Google's Borg system is a cluster manager that runs hundreds of thousands of jobs, from many thousands of different applications, across a number of clusters each with up to tens of thousands of machines. It achieves high utilization by combining admission control, efficient task-packing, over-commitment, and machine sharing with process-level performance isolation. It supports high-availability applications with runtime features that minimize fault-recovery time, and scheduling policies that reduce the probability of correlated failures. Borg simplifies life for its users by offering a declarative job specification language, name service integration, real-time job monitoring, and tools to analyze and simulate system behavior.
We present a summary of the Borg system architecture and features, important design decisions, a quantitative analysis of some of its policy decisions, and a qualitative examination of lessons learned from a decade of operational experience with it.
With the continuous delivery model of the Orion cloud IDE you may have already tried many of the awesome new features in the latest release. The official release of Orion 9.0provides an excellent time to review and promote what the project has been up to.
Ethereum is a community-driven project aiming to decentralize the internet and return it to its democratic roots. It is a platform for building and running applications which do not need to rely on trust and cannot be controlled by any central authority.
Finding a path to enlightenment in Programming Language Theory can be a tough one, particularly for programming pracitioners who didn’t learn it at school. This resource is here to help. Please feel free to ping me or send pull requests if you have ideas for improvement.
In-memory key-value stores play a critical role in data processing to provide high throughput and low latency data accesses. In-memory key-value stores have several unique properties that include (1) data intensive operations demanding high memory bandwidth for fast data accesses, (2) high data parallelism and simple computing operations demanding many slim parallel computing units, and (3) a large working set. As data volume continues to increase, our experiments show that conventional and general-purpose multicore systems are increasingly mismatched to the special properties of key-value stores because they do not provide massive data parallelism and high memory bandwidth; the powerful but the limited number of computing cores do not satisfy the demand of the unique data processing task; and the cache hierarchy may not well benefit to the large working set. In this paper, we make a strong case for GPUs to serve as special-purpose devices to greatly accelerate the operations of in-memory key-value stores. Specifically, we present the design and implementation of Mega-KV, a GPU-based in-memory key-value store system that achieves high performance and high throughput. Effectively utilizing the high memory bandwidth and latency hiding capability of GPUs, Mega-KV provides fast data accesses and significantly boosts overall performance. Running on a commodity PC installed with two CPUs and two GPUs, Mega-KV can process up to 160+ million key-value operations per second, which is 1.4-2.8 times as fast as the state-of-the-art key-value store system on a conventional CPU-based platform.
Over the last few years we have seen the rise of a new type of databases, known as NoSQL databases, that are challenging the dominance of relational databases. Relational databases have dominated the software industry for a long time providing mechanisms to store data persistently, concurrency control, transactions, mostly standard interfaces and mechanisms to integrate application data, reporting. The dominance of relational databases, however, is cracking.
A core focus of Microsoft Edge is delivering greater interoperability across browsers, so the web just works for users on any device and on any platform. The new Test Drive supports this goal by providing web developers with feature demos that demonstrate how to build interoperable code based on new web platform features. To make this process as easy as possible, we are excited to open-source all our feature demos on GitHub, so anyone can learn and reuse this code in any website (or contribute fixes!).
In the past, the Spark UI has been instrumental in helping users debug their applications. In the latest Spark 1.4 release, we are happy to announce that the data visualization wave has found its way to the Spark UI. The new visualization additions in this release includes three main components:
Timeline view of Spark eventsExecution DAGVisualization of Spark Streaming statistics
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