TS-Linux Introduction

Applications needing guaranteed response times, like audio/video players and games.

Applications focused on high processing speed, like databases and scientific computing.

Difficulties in OS scheduling due to the need to prioritize both real-time and general tasks.

Ensuring real-time applications get needed resources, while preventing background interference.

Extends Linux to support both real-time and throughput-oriented applications.

Delay in response time for an application.

Ensuring an application consistently meets its required response time.

The reduced speed of general-purpose apps when real-time apps need resources.

A method of scheduling tasks in an operating system to prioritize latency-sensitive tasks and ensure they meet deadlines.

The ability of the operating system to interrupt a lower-priority task to handle a higher-priority one.

Rules for allocating resources (like CPU time) to ensure real-time tasks get the resources they need and other tasks aren't impacted.

Applications focused on completing many tasks quickly, not necessarily responding instantly to each one.

Delay between an event and the OS recognizing it due to the timer's granularity.

Delay between an interrupt and the OS reacting due to processing other tasks.

Delay in the OS scheduling the application to handle an event after the interrupt.

Total time it takes for an application to respond to an event.

Timers that generate interrupts at fixed intervals.

Timers that trigger a single interrupt at a specific time.

Timers that don't generate interrupts; the OS checks for events periodically.

A timer system that combines periodic and one-shot timming with low overhead.

A version of Linux designed to handle time-sensitive tasks adequately.

Parts of operating system code that are modifying critical data structures & may disable interrupts temporarily.

A configurable knob in firm timers that determines a window of time after the scheduled timer expiration, allowing for kernel entry before the interrupt.

An interrupt generated by a timer when it expires.

A kernel data structure that stores tasks sorted by expiration time.

Advanced Programmable Interrupt Controller hardware in modern CPUs.

Application request to the kernel.

When the kernel takes control from the user space.

Code associated with a task that needs to be executed.

A single event that occurs once at a specific time.

An interrupt caused by an external event, not a timer.

Schedulable entity in the Linux kernel (like a process) with associated timers.

TS-Linux guarantees quality of service for real-time applications by addressing and fixing the sources of latency: timer latency, kernel preemption latency, and scheduling latency.

TS-Linux proves that real-time performance can be achieved on standard operating systems like Linux without requiring specialized hardware or a complete OS replacement.

TS-Linux uses admission control to manage CPU resource allocation for real-time and throughput tasks.

TS-Linux uses proportional period scheduling (PPS) to ensure time-sensitive tasks don't hog the CPU and throughput tasks still get a fair share.

These applications use real-time sensor data to understand and react to environmental changes, like traffic monitoring and emergency response.

These applications face challenges like computational intensity, real-time constraints, scalability, and resource requirements due to their sensor-based nature and real-time data processing requirements.

Middleware acts as a bridge between commodity operating systems and complex multimedia applications, providing essential services and abstractions for real-time, sensor-based applications.

Middleware should provide advanced APIs beyond simple socket communication to simplify the development of complex multimedia applications.

Middleware needs to provide real-time support to meet the timing requirements of multimedia applications.

Middleware needs to efficiently manage computational resources across distributed systems, balancing load and optimizing performance.

Middleware should seamlessly integrate various types of sensors and handle heterogeneous data streams.

Middleware should support control loops for dynamic sensor reconfiguration, enabling actuators and feedback systems.

The goal of middleware development is to create solutions that facilitate the creation and deployment of novel multimedia applications.

The delay caused when the kernel cannot immediately handle an interrupt because it's busy with another critical task.

Specific locations in kernel code where the kernel can safely pause its current task to check for and handle urgent events.

A segment of code that involves manipulating shared data structures, requiring exclusive access to prevent data corruption.

A technique that combines explicit preemption points with the ability to preempt the kernel outside critical sections, reducing the time the kernel is unavailable for interrupts.

A scheduling technique that allocates CPU time to applications based on their requested proportion within a defined time period.

A situation where a low-priority task holds a resource needed by a higher-priority task, leading to a delay for the high-priority task.

A mechanism where a low-priority task temporarily inherits the priority of a higher-priority task when it's using a shared resource, preventing delays.

TS-Linux employs a hybrid timer design that combines the strengths of one-shot, soft, and periodic timers, minimizing overhead while achieving precise timing.

TS-Linux uses a 'lock-breaking' technique to allow preemption of the kernel outside critical sections, minimizing the time the kernel is unavailable.

TS-Linux uses Proportional Period Scheduling, allowing applications to request a specific proportion of CPU time within a time interval, guaranteeing their performance.

TS-Linux utilizes Priority Inheritance, where a low-priority task temporarily inherits the priority of a higher-priority task during resource access, ensuring the high-priority task isn't delayed.

TS-Linux reserves a portion of CPU time for non-real-time tasks, ensuring that even when real-time applications are running, general-purpose tasks can still make progress.

A real-time application used to detect anomalies in an airport environment, like unusual behavior, using camera networks.

The difficulties that arise when monitoring a vast number of camera feeds, such as data overload and the limitations of human monitoring.

The potential for network bandwidth and processing capabilities to be overwhelmed by the massive amount of continuous data from surveillance cameras.

The process of filtering and processing data locally at the source to reduce unnecessary data transmission, only sending essential information to central systems.

The challenge of using human operators to effectively monitor a large number of surveillance camera feeds, due to cognitive limitations and the risk of error.

Mistakes made by surveillance systems, either incorrectly identifying normal events as anomalies (false positives) or failing to detect actual anomalies (false negatives).

An important consideration when developing large-scale, real-time applications, focusing on simplifying the development process for domain experts by abstracting away complex infrastructure details.

The ability to move processing tasks from sensor devices (edge) to centralized servers (cloud) as needed, without impacting the application's performance.

Maintaining the correct sequence of events and their relationships as they occur across distributed systems, ensuring accurate analysis and timely responses.

The ability of applications to handle data streams promptly, minimizing delays to enable timely decision-making and responses.

Using past data to gain context and enhance decision-making in real-time applications, identifying patterns and recurring issues.

The essential tools and frameworks needed to develop large-scale, real-time applications, supporting features like handling time-sensitive and historical data, distributed system management, and simplifying tasks for domain experts.

A sequence of steps used to analyze video streams in real-time, involving detection, tracking, recognition, and alarm raising.

A technology that records and manages real-time streams of data over time, allowing for analysis, correlation, and future retrieval.

A programming model designed for real-time stream analysis applications, offering simplicity and efficiency in handling time-sequenced data across distributed systems.

Data structures within PTS that are organized and indexed based on time, enabling temporal correlations and causality analysis in distributed environments.

PTS automatically handles live data retention, garbage collection, and persistence without developer intervention, balancing resource use with application needs.

PTS seamlessly supports retrieving both live and historical data, providing automatic archival for backend storage based on user-defined policies.

PTS offers a streamlined development experience for experts, with simple abstractions, minimal setup, and focus on high-level semantics in real-time applications.

PTS tackles critical challenges in real-time and multimedia systems, such as managing live and historic data, ensuring seamless temporal causality, and optimizing resources for real-time guarantees.

Similar to how MapReduce revolutionized big data processing, PTS simplifies real-time stream analysis applications, providing a clear and intuitive programming model.

PTS presents a powerful programming model tailored for building sophisticated real-time situation awareness applications, addressing key challenges in handling time-sensitive data.

A collection of multiple data streams grouped together under a single label, allowing computations to retrieve temporally correlated data across different modalities.

The designated stream within a stream group that other streams depend on, often representing the primary data source.

Streams in a stream group that depend on the anchor stream for their time reference, ensuring temporal correlation.

A function that retrieves data from all streams within a stream group, ensuring the items are correspondingly timestamped.

Named entities that act as communication channels in a distributed system, allowing threads to share data.

A primitive to place data into a channel, associating the data with a timestamp.

A primitive to retrieve data from a channel, allowing retrieval of data within specified time ranges.

A system that manages all the low-level operations related to channels, including data storage, retrieval, and synchronization.

Time is treated as a fundamental element in PTS, allowing applications to specify and query data based on time, making it essential for time-sensitive applications.

A traditional approach to video analysis where processing steps occur in a specific order, one after the other.

Breaking down a sequential video pipeline into smaller components that run concurrently on different machines, using PTS channels for communication and synchronization.

Named channels introduced between computations in a distributed pipeline to hold temporal data, ensuring data flow and synchronization.

Channels hold data with associated timestamps, preserving the temporal order, allowing computations to maintain the chronology of events.

Threads can discover and connect to channels dynamically, making it simple for components to share data effortlessly.

Each component interacts with channels independently, enabling easier scaling and maintenance, as components can be modified or added without disrupting the entire system.

Applications determine which data to store persistently, giving them flexibility in managing data lifespan and storage resources.

The top layer of the PTS architecture responsible for managing live data, reacting to new items, and maintaining a snapshot of the latest data.

The bottom layer of the PTS architecture responsible for archiving and retrieving data that needs to be persisted.

The process of identifying and removing outdated data from the Live Channel Layer to free up resources, based on the channel's retention policy.

Signals generated by the Live Channel Layer to indicate when data should be persisted.

Customizable functions that process data before persistence, allowing for compression, encryption, or summarization.

The way data moves through the PTS architecture, from live data creation, through persistence, and finally to retrieval by consumers.

Components that add new data to the PTS system.

Components that retrieve data from the PTS system.

The background processes that handle get and put operations from producers and consumers.

Applications can request data from specific time ranges by specifying the desired time bounds.

Developers can customize channel properties like retention policy, persistence, and pickling.

The same get and put operations are used for accessing both live and historical data.

PTS provides a smooth integration of live and historical data, allowing applications to combine real-time insights with historical context.

Processing video data from many cameras in real-time to detect events and understand situations, but it's difficult because of the huge amount of data and the complexity of the algorithms.

A system designed to make building large-scale, real-time applications easier by handling time-sensitive data from multiple sources, like cameras in a city.

The challenges include handling huge amounts of data from cameras, the need for fast processing power, and tracking objects across multiple cameras.

PTS makes it easier for developers to create applications that process time-sensitive data from many sources, like cameras, without worrying about complex distributed systems.

Threads are like individual tasks that process data within a PTS application. They can produce data or consume data from channels.

Channels are pathways for transferring data between threads. They store time-sequenced data objects, acting as a communication hub for workers.

It allows a thread to add a data item, along with its timestamp, to a channel, making sure that the data is kept in the correct order of events.

Timestamps are used to keep track of the order of events and ensure the causal relationships between different data items are maintained.

It retrieves data items from a channel based on their timestamps. You specify a time range, and the system retrieves items within that range.

It means that the order of events is preserved as data is processed. The output of a thread reflects the time order of the input data.

A video analytics application uses PTS to process data from multiple cameras, with threads detecting features or events in each video stream and correlating them based on timestamps.

It provides high-level programming abstractions, hiding the complexity of distributed systems, making it easier for experts to focus on the algorithm itself.

Middleware like PTS helps connect domain-specific algorithms with the underlying distributed systems, enabling the development of scalable real-time applications.

They can focus on developing algorithms and improving the application's effectiveness without worrying about the complexity of the distributed system.