Redefining End-to-End Monitoring: The Foundation - High-Performance Architecture

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Redefining End-to-End Monitoring with RTView Enterprise Monitortrade The Foundation ndash High-Performance Architecture

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E-Commerce App Billing App

Trading App Customer Service App

Evolution of High-Performance Applications

bull Resource pooling and shared services

bull Multiple tiers multiple vendors

bull Exponential Growth In Data Volumes and users

bull 247 operating windows

bull Real-time performance requirements

bull Global distributed data centers operations

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Silo Silo Silo Silo

Where Traditional Monitoring Failshellip

Normalization Correlation Analysis Prioritization Prediction (Pro)Action

Hosts DBMS App

Servers VMs

Manufacturing App

E-Commerce App Trading App

Manufacturing App Traditional Monitoring

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Real End-to-End Monitoring Requirements Across Tiers Across Vendors Enterprise-wide

bull High-Performance Architecture ndash Distributed cache-based architecture to minimize latency

bull Integrated Dynamic Service Model ndash Auto-generated user-validated

bull Sophisticated cross-correlations and visualizations ndash Both application service-centric and infrastructure-centric

views

ndash Visibility into the data that matter by role criticality severity

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High-Performance - Why the Business Cares

bull Health Monitoring ndash Are my critical services healthy If not why

bull Scalability Analysis ndash Is the system scaling If not why

bull Bottleneck Analysis ndash How do I continually improve

bull Event Analysis ndash What happened When Why --- again

bull Business alignment ndash Are my resources allocated to meet Business needs

bull Performance Analysis ndash Am I trending in the right direction

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RTView EM Architecture

Display Server

Config Server

Alert Server

Rules Engine

Cache Map

Dev QA Ops

Solution Packages (Distributed Data Servers)

EM Server Layer

Metadata Layer

Data Server Layer

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Distributed Cache-based Data Server Layer

bull Flexible architecture ndash tuned to your needs

ndash Configurable collection and retention granularity

bull Distributed storage optimized for large data sets

ndash No Central Bottleneck

ndash Metrics cached close to data sourcendash no lsquodroppedrsquo data

bull Distributed rules processing = low data transfer

overhead (Deltas only)

Solution Packages (Distributed Data Servers)

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RTView Cache

Why is Cache Capability Essential bull In-Memory Caching for Fast Data Capture

and Retrieval bull Only aged data written out to RDBMS bull Cache map unlocks performance and scalability

RDBMS

Fast

Slow

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Cache Architecture - Advanced Indexing

bull Designed for efficient handling of real-time multidimensional data ndash optimized for the insertionextraction of multi-dimensional time-sequenced data ndash Column-oriented to leverage specialized insert functionality to ensure extremely

fast performance bull Maintains the current indexed data set for asynchronous in-memory access

ndash crucial for analyzing rolling averages across application server sessions

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Solution Package Architecture Thresholds and Alert Logic Distributed

bull High-overhead operations performed in a distributed manner close to the data source ndash Data Collection amp Aggregation ndash Data CorrelationAnalysis ndash Rules ndash Historization (archiving)

bull Only changedrelevant data are transmitted back to Rules Engine and Display Server

bull Cache mapping allows for tracking multiple caches real time update

bull REST API allows for flexible data access to both cache and historian (DBMS)

RTView Cache

RDBMS

Solution Package

Logic

Data Source

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RTView Low Overhead = No Instrumentation

Other solutions include code injection and agents as a means of gaining visibility into execution details

- Both approaches incur unacceptably high overhead

- Both introduce instability into your code

- Inability to reduce the noise ndash lacking context and ability to prioritize alerts

Organizations require a low-cost low-overhead

architecture designed around low-latency application characteristics

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Metadata Layer Surfaces the Data that Matter

bull Metadata populates dynamic cache map and enables distributed architecture

ndash Complexcomposite rules aggregated and applied centrally

ndash Intelligent Partitioning for optimized data access

ndash Cache API enables non-disruptive scalability

Rules Engine

Cache Map

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Real-time Integration and Display

bull Dynamic service model population bull Metadata-driven correlation of key metrics bull Role-based selection of relevant data bull High-performance UI (HTML5Ajax)

ndash Trend chart processing optimized for real-time data visualization

Display Server

Config Server

Alert Server

Dev QA Ops

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Real End-to-End Monitoring Requirements Across Tiers Across Vendors Enterprise-wide

bull High-Performance Architecture ndash Distributed cache-based architecture to minimize latency

bull Integrated Dynamic Service Model ndash Auto-generated user-validated

bull Sophisticated cross-correlations and visualizations ndash Both application service-centric and infrastructure-centric

views

ndash Visibility into the data that matter by role criticality severity

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Select RTView Customers

Financial Services

Other

eCommerce Retail

Energy Telecom

500+ RTView

Customers

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For More Information

Visit us at SLcom

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Start an Evaluation

Watch a Video

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Thank You

Questions

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Redefining End-to-End Monitoring with RTView Enterprise Monitortrade The Foundation ndash High-Performance Architecture