A Systematic Analysis of Network Bandwidth Sharing in Smart Grid Environment

. The integration of Information and Communication Technology (ICT) into modern infrastructure systems has given birth to the concept of "Intelligent Infrastructure," with the smart grid as a prominent example. The smart management and efficient application of important electricity burden during agricultural production process has been realized by disposing energy efficiency management platform in rural areas. This paper investigates the applicability of grid computing within the context of intelligent infrastructure, with a particular emphasis on the smart grid. Grid computing, a distributed computing paradigm that utilizes the combined computational power of interconnected resources, has the potential to significantly improve the effectiveness, dependability, and sustainability of smart grid operations. In addition, the allocation and distribution of network resources by the grid's systems will be examined in depth. While downloading gigabyte to terabyte-sized files from the internet, we frequently encounter various technical issues such as ISP restrictions, link expiration, etc In the meantime, the information exchanging has been proposed among the energy efficiency management platform, the distributed energy systems, the distribution automation system and the data acquisition system. Therefore, the intelligent scheduling of distributed energy can be realized in rural production during the peak period of electricity load and the affluent period.


Introduction
The last decade has brought a revolution in enhancing the processing speed of computers, the storage technology, and the communication techniques.But the advancement in processing power has not yet met the requirements of the highly sophisticated or in computer terms computationally complex problems.Even supercomputers are not able to solve various computationally intensive problems.The easy way is to break the computation intensive problem into simpler problem and distribute it to several processing units.The evolution of distributed systems added a spice to this approach [2].This gave birth to the techniques like Cluster computing, Grid computing and Cloud Computing.All these technologies incorporate the use of geographically distributed heterogeneous systems to get over the large-scale problems.A grid is simply a network of computers in which every computer shares its resources with every other system connected to the same network [16].In an ideal grid all the resources are shared by a computer, so that whenever a user plugs into the network, it gets enormous processing power, storage, etc. as if his/her computer has turned into a super computer.Moreover, the user does not have to care about the origin of the resources, the situation is quite similar to the electric power grid where the users do not care about how and from where the electricity is reaching them, and they just want their bulb to glow when they turn the switch ON.The grid computing system can be as simple as a collection of homogeneous computers with same operating systems or as complex as an inter-networked group of heterogeneous computers with all different platforms [2].Though the concept of Grid computing is very similar to distributed computing, it is in fact a special type of distributed computing, but it has not yet been perfected as we are still lacking the Standards and Protocols needed for the implementation of grid computing for public use [10].Once the Scientists, programmers and engineers agree upon a reliable set of Protocols and standards, it will be easy and efficient for everyone to adopt the grid computing environment.In an age of rapid urbanization and digital transformation, the convergence of information technology and infrastructure systems has given birth to the concept of "Intelligent Infrastructure."This paradigm shift represents a fundamental transformation in the way we design, operate, and manage critical infrastructure, with an emphasis on enhancing efficiency, sustainability, and resilience.At the Forefront of this transformational landscape is the "Smart Grid," a technologically advanced and interconnected electrical grid system that uses Information and Communication Technology (ICT) to optimize energy generation, distribution, and consumption.The deployment of ICT within the smart grid has created a universe of opportunities for real-time monitoring, control, and decision-making, transforming the conventional power grid into a dynamic and adaptable network.However, the realization of these opportunities is contingent on the efficient use of computational resources, data processing, and communication infrastructure.The smart grid serves as a prominent example of "Grid Computing," a Distributed computing paradigm that offers a promising solution to the complex challenges posed by the integration of ICT in intelligent infrastructure.This paper investigates the central function that grid computing plays in the context of intelligent infrastructure, with a particular emphasis on its application within the smart grid domain.Grid computing serves as a catalyst for unleashing the full potential of intelligent infrastructure and the smart grid by leveraging the collective computational power of interconnected resources.This introduction lays the groundwork for a comprehensive examination of the complex interactions between grid computing, intelligent infrastructure, and ICT, as well as the far-reaching implications they have for the future of energy management, sustainability, and urban development.As we delve deeper into the intricate interaction between these domains, we discover the transformative impact of grid computing and its capacity to resolve the pressing challenges that lie ahead in the realm of modern infrastructure systems.Grid computing is predicated on the notion of resource aggregation.By sharing resources, internet-connected or simply connected systems generate a pool of resources that are then utilized equitably by the connected computers to increase speed and efficiency.Typically, individual computers are equipped with a resource limit.Once a process or collection of processes exhausts all available system resources, the limit is surpassed and the system ceases to accept additional burden.This can be a significant disadvantage for processes that require substantial computation power.Most computers are upgradable, meaning that their resources, such as storage space expansion, processing capacity improvement through the addition of more CPUs, etc., can be enhanced.However, the impact of these upgrades remains incremental.Conversely, so it stands.Grid computing establishes connections among computers in a manner that enables each system to utilize the collective reservoir of resources generated by summing the capabilities and power of all computers.It offers a performance increase of an exponential nature [3] in comparison to clustered systems.Grid computing aims to incorporate resources originating from geographically dispersed heterogeneous resources [5].To accomplish this, a logical entity was necessary in which the resources could be identified and collaborated upon as if they originated from the same system or organization.With security and interoperability considerations in mind, this issue was resolved through the definition of a grid protocol architecture [15].The grid protocol architecture, as illustrated in Figure 1, comprises five distinct layers.Grid computing architecture is a distributed computing strategy that integrates computing resources from multiple sources, including data centers, clusters, and geographically dispersed servers, to generate a virtual supercomputer.The purpose of this architecture is to provide a flexible, scalable, and high-performance computing environment suitable for a wide range of applications.The architecture of Grid Computing is illustrated in Figure 1.

Materials and Methods
To address the aforementioned issues, we have created an application that leverages the grid computing paradigm and offers exceptionally rapid download speeds, contingent upon the quantity of nodes comprising the grid [6].The plan is to divide the file into multiple sections proportional to the number of contributor systems in the grid that can download the file.Each node downloads a portion of the file, which is then joined at the requesting node once all portions have been finished.To execute the concept, it is necessary to designate an intranet grid in which every node is equipped with internet connectivity.Grid formulation would be a straightforward task for an organization in which all users are currently connected to a LAN and share the same internet connection.Nevertheless, it is possible to link locations with distinct ISPs by establishing virtual network interfaces and utilizing a routing device to connect them [11].Attaining desired results on agricultural operations becomes a challenging task in the absence of adequate upkeep.Consequently, through the utilization of cloud computing, IoT, networking, and various other technologies, it becomes effortless to oversee and maintain the crops, weather patterns, water consumption, and fertilizer application as necessary.The SmartFarm AgriTech System operates on Raspberry Pi and Arduino as its principal microcontrollers, facilitating control over the motor, relay switch, and sensors.Intelligent agriculture places significant emphasis on the synchronization of agricultural activities through the utilization of data collected from diverse origins, such as instrumental, historical, and geographical data.Occasionally, the technological sophistication of a system and its intelligence are not correlated.Intelligent systems are distinguished by their dual capability of data acquisition and analysis.Intelligent farming employs hardware (IoT) both prior to and after harvest to gather data and deliver practical insights for the purpose of overseeing all farm activities.The data, which is consistently well-organized and exhaustive of information regarding every aspect of finance and field operations, is accessible from any location on the planet.The cloud serves as a centralized repository for a wide variety of data, including images, text, video, and maps, which are collected in significant and varied quantities from various sources.In order to ensure precise data classification, machine learning models based on Artificial Intelligence (AI) are employed, such as the Support Vector Machine (SVM), among other models [16].
Our grid implementation can be accomplished in two ways: 1) Employing the Globus Toolkit [12], which is pre-existing and supplied by the Globus Alliance; This method facilitates the formulation of the grid using the Globus Resource Allocation Manager (GRAM), GIS, and GFTP (Grid FTP) [13].Grid applications can be developed utilizing the APIs provided.However, this strategy has several significant drawbacks.To begin with, the installation of the Globus toolkit necessitates hardware operating Linux or AIX, which certain users may not have access to.Furthermore, developers and consumers are uneasy with the incredibly complex nature of grid implementation using Globus [14].On our system, the Globus toolkit executes several processes or services that may be superfluous to our implementation.To circumvent the intricacy and disarray, we have adopted an alternative methodology.2) Construct our own Grid implementation -To this end, we initially designed the most appropriate paradigm for our implementation.Ruby has been selected as the programming language and event generator to facilitate the establishment of connections among the various systems comprising the network.Our method has the benefit of being compatible with systems running on any operating system or platform [16].Installation requires no additional effort; the entire application is delivered as a stand-alone unit.By exclusively executing the necessary services, the system achieves enhanced speed and efficiency.
Our grid implementation paradigm incorporates both client-server and peer-to-peer [15] communication architectures.The client establishing a connection to the central server in order to obtain a file initially requests information regarding available systems and resources.After obtaining the data, it establishes connections with all other systems within the grid, thereby initiating peer-to-peer communication [9].We constructed the network using a mesh topology so that the failure of a single node would not disrupt the operation of the others.a.An always-active central server awaits connections from clients in order to retrieve the necessary data.This centralized server maintains a log of each grid-connected node.The data comprises hardware resources such as the central processing unit (CPU), primary and secondary memory, as well as the available bandwidth on the system.b.A node that establishes a connection with the central server and obtains information regarding all other nodes.Additionally, the node transmits its own data to the central server and any other nodes with which it is in contact.c.If a novel system is integrated into the grid, both the central server and all clients exchange information by transmitting the data received from the new node or the central server.d.A downloader client submits a request to obtain a particular file.The file is partitioned based on the quantity of nodes or customers participating in the grid and the resources they possess.Each system is assigned a weight according to its bandwidth and additional computational resources.As an illustration, a system featuring 4 GB Ram and 3 mbps bandwidth is assigned the value 3, whereas a system with 2 GB RAM and 2 mbps bandwidth is assigned the value 2. The subsequent algorithm is utilized to partition the file into sections.Once the file has been divided into segments, it is transmitted to the interconnected nodes in order to facilitate the downloading process.Once the downloading process is finished at each node, the requesting node receives the downloaded file portion.f.Once every component has been received at the requesting node, it is united together to produce the complete file.g.The Grid Computing applications are illustrated in Figure 2. Numerous disciplines make use of grid computing, a distributed computing paradigm that generates a virtual supercomputer by combining computational resources from multiple sources.In recent years, interest in the agricultural sector-transforming potential of grid computing has increased significantly.A variety of applications and strategies are being explored by academics and professionals in an effort to resolve the complex challenges that contemporary agriculture confronts.This segment presents an extensive examination of the pertinent scholarly investigations in the field of agricultural grid computing, categorizing the corpus of work based on its fundamental themes.
Recent years have seen a substantial increase in interest in the potential of grid computing to transform the agricultural sector.Scholars and practitioners are investigating a wide range of applications and approaches to address the complex issues that modern agriculture faces.This section provides a thorough review of the relevant research in agricultural grid computing, organizing the body of literature according to its primary themes.

I.
Analyzing Crops and Predictively Crop modelling and predictive analysis have greatly benefited from grid computing.The application of grid technology to improve the accuracy and scalability of crop simulation models was demonstrated study [1].Using grid infrastructure to execute large-scale simulations of crop development, pest infestations, and yield estimates, their study concentrated on distributed computing systems. II.
Sensing networks and precision agriculture One important area that has benefited greatly from grid computing is precision agriculture.Study [2] presented a novel method for real-time data collecting and processing using sensor networks and grid technologies.The study focused on the effective handling of sensor data about crop health, weather, and soil moisture to facilitate data-driven decisions for precision farming. III.
Imaging and Remote Sensing In satellite imaging and remote sensing for agriculture, grid computing is essential.The use of grid technology to manage the enormous datasets produced by remote sensing was demonstrated by author's work [3].Their work improved crop health assessment and land utilization by investigating grid-based techniques for image processing and feature extraction.

IV.
Agricultural Weather Forecasting Weather forecasting models customized for agriculture have advanced thanks to the application of grid computing.Grid infrastructure was used in the Martinez et al. study [4] to increase the precision and promptness of weather forecasts.Their work focused on integrating grid-based parallel processing into numerical weather which will help farmers by giving them accurate meteorological data to make decisions. V.
Control of Insects and Diseases Grid computing's use in disease and pest management is a significant area of scientific interest.The potential of grid technology for agricultural pest monitoring and early identification was examined by Wang and Chen [6].Their research focused on using real-time sensor data processing to detect pest outbreaks and support quick reaction plans to minimize crop damage.

Discussion of Results
The conventional method that we employ daily to download files could be considerably sluggish, contingent upon the file size being downloaded.In contrast, our grid downloader implementation has the capability to exponentially increase it in proportion to the number of systems that are present in the network.Traditional approaches also encounter difficulties when the node from which we download files lacks sufficient resources.Insufficient RAM on the system could impede the generation of threads, consequently impacting the download speed.The resources for our grid downloader system will be sourced from the network nodes, ensuring that we have sufficient resources to complete the task [8].In order to compare the functionality of two downloaders, we established a grid of five connected nodes and initiated the download process for a 1.4 GB file.The download speeds of each node in the grid are detailed in Table 1.

Table 1: Node analysis against download Speed
As shown in Figure 3, the download velocities increase progressively as more nodes are added to the grid network.Throughout the assessment, the download pace of each node was maintained consistently in this analysis.In contrast, the conventional downloader scenario utilized a solitary node denoted as Node 1, maintaining a consistent download speed of 1 Mbps.Continuous measurements of the downloaded file size were conducted for both the grid downloader and the conventional downloader, with readings being taken every 10 seconds.The provided figure depicts the impact of the grid network's collaborative functionalities on download performance when compared to a method utilizing a single node.It provides information on how to optimize networks and increase download speeds .As grid size increases, the download rate of the uploader within the grid also increases.The nodes are introduced into the grid in the same sequence as specified in Table 4.1 Table 2 presents the grid downloader with five nodes, which downloads the file in segments simultaneously.The size of the downloaded file is also displayed every 10 seconds.Additionally, node 1, which is downloading the identical file from the same source, operates at a speed of 1 Mbps.Visual representation of the download rate achieved by a standard downloader comprising a solitary node is provided in Figure 4.This figure graphically represents the download speed and efficiency of the conventional downloading technique, which is dependent on the resources of a solitary dedicated node.This method's speed and efficiency limitations are effectively illustrated through a straightforward and unambiguous demonstration of the download performance characteristics when the download operation is entrusted to a solitary node.This provides valuable insight.The file size that is downloaded at 10-second intervals is detailed in Table 3.The relationship between the number of nodes, file size, and download speed is illustrated in Figure 5. Figure 5 visually represents the correlation between the quantity of nodes (1) and two critical variables, namely the download time and file size.The presented graph offers a comprehensive depiction of the temporal progression of the download procedure as a solitary node participates.It facilitates a more comprehensive understanding of the correlation between these variables by permitting the observation of the temporal components of the download process and its impact on the ultimate file size.

Conclusion
This paper provides an in-depth analysis of the grid architecture, elucidating the prospects, advantages, and obstacles associated with grid computing.Implementations of grid computing in the actual world and its applications in numerous industries have been described.In essence, the integration of smart grid computing into the agricultural industry represents a fundamental change towards cultivation methods that are more sustainable, productive, and efficient.By employing state-of-the-art technologies such as Internet of Things sensors, data analytics, and real-time monitoring, intelligent grids enable agricultural professionals to optimize resource allocation, reduce inefficiencies, and increase crop yield.By reducing energy usage and greenhouse gas emissions, this practice ensures food security for a developing global population while also making a positive environmental impact.Furthermore, intelligent grid computing promotes economic growth by fostering opportunities for innovative methodologies and mechanization and bolstering the competitiveness of the agricultural sector.This knowledge imparts producers with practical and implementable insights, empowering them to make informed decisions, mitigate potential risks, and adapt to changing ecological conditions.An overview of the research conducted by various scholars in the domain of grid computing has been provided, along with an examination of the conclusions they drew.Numerous challenges and issues pertaining to the grid environment have been delineated, accompanied by potential resolutions.The specified objectives have been unambiguously delineated, accompanied by recommendations for the necessary instruments and methodologies.Subsequently, it will be possible to develop a fault-tolerant grid downloader capable of identifying and detecting issues such as node failure.In the event that the download attempt at a particular node is unsuccessful, the fault-tolerant system will possess the capability to discern the affected component and allocate it to another node within the network.Furthermore, we shall develop a graphical user interface (GUI) for our application to ensure that individuals lacking a technical background can easily operate it.By introducing grid computing to ordinary users, we hope to inspire them to contribute to a worthy cause by sharing their resources.Considering the increasing demands for energy and food in the coming years, smart grid computing in agriculture presents itself as a potentially viable resolution, offering a path towards improved agricultural methodologies that are resilient, environmentally sustainable, and operationally efficient.This technological innovation functions as a cornerstone of a more prosperous and sustainable agricultural industry, offering benefits to both present and future generations.

Fig 3
Fig 3 Analysis of the Number of Nodes in Relation to Download Size and Time

Fig 4
Fig 4 Number of Nodes(1) against Time and Download File Size

Fig 5
Fig 5 Number of Nodes(1) versus Download File Size and Time

Table 2 :
Node Count in Relation to Download File Size and Time

Table 3 :
Number of Nodes against Time and Download File Size