Efficient Resource Discovery Research Articles

Efficient IoT resource discovery approach based on P2P networks and Fog Computing

The Internet of Things (IoT) objects, their provided resources and services, as well as their users, and the requests for these resources, are increasing. Consequently, it is becoming extremely difficult for users to find a specific IoT resource that fits their needs. The sheer number and wide range of equipment in IoT, coupled with the lack of standardized and coherent resource descriptions, have a significant impact on resource discovery. In this context, we propose a Fog-based Distributed Semantic IoT resource discovery (FDS-RD) solution. FDS-RD leverages semantic technologies and two levels of peer-to-peer (P2P) networks to provide a semantic and scalable IoT resource discovery. Additionally, to support real-time applications, FDS-RD capitalizes on the Fog computing paradigm to reduce the discovery time. Experimental results demonstrate that the proposed solution enhances the performance of the discovery process by increasing the relevance of the discovered resources and reducing the response time.

Resource discovery in inter-cloud environment: a review

The inter-cloud has emerged as a logical evolution to cloud computing extending computational scale and geographic boundaries through collaboration across individual cloud service providers (CSPs). Resource discovery in this large-scale, distributed and highly heterogeneous environment remains a fundamental challenge to enable effective cross-utilisation of resources and services. This review paper examines various resource discovery approaches in the inter-cloud outlining key challenges. Finally, the paper presents some ideas to build effective and efficient resource discovery strategies for the inter-cloud.

Attred: Attribute Based Resource Discovery for IoT

While the number of devices connected together as the Internet of Things (IoT) is growing, the demand for an efficient and secure model of resource discovery in IoT is increasing. An efficient resource discovery model distributes the registration and discovery workload among many nodes and allow the resources to be discovered based on their attributes. In most cases this discovery ability should be restricted to a number of clients based on their attributes, otherwise, any client in the system can discover any registered resource. In a binary discovery policy, any client with the shared secret key can discover and decrypt the address data of a registered resource regardless of the attributes of the client. In this paper we propose Attred, a decentralized resource discovery model using the Region-based Distributed Hash Table (RDHT) that allows secure and location-aware discovery of the resources in IoT network. Using Attribute Based Encryption (ABE) and based on predefined discovery policies by the resources, Attred allows clients only by their inherent attributes, to discover the resources in the network. Attred distributes the workload of key generations and resource registration and reduces the risk of central authority management. In addition, some of the heavy computations in our proposed model can be securely distributed using secret sharing that allows a more efficient resource registration, without affecting the required security properties. The performance analysis results showed that the distributed computation can significantly reduce the computation cost while maintaining the functionality. The performance and security analysis results also showed that our model can efficiently provide the required security properties of discovery correctness, soundness, resource privacy and client privacy.

Semantic-based discovery method for high-performance computing resources in cyber-physical systems

High-performance computing (HPC) systems with powerful computing capabilities are becoming increasingly significant in the large-scale cyber-physical systems (CPS), helping CPS process a huge number of real-time data. Nowadays, the efficiency of HPC resource management and discovery becomes a challenging problem in CPS, due to the complex characteristics of HPC resources and the growing demands of the users. Although lots of recent efforts have been conducted to the resource discovery in distributed systems, they cannot be well adapted for the cross-regional HPC environments, due to the lack of unified model for the resources description and consideration for the usability demands of non-expert users. In this paper, we propose novel techniques to try to solve the problem. Specifically, we first propose a unified semantic model named HPCRO for specifying cross-regional HPC resources, and apply ontology reasoning to obtain more semantic information for queries. Moreover, we propose a WordNet-based quick resource index list data structure called WQRIL to improve the query. Finally, according to the proposed model and data structure, we propose an efficient discovery method called ROLD for cross-regional HPC resources. Extensive experimental results demonstrate that, our proposals not only maintain efficient resource discovery performance, but also achieve the highest precision rate (94.76%), recall rate (92.34%) and F1-score (93.53%).

Rock, Paper, Scissors Game Based Model for Content Discovery in P2P MANETs

Resource discovery is a key challenge in dynamic environment such as Peer-to-Peer (P2P) MANETs. To leverage the lookup costs and efficiently discover the resources, the peers in a P2P network communicate with each other forming one-or-more overlay structure. And, thus the peer’s connections in such overlay networks plays a crucial role. To harness this, we propose a model that focuses on the underlying network topology as each virtual link of the overlay network is supported by a path in the underlying physical network. Also, we design a new resource discovery algorithm that uses the famous Rock-Paper-Scissors (RPS) game ideology. In this work, we present a Rock-Paper-Scissors-Game-Based (RPSGB) algorithm for content discovery in mobile peer-to-peer network. The proposed work focuses on providing efficient resource discovery scheme in such a dynamic network, using the game theory concepts. Our scheme is a light-weight model aimed to suit the unstructured architecture better. The major goal of this work, is to reduce the consumption of power, minimize the lookup cost, lower the bandwidth consumption, and increase the hit ratio. We compare our scheme with the traditional well-known benchmarked schemes. After evaluation, the simulation results justify the effectiveness of the proposed protocol. The obtained results show that the proposed scheme substantially decreases the network traffic, lowers the battery power and bandwidth consumption, while having good search efficiency. Also, the search latency is minimized. The result justifies that RPSGB algorithm proposes to make resource searching much more efficient, and improves the statistics against the posed challenges.

DAIS: dynamic access and integration services framework for cloud-oriented storage systems

This paper proposes a framework named Dynamic Access and Integration Services (DAIS) framework. The framework provides on-demand and large scale integrated data access in cloud computing environment. The sole motivation of this research paper is to enhance the performance of Cloud-Oriented Storage (COS) systems. DAIS framework consists of two basic modules namely (1) Adjacency COS Overlay Topology (ACOT) and (2) Cloud Storage Resource Discovery (CSRD). ACOT introduces a self-organizing well balanced m-way tree which removes the necessity of centralized approaches and adopts dynamic and scalable data access in cloud computing environment. CSRD module provides data integration mechanism and efficient resource discovery from geographically distributes COS systems. In this paper, Hadoop test-bed setup has been deployed for experiment and results analysis. All input and output data have been collected in the Hadoop Distributed File System (HDFS). In the experiments, performance, bandwidth and energy efficiency of the proposed DAIS framework have been demonstrated and compared with three different approaches like, Static DAIS, P2P and DSSM. To make it energy efficient, measurement unit and its mathematical model have been defined. Energy efficiency results have been compared with two popular approaches HBase and Hadoop-MR, and found better in most of the cases.

S-COAP: Semantic Enrichment of COAP for Resource Discovery

Internet of Things (IoT) paradigm is progressing at an enormous rate creating an expansive ecosystem of interconnected smart devices which needs to be controlled and managed by end applications or users. So, the solution is to discover the best device matching the user requirement in timely manner. Ensuring efficient and context aware resource discovery is a challenge in seamless operation of IoT applications. Semantic Intelligence can be built for resource discovery. In this paper, we propose semantic enrichment of COAP protocol to discover the resources. Resources are semantically modelled, and COAP protocol CORE Link format is modified to incorporate semantic information. Also, we have proposed a framework which is aware of the social relationship between the devices to accommodate distributed topologies. We conducted the experiments in COOJA simulator on CONTIKI OS with COAP protocol semantically enriched. COAP CORE Link format and Resource Directory were semantically enriched, and its effectiveness was measured.

Intercloud Resource Discovery: A Future Perspective using Blockchain Technology

Intercloud is a single logical entity orchestrating resources from different individual clouds providing on-demand resource provisioning in a seamless manner. However, achieving efficient resource discovery in the intercloud environment remains a challenging task owing to the heterogeneity of resources and diversity of cloud platforms. The paper briefs about intercloud resource discovery, outlines the current work done using existing approaches and examines the challenges involved. Finally, the paper explains the concept of blockchain and presents an innovative conceptual model for efficient resource discovery in intercloud.

A novel multilevel index model for distributed service repositories

This study, based on the theory of equivalence relations, proposes a novel multilevel index model for decentralized service repositories to eliminate redundant information and enhance the time-management quality of the service retrieval process of the service repository architecture. An efficient resource discovery algorithm based on Discrete Hash Tables is presented to enable efficient and effective retrieval services among different distributed repositories. The performance of the proposed model and the supporting algorithms have been evaluated in a distributed environment. Experimental results validate the effectiveness of our proposed indexing model and search algorithm.

Semantic Grid resource discovery based on SKOS ontology

In large-scale distributed environments like dynamic Grids, an efficient and scalable resource discovery mechanism is required. Hybrid techniques based on node clustering are considered among solutions that guarantee the efficiency and the scalability at the same time. However, lack of a good representation of nodes in these environments often results in an irrelevant grouping of nodes. Combining hybrid techniques with Semantic Web technologies would bring more benefits to resource discovery. This paper introduces a semantic clustering of nodes based on their domains of interest to form groups called federations. It presents a new process for constructing federations and a three-layered overlay network. The proposed process is based on a SKOS lightweight ontology that describes domains of applications in the Grid. We conduct extensive simulations to evaluate the performance of the proposed approach; the experimental results demonstrate its efficiency and its ability to scale up with the system size.

Dynamic Resource Discovery Based on Preference and Movement Pattern Similarity for Large-Scale Social Internet of Things

Given the wide range deployment of disconnected delay-tolerant social Internet of Things (SIoT), efficient resource discovery remains a fundamental challenge for large-scale SIoT. The existing search mechanisms over the SIoT do not consider preference similarity and are designed in Cartesian coordinates without sufficient consideration of real-world network deployment environments. In this paper, we propose a novel resource discovery mechanism in a 3-D Cartesian coordinate system with the aim of enhancing the search efficiency over the SIoT. Our scheme is based on both of preference and movement pattern similarity to achieve higher search efficiency and to reduce the system overheads of SIoT. Simulation experiments have been conducted to evaluate this new scheme in a large-scale SIoT environment. The simulation results show that our proposed scheme outperforms the state-of-the-art resource discovery schemes in terms of search efficiency and average delay.

Towards an efficient distributed cloud computing architecture

Cloud computing is an emerging field in computer science. Users are utilizing less of their own existing resources, while increasing usage of cloud resources. There are many advantages of distributed computing over centralized architecture. With increase in number of unused storage and computing resources and advantages of distributed computing resulted in distributed cloud computing. In the distributed cloud environment that we propose, resource providers (RP) compete to provide resources to the users. In the distributed cloud all the cloud computing and storage services are offered by distributed resources. In this architecture resources are used and provided by the users in a peer to peer fashion. We propose using multi-valued distributed hash tables for efficient resource discovery. Leveraging the fact that there are many users providing resources such as CPU and memory, we define these resources under one key to easily locate devices with equivalent resources. We have evaluated the performance of resource discovery mechanisms for the distributed cloud and distributed cloud storage and compared the results with existing DHTs, peer to peer clients such as VUZE [1] and explored the feasibility and efficiency of the proposed schemes in terms of resource/service discovery and allocation. We use a simultaneous Auction mechanism and select a set of winners once we receive all contributions or bids. In a real world scenario, users request resources with multiple capabilities, and in order to find such resources we use a contribution mechanism where service providers will provide a contribution price to users for providing a resource. Users use our proposed auction mechanism to select the resources from the set of resource providers. We show that Nash equilibrium can be achieved and how we can avoid the problem of free riders in the distributed cloud.

A service oriented broker-based approach for dynamic resource discovery in virtual networks

Abstract In the past few years, the concept of network virtualization has received significant attention from industry and research fora. This concept applies virtualization to networking infrastructures by enabling the dynamic creation of several co-existing logical network instances (or virtual networks) over a shared physical network infrastructure (or substrate network). Due to the potential it offers in terms of diversifying existing networks and ensuring the co-existence of heterogeneous network architectures on top of shared substrates, network virtualization is often considered as an enabler of a polymorphic Internet and a cornerstone of the future Internet architecture. One of the challenges associated with the network virtualization concept is the description, publication, and discovery of virtual resources that can be composed to form virtual networks. To achieve those tasks, there is a need for an expressive information model facilitating information representation and sharing, as well as an efficient resource publication and discovery framework. In this paper, we propose a service oriented, broker-based framework for virtual resource description, publication, and discovery. This framework relies on a novel service-oriented hierarchical business model and an expressive information model for resources/services description. The detailed framework’s architecture is presented, and its operation is illustrated using a REST-based content distribution scenario. Furthermore, a proof-of-concept prototype implementation realized using various technologies/tools (e.g. Jersey, JAXB, PostgreSQL, and Xen cloud platform) is presented along with a detailed performance analysis of the system. When compared to existing virtual resource discovery frameworks, our broker-based virtual resource discovery framework offers signification performance improvements of the virtual resources’ discovery operation, in terms of response time (92.8% improvement) and incurred network load (77.3% improvement), when dealing with multiple resource providers. Furthermore, relying on a broker as intermediary role simplifies the resources’ discovery and selection operations, and improves the overall efficiency of the virtual network embedding process.

Resource and application-aware resource discovery in computing environments

Efficient resource discovery plays a vital role in the effective management of resources and applications in heterogeneous computing environments. Therefore, the knowledge of applications' behavior and resources' usage pattern improves resource discovery decisions. This knowledge can be provided for the resource discovery mechanism by cooperating with the load balancing mechanism. In this paper, we formulate their cooperation by considering some parameters that represent applications' behavior and resources' usage patterns and extract the relation between them to introduce a formula using mathematical methods. Further, the resource discovery mechanism uses the formula to predict resources' load before assigning them new processes and thus it prevents resource overloading which happens frequently in computing environments.

THE EFFICIENCY CHALLENGES OF RESOURCE DISCOVERY IN GRID ENVIRONMENTS

Resource discovery is one of the most important services that significantly affects the efficiency of grid computing systems. The inherent dynamic and large-scale characteristics of grid environments make their resource discovery a challenging task. In recent years, different approaches have been proposed for resource discovery, attempting to tackle the challenges of grid environments and improve the efficiency. Being aware of these challenges and approaches is worthwhile in order to choose an appropriate approach according to the application in different organizations. This study reviews the most important factors that should be considered and challenges to be tackled in order to develop an efficient grid resource discovery system.

AMHC: Adaptive Multi-Hop Clustering based Resource Discovery Architecture for Large Scale MANETs

In this study we propose an efficient clustering protocol called AMHC used for resource discovery in large scale Mobile Ad hoc Networks (MANETs). AMHC is an Adaptive Multi-Hop Clustering generating several non-overlapping network localities (clusters) with explicit elected cluster-heads. Every cluster member is on average d hops away from its cluster-head, where d is an integer parameter for the protocol. The generated set of clusters are highly stable and has low restructuring frequency that takes into consideration the dynamic network topology due to nodes mobility and depleted energy. The head election process is a distributed process based on a node's weight formula calculated by every node independently. The node's weight involves the current energy level, the current neighborhood degree and distance (in number of hops) between the nominated head and the voting node. The cluster-head is responsible of coordinating intra-cluster and inter-cluster resource discovery activities. Inter-cluster communication is handled through gateway nodes which hear from more than one cluster and able to connect clusters with each other. The aim of AMHC is to identify all the possible gateways for creating highly fault-tolerant architecture. AMHC is an asynchronous, scalable and robust architecture capable of handling large amount of resource queries with high degree of power and communication efficiency. We conducted a comparative study using simulation to demonstrate AMHC's efficiency and superiority against other recently proposed clustering algorithms in the literature. The comparison is based on: number of generated clusters, average cluster size, cluster stability and nodes re-affiliation. These results show a lot of promise for AMHC as efficient, energy-aware, load-balance and fault tolerant resource discovery architecture for large-scale MANETs.

An efficient media ports resource discovery for service networks

Interest in service specific overlay networks SSONs has been increased in recent years, where each overlay is designed to meet the user's specific requirements. It is considered an effective means to support end-to-end quality of service guarantees. A key component of these overlay networks is the media ports MPs. MPs are network side functions that provide value added functions such as catching, synchronisation and adaptation. Efficient overlay networks construction requires an efficient and dynamic MPs resource discovery approach. In this paper, the researchers propose a new resource discovery scheme for MPs. The proposed approach clusters MPs into groups of similar types. Clusters are disjoint with no global entity. To discover MPs, a modified version of random walk RW called RW+ is presented. The proposed RW+ allows network nodes to learn about existing clusters and resources in an efficient way which renders RW+ to be partially random. This greatly enhances the performance and the efficiency of the resource discovery scheme. Simulation experiments demonstrate the effectiveness of the proposed scheme. In particular, it shows that RW+ can achieve a higher success rate and a lower response time and network load.

New Approaches with Chord in Efficient P2p Grid Resource Discovery

New Approaches with Chord in Efficient P2p Grid Resource Discovery

An efficient resource discovery framework for pure unstructured peer-to-peer systems

In this paper, we propose an efficient resource discovery framework allowing pure unstructured peer-to-peer systems to respond to requests at run time with a high success rate while preserving the local autonomy of member machines. There are five units in the proposed framework that respectively gather information about the status of resources, make decisions, detect the states of member machines, discover resources to respond to requests in normal and dynamic conditions, and balance the load of local machines. Efficient resource discovery is achieved by the deployment of a newly introduced mechanism that is placed on every machine allowing it to figure out its states before and after accepting other machines’ requests for its resources using a state model and deciding whether to accept or reject those requests. This state model accurately estimates the machine’s state based on the resources and processes of the machine before and after accepting the request. We have experimentally compared the proposed mechanism with random, learning-based, and state-based search mechanisms with regard to the number of missed requests, network bandwidth due to transferred messages, number of associated machines in a discovery operation, time required to process information in discovery operation, processing time in machines, and the number of faults per request. The results show significant improvement of some of these parameters, specially network bandwidth and the number of missed requests in a dynamic condition, under our framework.

A performance, semantic and service quality-enhanced distributed search engine for improving geospatial resource discovery

Geospatial resource discovery is a critical step for developing geographic science applications. With the increasing number of geospatial resources available online, many Spatial Data Infrastructure (SDI) components (e.g. catalogues and portals) have been developed to help manage and discover geospatial resources. However, efficient and accurate geospatial resource discovery is still a big challenge because of the heterogeneity and complexity of decentralized network environments and interdisciplinary semantics. In this article, we report a search engine framework for efficient geospatial resource discovery, which reduces integration costs by leveraging existing Geospatial Cyberinfrastructure (GCI) components. Specifically, (1) the framework provides integration capability and flexibility by adopting the brokering approach, implementing a ‘plug-in’-based framework for metadata processing and proposing a dynamically configurable search workflow; (2) the asynchronous messaging and batch processing-based metadata record retrieval mode enhances the search performance and user interactivity; (3) an embedded semantic support system improves the discovery recall level and precision by providing semantic-based search rule creation and result similarity evaluation functions and (4) the engine assists user decision-making by integrating a service quality monitoring and evaluation system, data/service visualization tools, multiple views and additional information. Experiments and a search example show that the proposed engine helps both scientists and general users search for more accurate results with enhanced performance and user experience through a user-friendly interface.