The Morning Paper

SEPTEMBER 10, 2019

Procella: unifying serving and analytical data at YouTube Chattopadhyay et al., Broadly, these can be categorized as: reporting and dashboarding, embedded statistics in pages, time-series monitoring, and ad-hoc analysis. Oh, and in additional to low latency, “ we require access to fresh data.” VLDB’19.

Analytics 75

Analytics Latency Cache Google 75

ScaleOut Software

JULY 14, 2021

Whether it’s health-tracking watches, long-haul trucks, or security sensors, extracting value from these devices requires streaming analytics that can quickly make sense of the telemetry and intelligently react to handle an emerging issue or capture a new opportunity.

IoT 52

IoT Azure Analytics Serverless 52

Scalegrid

JANUARY 8, 2024

The partnership between AI and cloud computing brings about transformative trends like enhanced security through intelligent threat detection, real-time analytics, personalization, and the implementation of edge computing for quicker on-site decision-making. Key among these trends is the emphasis on security and intelligent analytics.

Artificial Intelligence 147

Artificial Intelligence Cloud Scalability Analytics 147

Dynatrace

SEPTEMBER 13, 2023

This is where unified observability and Dynatrace Automations can help by leveraging causal AI and analytics to drive intelligent automation across your multicloud ecosystem. The Site Reliability Guardian also helps keep your production environment safe and secure through automated change impact analysis.

AWS 236

AWS Efficiency Azure Cloud 236

Dynatrace

DECEMBER 15, 2022

Besides the traditional system hardware, storage, routers, and software, ITOps also includes virtual components of the network and cloud infrastructure. This includes response time, accuracy, speed, throughput, uptime, CPU utilization, and latency. ITOps relies on manual correlations and dashboards for analysis. Performance.

Artificial Intelligence 191

Artificial Intelligence DevOps Hardware Virtualization 191

ScaleOut Software

JULY 21, 2020

ScaleOut StateServer® Pro Adds Analytics to In-Memory Data Grids . By transparently distributing stored objects across a cluster of servers (physical or virtual), it automatically scales performance for fast-growing workloads and maintains consistently low access latency. In-Memory Data Grids for Fast-Changing Data.

Analytics 52

Analytics Airlines Cache Scalability 52

Scalegrid

JANUARY 12, 2024

Key Takeaways A cloud workload encompasses any application or service running on a cloud infrastructure, facilitating tasks ranging from basic functions to advanced data analysis with the help of resources like databases, collaboration tools, and disaster recovery systems. Additionally.

Cloud 130

Cloud Virtualization Storage Efficiency 130

ScaleOut Software

JULY 21, 2020

ScaleOut StateServer® Pro Adds Analytics to In-Memory Data Grids . By transparently distributing stored objects across a cluster of servers (physical or virtual), it automatically scales performance for fast-growing workloads and maintains consistently low access latency. In-Memory Data Grids for Fast-Changing Data.

Analytics 52

Analytics Airlines Cache Scalability 52

Dynatrace

APRIL 26, 2022

STM generates traffic that replicates the typical path or behavior of a user on a network to measure performance for example, response times, availability, packet loss, latency, jitter, and other variables). One use case for STM is to model the behavior of a customer in the form of a flow of transactions along the buyer’s journey.

Monitoring 192

Monitoring Social Media IoT Metrics 192

Percona

JUNE 22, 2023

Number of slow queries recorded Select types, sorts, locks, and total questions against a database Command counters and handlers used by queries give an overall traffic summary Along with this, PMM also comes with Query Analytics giving much detailed information about queries getting executed.

Performance 102

Performance Monitoring Traffic Database 102

ScaleOut Software

JULY 19, 2021

Real-Time Device Tracking with In-Memory Computing Can Fill an Important Gap in Today’s Streaming Analytics Platforms. The Limitations of Today’s Streaming Analytics. How are we managing the torrent of telemetry that flows into analytics systems from these devices? The list goes on.

IoT 78

IoT Analytics Big Data Architecture 78

All Things Distributed

JUNE 7, 2017

Some applications – medical equipment, industrial machinery, and building automation are just a few – can't rely exclusively on the cloud for control, and require some form of local storage and execution. Some applications may also rely on timely decisions: when maneuvering heavy machinery, an absolute minimum of latency is critical.

AWS 118

AWS IoT Lambda Storage 118

Highly Scalable

MAY 1, 2012

Statistical analysis and mining of huge multi-terabyte data sets is a common task nowadays, especially in the areas like web analytics and Internet advertising. Analysis of such large data sets often requires powerful distributed data stores like Hadoop and heavy data processing with techniques like MapReduce. Case Study.

Analytics 191

Analytics Traffic Big Data Efficiency 191

ACM Sigarch

DECEMBER 6, 2018

smart cameras & analytics) to interactive/immersive environments and autonomous driving (e.g. Each of these categories opens up challenging problems in AI/visual algorithms, high-density computing, bandwidth/latency, distributed systems. cameras) in many usages ranging from digital security/surveillance and automated retail (e.g.

Wireless 54

Wireless IoT Analytics Architecture 54

All Things Distributed

JULY 11, 2011

Spot Instances are ideal for use cases like web and data crawling, financial analysis, grid computing, media transcoding, scientific research, and batch processing. Driving Storage Costs Down for AWS Customers. Expanding the Cloud - The AWS Storage Gateway. Driving down the cost of Big-Data analytics. At werner.ly

AWS 85

AWS Storage Cloud Big Data 85

Alex Russell

MARCH 6, 2021

A then-representative $200USD device had 4-8 slow (in-order, low-cache) cores, ~2GiB of RAM, and relatively slow MLC NAND flash storage. Sadly, data on latency is harder to get, even from Google's perch, so progress there is somewhat more difficult to judge. The Moto G4 , for example.

Performance 124

Performance Network Cache Metrics 124

The Netflix TechBlog

SEPTEMBER 2, 2020

Edgar helps Netflix teams troubleshoot distributed systems efficiently with the help of a summarized presentation of request tracing, logs, analysis, and metadata. With request tracing and additional data from logs, events, metadata, and analysis, Edgar is able to show the flow of a request through our distributed system?

Latency 296

Latency Transportation Engineering Traffic 296

ScaleOut Software

MAY 15, 2014

For example, the IMDG must be able to efficiently create millions of objects in each server to make use of its huge storage capacity. Likewise, object access paths must be heavily multi-threaded and avoid lock contention to minimize access latency and maximize throughput.

Cache 40

Cache Servers Network Design 40

Smashing Magazine

JANUARY 11, 2021

This guide has been kindly supported by our friends at LogRocket , a service that combines frontend performance monitoring , session replay, and product analytics to help you build better customer experiences. Study common complaints coming into customer service and sales team, study analytics for high bounce rates and conversion drops.

Performance 145

Performance Cache Media Metrics 145

Smashing Magazine

JANUARY 6, 2020

Study common complaints coming into customer service and sales team, study analytics for high bounce rates and conversion drops. Run performance experiments and measure outcomes — both on mobile and on desktop (for example, with Google Analytics ). Treo Sites provides competitive analysis based on real-world data.

Performance 145

Performance Cache Network Metrics 145

All Things Distributed

JUNE 21, 2018

Use cases such as gaming, ad tech, and IoT lend themselves particularly well to the key-value data model where the access patterns require low-latency Gets/Puts for known key values. The purpose of DynamoDB is to provide consistent single-digit millisecond latency for any scale of workloads.

Database 167

Database AWS Games Latency 167

Alex Russell

JULY 14, 2021

Here, native apps are doing work related to their core function; storage and tracking of user data are squarely within the four corners of the app's natural responsibilities. Such enforcement is not challenging for Google, given its existing binary analysis infrastructure. How can that be? Apple's right to worry about engine security.

Google 121

Google Mobile Engineering Internet 121

All Things Distributed

NOVEMBER 15, 2010

For example, the most fundamental abstraction trade-off has always been latency versus throughput. Modern CPUs strongly favor lower latency of operations with clock cycles in the nanoseconds and we have built general purpose software architectures that can exploit these low latencies very well. Where to go from here?

AWS 136

AWS Latency Programming Architecture 136