DATA IN CLOUD
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The document discusses data in the cloud and virtual data centers (VDCs). It describes VDCs as facilities that house computer systems and associated telecommunications and storage components. The document outlines the components of a VDC including front-end, middle-tier, and back-end servers. It also discusses the architecture of VDCs and the functional requirements and issues they face, such as reliability, power needs, and cooling. Additionally, the document provides an overview of cloud infrastructure using AWS as an example and describes the iSCSI standard for transporting block storage over IP networks.
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3. Networking technologies such as peer-to-peer networks, client-server networks, and different network topologies.
4. Data management including database software, physical data storage, and storage area networks.
5. Technology services including IT support and management services.
Data processing in Cyber-Physical Systems
Overview of the data processing types, requirements, and alternatives in cyber-physical systems from the Internet of Things to Fog to Cloud platforms.
Cloud computing
Cloud computing architecture uses a client-server model to deliver computing resources over the Internet. It has two main parts - the front end and back end. The front end includes interfaces like web browsers that clients use to access cloud services. The back end is owned by infrastructure providers and manages resources like data storage, security, virtual machines, and servers to provide cloud computing services. Key components of the architecture include clients, applications, services, runtime environment, storage, infrastructure, management systems, security controls, and the Internet that connects the front and back end.
The Evolving Data Center – Past, Present and Future
The journey to Cloud is not linear. Realistically, most environments will have workloads that continue to run on both physical and virtualized infrastructures for some time. Join Cisco’s Data Centre Experts, as they outline the key technologies transforming the Data Centre, enabling an intelligent infrastructure which will support physical, virtualized and cloud applications as part of Cisco’s Unified Data Centre Architecture.
IRJET - Importance of Edge Computing and Cloud Computing in IoT Technolog...
This document discusses the importance of edge computing and cloud computing for processing real-time and time-sensitive data from IoT devices. It explains that edge computing is better suited than cloud computing for applications that require low latency and instant decision making, as it processes data closer to the source where it is generated. The document provides examples of applications that benefit from edge computing, such as traffic management and healthcare. It also describes the different services provided by cloud computing, and concludes that a combination of edge computing and cloud computing can maximize their benefits while reducing their individual drawbacks.
Edge Comp.pptx
Edge computing is a distributed computing paradigm that processes data close to where it is generated by IoT devices and sensors, rather than sending all data to a centralized cloud for processing. This reduces latency and network congestion. Edge computing provides resources like data analysis and AI locally to data sources. It is important for applications that require real-time, low-latency responses and for reducing load on networks and servers. Edge computing will be increasingly important as more IoT devices are deployed.
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More from Rashmi Agale
Cloud computing
This document provides an overview of cloud computing. It defines cloud computing as distributed computing over the internet that delivers computing services, such as email and data storage. The document traces the history of cloud computing concepts back to the 1950s. It describes the key components of cloud computing including clients, data centers, distributed servers, and a central server that administers the system. The document also covers cloud service models, deployment options, benefits, applications, and provides a brief history of cloud computing's evolution.
Cloud Patterns And Applications
This document discusses cloud computing platforms and concepts. It introduces Apache CloudStack as an open-source infrastructure as a service platform that allows users to provision cloud resources on demand. It also discusses Amazon EC2 and S3 cloud services, intercloud networking, common design patterns like facades, adapters and proxies, and distributed computing architectures like MapReduce. The document provides an overview of cloud computing topics at a high level.
Introduction to Cloud Computing
This document provides an introduction to cloud computing. It defines cloud computing as a model that enables on-demand access to a shared pool of configurable computing resources over the internet. It describes the essential characteristics of cloud computing including on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service. It also outlines the different deployment models including private cloud, community cloud, public cloud and hybrid cloud. Additionally, it discusses the different service models of SaaS, PaaS and IaaS and presents the NIST guidelines for implementing cloud computing solutions.
QUALITY OF SERVICE(QoS) OF CLOUD
This document discusses quality of service (QoS) aspects of cloud computing, including QoS management, auto scaling, load balancing, and resource scheduling. It provides details on each of these topics: for QoS management, it lists the phases involved; for auto scaling, it describes scaling resources according to user needs; for load balancing, it discusses algorithms like batch mode and online mode heuristic scheduling; and for resource scheduling, it outlines algorithms like genetic, bee, ant colony, and workflow. The document aims to explain how these techniques help provide quality service in cloud computing environments.
Cloud Security
This document discusses cloud security risks and provides an overview of cloud security. It outlines various security risks in cloud computing including insider and outsider attacks, privacy and trust issues, and vulnerabilities in operating systems, virtualization, and shared images. It also describes the Xoar system for improving security by reducing the trusted computing base and limiting privileges and interfaces of system components. Finally, it discusses the need for a trusted virtual machine monitor to prevent the cloud provider from accessing the system.
realtime- transaction Processing System
This document appears to be a presentation about real-time transaction processing systems given by Kunal Mahurkar under the guidance of Mrs. Shaikh. It discusses key aspects of real-time transaction processing systems including how they are measured by the number of transactions they can process in a given time period, the importance of avoiding data corruption, needing modular growth to add or replace hardware/software, and different types of deadlines for transactions (hard, firm, soft). It also mentions using main memory databases and server processes.
Multimedia database
This document discusses multimedia databases. A multimedia database is a collection of related multimedia data managed by a multimedia database management system. It supports different data types and allows for creation, storage, access, querying and control of multimedia data. Key components include a query interface, query processing, indexing, buffer management and storage management. Challenges include large storage needs, processing time requirements, and temporal properties of some multimedia data types. Application areas mentioned are digital libraries, news on demand, video on demand, and music databases.
Couch db
This document provides an overview of CouchDB, a document-oriented NoSQL database. It discusses key CouchDB concepts like using JSON documents to store data, JavaScript-based MapReduce functions to query data, and an HTTP-based API. The document also covers CouchDB features such as replication and eventual consistency. It provides pros and cons of CouchDB and compares it to MongoDB. Screenshots of the CouchDB web interface are included.
CouchDB
This document provides an overview of CouchDB, a document-oriented NoSQL database. It discusses key CouchDB concepts like using JSON documents to store data, JavaScript-based MapReduce functions to query data, and an HTTP-based API. It also covers CouchDB features such as replication and eventual consistency. Pros noted are flexibility in data schemas and parallel indexing for queries. Cons include needing to pre-define views for queries and implementing join/sort logic client-side. Related projects like PouchDB and TouchDB are also mentioned.
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Embedded machine learning-based road conditions and driving behavior monitoring
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEM
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...
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DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
As digital technology becomes more deeply embedded in power systems, protecting the communication
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Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
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detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
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train and test our model. The results of our experiments show that our CNN-LSTM method is much better
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This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
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A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
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The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Computational Engineering IITH Presentation
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Recycled Concrete Aggregate in Construction Part III
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DATA IN CLOUD
- 1. DATA IN CLOUD Guided By Presented By Deepali Ujalamkar Pradnya Madulkar COMPUTER DEPARTMENT,AISSMS COE,PUNE Department of computer engineering,AISSMS,Pune 1
- 2. Index • VDC -Introduction -Components -Architecture • Functional requirements and Issues faced by of VDC • Cloud Infrastructure • Hybrid Storage Networking technique • iSCSI -Components -Architecture Department of computer engineering,AISSMS,Pune 2
- 3. What is VDC • Virtual Data Center(VDC) is a facility used to house computer system and is associated with the components such as telecommunication and storage system. • Cloud computing is a method of delivering computing services from large highly virtualized data center to many independent end uses using shared applications and pooled resources. • Data center generally includes data communication, environmental controls and security devices. Department of computer engineering,AISSMS,Pune 3
- 4. Components of VDC • Front end(Web server) The front end web server is one of the edge of data center and is responsible for handling the static requests. • Middle tier(Application server) The middle Tier application server is responsible foe handling the dynamic web contents and requests. • Back end(Database server) The back end database server is responsible for handling the complex database transactions. Department of computer engineering,AISSMS,Pune 4
- 5. Architecture of VDC Department of computer engineering,AISSMS,Pune 5
- 6. Functional Requirements of VDC • Location • Power • Temperature • Structured cabling Department of computer engineering,AISSMS,Pune 6
- 7. Issues In VDC • Reliability Factors that are responsible for the reliability of data centers are -temperature -Dust -Vibration -High/low humidity • Power • Space risk • Site location • Heat and cooling Department of computer engineering,AISSMS,Pune 7
- 8. CLOUD INTERCONNE CT EC2 INSTANC E EC2 INSTANC E EC2 INSTANCE NAT INTERN ET SQS CLOUD WATCH CLOUD FRONT ELASTIC CACHE ELASTIC BEANSTALK ELASTIC LOAD BALANCING AWS MANAGEMENT CONSOLE S3 EB S Simple DB AWS Storage Servers Figure showing CLOUD INFRASTRUCTURE Department of computer engineering,AISSMS,Pune 8
- 9. iSCSI (Internet Small Computer System Interface) • Defination:-is a storage networking standard that enables the transport of block I/O data over the IP network. • Components of iSCSI o iSCSI Client o iSCSI Server o Initiator Portal o Target Portal Department of computer engineering,AISSMS,Pune 9
- 10. Department of computer engineering,AISSMS,Pune 10
- 11. Department of computer engineering,AISSMS,Pune 11