RNN-Assisted Feature-Extraction VMD for Load Classification in Cloud Computing Platform

2021 ◽  
Author(s):  
Yiming Ma ◽  
Haixin Wang ◽  
Junyou Yang ◽  
Zhe Chen ◽  
Jingwei Yuan ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Feature Extraction ◽  
Power System ◽  
Data Storage ◽  
Peak Load ◽  
Smart Meters ◽  
Peak Period ◽  
Control Center ◽  
System A ◽  
Cloud Computing System

Cloud computing can improve the calculation and data storage ability for the control center in the power system. A new framework of the cloud-based control center is proposed in this paper. This cloud computing system can collect the load data from smart meters of the grid and classify demand-side management (DSM) loads that meet the specific requirements. The selected loads belong to the off-peak period (from 21:00 to 07:00 next day) and can contribute to shifting the night peak load. A feature extraction combined with Variational Mode Decomposition (FE-VMD) of the loads which can be trained in recurrent neural network (RNN) is proposed in this paper. Using the feature value to replace the actual load data, input data can be significantly reduced which is suitable for a vast amount of load in the power system. A case study of real load data from 200,000 customers has been classified with this method, and the accuracy is compared with the other methods. From simulation with MATLAB, it can be seen that the FE-VMD combined with the RNN method provides the best result of 89.8% recognition accuracy among these methods.

scholarly journals Exploiting Coarse-Grained Parallelism Using Cloud Computing in Massive Power Flow Computation

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2268 ◽  
Author(s):  
Dong-Hee Yoon ◽  
Sang-Kyun Kang ◽  
Minseong Kim ◽  
Youngsun Han
Keyword(s):  
Cloud Computing ◽  
Power Systems ◽  
Power System ◽  
Power Flow ◽  
Computation Time ◽  
Coarse Grained ◽  
Contingency Analysis ◽  
Flow Computation ◽  
Cloud Computing System ◽  
The Impact

We present a novel architecture of parallel contingency analysis that accelerates massive power flow computation using cloud computing. It leverages cloud computing to investigate huge power systems of various and potential contingencies. Contingency analysis is undertaken to assess the impact of failure of power system components; thus, extensive contingency analysis is required to ensure that power systems operate safely and reliably. Since many calculations are required to analyze possible contingencies under various conditions, the computation time of contingency analysis increases tremendously if either the power system is large or cascading outage analysis is needed. We also introduce a task management optimization to minimize load imbalances between computing resources while reducing communication and synchronization overheads. Our experiment shows that the proposed architecture exhibits a performance improvement of up to 35.32× on 256 cores in the contingency analysis of a real power system, i.e., KEPCO2015 (the Korean power system), by using a cloud computing system. According to our analysis of the task execution behaviors, we confirmed that the performance can be enhanced further by employing additional computing resources.

Cloud computing system for small and medium corporations

2017 ◽  
Vol 7 (1.1) ◽  
pp. 173
Author(s):  
R. Vijaya Arjunan ◽  
K. Vinayaka Kamath
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Global Economy ◽  
Business Processes ◽  
Small And Medium Enterprises ◽  
Computing System ◽  
Software As A Service ◽  
Business Operations ◽  
Cloud Computing System ◽  
Medium Enterprises

Cloud computing provides services that allocate infrastructure resources using the Internet as a medium and data storage on an external server. Small and medium corporations are the foundation of any flourishing economy for a growing nation which seeks new and innovative methods to reduce the way they manage their resources. Over a couple of decades, Information technology (IT) has created a significant impact in improving the lives of people and alsoon the global economy due to tremendous digital transformation. With the growth of the Small and medium corporations, IT is creating some real impact in enabling these industries to undergo adigital transformation of their business processes while they continue to grow. Small and medium enterprises (SME’s) are usually identified as the dominant force for the growth of any country's economy. In the cloud computing environment, the SME's need not have the in-house infrastructure so they can give up on any initial expenditure for setting up and instead they can utilize the resources available on the cloud and pay as per their requirement and usage.This paper presents the results of a comprehensive interpretation to research some of the most commonly used SaaS (Software-as-a-Service) implementations in the domain of Cloud Computing firstly to identify the weaknesses of the traditional computing approach for SME’s, and secondly to identify the aspects of these weaknesses that can be overcome by implementing cloud computing.In this paper, we provided the overview of various cloud computing models and literature survey of these models. This study extends to create an own cloud computing system for small and medium corporations. We will be using Software-as-a-Service (SaaS) approach and see how small and medium corporations can leverage on this for their business operations.

Research of Smart Meter Massive Data Storage Based on Cloud Computing Platform

2013 ◽  
Vol 341-342 ◽  
pp. 1434-1438
Author(s):  
Weng Ting Li ◽  
Yan Zheng ◽  
Shao Bo Liu ◽  
Zhao Zhi Long ◽  
Zhi Cheng Li
Keyword(s):  
Cloud Computing ◽  
Smart Grid ◽  
Data Storage ◽  
Massive Data ◽  
Smart Meter ◽  
Smart Meters ◽  
Computing Platform ◽  
Cloud Computing Platform ◽  
Distributed Computing Platform ◽  
Grid Operation

With the comprehensive construction of the smart grid, the smart grid operation control and interactive service system will be initially formed. The smart terminal of smart grid are smart meters, and they produce a large number of various data all the time. That how to most effectively manage these massive data storage is an important research point for improving the intelligence service. This paper studies the smart meter massive data storage management based on cloud computing platform. The Hadoop distributed computing platform for smart meter massive data management is reliable, efficient, scalable storage.

scholarly journals Application of mobile cloud computing in emergency health care

2019 ◽  
Vol 8 (3) ◽  
pp. 1088-1095
Author(s):  
Shihab A. Hameed ◽  
Ali Nirabi ◽  
Mohamed Hadi Habaebi ◽  
Alaa Haddad
Keyword(s):  
Health Care ◽  
Cloud Computing ◽  
Data Storage ◽  
Mobile Application ◽  
Mobile Cloud Computing ◽  
Medical Center ◽  
Professional Service ◽  
Mobile Cloud ◽  
Emergency Health Care ◽  
Cloud Computing System

Mobile applications in emergency health care help maintain patient confidentiality and manage patient records, data storage. Compiles and analyzes care of better quality care. new implementations come with new goals and technologies like using mobile application with cloud computing system and reducing the responding time to safe the patient life and give the patient best health care professional service transition to using of mobile application in emergency healthcare, this paper will present (MCCEH) mobile cloud computing in emergency health care model, mainly reducing the wasting time in emergency health care, The process starting once the accident occurred and the patient run the application, mobile application will detect the patient location and allow him to book nearest medical center or specialist in some emergency cases once the patient did the booking will send help request to medical center this process will include an online pre-register patient in the medical center to save time of patient registration, MCCEH model allows the patients to review the previous feedback and experiences of each specialist or medical center and allows doctors to be able to stay in contact with their patients more often and by communication through mobiles applications and share messages and photos of the accident or emergency case itself.

scholarly journals Analysis of Load Balancing Techniques in Cloud Computing

2005 ◽  
Vol 4 (2) ◽  
pp. 737-741 ◽  
Author(s):  
Amandeep Sidhu ◽  
Supriya Kinger
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Data Storage ◽  
Cloud Storage ◽  
Job Scheduling ◽  
Single Node ◽  
Computing Paradigm ◽  
System A ◽  
Share Data ◽  
Scalable Network

Cloud Computing is an emerging computing paradigm. It aims to share data, calculations, and service transparently over a scalable network of nodes. Since Cloud computing stores the data and disseminated resources in the open environment. So, the amount of data storage increases quickly. In the cloud storage, load balancing is a key issue. It would consume a lot of cost to maintain load information, since the system is too huge to timely disperse load. Load balancing is one of the main challenges in cloud computing which is required to distribute the dynamic workload across multiple nodes to ensure that no single node is overwhelmed. It helps in optimal utilization of resources and hence in enhancing the performance of the system. A few existing scheduling algorithms can maintain load balancing and provide better strategies through efficient job scheduling and resource allocation techniques as well. In order to gain maximum profits with optimized load balancing algorithms, it is necessary to utilize resources efficiently. This paper discusses some of the existing load balancing algorithms in cloud computing and also their challenges.

scholarly journals Proposal of security preserving machine learning of IoT

2018 ◽  
Vol 7 (2) ◽  
pp. 26 ◽  
Author(s):  
Hirofumi Miyajima ◽  
Norio Shiratori ◽  
Hiromi Miyajima
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Computing System ◽  
Secure Multiparty Computation ◽  
Edge Computing ◽  
Multiparty Computation ◽  
Learning Method ◽  
System A ◽  
Cloud Computing System ◽  
Learning Data

The use of cloud computing system, which is the basic technology supporting ICT, is expanding. However, as the number of terminals connected to it increases, the limit of the capability is also becoming apparent. The limit of its capacity leads to the delay of significant processing time. As an architecture to improve this, the edge computing system has been proposed. This is known as a new paradigm corresponding the conventional cloud system. In the conventional cloud system, a terminal sends all data to the cloud and the cloud returns the result to the terminal or a thing directly connected to it. On the other hand, in the edge system, a plural of servers called edges are connected directly or to close distance between the cloud and the terminal (or thing). Then, let us consider the case of machine learning that requires big data. The purpose of learning is to find out the relationship (information) lurking in from the collected data. In order to realize this, a system with several parameters is assumed and estimated by repeatedly updating the parameters with learning data. Further, there is the problem of the security for learning data. In other words, users of cloud computing cannot escape the concern about the risk of information leakage. How can we build a cloud computing system to avoid such risks? Secure multiparty computation is known as one method of realizing safe computation. It is called SMC (Secure Multiparty Computation). Many studies on learning methods considering on SMC have also been proposed. Then, what kind of learning method is suitable for edge computing considering on SMC? In this paper, learning method suitable for edge computing considering on SMC is proposed. It is shown using an edge system composed of a client and m servers. Learning data are shared m pieces of subsets for m servers, learning is performed simultaneously in each server and system parameters are updated in the client using their results. The idea of learning method is shown using BP algorithm for neural network. The effectiveness is shown by numerical simulations.

scholarly journals Security in Cloud Computing Using Hash Algorithm: A Neural Cloud Data Security Model

2018 ◽  
Vol 12 (6) ◽  
pp. 143 ◽  
Author(s):  
Osama Harfoushi ◽  
Ruba Obiedat
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
Data Privacy ◽  
Security Model ◽  
Application Development ◽  
Cloud Data ◽  
Computing Services ◽  
Cloud Computing System ◽  
Cloud Computing Services

Cloud computing is the delivery of computing resources over the Internet. Examples include, among others, servers, storage, big data, databases, networking, software, and analytics. Institutes that provide cloud computing services are called providers. Cloud computing services were primarily developed to help IT professionals through application development, big data storage and recovery, website hosting, on-demand software delivery, and analysis of significant data patterns that could compromise a system’s security. Given the widespread availability of cloud computing, many companies have begun to implement the system because it is cost-efficient, reliable, scalable, and can be accessed from anywhere at any time. The most demanding feature of a cloud computing system is its security platform, which uses cryptographic algorithm levels to enhance protection of unauthorized access, modification, and denial of services. For the most part, cloud security uses algorithms to ensure the preservation of big data stored on remote servers. This study proposes a methodology to reduce concerns about data privacy by using cloud computing cryptography algorithms to improve the security of various platforms and to ensure customer satisfaction.

scholarly journals Prediction of Cloud Computing Resources Based on the Open Source Monitoring System

2020 ◽  
Vol 6 (3) ◽  
pp. 100-106
Author(s):  
K. Kucherova
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Source Monitoring ◽  
Computing System ◽  
Operating Conditions ◽  
Software Products ◽  
Database Structure ◽  
Cloud Computing System ◽  
Distributed Cloud ◽  
Globally Distributed

The paper describes the universal approach for monitoring the data storage of a globally distributed cloud computing system, which allows you to automate creation of new metrics in the system and predict their behavior for the end users. Since the existing monitoring software products provide built-in scheme only for system metrics like RAM, CPU, disk drives, network traffic, but don’t offer solutions for business functions, IT companies have to design specialized database structure (DB). The data structure proposed in this paper for storing the monitoring statistics is universal and allows you to save resources when orginizing database monitoring on the scale of the GDCCS. The goal of the research is to develop a universal model for monitoring and forecasting of data storage in a globally distributed cloud computing system and its adequacy to real operating conditions.

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