Utilization Of Shared Resources Research Articles

A Mathematical Programming Approach for IoT-Enabled, Energy-Efficient Heterogeneous Wireless Sensor Network Design and Implementation.

The Internet of Things (IoT) is playing a pivotal role in transforming various industries, and Wireless Sensor Networks (WSNs) are emerging as the key drivers of this innovation. This research explores the utilization of a heterogeneous network model to optimize the deployment of sensors in agricultural settings. The primary objective is to strategically position sensor nodes for efficient energy consumption, prolonged network lifetime, and dependable data transmission. The proposed strategy incorporates an offline model for placing sensor nodes within the target region, taking into account the coverage requirements and network connectivity. We propose a two-stage centralized control model that ensures cohesive decision making, grouping sensor nodes into protective boxes. This grouping facilitates shared resource utilization, including batteries and bandwidth, while minimizing box number for cost-effectiveness. Noteworthy contributions of this research encompass addressing connectivity and coverage challenges through an offline deployment model in the first stage, and resolving real-time adaptability concerns using an online energy optimization model in the second stage. Emphasis is placed on the energy efficiency, achieved through the sensor consolidation within boxes, minimizing data transmission hops, and considering energy expenditures in sensing, transmitting, and active/sleep modes. Our simulations on an agricultural farmland highlights its practicality, particularly focusing on the sensor placement for measuring soil temperature and humidity. Hardware tests validate the proposed model, incorporating parameters from the real-world implementation to enhance calculation accuracy. This study provides not only theoretical insights but also extends its relevance to smart farming practices, illustrating the potential of WSNs in revolutionizing sustainable agriculture.

Mapping a shared care model in complex gastrointestinal surgery: A qualitative study of queues and stakeholders within a Canadian general surgery practice.

Shared Care Models (SCMs), in which a team of clinicians share in patient care and resource utilization, represent an opportunity for surgeon-level system change. We aimed to identify the queues and stakeholders within a complex gastrointestinal surgical care pathway to demonstrate the implications of a SCM on system efficiency. A multidisciplinary group of surgeons and care navigators working in SCMs were asked to develop a patient encounter map through consensus to illustrate relevant queues and stakeholders within a SCM. Fifteen surgeon-related queues were identified, each representing a point of potential delay to care in the patient's journey that could be addressed by shared care. A final patient encounter map was created, and advantages and challenges of SCMs were also described from multidisciplinary group discussions. The numerous queues identified in this map ultimately reflected opportunities for more efficient care navigation under a SCM through increased surgeon availability and shared resource utilization.

GI multisociety strategic plan on environmental sustainability.

GI multisociety strategic plan on environmental sustainability.

Modular interactive computation scheme for the internet of things assisted robotic services

Internet of Things (IoT) assisted robotic applications are becoming prominent in smart cities for granting autonomous services for end-users. The IoT-enabled edge-computing paradigm provides ubiquitous computing and resource access for the robots for robust services. The interactive sessions and partial computations of the robots result in unnecessary exploitation of real-time resources. This paper introduces a swarm intelligence-based modular interactive computation scheme (MICS) for IoT-assisted robots to address the incompleteness in resource utilization and reduce computational complexity. In this scheme, particle swarm optimization (PSO) is incorporated with intelligent decision-making. The role of swarm intelligence is to improve interactive computations using different agents that provide offloading support. The flexible sharing of swarm agents provides shared resource utilization and comprehensive calculations for robot-based assistance. The proposed scheme performs computing and interaction decisions based on the robot inputs and its parallel processing feature. The proposed scheme's performance is verified using the metrics interaction span, decision complexity, offloading ratio, computing time, and outages. The proposed scheme achieves a 12.74% high interaction span, 6.68% less decision complexity, 11.69% less offloading ratio, and 19.41% less computing time under different intervals. Based on the resource availability analysis, MICS achieves 62.1% compared to IoT-IF 25.6%, SF-EM 32.9, and 25.61%. Further MICS offloading ratio achieves up to 8.5% following IoT-IF 11.9%, SF-EM 10.1%, and SDF 9.1%.

Analysis of Enterprise Shared Resource Invocation Scheme based on Hadoop and R

The response rate and performance indicators of enterprise resource calls have become an important part of measuring the difference in enterprise user experience. An efficient corporate shared resource calling system can significantly improve the office efficiency of corporate users and significantly improve the fluency of corporate users' resource calling. Hadoop has powerful data integration and analysis capabilities in resource extraction, while R has excellent statistical capabilities and resource personalized decomposition and display capabilities in data calling. This article will propose an integration plan for enterprise shared resource invocation based on Hadoop and R to further improve the efficiency of enterprise users' shared resource utilization, improve the efficiency of system operation, and bring enterprise users a higher level of user experience. First, we use Hadoop to extract the corporate shared resources required by corporate users from the nearby resource storage computer room and terminal equipment to increase the call rate, and use the R function attribute to convert the user’s search results into linear correlations, according to the correlation The strong and weak principles are displayed in order to improve the corresponding speed and experience. This article proposes feasible solutions to the shortcomings in the current enterprise shared resource invocation. We can use public data sets to perform personalized regression analysis on user needs, and optimize and integrate most relevant information.

Client-Side Journaling for Durable Shared Storage

Hardware consolidation in the datacenter often leads to scalability bottlenecks from heavy utilization of critical resources, such as the storage and network bandwidth. Client-side caching on durable media is already applied at block level to reduce the storage backend load but has received criticism for added overhead, restricted sharing, and possible data loss at client crash. We introduce a journal to the kernel-level client of an object-based distributed filesystem to improve durability at high I/O performance and reduced shared resource utilization. Storage virtualization at the file interface achieves clear consistency semantics across data and metadata, supports native file sharing among clients, and provides flexible configuration of durable data staging at the host. Over a prototype that we have implemented, we experimentally quantify the performance and efficiency of the proposed Arion system in comparison to a production system. We run microbenchmarks and application-level workloads over a local cluster and a public cloud. We demonstrate reduced latency by 60% and improved performance up to 150% at reduced server network and disk bandwidth by 41% and 77%, respectively. The performance improvement reaches 92% for 16 relational databases as clients and gets as high as 11.3x with two key-value stores as clients.

Resource Sharing in the Logistics of the Offshore Wind Farm Installation Process based on a Simulation Study

This present contribution examines by means of a discrete event and agent-based simulation the potential of a joint use of resources in the installation phase of offshore wind energy. To this end, wind farm projects to be installed simultaneously are being examined, the impact of weather restrictions on the processes of loading, transport and installation are also taken into consideration, and both the wind farm specific resource allocation and the approach of a resource pool or resource sharing, respectively, are being implemented. This study is motivated by the large number of wind farms that will be installed in the future and by the potential savings that might be realized through resource sharing. While, so far, the main driver of the resource sharing approach has been the end consumer market, it has been applied in more and more areas, even in relatively conservative industries such as logistics. After the presentation of the backgrounds and of the underlying methodology, and the description of the prior art in this context, the network of the offshore wind energy installation phase will be described. This is the basis for the subsequent determination of the savings potential of a shared resource utilization, which is determined by the performance indicators such as the total installation time and degree of utilization of the resources. The results of the simulation show that weather restrictions have a significant effect on the installation times and the usage times of the resources as well as on their degree of utilization. In addition, the resource sharing approach, has been identified to have significant savings potential for the offshore wind energy installation.

Resource Sharing Centric Dynamic Voltage and Frequency Scaling for CMP Cores, Uncore, and Memory

With the breakdown of Dennard’s scaling over the past decade, performance growth of modern microprocessor design has largely relied on scaling core count in chip multiprocessors (CMPs). The challenge of chip power density, however, remains and demands new power management solutions. This work investigates a coordinated CMP systemwide Dynamic Voltage and Frequency Scaling (DVFS) policy centered around shared resource utilization. This approach represents a new angle on the problem, differing from the conventional core-workload-driven approaches. The key component of our work is per-core DVFS leveraging a technique similar to TCP Vegas congestion control from networking. This TCP Vegas–based DVFS can potentially identify the synergy between power reduction and performance improvement. Further, this work includes uncore (on-chip interconnect and shared last level cache) and main memory DVFS policies coordinated with the per-core DVFS policy. Full system simulations on PARSEC benchmarks show that our technique reduces total energy dissipation by over 47% across all benchmarks with less than 2.3% performance degradation. Our work also leads to 12% more energy savings compared to a prior work CMP DVFS policy.

Game Theory as a tool for dispute management in shared resource utilization

Game Theory as a tool for dispute management in shared resource utilization

Research on Virtual Cellular Manufacturing Scheduling Based on the Scale-Free Random Network Model

This paper studies the resource conflict resolution problem for virtual cellular manufacturing inter-cell scheduling. The existing literatures on resource conflict of inter-cell scheduling don't take account into delay within building a model. They try to resolve conflict according to coordination strategy, but it is always accompanied by delay. In view of this deficiency and new characteristics of virtual cellular inter-cell scheduling problem, we consider the tardiness of all products. A hybrid multi-objective particle swarm optimization algorithm is proposed, and considers task priority of shared resource. A new decoding method which simulates market mechanism solves resource conflict problem. The feasible task scheduling shortens total flow time and tardiness, improves the utilization of shared resource. Finally, an example is used to verify its feasibility and validity.

The Resource Conflict Resolution Problem for Virtual Cellular Manufacturing Inter-Cell Scheduling under the Conditions of Resource Sharing

This paper studies the resource conflict resolution problem for virtual cellular manufacturing inter-cell scheduling. The existing literatures on resource conflict of inter-cell scheduling don't take account into delay within building a model.They try to resolve conflict according to coordination strategy, but it is always accompanied by delay. In view of this deficiency and new characteristics of virtual cellular inter-cell scheduling problem, we consider the tardiness of all products. A hybrid multi-objective particle swarm optimization algorithm is proposed, and considers task priority of shared resource. A new decoding method which simulates market mechanism solves resource conflict problem. The feasible task scheduling shortens total flow time and tardiness, improves the utilization of shared resource. Finally, an example is used to verify its feasibility and validity.