Showing papers on "Shared resource published in 2023"

VECMAN: A Framework for Energy-Aware Resource Management in Vehicular Edge Computing Systems

Abstract: In Vehicular Edge Computing (VEC) systems, the computing resources of connected Electric Vehicles (EV) are used to fulfill the low-latency computation requirements of vehicles. However, local execution of heavy workloads may drain a considerable amount of energy in EVs. One promising way to improve the energy efficiency is to share and coordinate computing resources among connected EVs. However, the uncertainties in the future location of vehicles make it hard to decide which vehicles participate in resource sharing and how long they share their resources so that all participants benefit from resource sharing. In this paper, we propose VECMAN, a framework for energy-aware resource management in VEC systems composed of two algorithms: (i) a resource selector algorithm that determines the participating vehicles and the duration of resource sharing period; and (ii) an energy manager algorithm that manages computing resources of the participating vehicles with the aim of minimizing the computational energy consumption. We evaluate the proposed algorithms and show that they considerably reduce the vehicles’ computational energy consumption compared to the state-of-the-art baselines. Specifically, our algorithms achieve between 7 and 18 percent energy savings compared to a baseline that executes workload locally and an average of 13 percent energy savings compared to a baseline that offloads vehicles’ workloads to RSUs.

Beyond the Library Collections: Proceedings of the 2022 Erasmus Staff Training Week at ULiège Library

Abstract: No library can buy or hold everything its patrons need. At a certain point, librarians need to pool their resources and collaborate to provide access to what they don't have: Collaboration and partnership, centralized and shared collection storage, digitization projects, interlibrary loan and resource sharing, purchase on demand, PDA and EBA are notably key to success. The 2022 edition of the Erasmus Mobility Staff Training week organized at the University of Li\`ege Library focused on services, projects and policies that libraries can deploy and promote to increase and ease access to materials that do not belong to their print or electronic holdings. More than 20 librarians, managers, and researchers in library science share their experiences and visions in this book. Keywords: Collaboration between libraries; Collection Development; Academic Libraries; Research Libraries; Interlibrary Loan; ILL; Resource Sharing; Purchase on Demand; Patron-Driven Acquisition; PDA; Evidence-Based Acquisition; EBA; Alma; Koha; RapidILL; Impala; Subito; Library systems; Digitalization; Workflows

Resource Sharing and Trading of Blockchain Radio Access Networks: Architecture and Prototype Design

Abstract: Recently, blockchain radio access network (B-RAN) arises as an innovative paradigm for the sixth-generation (6G) wireless communications to build cooperative trust, aggregate wireless resources, and schedule inter-and intra-network tasks among independent network entities. It establishes an open platform based on blockchain to provide diverse wireless services and applications, such as radio access, Internet of Things (IoT), and mobile edge computing, via trusted interactions with enhanced security and efficiency. As a distinctive feature, B-RAN enables secure and efficient resource sharing and trading by aggregating, pooling, and coordinating resources from multiple resource hosts and owners across subnetworks. Therefore, an implementable architecture along with various functional modules shall be delicately designed. This work aims to establish a unified architecture with enhanced efficiency, security, compatibility, and flexibility for resource sharing and trading in B-RAN. Specifically, we develop a six-layer architecture that incorporates a number of novel features such as enhanced blockchain structures, secure interaction methods, efficient service mechanisms, and scalable transaction patterns. We design a number of pluggable functional modules in each layer to support diverse functions, services, and applications of resource sharing and trading. At last, we implement a practical prototype based on the layered architecture for resource-limited devices. Multiple experiments are presented to verify the performance of the proposed architecture from different aspects.

A new resource sharing protocol in the light of token-based strategy for distributed system

Abstract: One of the highly researched areas in distributed system is mutual exclusion. To avoid any inconsistent state of system, more than one processes executing on different processors are not allowed to invoke their critical sections simultaneously for the purpose of resource sharing. As a solution to such resource allocation issues, token-based strategy for distributed mutual exclusion algorithms as a prime classification of solutions is one of the most popular and significant ways to handle mutual exclusion in this field. Through this research article, we propose a novel token-based distributed mutual exclusion algorithm. The proposed solution is scalable and has better results in terms of message complexity compared to existing solutions. In this proposed art of work, the numbers of messages exchange per critical section invocation are 3(⌈log N⌉ - 1), 3⌈(⌈log (N + 1) ⌉ - 1)/2⌉ and 6[⌈log (N + 1)⌉ + 2(2(-⌈log (N+1)⌉) - 1)] in case of light load, medium load and high load situations respectively.

Edge Trusted Sharing: Task-Driven Decentralized Resources Collaborate in IoT

Abstract: 6G is committed to providing a fully connected world. The deployment and application of 6G technology in the Internet of Things (IoT) can efficiently collaborate IoT resources and realize resource sharing, which mainly encourages IoT development. However, due to the lack of trust between IoT resources, security and privacy become the main challenges. As an emerging technology, blockchain can solve the trust-absence issues and provide more benefits, but the introduction of blockchain also brings problems for IoT resource collaboration and sharing. This article first proposes a blockchain-enabled edge resource sharing (BEERS) architecture by combining blockchain and edge computing technology. Then, based on a typical resource sharing and collaboration scenario, the joint optimization problem of resource scheduling and task assignment (JRSTA) is constructed. We decompose JRSTA into a primal problem and a master problem and design a greedy-based task assignment (GBTA) algorithm to solve the primal problem. Based on the GBTA algorithm, the resource scheduling and task assignment (RSTA) algorithm is developed. Next, we design a layered parallel edge resource scheduling and task assignment (LPRSTA) mechanism to improve practicability. Finally, we analyze the security and the performance of our proposed architecture and algorithms. The results show that the proposed architecture can support the secure collaboration of IoT resources, and the proposed algorithm can achieve effective resource scheduling and task assignment.

Next-generation networks: Necessity of edge sharing

Abstract: Resource sharing is fundamental to the design of telecommunication networks. The technology, economic and policy forces shaping the transition to next-generation digital networking infrastructure—characterized here as “5G+” (for 5G and beyond)—make new and evolved forms of edge sharing a necessity. Despite this necessity, most of the economic and policy research on Network Sharing Agreements (NSAs) has focused on sharing among service providers offering retail services via networks owned and operated by legacy fixed and mobile network operators (MNOs). In this essay, we make the case for why increased and more dynamic options for sharing, in particular of end-user owned network infrastructure, should be embraced for the future of NSAs. Furthermore, we explain how such a novel sharing paradigm must be matched by appropriate regulatory policies.

V10: Hardware-Assisted NPU Multi-tenancy for Improved Resource Utilization and Fairness

Abstract: Modern cloud platforms have deployed neural processing units (NPUs) like Google Cloud TPUs to accelerate online machine learning (ML) inference services. To improve the resource utilization of NPUs, they allow multiple ML applications to share the same NPU, and developed both time-multiplexed and preemptive-based sharing mechanisms. However, our study with real-world NPUs discloses that these approaches suffer from surprisingly low utilization, due to the lack of support for fine-grained hardware resource sharing in the NPU. Specifically, its separate systolic array and vector unit cannot be fully utilized at the same time, which requires fundamental hardware assistance for supporting multi-tenancy. In this paper, we present V10, a hardware-assisted NPU multi-tenancy framework for improving resource utilization, while ensuring fairness for different ML services. We rethink the NPU architecture for supporting multi-tenancy. V10 employs an operator scheduler for enabling concurrent operator executions on the systolic array and the vector unit and offers flexibility for enforcing different priority-based resource-sharing mechanisms. V10 also enables fine-grained operator preemption and lightweight context switch in the NPU. To further improve NPU utilization, V10 also develops a clustering-based workload collocation mechanism for identifying the best-matching ML services on a shared NPU. We implement V10 with an NPU simulator. Our experiments with various ML workloads from MLPerf AI Benchmarks demonstrate that V10 can improve the overall NPU utilization by 1.64×, increase the aggregated throughput by 1.57×, reduce the average latency of ML services by 1.56×, and tail latency by 1.74× on average, in comparison with state-of-the-art NPU multi-tenancy approaches.

Research on the Reform of Laboratory Experimental Teaching of Automotive Majors in Local Undergraduate Institutions Based on Cloud Sharing

Abstract: The development of network education resource cloud sharing in local undergraduate institutions can take advantage of cloud computing technology to achieve optimal integration and sharing of network automotive professional education resources, reduce resource construction costs, promote uniform distribution of resources, further improve the utilization rate of education resources, and promote the reform of laboratory experimental teaching of automotive majors in local undergraduate institutions. The current situation of laboratory experimental teaching of automotive majors in local undergraduate colleges and universities is analyzed, and the application mode of cloud sharing of educational resources of automotive majors in local undergraduate colleges and universities is discussed.

Average-Case Analysis of Greedy Matching for Large-Scale D2D Resource Sharing

Abstract: Given the proximity of many wireless users and their diversity in consuming local resources (e.g., data-plans, computation and energy resources), device-to-device (D2D) resource sharing is a promising approach towards realizing a sharing economy. This paper adopts an easy-to-implement greedy matching algorithm with distributed fashion and only sub-linear O(log n) parallel complexity (in user number n) for large-scale D2D sharing. Practical cases indicate that the greedy matching's average performance is far better than the worst-case approximation ratio 50% as compared to the optimum. However, there is no rigorous average-case analysis in the literature to back up such encouraging findings and this paper is the first to present such analysis for multiple representative classes of graphs. For 1D linear networks, we prove that our greedy algorithm performs better than 86.5% of the optimum. For 2D grids, though dynamic programming cannot be directly applied, we still prove this average performance ratio to be above 76%. For the more challenging Erdos-Renyi random graphs, we equivalently reduce to the asymptotic analysis of random trees and successfully prove a ratio up to 79%. Finally, we conduct experiments using real data to simulate realistic D2D networks, and show that our analytical performance measure approximates well practical cases.

Decentralized Chat Application using Blockchain Technology

Abstract: Abstract: Decentralized application make use of peer-to-peer networks, this ensures that no network failure can occur due to central node failure. Blockchain serves as an immutable ledger which allows messaging to take place in a decentralized manner. A decentralized application for communication and resource sharing is need in today’s world, where keeping data on a centralized server can be risky and costly experience. With the help of various consensus, we can implement different ways to share resources and communicate. Together with Blockchain and Decentralized Applications, we can create a secure and reliable messaging application that overcomes the drawbacks of traditional messaging applications.

A Review on Sustainable Resource Management in Cloud Environment

Abstract: <p>Cloud computing (CC) has become the best way to manage a large-scale computing paradigm. Resource management (RM) in the cloud environment is critical due to the heterogeneity of computing resource types and their inter-dependencies. Recently, significant research has been done on RM techniques focusing on efficiently sharing resources among multiple users. Machine learning techniques manage various resource management functions, such as VM consolidation, resource optimization, energy optimization, workload estimation, and task scheduling. This article contributes a comprehensive and concise review of RM approaches within cloud environments. Moreover, it suggests design goals and research challenges that must be considered while developing novel RM strategies.</p>

Research on College English Resources Sharing Platform Based on Distributed Storage

Abstract: Distributed storage provides a more secure and convenient way of storing data by linking nodes together through computer networks. In order to meet the needs of online college English teaching, this paper builds a college English resource sharing platform based on the distributed storage IPFS technology, which decentralises data storage on multiple independent devices, significantly reducing the system’s construction cost and energy consumption. IPFS supports the creation of fully distributed applications where large files will be split into small file blocks stored in multiple server nodes and the resource platform invokes the deployed smart contracts to provide a basis for future resource copyright disputes. At the same time, IPFS provides a high-throughput content-addressable block storage model that can provide support for a wider range of applications.

Fine-grained Policy-driven I/O Sharing for Burst Buffers

Abstract: A burst buffer is a common method to bridge the performance gap between the I/O needs of modern supercomputing applications and the performance of the shared file system on large-scale supercomputers. However, existing I/O sharing methods require resource isolation, offline profiling, or repeated execution that significantly limit the utilization and applicability of these systems. Here we present ThemisIO, a policy-driven I/O sharing framework for a remote-shared burst buffer: a dedicated group of I/O nodes, each with a local storage device. ThemisIO preserves high utilization by implementing opportunity fairness so that it can reallocate unused I/O resources to other applications. ThemisIO accurately and efficiently allocates I/O cycles among applications, purely based on real-time I/O behavior without requiring user-supplied information or offline-profiled application characteristics. ThemisIO supports a variety of fair sharing policies, such as user-fair, size-fair, as well as composite policies, e.g., group-then-user-fair. All these features are enabled by its statistical token design. ThemisIO can alter the execution order of incoming I/O requests based on assigned tokens to precisely balance I/O cycles between applications via time slicing, thereby enforcing processing isolation. Experiments using I/O benchmarks show that ThemisIO sustains 13.5-13.7% higher I/O throughput and 19.5-40.4% lower performance variation than existing algorithms. For real applications, ThemisIO significantly reduces the slowdown by 59.1-99.8% caused by I/O interference.

Sharing technology of party history learning educational resources of party members in colleges and universities based on big data analysis

Abstract: With the rapid development of information technology and the rapid growth of data, the era of big data has come. Rational use of big data technology to improve the contribution quality of network teaching resources, to achieve comprehensive sharing of teaching resources, will be the trend of future development. The rapid development of big data technology has created good opportunities for improving the network informatization level of party construction in colleges and universities. In the era of big data, the innovation of party construction in colleges and universities has its necessity and possibility. However, traditional resource-sharing technology can not accurately identify heterogeneous resources, resulting in a poor sharing effect. Therefore, based on big data analysis, this paper studies the sharing technology of educational resources for party history study of party members in colleges and universities. By constructing a resource-sharing model, semantic recognition of heterogeneous educational resources is carried out, and semantic relations between concepts are obtained by reasoning. Based on similar semantics, big data analysis algorithm is used to calculate semantic similarity, and other resources associated with the resource are retrieved according to semantic similarity to realize resource-sharing. In the experimental demonstration, the semantic similarity between different concepts is calculated by the design method and the traditional method respectively. Experimental results show that the semantic similarity calculated by the resource-sharing technology based on big data analysis is closer to the expected value. The semantic similarity can also better identify heterogeneous resources and achieve a better sharing effect. The learning education resource-sharing technology based on a big data analysis algorithm is feasible, which can lay a foundation for resource-sharing technology and help users rationally use more educational resources.

Collaboration Patterns and Impact of Sharing at CHIIR

Abstract: We studied the collaboration patterns of CHIIR authors, and found that most papers are collaborative. A core of 33% of the CHIIR researchers are directly connected and frequently co-author, and several disconnected clusters also make frequent CHIIR contributions. We also studied citation impact of the CHIIR papers and show that in relation to research design type, theoretical and empirical papers tend to receive more citations than resource papers. With regards to sharing and re-use, papers that share at least one resource tend to have significantly higher citation impact—in particular when sharing data resources and design resources. Re-using resources does not significantly increase citation impact in itself.

Design of College Online Resource Sharing and Practice Platform Assisted by Opensource Public Cloud Big Data

Abstract: The educational resource sharing platform is designed and developed with open source tools such as Spark computing engine, Hive data warehouse, MySQL database, InfluxDB timing library and Akka toolkit. Combined with the characteristics of private cloud computing technology, it gives full play to the advantages of cloud computing, which can save investment costs, provide secure storage and improve the level of resource sharing in the construction of information platforms. Design and implement an educational resource sharing scheme; implement a resource search algorithm based on a complex network-based resource search model; design and implement the platform's resource processing module, blog module, circle module, user management module, etc.

Ontology-based Evaluation of ABAC Policies for Inter-Organizational Resource Sharing

Abstract: Attribute-based Access Control (ABAC), as the name suggests, determines whether an access request be granted based on the attributes or characteristics of the requesting user, those of the requested resource, and the environmental condition in which the request is generated. An important advantage of such an identity-agnostic model is that access control can be imposed even on users from other organizations if they are able to prove their attributes to the reference monitor of the organization whose resources are being accessed. It would, however, require a mechanism for mapping the attributes and their values among these organizations. We propose an ontology based method for addressing this requirement. Besides meeting the needs of collaborative accesses, we show how such an approach can be made to naturally support hierarchical ABAC policies as well as controlled relaxation during policy enforcement.

Anti-windup coordination strategy around a fair equilibrium in resource sharing networks

Abstract: This paper aims at coordinating interconnected agents where the control input of each agent is limited by the control input of others. In that sense, the systems have to share a limited resource over a network. Such problems can arise in different areas and it is here motivated by a simplified district heating example. When the shared resource is insufficient for the combined need of all systems, the resource will have to be shared in an optimal fashion. In this scenario, we want the systems to automatically converge to an optimal equilibrium. The contribution of this paper is the proposal of a control architecture where each separate system is controlled by a local PI controller. The controllers are then coordinated through a global rank-one anti-windup signal. It is shown that the equilibrium of the proposed closed-loop system minimizes the infinity-norm of stationary state deviations. Convergence is investigated via a randomized numerical study, and a district heating-inspired example illustrates the utility of the proposed method.

Resource Sharing Platform Based on Machine Learning and Data Fusion Technology

Abstract: Artificial intelligence can change the traditional teaching methods to assist teaching by evaluating in the process of teaching, and can also provide personalized services to guide students in the learning process, thus improving the learning efficiency and education quality and comprehensive quality of teachers and students. Therefore, this paper proposes to build an ER sharing platform and its supporting software platform based on machine learning (ML) and data fusion (DF) technology (TensorFlow) to promote the application in education and teaching. This paper realizes resource sharing among schools through the platform. Using big data technology to provide decision support for teaching and learning, improve teaching quality and teaching effectiveness to achieve effective cultivation of students' personalized development standards. Based on the data collection and application system, a new and applicable big data system for education and teaching is constructed. It achieves the goals of sharing educational resources (ER), helping teachers to carry out flexible and effective educational activities and improve teaching efficiency according to students' different thinking patterns, and improving the level and quality of education: comprehensively enhancing teachers' and students' interest in learning and awareness of methods and comprehensive quality.

Construction of a Human Resource Sharing System Based on Blockchain Technology

Abstract: Human resource data sharing is very important for the cooperation of human resource institutions. Since the human resource data-sharing service needs to take into account the needs of individuals and talent service institutions, it faces issues such as the security of information sharing and the traceability of information. Therefore, this paper constructs a human resource data-sharing service system based on blockchain technology. Its trust mechanism is based on the Fabric alliance chain, and the system makes full use of its advantages of decentralization and consensus. Our research mainly includes data sharing architecture design, consensus mechanism analysis, smart contract design, data sharing process, and blockchain construction. The contribution of this paper is mainly in two aspects. On the one hand, it explores the trust mechanism of human resource data sharing and gives the scheme of the Fabric alliance chain. On the other hand, the overall architecture and smart contract design are given on the construction of the blockchain, which provides a reference for future research on human resource data sharing.

An intelligent network slicing framework for dynamic resource sharing in multi-access edge computing enabled networks

Abstract: Dynamic resource sharing in multi-access edge computing (MEC) enabled networks has gained tremendous interest in the recent past, paving the way for the realization of beyond fifth generation (B5G) communication networks. To enable efficient and dynamic resource sharing, Network Slicing has appeared as a promising solution, virtualizing the network resources in the form of multiple slices employed by the end-users requiring strict latency, proximate computations, and storage demands. In literature, network slicing is primarily studied in the context of communication resource slicing, and little research has been devoted to jointly slicing communication, energy, and MEC resources. In this paper, we, therefore, proposed a joint network-slicing framework that considers 1) communication resources, 2) compute resources, 3) storage resources, and 4) energy resources, and intelligently and dynamically shares the resources between different slices, aiming to improve tenants' overall utility. To this end, we formulated a utility maximization problem as Markov-chain Decision Process. We utilized a tenant's manager that employs a deep reinforcement learning technique named "deep deterministic policy gradient" to enable dynamic resource sharing. Simulation results reveal the effectiveness of the proposed scheme.

Construction of Maker Education Resource Sharing Platform Based on Web Technology

Abstract: AbstractAt present, what maker education lacks is not a certain software or hardware, but a system. This platform should connect students, teachers and education authorities in series. This series is not a series of products, but a series of data. Now the exam oriented education industry is very concerned about the construction of three links and two platforms, and the same is true of maker education. Considering the practicability and economy, building a network teaching platform based on Web technology is an important task and development direction of the current construction of educational resource sharing platform. The summary of the construction of maker education platform may have some enlightenment.KeywordsWeb technologyMakerEducational resource sharingPlatform construction

Resource sharing system of college English education based on wireless sensor network

Abstract: In order to solve the problems of low efficiency and poor security of college English education resource sharing, a resource sharing system of college English education based on wireless sensor network is proposed. The overall function of the resource sharing system is designed. In the hardware design of the system, the hardware of the wireless sensor node is designed. The model of PIC18LF6680 microprocessor is selected, and the standard RS232 serial port is extended to MAX232 level converter. Digital sensors are designed to realise the high-speed transmission of shared resources. On this basis, the hierarchical structure of the system is designed. In the software part, Kalman filter is used to detect noise and measure noise. According to different network conditions, the delay of wireless network system is controlled. The results show that the maximum sharing efficiency of the system is 98%, and the energy consumption of the system is low.

Kerjasama Jaringan Perpustakaan di Indonesia:

Abstract: Collaborative Partnerships are critical to driving demand and meeting Demand readers. The availability of collaboration and networking services in the system library for all individuals is critical to the success of the push. This increases service user and technical capacity, enhances resource sharing, reduces duplication, and provides efficient services. When creating the basis for collaboration and networking in libraries in general, several factors must be considered. Keywords: National Library, Collaboration, Library Network, National Library Collaboration, Collaboration Network

Design of an English Web-based Teaching Resource Sharing Platform based on Mobile Web Technology

Abstract: Thanks to the booming technology of computers and multimedia, student-centered online teaching resource platforms have become an important way for students to learn. However, English teaching resource platforms at the present stage fail to effectively integrate the massive and scattered learning resources. Based on this, the study proposes an English online teaching resource sharing platform based on mobile Web technology, using the SOAP protocol to deploy heterogeneous data resources as Web services to achieve interchangeability between heterogeneous resources. In addition, to enhance the efficient use of learning resources by students, the study proposes a hybrid algorithm based on collaborative filtering algorithm and sequential pattern mining algorithm to achieve personalized sequential recommendation for students. The results show that the platform created by the study exhibits excellent performance in terms of resource transfer capability, achieves efficient teaching resource sharing in a short response time and also shows that the proposed recommendation algorithm is highly accurate. Keywords—Web service; SOAP; English teaching resources; sharing platform; personalised recommendation

A Security-aware Network Function Sharing Model for 5G Slicing

Abstract: Sharing Virtualized Network Functions (VNFs) among different slices in Fifth Generation (5G) is a potential strategy to simplify the system implementation and utilize 5G resources efficiently. In this paper, we propose a security-aware VNF sharing model for 5G networks. The proposed optimization model satisfies the service requirements of various slices, enhances slice security by isolating their critical VNFs, and enhances resource utilization of the underlying physical infrastructure. The model tries to systematically decide on sharing a particular VNF based on two groups of constraints; the first group of constraints is common assignment constraints used in the existing literature. The second group is the novel security constraints that we propose in this work; the maximum traffic allowed to be processed by the VNF and the exposure of the VNF to procedures sourced via untrusted users or access networks. This sharing problem is formalized to allow for procedure-level modeling that satisfies the requirements of slice requests in 5G systems. The model is tested using standard VNFs and procedures of the 5G system rather than generic ones. The numerical results of the model show the benefits and costs of applying the security constraints along with the network performance in terms of different metrics.

Research on network resource sharing based on distributed ledger technology

Abstract: Network resource sharing needs more flexible and efficient for diversified services with the rapid evolution of 5G network and IoT technology, the current sharing cannot support highly decentralized resource collaboration and sharing. In this paper, we proposed a solution to establish a sharing ecosystem by DLT, and research the relationship and activities of roles in this ecosystem. Based on these, we research the general framwork of network resource sharing based on DLT and give the scenarios and application by using it.