless ad hoc networks (hereafter,mobile ad hoc grids). Clearly, such grids need specialized resource discovery and scheduling mechanisms. To the best of our knowledge, though, the research on these mechanisms for mobile ad hoc grids is still preliminary. Besides, and more importantly, it has approached discovery and scheduling as separate mechanisms, which, we argue, is not suitable for mobile ad hoc grids. In this paper, we propose the integration of resource discovery and scheduling for mobile ad hoc grids into a single protocol called DICHOTOMY (Discovery and sCHeduling prOTOcol for MobilitY). This protocol allows computational tasks to be distributed appropriately in a mobile ad hoc grid, while mitigating the overhead of discovery messages exchanged among the nodes. Our experiments show that the protocol: (i) does proper scheduling, allowing an efficient load balancing among the nodes and helping with lowering the average completion time of tasks; (ii) keeps the discovery efficiency at acceptable levels in mobility scenarios and (iii) scales very well with respect to an increasing number of nodes, both in the total amount of energy savings due to packet transmissions and the distribution of such savings among the nodes.