A real-time database system is a database where timely responses to user requests are needed. There are hard and soft real-time database systems. A real-time database uses main memory for faster access and retrieval of data. Transactions must meet deadlines and indexing is done using T-trees to efficiently store and retrieve data from main memory. The Adaptive Earliest Deadline scheduling algorithm is used to manage large volumes of transactions in real-time databases.
This document discusses real-time scheduling algorithms. It begins by defining real-time systems and their key properties of timeliness and predictability. It then discusses two common real-time scheduling algorithms: fixed-priority Rate Monotonic scheduling and dynamic-priority Earliest Deadline First scheduling. It covers how each algorithm prioritizes and orders tasks, and analyzes their schedulability and utilization bounds. It concludes by comparing the two approaches.
1. Real-time systems are systems where the correctness depends on both the logical result and the time at which the results are produced.
2. Real-time systems have performance deadlines where computations and actions must be completed. Deadlines can be time-driven or event-driven.
3. Real-time systems are classified as hard, firm, or soft depending on how critical meeting deadlines are. They are used in applications like medical equipment, automotive systems, and avionics.
An explicitly parallel program must specify concurrency and interaction between concurrent subtasks.
The former is sometimes also referred to as the control structure and the latter as the communication model.
This document provides an overview of real-time operating systems. It discusses the importance of timeliness in real-time systems and defines three types of time constraints: hard, soft, and firm. Key design issues for real-time systems are predictability and maintaining temporal consistency between the system and its environment. Real-time tasks have deadlines that must be met through scheduling approaches like static priority-driven preemptive scheduling. A major challenge is the priority inversion problem that can occur when higher priority tasks are blocked by lower priority tasks.
Parallel computing is computing architecture paradigm ., in which processing required to solve a problem is done in more than one processor parallel way.
A real-time database system is a database where timely responses to user requests are needed. There are hard and soft real-time database systems. A real-time database uses main memory for faster access and retrieval of data. Transactions must meet deadlines and indexing is done using T-trees to efficiently store and retrieve data from main memory. The Adaptive Earliest Deadline scheduling algorithm is used to manage large volumes of transactions in real-time databases.
This document discusses real-time scheduling algorithms. It begins by defining real-time systems and their key properties of timeliness and predictability. It then discusses two common real-time scheduling algorithms: fixed-priority Rate Monotonic scheduling and dynamic-priority Earliest Deadline First scheduling. It covers how each algorithm prioritizes and orders tasks, and analyzes their schedulability and utilization bounds. It concludes by comparing the two approaches.
1. Real-time systems are systems where the correctness depends on both the logical result and the time at which the results are produced.
2. Real-time systems have performance deadlines where computations and actions must be completed. Deadlines can be time-driven or event-driven.
3. Real-time systems are classified as hard, firm, or soft depending on how critical meeting deadlines are. They are used in applications like medical equipment, automotive systems, and avionics.
An explicitly parallel program must specify concurrency and interaction between concurrent subtasks.
The former is sometimes also referred to as the control structure and the latter as the communication model.
This document provides an overview of real-time operating systems. It discusses the importance of timeliness in real-time systems and defines three types of time constraints: hard, soft, and firm. Key design issues for real-time systems are predictability and maintaining temporal consistency between the system and its environment. Real-time tasks have deadlines that must be met through scheduling approaches like static priority-driven preemptive scheduling. A major challenge is the priority inversion problem that can occur when higher priority tasks are blocked by lower priority tasks.
Parallel computing is computing architecture paradigm ., in which processing required to solve a problem is done in more than one processor parallel way.
Clock synchronization in distributed systemSunita Sahu
This document discusses several techniques for clock synchronization in distributed systems:
1. Time stamping events and messages with logical clocks to determine partial ordering without a global clock. Logical clocks assign monotonically increasing sequence numbers.
2. Clock synchronization algorithms like NTP that regularly adjust system clocks across the network to synchronize with a time server. NTP uses averaging to account for network delays.
3. Lamport's logical clocks algorithm that defines "happened before" relations and increments clocks between events to synchronize logical clocks across processes.
The document discusses temporal databases, which store information about how data changes over time. It covers several key points:
- Temporal databases allow storage of past and future states of data, unlike traditional databases which only store the current state.
- Time can be represented in terms of valid time (when facts were true in the real world) and transaction time (when facts were current in the database). Temporal databases may track one or both dimensions.
- SQL supports temporal data types like DATE, TIME, TIMESTAMP, INTERVAL and PERIOD for representing time values and durations.
- Temporal information can describe point events or durations. Relational databases incorporate time by adding timestamp attributes, while object databases
This document discusses real-time operating systems (RTOS). It begins by defining an RTOS and distinguishing it from traditional operating systems by its ability to respond to external events in a timely manner. It describes the different types of RTOS based on timing constraints. It then covers key RTOS concepts like preemptive priority scheduling, multitasking, inter-task communication, priority inheritance, and memory management. The document also discusses the Nucleus RTOS and whether RTOS will replace traditional operating systems.
Memory system, and not processor speed, is often the bottleneck for many applications.
Memory system performance is largely captured by two parameters, latency and bandwidth.
Latency is the time from the issue of a memory request to the time the data is available at the processor.
Bandwidth is the rate at which data can be pumped to the processor by the memory system.
Scheduling refers to allocating computing resources like processor time and memory to processes. In cloud computing, scheduling maps jobs to virtual machines. There are two levels of scheduling - at the host level to distribute VMs, and at the VM level to distribute tasks. Common scheduling algorithms include first-come first-served (FCFS), shortest job first (SJF), round robin, and max-min. FCFS prioritizes older jobs but has high wait times. SJF prioritizes shorter jobs but can starve longer ones. Max-min prioritizes longer jobs to optimize resource use. The choice depends on goals like throughput, latency, and fairness.
The document discusses different models for distributed systems including physical, architectural and fundamental models. It describes the physical model which captures the hardware composition and different generations of distributed systems. The architectural model specifies the components and relationships in a system. Key architectural elements discussed include communicating entities like processes and objects, communication paradigms like remote invocation and indirect communication, roles and responsibilities of entities, and their physical placement. Common architectures like client-server, layered and tiered are also summarized.
Real-time operating systems (RTOS) are specialized operating systems designed to run applications with precise timing and high reliability. An RTOS is single-tasked compared to general purpose OSs which run multiple tasks simultaneously. There are three main types of RTOS: hard, firm, and soft. An RTOS provides functions like task management, scheduling, resource allocation, and interrupt handling. Common applications of RTOS include web servers, aircraft control systems, medical devices, and industrial automation.
This document discusses different types of mainframe systems, beginning with batch systems where users submit jobs offline and jobs are run sequentially in batches. It then describes multiprogrammed systems which allow multiple jobs to reside in memory simultaneously, improving CPU utilization. Finally, it covers time-sharing systems which enable interactive use by multiple users at once through very fast switching between programs, minimizing response time. The key difference between multiprogrammed and time-sharing systems is the prioritization of maximizing CPU usage versus minimizing response time respectively.
The Scheduler.
What if two tasks have the same priority are ready?
Task object data.
System tasks.
Hello World application using RTOS.
References and Read more
The document discusses memory management in the Amoeba distributed operating system. Key points:
- Amoeba uses a simple memory model where processes are divided into segments that are stored contiguously in physical memory, with no paging or swapping. This allows for high performance but limits processes to the available physical memory.
- Segments act as addressable blocks of memory that can contain code or data and have capabilities controlling access. Processes map and unmap segments in their virtual address space as needed.
- Shared memory communications and a main memory file server are enabled through segments that can be read or written by any process with the right capabilities.
These slides are based on Distributed Transactions, which is also a type of internet transaction. Distributed Transaction is a database transaction in which two or more servers are involved.
This document summarizes a seminar on parallel computing. It defines parallel computing as performing multiple calculations simultaneously rather than consecutively. A parallel computer is described as a large collection of processing elements that can communicate and cooperate to solve problems fast. The document then discusses parallel architectures like shared memory, distributed memory, and shared distributed memory. It compares parallel computing to distributed computing and cluster computing. Finally, it discusses challenges in parallel computing like power constraints and programmability and provides examples of parallel applications like GPU processing and remote sensing.
The document discusses naming in distributed systems. It covers desirable features of naming systems like location transparency and location independence. It differentiates between human-oriented and system-oriented names. It also discusses name spaces, name servers, name resolution including recursive and iterative approaches, and name caching.
Manchester & Differential Manchester encoding schemeArunabha Saha
The two main variants of biphase encoding techniques are discussed here. Manchester and Differential Manchester encoding scheme are explained with examples. Comparison between several classes of polar encoding techniques are done along with the exposure about the advantages and disadvantages of both schemes.
This document summarizes distributed computing. It discusses the history and origins of distributed computing in the 1960s with concurrent processes communicating through message passing. It describes how distributed computing works by splitting a program into parts that run simultaneously on multiple networked computers. Examples of distributed systems include telecommunication networks, network applications, real-time process control systems, and parallel scientific computing. The advantages of distributed computing include economics, speed, reliability, and scalability while the disadvantages include complexity and network problems.
This document discusses real-time operating systems (RTOS). It defines RTOS as operating systems that are able to respond to inputs immediately within a specified time delay. It compares RTOS to general operating systems and discusses the types, characteristics, functions, and applications of RTOS. Examples of RTOS like VxWorks are provided. The key functions of an RTOS include task management, scheduling, resource allocation, and interrupt handling. RTOS are widely used in applications that require deterministic responses like avionics, medical devices, industrial automation, and more.
This document summarizes a student's research project on improving the performance of real-time distributed databases. It proposes a "user control distributed database model" to help manage overload transactions at runtime. The abstract introduces the topic and outlines the contents. The introduction provides background on distributed databases and the motivation for the student's work in developing an approach to reduce runtime errors during periods of high load. It summarizes some existing research on concurrency control in centralized databases.
This document describes a multiprocessor database architecture for real-time systems. The architecture uses a main memory database to meet strict timing constraints of tens of microseconds for control tasks. It supports direct, concurrent shared memory access and allows developers to specify real-time constraints like access times within application code. Remote database access is also supported through remote procedure calls while hiding latency.
Firebase - cloud based real time databaseGlenn Bech
This document discusses Firebase, a cloud-based real-time database. It provides an overview of Firebase and how to get started using it with Angular and JavaScript. Examples are given of how to write and read data from Firebase in real-time and how to do basic searches and sorting of data.
Clock synchronization in distributed systemSunita Sahu
This document discusses several techniques for clock synchronization in distributed systems:
1. Time stamping events and messages with logical clocks to determine partial ordering without a global clock. Logical clocks assign monotonically increasing sequence numbers.
2. Clock synchronization algorithms like NTP that regularly adjust system clocks across the network to synchronize with a time server. NTP uses averaging to account for network delays.
3. Lamport's logical clocks algorithm that defines "happened before" relations and increments clocks between events to synchronize logical clocks across processes.
The document discusses temporal databases, which store information about how data changes over time. It covers several key points:
- Temporal databases allow storage of past and future states of data, unlike traditional databases which only store the current state.
- Time can be represented in terms of valid time (when facts were true in the real world) and transaction time (when facts were current in the database). Temporal databases may track one or both dimensions.
- SQL supports temporal data types like DATE, TIME, TIMESTAMP, INTERVAL and PERIOD for representing time values and durations.
- Temporal information can describe point events or durations. Relational databases incorporate time by adding timestamp attributes, while object databases
This document discusses real-time operating systems (RTOS). It begins by defining an RTOS and distinguishing it from traditional operating systems by its ability to respond to external events in a timely manner. It describes the different types of RTOS based on timing constraints. It then covers key RTOS concepts like preemptive priority scheduling, multitasking, inter-task communication, priority inheritance, and memory management. The document also discusses the Nucleus RTOS and whether RTOS will replace traditional operating systems.
Memory system, and not processor speed, is often the bottleneck for many applications.
Memory system performance is largely captured by two parameters, latency and bandwidth.
Latency is the time from the issue of a memory request to the time the data is available at the processor.
Bandwidth is the rate at which data can be pumped to the processor by the memory system.
Scheduling refers to allocating computing resources like processor time and memory to processes. In cloud computing, scheduling maps jobs to virtual machines. There are two levels of scheduling - at the host level to distribute VMs, and at the VM level to distribute tasks. Common scheduling algorithms include first-come first-served (FCFS), shortest job first (SJF), round robin, and max-min. FCFS prioritizes older jobs but has high wait times. SJF prioritizes shorter jobs but can starve longer ones. Max-min prioritizes longer jobs to optimize resource use. The choice depends on goals like throughput, latency, and fairness.
The document discusses different models for distributed systems including physical, architectural and fundamental models. It describes the physical model which captures the hardware composition and different generations of distributed systems. The architectural model specifies the components and relationships in a system. Key architectural elements discussed include communicating entities like processes and objects, communication paradigms like remote invocation and indirect communication, roles and responsibilities of entities, and their physical placement. Common architectures like client-server, layered and tiered are also summarized.
Real-time operating systems (RTOS) are specialized operating systems designed to run applications with precise timing and high reliability. An RTOS is single-tasked compared to general purpose OSs which run multiple tasks simultaneously. There are three main types of RTOS: hard, firm, and soft. An RTOS provides functions like task management, scheduling, resource allocation, and interrupt handling. Common applications of RTOS include web servers, aircraft control systems, medical devices, and industrial automation.
This document discusses different types of mainframe systems, beginning with batch systems where users submit jobs offline and jobs are run sequentially in batches. It then describes multiprogrammed systems which allow multiple jobs to reside in memory simultaneously, improving CPU utilization. Finally, it covers time-sharing systems which enable interactive use by multiple users at once through very fast switching between programs, minimizing response time. The key difference between multiprogrammed and time-sharing systems is the prioritization of maximizing CPU usage versus minimizing response time respectively.
The Scheduler.
What if two tasks have the same priority are ready?
Task object data.
System tasks.
Hello World application using RTOS.
References and Read more
The document discusses memory management in the Amoeba distributed operating system. Key points:
- Amoeba uses a simple memory model where processes are divided into segments that are stored contiguously in physical memory, with no paging or swapping. This allows for high performance but limits processes to the available physical memory.
- Segments act as addressable blocks of memory that can contain code or data and have capabilities controlling access. Processes map and unmap segments in their virtual address space as needed.
- Shared memory communications and a main memory file server are enabled through segments that can be read or written by any process with the right capabilities.
These slides are based on Distributed Transactions, which is also a type of internet transaction. Distributed Transaction is a database transaction in which two or more servers are involved.
This document summarizes a seminar on parallel computing. It defines parallel computing as performing multiple calculations simultaneously rather than consecutively. A parallel computer is described as a large collection of processing elements that can communicate and cooperate to solve problems fast. The document then discusses parallel architectures like shared memory, distributed memory, and shared distributed memory. It compares parallel computing to distributed computing and cluster computing. Finally, it discusses challenges in parallel computing like power constraints and programmability and provides examples of parallel applications like GPU processing and remote sensing.
The document discusses naming in distributed systems. It covers desirable features of naming systems like location transparency and location independence. It differentiates between human-oriented and system-oriented names. It also discusses name spaces, name servers, name resolution including recursive and iterative approaches, and name caching.
Manchester & Differential Manchester encoding schemeArunabha Saha
The two main variants of biphase encoding techniques are discussed here. Manchester and Differential Manchester encoding scheme are explained with examples. Comparison between several classes of polar encoding techniques are done along with the exposure about the advantages and disadvantages of both schemes.
This document summarizes distributed computing. It discusses the history and origins of distributed computing in the 1960s with concurrent processes communicating through message passing. It describes how distributed computing works by splitting a program into parts that run simultaneously on multiple networked computers. Examples of distributed systems include telecommunication networks, network applications, real-time process control systems, and parallel scientific computing. The advantages of distributed computing include economics, speed, reliability, and scalability while the disadvantages include complexity and network problems.
This document discusses real-time operating systems (RTOS). It defines RTOS as operating systems that are able to respond to inputs immediately within a specified time delay. It compares RTOS to general operating systems and discusses the types, characteristics, functions, and applications of RTOS. Examples of RTOS like VxWorks are provided. The key functions of an RTOS include task management, scheduling, resource allocation, and interrupt handling. RTOS are widely used in applications that require deterministic responses like avionics, medical devices, industrial automation, and more.
This document summarizes a student's research project on improving the performance of real-time distributed databases. It proposes a "user control distributed database model" to help manage overload transactions at runtime. The abstract introduces the topic and outlines the contents. The introduction provides background on distributed databases and the motivation for the student's work in developing an approach to reduce runtime errors during periods of high load. It summarizes some existing research on concurrency control in centralized databases.
This document describes a multiprocessor database architecture for real-time systems. The architecture uses a main memory database to meet strict timing constraints of tens of microseconds for control tasks. It supports direct, concurrent shared memory access and allows developers to specify real-time constraints like access times within application code. Remote database access is also supported through remote procedure calls while hiding latency.
Firebase - cloud based real time databaseGlenn Bech
This document discusses Firebase, a cloud-based real-time database. It provides an overview of Firebase and how to get started using it with Angular and JavaScript. Examples are given of how to write and read data from Firebase in real-time and how to do basic searches and sorting of data.
The goal of the MonetDB/DataCell project is to exploit the power of Relational DBMS (RDBMS) for efficient processing of continues queries over streaming data. This presentation first identifies the essential differences between processing one-time queries and continues queries. It then presents the current archtecture of MonetDB/DataCell and some ideas of how to extend an existing RDBMS with just a handful of new components to handle continues queries.
The presentation was presented by Ying Zhang (Centrum Wiskunde & Informatica) at the PlanetData project Meeting on February 28 - March 4, 2011 in Innsbruck, Austria.
The document outlines topics to be covered in a Monday presentation on Linux fundamentals, including Unix basics, Linux installation management using CentOS, file systems, user and group management, and secure shell (SSH). Specifically, it will discuss the history and types of Unix, the layers of Unix including the kernel and shell, hardware requirements and installation methods for CentOS, commands for adding users and groups, and using SSH to remotely access machines and transfer files securely.
Virtualization allows resources like servers and operating systems to be divided into virtual environments. The document discusses virtualization and defines key concepts in Active Directory like domains, forests, global catalogs, and organizational units. It provides details on the minimum hardware requirements for Windows Server 2008 R2 and explains that Active Directory is a special database designed to handle many read operations on a large number of objects.
The CPU, or central processing unit, is the brain of the computer that performs essential functions like fetching instructions, decoding instructions, executing instructions, and writing outputs back to memory. It has a clock that synchronizes its internal logic units and determines its processing speed. CPUs can have multiple cores and require compatible software to take advantage of parallel processing. They generate a lot of heat during operation and require cooling methods like fans or liquid cooling. When selecting or upgrading a CPU, users must check the motherboard compatibility and consider factors like the core count and clock speed. Common CPU problems include overheating, improper installation, and incompatibility issues that can be troubleshooted through diagnostic checks.
Linux is a free, open-source operating system based on UNIX with a modular kernel. It uses processes, threads, virtual memory, and files systems. Device drivers allow access to hardware via the block I/O system. Interprocess communication includes signals, pipes, shared memory, and semaphores. Security features authentication via PAM and access controls permissions via user and group IDs.
What’s Firebase you might ask. Basically it’s a cloud service storing your data and feeds your web application with real-time data. It can act as a normal REST endpoint and scales with your project. Firebase will act as your backend and in most cases you won’t have to bother with backend and servers at all. Sounds interesting right?!
The presentation can be viewed in Swedish here: http://lanhed.se/firebase-presentation/
This document summarizes a meetup on Firebase hosted by Amrit Sanjeev. It introduces Firebase and its features including realtime database, user authentication, hosting, and security. It provides code examples for adding Firebase dependencies, reading and writing data to the realtime database, and enabling offline support. The document also discusses Firebase's reliability, data retention policies, and security measures.
Want to build chat applications, online games and other exciting stuff? Firebase is here to help you developing all these amazing things.
Go through these slides to learn about Firebase, and how to use it.
Linux uses a unified, hierarchical file system to organize and store data on disk partitions. It places all partitions under the root directory by mounting them at specific points. The file system is case sensitive. The Linux kernel manages hardware resources and the file system, while users interact through commands interpreted by the shell. Journaling file systems like ext3 and ReiserFS were developed to improve robustness over ext2 by logging file system changes to reduce the need for integrity checks after crashes. Ext4 further improved on this with features like larger maximum file sizes and delayed allocation.
This document discusses threads and threading in Linux. It begins with an overview of threads, their similarities and differences to processes, and how they are implemented in Linux using the Native POSIX Threads Library (NPTL). It then covers key pthreads APIs for thread management, including thread creation, termination, joining threads, attributes, cancellation, and thread-specific data. The document provides code examples to demonstrate the use of various pthreads functions.
The document discusses Linux file systems. It begins with an overview of file system architecture, including inodes, dentries, superblocks, and how data is never erased but overwritten. It then covers various local file systems like Ext2, Ext3, Ext4, ReiserFS, and XFS. Next it discusses log-structured and pseudo file systems. It also covers network file systems like NFS and CIFS. Finally it summarizes cluster, distributed, and Hadoop file systems. The document provides a technical overview of Linux file system types, structures, features and capabilities.
In-memory databases (IMDBs) store data primarily in RAM for faster access than disk-based databases. While an older concept, IMDBs have become more practical due to lower RAM costs, multi-core CPUs, and 64-bit systems allowing more memory. IMDBs have different architectures, data representations, indexing, and query processing optimized for memory versus disk. They also face challenges in providing durability without disk and scaling to very large data sizes.
Firebase is a server and database that allows developers to interact with data through API calls. It allows syncing of data across multiple applications. Developers only need to write front end code, while Firebase securely monitors the data. Firebase offers easy hosting and a tool called Firebase Forge for viewing, editing, deleting data, users, databases, and permissions in real time. The document provides resources for Firebase documentation, code examples, tutorials for using Firebase with technologies like jQuery and AngularJS, and contact details.
This deck gives an overview of Firebase. Firebase allows mobile developers to develop a quality app, grow the user base and monetize from it, through cross-platform SDKs. With Firebase Analytics at it's core, you will be able to have a clear 360 view of your app without having to juggle between multiple dashboards.
Firebase Adventures - Real time platform for your appsJuarez Filho
Firebase is a powerful platform to use on your projects, built in support for web or native apps. Features like: real time, user authentication, static hosting, mobile offline support, REST API, integrations with Zapier and much more.
Check this presentation to have a short getting start in this amazing platform and let's create extraordinary real time apps with Firebase. \o/
This document discusses real-time operating systems for embedded systems. It defines embedded systems and real-time constraints. It describes the components of an RTOS including task management, inter-task communication, dynamic memory allocation, timers, and device I/O. It discusses when an RTOS is necessary compared to a general purpose OS and provides examples of common RTOSes.
Introduction to Firebase [Google I/O Extended Bangkok 2016]Sittiphol Phanvilai
This document provides an overview of developing a mobile application with Firebase. It discusses that developing a mobile app requires functionality like authentication, databases, storage, analytics etc. It then summarizes the key services Firebase provides for mobile development including authentication, realtime database, storage, hosting, cloud messaging, remote config, testing services, crash reporting, dynamic links, invites, monetization services and analytics. The document demonstrates how to integrate and use these Firebase services in a mobile app with code snippets. It positions Firebase as providing an easy way to add functionality to an app without needing to hire backend engineers.
Transaction processing systems carry out the processes related to organizational transactions. They have properties like atomicity, consistency, isolation, and durability. There are two types - batch processing, which collects and stores data for later updating, and real-time processing, which immediately updates data. Data validation ensures the correct type and value of input data. Transaction processing systems were initially driven by business needs to computerize manual processes and have since developed with technology.
Transaction processing systems carry out seven processes related to organizational transactions. There are two types: batch processing, which collects and stores data for later updating of databases; and real-time processing, which immediately updates databases as transactions occur. Ensuring accurate data through validation is important for transaction processing. Historical significance includes computerizing manual business processes like payrolls, driven initially by punch cards and later technology.
Transaction processing systems handle transactions by carrying out seven key processes. There are two main types: batch processing, which collects and stores data for later updating of databases; and real-time processing, which immediately updates databases as transactions occur. Ensuring accurate data through validation is important for transaction processing systems.
This document discusses real-time data and real-time systems. It defines real-time systems as systems where the correctness depends on both the logical result and the time the result is produced. Real-time systems must respond to events in a fast and predictable way. The document discusses soft, firm, and hard deadlines and gives examples of hard real-time systems like nuclear reactor control. It also discusses challenges in validating that a real-time system can meet all its timing constraints and potential solutions like real-time operating systems and distributed systems.
Transaction processing systems collect, store, modify, and retrieve organizational transactions. A transaction is an event that generates or modifies data eventually stored in the system. TPSs should pass the ACID test to ensure transaction integrity and consistency. TPSs use databases, files, and data warehouses to efficiently store and retrieve transaction data. Proper backup and recovery procedures, like journaling and checkpoints, are necessary to ensure transaction processing can continue in the event of failures.
The document provides information about real-time systems and real-time operating systems (RTOS). It defines a real-time system as one where the correctness depends not only on logical results but also the time when results are delivered. An RTOS is designed to meet the strict timing constraints of real-time applications through features like multitasking, interrupt handling, and predictable scheduling. Key considerations for selecting an RTOS include its real-time capabilities, footprint, interrupt latencies, available APIs, and development tools.
There are many operating systemsReal-Time Operating SystemReal-t.pdfankitmobileshop235
There are many operating systems
Real-Time Operating System
Real-time applications usually are executed on top of a Real-time Operating System (RTOS).
Specific scheduling algorithms can be designed. When possible, static cyclic schedules are
calculated off-line.
Real-time systems are those systems in which the correctness of the system depends not only on
the logical result of computation, but also on the time at which the results are produced.
RTOS is therefore an operating system that supports real-time applications by providing
logically correct result within the deadline required. Basic Structure is similar to regular OS but,
in addition, it provides mechanisms to allow real time scheduling of tasks.
Though real-time operating systems may or may not increase the speed of execution, they can
provide much more precise and predictable timing characteristics than general-purpose OS.
A real-time system is defined as a data processing system in which the time interval required to
process and respond to inputs is so small that it controls the environment. The time taken by the
system to respond to an input and display of required updated information is termed as the
response time. So in this method, the response time is very less as compared to online
processing.
Real-time systems are used when there are rigid time requirements on the operation of a
processor or the flow of data and real-time systems can be used as a control device in a dedicated
application. A real-time operating system must have well-defined, fixed time constraints,
otherwise the system will fail. For example, Scientific experiments, medical imaging systems,
industrial control systems, weapon systems, robots, air traffic control systems, etc.
Design considerations
Designing a proper RTOS architecture needs some delicate decisions. The basic services like
process management, inter-process communication, interrupt handling, or process
synchronization have to be provided in an efficient manner making use of a very restricted
resource budget.
Multi-core architectures need special techniques for process management, memory management,
and synchronization. The upcoming Wireless Sensor Networks (WSN) generate special demands
for RTOS support leading to dedicated solutions. Another special area is given by multimedia
applications. Very high data rates have to be supported under (soft) RT constraints.
The key difference between general-computing operating systems and real-time operating
systems is the need for \" deterministic \" timing behavior in the real-time operating systems.
Formally, \"deterministic\" timing means that operating system services consume only known
and expected amounts of time. In theory, these service times could be expressed as mathematical
formulas. These formulas must be strictly algebraic and not include any random timing
components. Random elements in service times could cause random delays in application
software and could then make the application randomly .
This document discusses key enabling technologies for the Internet of Things (IoT). It covers wireless sensor networks, cloud computing, big data analytics, and embedded systems. Wireless sensor networks use small nodes to monitor environments and pass data through the network to a central location. Cloud computing provides on-demand access to applications, storage, and processing over the internet. Big data analytics involves collecting and analyzing large datasets to discover useful patterns. Embedded systems are computer systems designed for specific control tasks that are integrated with other devices.
ER Publication,
IJETR, IJMCTR,
Journals,
International Journals,
High Impact Journals,
Monthly Journal,
Good quality Journals,
Research,
Research Papers,
Research Article,
Free Journals, Open access Journals,
erpublication.org,
Engineering Journal,
Science Journals,
Information Processess and Technology HSC Transaction processing systemspezhappy99
The document traces the history of information technology from early paper-based systems and initial computerized systems used for large projects, to the current era where data is electronically stored and used for decision making, enabled by the widespread adoption of information technology across most activities. It discusses the transition from paper files to databases being ubiquitous and how data is now leveraged to identify inefficiencies and quantify the impact of decisions.
1. The document discusses how organizations can leverage data, analytics, and insights to fundamentally change and pioneer new business models.
2. It emphasizes that data analytics cannot be accomplished in a silo and must involve the entire organization. Modern cloud platforms, software methodologies, and data tools are needed.
3. Examples are provided of how various organizations have used tools like Pivotal Greenplum to gain insights from data to solve problems in areas like predictive maintenance, risk management, and national security.
Transaction processing systems carry out the processes involved in organizational transactions, such as exchanging goods or services for payment. They have certain key properties to ensure transactions are properly processed, including being atomic, consistent, isolated, and durable (ACID properties). Transaction processing can occur through either batch processing, where data is collected and updated later in batches, or real-time processing, where updating occurs immediately. Proper data validation, storage and retrieval, as well as backup procedures, are important components of transaction processing systems. Issues related to these systems include the changing nature of work, potential for bias, and ensuring data security, accuracy and integrity.
Real-time databases are optimized for predictable response times compared to general purpose databases. They often relax ACID properties like durability to meet deadlines. Transactions in real-time databases have deadlines and priorities to schedule access. Main memory databases improve speed by storing the entire database in RAM, but require disk storage for backups, logs, and larger datasets. Adaptive Earliest Deadline scheduling algorithms prioritize transactions likely to meet deadlines over those that may miss them. Disk-based scheduling like scan policies can enable soft real-time constraints for databases.
The document outlines 8 requirements for real-time stream processing systems:
1. Keep the data moving by processing messages in-stream without costly storage operations to achieve low latency.
2. Allow continuous queries to run without end so the system can monitor data streams in real-time.
3. Scale out on commodity hardware to handle high data volumes by distributing load across clusters.
4. Support declarative queries to easily express application logic and reduce programming costs.
5. Provide load management to dynamically allocate resources and handle bursts and variations in stream volumes.
6. Expose stream processing as a service so multiple applications can share common infrastructure.
7. Handle out-of-order and
Information Storage and Management notes ssmeena ssmeena7
This document provides an introduction to information storage and management. It discusses why information storage has become important in the digital age, with data being created at an ever-increasing rate. It defines what data and information are, and describes how individuals and businesses collect and analyze data. It also outlines the key elements of data centers, including applications, databases, servers, networks, and storage arrays. Finally, it discusses challenges in managing information and the concept of information lifecycles over time.
An embedded system is a combination of hardware and software that is designed to perform a dedicated function. Embedded systems monitor, respond to, or control external environments through sensors, actuators and other I/O interfaces. They must meet real-time constraints imposed by the environment. Common applications include industrial automation, medical devices, vehicles, and consumer electronics.
This document discusses transaction processing and information reporting systems. It describes how transaction processing systems capture and process data from business transactions through stages of data entry, processing, file/database updates, document generation, and inquiry processing. Real-time and batch processing are compared. Information reporting systems provide managers with periodic reports, exception reports, and on-demand reports to support decision making. Decision support systems help managers with intelligence, design, and choice activities in the decision making process.
Are all real-time distributed applications supposed to be designed the same way? Is the design for a UAV-based
application the same as that of a command-and-control application? This paper characterizes the lifecycle of data in real-time applications—from creation to consumption. The paper
covers questions that architects should ask about data management—creation, transmission, validation,
enrichment, and consumption; questions that will determine the foundation of their project.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
2. A system which its basic specifications and design
correctness arguments have ability to meet its time
constraints.
Correctness depend on both logical correctness
and timeliness of its actions
Deal with temporal data
Response must be produced within a specific
time , otherwise data become out dated
3. A database system
which uses real time
processing to handle
workloads whose state is
constantly changing.
Timely execution of
transactions with the
ACID properties.
Operations execute with
predictable response ,
and with application-acceptable
levels of
logical and temporal
consistency of data
4. Transaction :sequence of read and write
operations.
ACID properties:
Atomicity : transaction is done either completely
or not at all
Consistency :transactions are executed in a given
sequence
Isolation : actions of a transaction are not visible
to any other transactions until it is committed
Durability : transactions of a database are
permanent
5. Timing constraint associated with transactions with
deadlines.
Timing constraint types:
Hard : must execute before deadline
Firm: abort if not completed by deadline
Soft: diminished value if completed after deadline
6. As the complexity of Real Time Systems going up ,
the amount of transactions to be handled by real
time systems increases.
Conventional databases do not support timing and
temporal requirements.
Design objectives of
conventional databases
are not appropriate
for real time applications.
7. Soft real time database systems
These are databases used in non-critical real time systems
where missed transactions only degrade system quality.
E.g. databases in banking, stock market, and airline reservation
systems
Hard real time database systems
These are databases used in critical real-time systems used in
places such as nuclear power plants.
E.g. databases in early warning systems, Emergency alarm systems,
MDARTS
MDARTS stands for multiprocessor database architecture for real time systems. This is
used mainly in control applications, such as controlling machine tools and robots etc
8. Has The ACID properties
Has timing constraints
Timeliness is more important than correctness
Queries to the database should have soft or hard deadlines
Data returned must have both absolute consistency and relative
consistency
Deal with temporal data that become outdated after a certain
time
Not all data are permanent but temporal, e.g., sensor data or stock
prices
Both real-time scheduling & database technologies can be applied
to real-time data management
9. Data items reflects the state of environment.
Data from sensors. e.g. temperature,
humidity, pressure etc
Derived data. e.g. rate of reaction
Input to actuators. e.g. amount of chemical
Archival data. e.g. history of(interaction
with) environment
Static data(as in conventional data bases)
10. Real time databases have to deal with temporal
data compared to static data as the case of
traditional databases.
Unlike traditional databases , timing constraints
are associated with the different operations carried
out on real time databases.
Main objective of conventional databases is to
provide fast “average” responce.But RTDBs focus
on average transactions miss their deadlines(also
the cost incurred for late transactions).
11. More efficient way of
handling large amount
of data.
Specification of time
constraints.
Improved overall
timeliness.
Reduce development
cost.
Avoid redundant data.
12. Dealing with time
constraints and violations.
Get the maximum benefit
from results which generate
from actions completed in
time.
Minimize the damage which
occurs from actions that
delayed or not executed in
time.
13. Telecommunication systems
Routers and network management systems
Telephone switching systems
Control systems
Automatic tracking and object positioning
Engine control in automobiles
Multimedia servers for real-time streaming
E-commerce and e-business
Stock market: program stock trading
Financial services: e.g. credit card transactions
Web based data services