Resource Discovery Mechanism Research Articles

ExaSU: a mathematical model for selecting the structured or unstructured resource discovery mechanism in distributed exascale computing environments

ExaSU: a mathematical model for selecting the structured or unstructured resource discovery mechanism in distributed exascale computing environments

Finding Internet of Things resources: A state-of-the-art study

Since its emergence, the Internet of Things (IoT) has aimed to join the physical world to the virtual world with a basic vision, which is to create intelligent spaces in which users interact seamlessly with IoT objects. Therefore, the user will be surrounded by a large number of services offered by these connected objects which he/she will inevitably interact with them. However, the IoT environments are characterized by a large number of heterogeneous connected objects. These characteristics make it difficult to find their adjacent resources. Importantly, because the traditional discovery solutions are inefficient, therefore, a mechanism that makes it possible to dynamically inventory and control the resources present in an IoT environment is crucial. In fact, a resource discovery mechanism basically aims to dynamically and regularly update the base of available resources in the IoT environment. To meet these needs, many research works were conducted where many architectures have been proposed to provide solutions for resource discovery in the IoT environment. In this work, we provide a state-of-the-art study of resource discovery techniques in IoT environment. First, the resource discovery techniques are classified according to their search approaches. Then, a review of a selection of recent works that propose solutions for resource discovery in the IoT environment is provided. Finally, the study gives some implications for further study and a number of guidelines that helps to meet a large part of the requirements of a resource discovery solution in the IoT environment.

A Decentralized Approach for Resource Discovery using Metadata Replication in Edge Networks

Recent advancements in distributed systems have enabled deploying low-latency edge applications (i.e., IoT applications) in proximity to the end-users, respectively, in edge networks. The stringent requirements combined with heterogeneous, resource-constrained and dynamic edge networks make the deployment process a challenging task. Besides that, the lack of resource discovery features make it particularly difficult to fully exploit available resources (i.e., computational, storage, and IoT resources) provided by low-powered edge devices. To that end, this article proposes a decentralized resource discovery mechanism that enables discovering resources in an automatic manner in edge networks. Through replicating resource descriptions (i.e., metadata), edge devices exchange information about available resources within their scope in a peer-to-peer manner. To handle the resource discovery complexity, we propose a solution to built edge networks as a flat model and enable edge devices to be organized in clusters. Our approach supports the system in coping with the dynamicity and uncertainty of edge networks. We discuss the architecture, processes of the approach, and the experiments we conducted on a testbed to validate its feasibility on resource-constrained edge networks.

Resource-Aware Dynamic Service Deployment for Local IoT Edge Computing: Healthcare Use Case

Edge Computing is a novel computing paradigm moving server resources closer to end-devices. In the context of IoT, Edge Computing is a centric technology for enabling reliable, context-aware and low-latency services for several application areas such as smart healthcare, smart industry and smart cities. In our previous work, we have proposed a three-tier IoT Edge architecture and a virtual decentralized service platform based on lightweight microservices, called nanoservices, running on it. Together, these proposals form a basis for virtualizing the available local computational capacity and utilizing it to provide localized resource-efficient IoT services based on the applications’ need. Furthermore, locally-deployed functions are resilient to access network problems and can limit the propagation of sensitive user data for improved privacy. In this paper, we propose an automatic service and resource discovery mechanism for efficient on-the-fly deployment of nanoservices on local IoT nodes. As use case, we have selected a healthcare remote monitoring scenario, which requires high service reliability and availability in a highly dynamic environment. Based on the selected use case, we propose a real-world prototype implementation of the proposed mechanism on Raspberry Pi platform. We evaluate the performance and resource-efficiency of the proposed resource matching function with two alternative deployment approaches: containerized and non-containerized deployment. The results show that the containerized deployment is more resource-efficient, while the resource discovery and matching process takes approximately 6–17 seconds, where containerization adds only 1–1.5 seconds. This can be considered a feasible price for streamlined service management, scalability, resource-efficiency and fault-tolerance.

Resource discovery mechanisms in pure unstructured peer-to-peer systems: a comprehensive survey

The concept of unstructured Peer-to-Peer (P2P) systems—setting free from any structural constraints—has put forward an appropriate paradigm for sharing a wide assortment of resources in a distributed-sharing manner efficiently. More importantly, unstructured P2P systems’ architecture and concepts have permeated diverse spheres of today’s successful and world-famous computer science areas, including NoSQL databases for excellently sharing data. However, pinpointing any given appropriate resource in such massive systems, namely unstructured P2P systems, is a challenging task; the two of the most dispensable rationales behind this proclaim are large scales of such systems and unstructured nature of overlay networks. Finding a decent resource with low response time, low bandwidth consumption, and high success-rate has played a crucial role in both the overall system performance and the functionality of P2P systems. Briefly, an efficacious resource discovery mechanism is the lifeblood of any productive P2P system. Given these points, in this study, we present an exhaustive survey on state-of-the-art resource discovery mechanisms employed in file-sharing pure unstructured P2P systems; we offer a new resource discovery categorization accordingly. Furthermore, we deeply delve into a plethora of resource searching methods and their merits as well as demerits to furnish the paper with an in-depth evaluation.

Knowledge management practice of medical cloud logistics industry: transportation resource semantic discovery based on ontology modelling

PurposeThe booming of the Internet of things (IoT) and artificial intelligence (AI) techniques contributes to knowledge adoption and management innovation for the healthcare industry. It is of great significance to transport the medical resources to required places in an efficient way. However, it is difficult to exactly discover matched transportation resources and deliver to its destination due to the heterogeneity. This paper studies the medical transportation resource discovery mechanism, leading to efficiency improvement and operational innovation.Design/methodology/approachTo solve the transportation resource semantic discovery problem under the novel cloud environment, the ontology modelling approach is used for both transportation resources and tasks information modes. Besides, medical transportation resource discovery mechanism is proposed, and resource matching rules are designed including three stages: filtering reasoning, QoS-based matching and user preferences-based rank to satisfy personalized demands of users. Furthermore, description logic rules are built to express the developed matching rules.FindingsAn organizational transportation case is taken as an example to describe the medical transportation logistics resource semantic discovery process under cloud medical service scenario. Results derived from the proposed semantic discovery mechanism could assist operators to find the most suitable resources.Research limitations/implicationsThe case study validates the effectiveness of the developed transportation resource semantic discovery mechanism, contributing to knowledge management innovation for the medical logistics industry.Originality/valueTo improve task-resource matching accuracy under cloud scenario, this study develops a transportation resource semantic discovery procedure from the viewpoint of knowledge management. The novel knowledge management practice contributes to operational management of the cloud medical logistics service by introducing ontology modelling and creative management.

Efficient Algorithm for Identification and Cache Based Discovery of Cloud Services

Efficient resource identification and discovery is the primary requirements for cloud computing services, as it assists in scheduling and managing of cloud applications. Cloud computing is a revolution of the economic model rather than technological. It takes advantage of several technologies that were tested and modified by replacing the local use of computers with centralized shared resources that are managed and stored by Cloud Service Providers (CSPs) in a transparent manner for Cloud Consumers (CCs). With this new use, various cloud services have appeared and it is mainly classified into three broad categories i.e., Infrastructure as a service (IaaS), Software as a service (SaaS) and Platform as a service (PaaS). Each of these cloud services provides several benefits to the CCs through their respective Quality of Service (QoS) metric. Among the cloud service models, most of the QoS attribute and metric are identical and some are different. The vendors of cloud have focused their objectives on the development of scalability, resource consumption and performance, other characteristics of cloud have been ignored. While CSPs face challenging difficulties in publishing cloud services that displays their cloud resources, at the same time CCs do not have standard mechanism for cloud resource discovery, automated cloud services selection, and easy use of cloud services. In this frame, this paper puts forward a set of QoS metric for SaaS, IaaS, PaaS services and propose (i) An efficient algorithm for identifying the cloud services based on the QoS metric given by the cloud consumer using decision tree classification algorithm (ii) An efficient algorithm for Cloud service resource registry which aims to enable CSPs to register their services with its QoS attributes and (iii) A Cloud service resource discovery that search for the suitable cloud service and their attributes in the cloud service registry that meets the CCs application requirements using Split and Cache (SAC) algorithm. Our new approach makes the provisioning of cloud service possible by ease of resource identification, publication, discovery based on dynamic QoS attributes via web GUI interface backed by series of test that has validated and the proposed approach is feasible and sound. The recommended solution is important: instead of putting effort in locating, learning about the services and evaluating them, CCs can easily identify, discover the services, select and use the required cloud resources. The efficiency of our algorithms was assessed through experiments using CloudSim, which primarily decreases the response time, CPU utilization and memory consumption for identifying and searching the cloud services and increases the accuracy of the CSPs list retrieved along with their QoS attributes.

Context-aware RAON middleware for opportunistic network

Application development and deployment on Mobile Ad Hoc Networks (MANET) is a major challenge in the widespread use of MANET. The increasing D2D communication in 5G networks has renewed interest in an effective middleware design for MANET where application developers face various challenges such as unstable connectivity, high error rate, mobility induced disruption and disconnection, and limited battery power. We find that unstructured overlay network provides a good abstraction to facilitate application development and deployment on MANET. In this paper, we present the design of a middleware that builds a Resource-Aware Overlay Network (RAON), which is an unstructured overlay network of nodes engaged in the application that employs a query–reply mechanism for resource discovery. RAON is enhanced with features such as proactive neighbor replacement, congestion-aware data download and cooperative caching. Simulation results show that these features are effective in reducing query delay, improving data availability, and balancing node power consumption with protocol performance. We also present the middleware software design that offers the API based on node and path abstractions to applications. The middleware implements a generic context framework for acquiring device and user context. We discuss the implementation of application-level multicast and credit-based file-sharing applications using the middleware API. The middleware is implemented in Java J2ME on Android, which is tested in an ad hoc network of Nexus 7 devices running OLSR.

A self-adaptive structuring for large-scale P2P Grid environment: design and simulation analysis

Resource management is a key issue for large-scale grid environments. In particular, the resource discovery mechanism used highly impacts on the efficiency of resource sharing and cooperative computing. Meanwhile, the increasing size of resources and users in large-scale distributed systems has lead to a scalability problem. The self-organising, fault tolerance and decentralised nature of P2P technology, which helps to reduce the management cost of grid infrastructure, is a good basis for resolving both aforementioned problems. In this context, we propose a self-adaptive structuring model for large-scale P2P grid environments. The proposed specification, called P2P4GS, is generic, i.e. not linked to a particular P2P architecture. Our structuring approach is completely distributed, and requires only local knowledge about neighbouring nodes to implement node virtual community. Indeed, given a node's connection and based on its neighbours, we dynamically create virtual communities or clusters. A particular node called ISP (Information System Proxy) acts as service directory within each cluster. In addition, to provide an efficient lookup mechanism in our system, we proposed to build a spanning tree constituted by the set of ISPs. An experimental validation, through simulation, shows that our approach ensures a high scalability in terms of clusters distribution and communication cost.

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.

Peer Clouds: A P2P-Based Resource Discovery Mechanism for the Intercloud

The Intercloud represents the next logical step in the evolution of cloud computing overcoming issues of data/vendor lock-in and dealing with volatile service requests. However, resource discovery across heterogeneous Cloud Service Providers (CSPs) remains a challenge. In this paper we present a P2P-based distributed resource discovery mechanism based on spatial-awareness of cloud data-centers belonging to different Cloud Service Providers. The scheme is based upon exploiting location information of Data Centers and organizing them into DHT peers for optimal communication. It thus allows for QoS-compliant resource/service provisioning across Cloud Service Providers (CSPs). Simulation results establish the effectiveness of the proposed scheme.

A formal approach for the specification and verification of a Trustworthy Human Resource Discovery mechanism in the Expert Cloud

Expert Cloud as a new class of Cloud computing systems enables its users to request the skill, knowledge and expertise of people without any information of their location by employing Internet infrastructures and Cloud computing concepts. One of the most important service in Expert Cloud is to search trustworthy peoples in order to benefit from their knowledge and skills. In this paper we propose a new and applicable method for Trustworthy Human Resource Discovery (THRD) in Expert Cloud by introducing a resource discovery and trust evaluating method, implement the proposed method by using ASP.Net and SQL in Expert Cloud; verify the structures and compositions of THRD by describing the behavioral models and state diagrams; define a Kripke structure with marked states to provide the formal relationship between the expanded model and the original state diagram structures; define the expected properties of the structures and compositions of THRD by means of temporal logic languages; and implement the proposed model by NuSMV model checker. The results show that the proposed method can discover trustworthy people efficiently and is sound, complete, reachable, fair, deadlock-free and consistent.

IMPLEMENTATION OF RESOURCE DISCOVERY MECHANISMS ON PEERSIM: ENABLING UP TO ONE MILLION NODES P2P SIMULATION

The objective of this study was to implement resource discovery mechanisms onto a Peer-to-Peer (P2P) simulator. Resource discovery mechanisms such as random walk, restricted random walk, breadth-first search, intelligent-BFS, depth-first search, adaptive probabilistic search, and blackboard resource discovery mechanism had been adopted onto PeerSim. The proposed implementation has enabled the simulation for resource discovery in P2P simulator which capable of running up to one million nodes. Brief information about each resource discovery techniques that are implemented onto PeerSim are discussed and presented.

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.

A Scalable and Self-Configuring Architecture for Service Discovery in the Internet of Things

The Internet of Things (IoT) aims at connecting billions of devices in an Internet-like structure. This gigantic information exchange enables new opportunities and new forms of interactions among things and people. A crucial enabler of robust applications and easy smart objects' deployment is the availability of mechanisms that minimize (ideally, cancel) the need for external human intervention for configuration and maintenance of deployed objects. These mechanisms must also be scalable, since the number of deployed objects is expected to constantly grow in the next years. In this work, we propose a scalable and self-configuring peer-to-peer (P2P)-based architecture for large-scale IoT networks, aiming at providing automated service and resource discovery mechanisms, which require no human intervention for their configuration. In particular, we focus on both local and global service discovery (SD), showing how the proposed architecture allows the local and global mechanisms to successfully interact, while keeping their mutual independence (from an operational viewpoint). The effectiveness of the proposed architecture is confirmed by experimental results obtained through a real-world deployment.

Geo-Chord: Geographical Location based Chord Protocol in Grid Computing

Grid computing is a service for sharing computer resources and data storage capacity over the internet. As resource requirements of recent applications increased greatly, grid systems have gained importance in the last decade. Resource discovery is the essential job in Grid computing which provides searching and identifying necessary resources for given processes. There are various Resource Discovery mechanisms in which Peer-to-Peer (P2P) technology witnessed a rapid development. Current Peer-to-Peer system is based on DHTs struggle with routing locality because of random node ID assignment. Locality awareness is one of the important properties of P2P Systems, which builds and operates their topology independently in the underlying network topology. Among structured P2P system Chord is well-known protocol due to its simplicity, dynamicity, scalability and flexibility in node join and departure. Due to the use of logical overlay network, Chord suffered from high routing latency and low efficiency in data lookup. In this paper, Geo-Chord is introduced, which is based on geographical location of nodes. Based on the Euclidian distance among nodes neighbors are identified and many Region-Rings are formed. Ring heads of Regions form the main Chord ring. Geo-Chord makes use of the physical network topology of the overlay network to demonstrate more efficient key lookup. Simulation results show that our proposed algorithm performs better in terms of average number of messages, hops and average communication time.

A model for communication between resource discovery and load balancing units in computing environments

Resource overloading causes one of the main challenges in computing environments. In this case, a new resource should be discovered to transfer the extra load. However, this results in drastic performance degradation. Thus, it is of high importance to discover the appropriate resource at first. So far, several resource discovery mechanisms have been introduced to overcome this challenge, a majority of which neglect the fact that this important decision should be made in cooperation with other units existing in a computing environment. One of the units is load balancing. In this paper, we propose a model for communication between resource discovery and load balancing units in a computing environment. Based on the model, resource discovery and load balancing decisions are made cooperatively considering the behavior of running processes and resources capacities. These considerations make decisions more precise. In addition, the model presents the loosest type of coupling between resource discovery and load balancing units, i.e., message coupling. This feature provides a better scalability in size for the model. Comparative results show that the proposed model increases scalability in size by 7 to 15 %, cuts message transmission rate by 15 % and improves hit rate by 51 %.

Semantic-enabled CARE Resource Broker (SeCRB) for managing grid and cloud environment

Grid computing is mainly helpful for executing high-performance computing applications. However, conventional grid resources sometimes fail to offer a dynamic application execution environment and this increases the rate at which the job requests of users are rejected. Integrating emerging virtualization technologies in grid and cloud computing facilitates the provision of dynamic virtual resources in the required execution environment. Resource brokers play a significant role in managing grid and cloud resources as well as identifying potential resources that satisfy users' application requests. This research paper proposes a semantic-enabled CARE Resource Broker (SeCRB) that provides a common framework to describe grid and cloud resources, and to discover them in an intelligent manner by considering software, hardware and quality of service (QoS) requirements. The proposed semantic resource discovery mechanism classifies the resources into three categories viz., exact, high-similarity subsume and high-similarity plug-in regions. To achieve the necessary user QoS requirements, we have included a service level agreement (SLA) negotiation mechanism that pairs users' QoS requirements with matching resources to guarantee the execution of applications, and to achieve the desired QoS of users. Finally, we have implemented the QoS-based resource scheduling mechanism that selects the resources from the SLA negotiation accepted list in an optimal manner. The proposed work is simulated and evaluated by submitting real-world bio-informatics and image processing application for various test cases. The result of the experiment shows that for jobs submitted to the resource broker, job rejection rate is reduced while job success and scheduling rates are increased, thus making the resource management system more efficient.