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The Economics of Scaling Cassandra - By Alex Bordei, Techie Product Manager at Bigstep This presentation was made during the "Cassandra Summit 2014" Event, in London. We benchmarked Cassandra on a number of configurations and we show what's the scaling profile. We test Cassandra on Docker as well as Cassandra's In-memory feature. Follow Alex on Twitter: @alexandrubordei Bigstep on Twitter: @BigStepInc If you have any questions, let us know at hello@bigstep.com and we'll do our best to answer. Stay informed: http://blog.bigstep.com/

OSNoise Tracer: Who Is Stealing My CPU Time?

In the context of high-performance computing (HPC), the Operating System Noise (osnoise) refers to the interference experienced by an application due to activities inside the operating system. In the context of Linux, NMIs, IRQs, softirqs, and any other system thread can cause noise to the application. Moreover, hardware-related jobs can also cause noise, for example, via SMIs. HPC users and developers that care about every microsecond stolen by the OS need not only a precise way to measure the osnoise but mainly to figure out who is stealing cpu time so that they can pursue the perfect tune of the system. These users and developers are the inspiration of Linux's osnoise tracer. The osnoise tracer runs an in-kernel loop measuring how much time is available. It does it with preemption, softirq and IRQs enabled, thus allowing all the sources of osnoise during its execution. The osnoise tracer takes note of the entry and exit point of any source of interferences. When the noise happens without any interference from the operating system level, the tracer can safely point to a hardware-related noise. In this way, osnoise can account for any source of interference. The osnoise tracer also adds new kernel tracepoints that auxiliaries the user to point to the culprits of the noise in a precise and intuitive way. At the end of a period, the osnoise tracer prints the sum of all noise, the max single noise, the percentage of CPU available for the thread, and the counters for the noise sources, serving as a benchmark tool.

Virtual training Intro to Kapacitor

Memory, Big Data, NoSQL and Virtualization

University of Moratuwa, Katubedda, Sri Lanka

In-memory processing has started to become the norm in large scale data handling. This is aclose to the metal analysis of highly important but often neglected aspects of memory accesstimes and how it impacts big data and NoSQL technologies.We cover aspects such as the TLB, the Transparent Huge Pages, the QPI Link, Hyperthreading and the impact of virtualization on high-memory footprint applications. We present benchmarks of various technologies ranging from Cloudera’s Impala to Couchbase and how they are impacted by the underlying hardware.The key takeaway is a better understanding of how to size a cluster, how to choose a cloud provider and an instance type for big data and NoSQL workloads and why not every core or GB of RAM is created equal.

Deswik Software Suite v5.0: Pseudoflow pit optimization algorithm

Deswik

Pit Optimization was introduced to Deswik CAD in v4.2. Up until v5.0, it has used the Lerchs-Grossman algorithm. V5.0 comes with an alternative pit optimization algorithm, called Pseudoflow. It provides the same resultant pit shells as Lerchs-Grossman, but faster. The Pseudoflow Pit Optimization Algorithm is more efficient at removing “redundant” options, and therefore reduces processing requirements.

3 Ways To UP Your OPs Game

David Tesar

The document discusses DevOps concepts including development, build, test, production and pre-production environments. It shows different ecosystem models including Microsoft, heterogeneous, and optimized resources. It provides examples of configuration of repeatable resources like virtual machines, SQL databases and websites into resource groups for deployment. The last sections discuss benefits of DevOps like accelerated delivery, optimized resources, faster issue detection and remediation.

Stor4NFV: Exploration of Cloud native Storage in OPNFV - Ren Qiaowei, Wang Hui

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eGWM

John Green

- The document describes a new imaging workflow called eGWM that uses a one-way wave equation algorithm to output pre-stack image gathers from wave equation migration. - eGWM pre-stack gathers can be used for AVO and pre-stack inversion analysis to robustly understand amplitudes and reduce drilling risk in complex areas like below salt or basalt. - eGWM fully preserves amplitudes through multi-path and multi-arrival imaging, making quantitative amplitude analysis achievable in complex areas.

Svelte as a Reactive Web Framework

Object Compaction in Cloud for High Yield

In file systems, large sequential writes are more beneficial than small random writes, and hence many storage systems implement a log structured file system. In the same way, the cloud favors large objects more than small objects. Cloud providers place throttling limits on PUTs and GETs, and so it takes significantly longer time to upload a bunch of small objects than a large object of the aggregate size. Moreover, there are per-PUT calls associated with uploading smaller objects. In Netflix, a lot of media assets and their relevant metadata is generated and pushed to cloud. We would like to propose a strategy to compact these small objects into larger blobs before uploading them to Cloud. We will discuss how to select relevant smaller objects, and manage the indexing of these objects within the blob along with modification in reads, overwrites and deletes. Finally, we would showcase the potential impact of such a strategy on Netflix assets in terms of cost and performance.

Virtual training Intro to the Tick stack and InfluxEnterprise

20170926 cern cloud v4

Tim Bell from CERN gave a presentation on "Understanding the Universe through Clouds" at OpenStack UK Days on September 26th, 2017. Some key points: - CERN operates one of the world's largest private OpenStack clouds to support the Large Hadron Collider, with over 8000 hypervisors and 33,000 VMs. - The Worldwide LHC Computing Grid distributes and analyzes LHC data across 600 PB of storage and 750k CPU cores at 170 sites in 42 countries. - CERN has been an early adopter of OpenStack technologies like Nova, Glance, Horizon, and Neutron since 2011 and contributes code back to the community. - New services like Mag

20161025 OpenStack at CERN Barcelona

CERN operates the largest machine on Earth, the Large Hadron Collider (LHC), which produces over 1 billion collisions per second and records over 0.5 petabytes of data per day. CERN relies heavily on OpenStack, with over 190,000 CPU cores and 5,000 VMs under OpenStack management, accounting for over 90% of CERN's computing resources. CERN plans to add over 100,000 more CPU cores in the next 6 months and explores using public clouds and containers to help process the massive amount of data generated by the LHC.

K8S Certifications - Exam Cram

Michael Man

Transition to high-speed WORDPRESS using KUSANAGI

Sumito Tsukada

The document discusses transitioning a WordPress environment to a high-speed configuration using the KUSANAGI image. It summarizes the previous and current environments, stress test results showing improved performance, and issues encountered with certain plugins that were addressed. In conclusion, KUSANAGI can help speed up WordPress but care needs to be taken with plugins, and sites should be checked before enabling caching.

Nova, Folsom presentation, Compute PTL

laurabeckcahoon

Vishvananda Ishaya presented the state of the OpenStack Compute (Nova) project. Nova is now stable and operational in real deployments. For the upcoming Folsom release, plans include breaking out block storage and network into separate projects/repositories while maintaining compatibility. Other goals are improving AWS compatibility, command line interfaces, operational support, security, stability, scaling and careful migration between releases.

Approaching hyperconvergedopenstack

Ikuo Kumagai

The Basic Introduction of Open vSwitch

Te-Yen Liu

This document provides an overview of Open vSwitch, including what it is, its main components, features, and how it can be used to build virtual network topologies. Open vSwitch is a software-defined networking switch that can be used to create virtual networks and handle network traffic between virtual machines and tunnels. It uses a distributed database, ovsdb-server, and a userspace daemon, ovs-vswitchd, to implement features like virtual switching, tunneling protocols, and OpenFlow support. Examples are provided for using Open vSwitch with KVM virtual machines and GRE tunnels to create virtual network topologies.

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