MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. Many real world tasks are expressible in this model, as shown in the paper.

Programs written in this functional style are automatically parallelized and executed on a large cluster of commodity machines. The run-time system takes care of the details of partitioning the input data, scheduling the program's execution across a set of machines, handling machine failures, and managing the required inter-machine communication. This allows programmers without any experience with parallel and distributed systems to easily utilize the resources of a large distributed system.

Our implementation of MapReduce runs on a large cluster of commodity machines and is highly scalable: a typical MapReduce computation processes many terabytes of data on thousands of machines. Programmers find the system easy to use: hundreds of MapReduce programs have been implemented and upwards of one thousand MapReduce jobs are executed on Google's clusters every day.

/pdf/mapreduce-simplified-data-processing-on-large-clusters-16emdgg5h5.pdf

MapReduce: simplified data processing on large clusters

MapReduce is a programming model and an associated implementation for processing and generating large datasets that is amenable to a broad variety of real-world tasks. Users specify the computation in terms of a map and a reduce function, and the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks. Programmers find the system easy to use: more than ten thousand distinct MapReduce programs have been implemented internally at Google over the past four years, and an average of one hundred thousand MapReduce jobs are executed on Google's clusters every day, processing a total of more than twenty petabytes of data per day.

/pdf/mapreduce-simplified-data-processing-on-large-clusters-1zth35tzla.pdf

世界経済・社会統計 = World development indicators

https://minerva-access.unimelb.edu.au/bitstream/handle/11343/197439/1207.0203v1.pdf

Internet of Things (IoT): A vision, architectural elements, and future directions

https://link.springer.com/content/pdf/10.2307%2F3343168.pdf

Development as Freedom

We present nesC, a programming language for networked embedded systems that represent a new design space for application developers. An example of a networked embedded system is a sensor network, which consists of (potentially) thousands of tiny, low-power "motes," each of which execute concurrent, reactive programs that must operate with severe memory and power constraints.nesC's contribution is to support the special needs of this domain by exposing a programming model that incorporates event-driven execution, a flexible concurrency model, and component-oriented application design. Restrictions on the programming model allow the nesC compiler to perform whole-program analyses, including data-race detection (which improves reliability) and aggressive function inlining (which reduces resource consumption).nesC has been used to implement TinyOS, a small operating system for sensor networks, as well as several significant sensor applications. nesC and TinyOS have been adopted by a large number of sensor network research groups, and our experience and evaluation of the language shows that it is effective at supporting the complex, concurrent programming style demanded by this new class of deeply networked systems.

/pdf/the-nesc-language-a-holistic-approach-to-networked-embedded-3nbdmnogzq.pdf

The nesC language: a holistic approach to networked embedded systems

We present TinyOS, a flexible, application-specific operating system for sensor networks, which form a core component of ambient intelligence systems. Sensor networks consist of (potentially) thousands of tiny, low-power nodes, each of which execute concurrent, reactive programs that must operate with severe memory and power constraints. The sensor network challenges of limited resources, event-centric concurrent applications, and low-power operation drive the design of TinyOS. Our solution combines flexible, fine-grain components with an execution model that supports complex yet safe concurrent operations. TinyOS meets these challenges well and has become the platform of choice for sensor network research; it is in use by over a hundred groups worldwide, and supports a broad range of applications and research topics. We provide a qualitative and quantitative evaluation of the system, showing that it supports complex, concurrent programs with very low memory requirements (many applications fit within 16KB of memory, and the core OS is 400 bytes) and efficient, low-power operation.We present our experiences with TinyOS as a platform for sensor network innovation and applications.

/pdf/tinyos-an-operating-system-for-sensor-networks-1d6c7am070.pdf

TinyOS: An Operating System for Sensor Networks

We propose a new design for highly concurrent Internet services, which we call the staged event-driven architecture (SEDA). SEDA is intended to support massive concurrency demands and simplify the construction of well-conditioned services. In SEDA, applications consist of a network of event-driven stages connected by explicit queues. This architecture allows services to be well-conditioned to load, preventing resources from being overcommitted when demand exceeds service capacity. SEDA makes use of a set of dynamic resource controllers to keep stages within their operating regime despite large fluctuations in load. We describe several control mechanisms for automatic tuning and load conditioning, including thread pool sizing, event batching, and adaptive load shedding. We present the SEDA design and an implementation of an Internet services platform based on this architecture. We evaluate the use of SEDA through two applications: a high-performance HTTP server and a packet router for the Gnutella peer-to-peer file sharing network. These results show that SEDA applications exhibit higher performance than traditional service designs, and are robust to huge variations in load.

/pdf/seda-an-architecture-for-well-conditioned-scalable-internet-25bi8q5ubv.pdf

SEDA: an architecture for well-conditioned, scalable internet services

Traditional problem determination techniques rely on static dependency models that are difficult to generate accurately in today's large, distributed, and dynamic application environments such as e-commerce systems. We present a dynamic analysis methodology that automates problem determination in these environments by 1) coarse-grained tagging of numerous real client requests as they travel through the system and 2) using data mining techniques to correlate the believed failures and successes of these requests to determine which components are most likely to be at fault. To validate our methodology, we have implemented Pinpoint, a framework for root cause analysis on the J2EE platform that requires no knowledge of the application components. Pinpoint consists of three parts: a communications layer that traces client requests, a failure detector that uses traffic-sniffing and middleware instrumentation, and a data analysis engine. We evaluate Pinpoint by injecting faults into various application components and show that Pinpoint identifies the faulty components with high accuracy and produces few false-positives.

/pdf/pinpoint-problem-determination-in-large-dynamic-internet-1zglcww7fn.pdf

Pinpoint: problem determination in large, dynamic Internet services

Current distributed systems, even the ones that work, tend to be very fragile: they are hard to keep up, hard to manage, hard to grow, hard to evolve, and hard to program. In this talk, I look at several issues in an attempt to clean up the way we think about these systems. These issues include the fault model, high availability, graceful degradation, data consistency, evolution, composition, and autonomy.These are not (yet) provable principles, but merely ways to think about the issues that simplify design in practice. They draw on experience at Berkeley and with giant-scale systems built at Inktomi, including the system that handles 50% of all web searches.

Eric Brewer

Papers

The nesC language: a holistic approach to networked embedded systems

TinyOS: An Operating System for Sensor Networks

SEDA: an architecture for well-conditioned, scalable internet services

Pinpoint: problem determination in large, dynamic Internet services

Towards robust distributed systems (abstract)