introduction to cloud native computing

ishi systems

Cloud Native ComputingWhite Paper

Introduction to

2015

www.ishisystems.com

© 2015 Ishi Systems Inc. All Rights Reserved. This document is a property of Ishi Systems and may not be reproduced in whole or in part without consent.

Table of Contents

Background and Overview 2

Cloud Platform for Distributed Systems 4

Tech Components of Cloud Native Computing 6

Organizational Perspective 7

Summary 9

About ISHI Systems 10

Glossary 11

Next generation companies like UBER, Netflix, AirBnB and

Snapchat are transforming how business is done.

They deliver products at rapid pace and at really low costs that

it is unraveling established business models and practices which

depend on deliberate and predictable schedules.

A key enabler of the new generation of companies is a

Cloud Native Computing model. This model encompasses

technology, process and organizational approach that is built

around efficiency, automation, scale and reliability provided

by public and private cloud platforms.

Businesses and enterprises risk massive disruptions if they do

not reinvent themselves to match the efficiency, speed and

scale inherent to next generation organizations.

This whitepaper provides a brief introduction to Cloud Native

Computing including background, technology and organizational

aspects for business and IT leaders to evaluate and understand.

Executive Summary

Table of Contents

Distributed Systems

This document aims to introduce the concept of Cloud Native Computing and the problem context it attempts to address.

Application development and infrastructure platforms are undergoing significant changes driven by need for rapid application releases, efficiency, hyper scale and fault tolerance.

Traditional monolithic architectures are proving to be extremely expensive to develop, deploy and operate as scale, availability and reliability requirements grow.

Distributed Systems are rapidly gaining momentum in enterprise environments as its advantage in addressing issues with traditional architectures are becoming clear. While Distributed Systems are very attractive, it requires considerable and sophisticated systems engineering capability to manage and operate. Enterprises are continuously evaluating tools and platforms that can simplify the development and management of distributed systems.

Cloud Native Computing defines a technology, operations and organizational model that will enable enterprises to operate in this new environment effectively and efficiently.

Monolithic Systems and Scale Challenges Monolithic Systems combine disparate application functionality and concerns into a single large component with deep and brittle dependencies among them.

As scale and size of applications and data grow, it is increasingly difficult to manage the availability, performance, reliability, scalability, manageability and cost with traditional monolithic architectures.

Monolithic architecture present the following challenges

• Long development cycles • Lock-in to particular technology stack without the ability to adopt the best tool for the job • Considerable impedance to rapid releases and deployments • Technology stack insufficiency could result in re-write the entire application • Scaling logic is usually application specific and is expensive to develop and maintain

Distributed Systems attempt to solve the challenges posed by Monolithic Architectures by architecturally decomposing various functional and non-functional concerns interacting using consistent interfaces.

Background & Overview

“A distributed system is a software system in which components located on networked computers communicate and coordinate their actions by passing messages”

– Wikipedia

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Key characteristics of a distributed application

• Availability: Distributed Systems are designed for high availability by eliminating a single point of failure, allow rapid recovery in the event of partial system failures and graceful degradation when problems occur.• Performance: Distributed Systems achieve high performance by allowing for parallelization of user request or task processing across a cluster of compute nodes. • Consistency: Consistency ensures that data written to a system is valid and up to date to all distributed components reading the data. Distributed Systems follow the CAP theorem with a focus on eventual consistency and is built around this possibility.• Scalability: Distributed Systems are designed to be horizontally scalable with ability to elastically scale selectively the distributed component under stress.• Manageability: Distributed systems introduce considerably more moving parts leading to multiple points and scenarios of failure. Important considerations for effective operational management are the visibility to monitor and diagnose problems as they occur, the ease of rolling out partial or complete updates to the system and the ability to recover successfully from failures.• Cost: Cost efficiency in a distributed system is typically multi-dimensional – it makes efficient use of hardware resources by scaling up and down only as required, and supports automation thereby reducing or simplifying manual operational support for deployment and operations.

The industry is in the process of slowly transitioning from monolithic systems to distributed systems driven by scale and availability requirements. Industry leaders like Facebook, Google and LinkedIn are driving the transition of core systems like data stores, messaging, searching, caching, logging and storage systems to a distributed architecture.

Following the same lead, organizations are also transforming their business applications into a distributed architecture. Netflix, Twitter are good examples of organizations running their entire application portfolio in a distributed manner.

Monolithic Systems and Scale Challenges


Distributed Systems Adoption




Cloud Platform for DistributedSystems

Cloud Native Computing is a comprehensive cloud platform strategy to design, develop and manage distributed applications.

Cloud Native Computing defines technology, architecture, organizational structure, application delivery and operations driven by the need for operational efficiency,hyper scale, fault tolerance and rapid application releases. Cloud Native Computing aims to achieve 10x-50x improvement in IT efficiency driven by the following key characteristics:

• End to End Operations Automation • High Developer Velocity • Increased Utilization • Automatic Fault Tolerance • Auto Scaling

Key tenets of Cloud Native Computing

• Distributed – Designed for distributed applciations • Multi-tenant – Run heterogeneous applications on the same platform improving efficiency • Fault Tolerant – Built around inevitable hardware and software failures without impacting applications • Scalable – scales to 10s of 1000s of nodes • Automation Ready – To enable a small operational footprint and the lend itself to easy management everything is automated including environment provisioning, scale up, scale down, recovery and rollback. • Continuous Deployment – Ability to do end to end deployment triggered by any relevant event • Efficient – Efficient utilization of resources leveraging bin packing and resource pooling • Secure – Enables secure, isolated application and data environments

Distributed

Cloud Native Computing platforms are built for distributed applications and address key cross-cutting concerns relevant to distributed applications such as scalability, fault tolerance and efficiency.

Multi - Tenant

The platform is fundamentally multi-tenant. Applications with diverse resource requirements are deployed on the same environment to take advantage of resource synergies that exist.

For example a long running web application coexists with batch analytics services tapping into available resources when demand for the web application is low.




Fault Tolerant

Hardware and software failures are inevitable while operating large distributed applications. The platform takes care of handling failures and ensures that applications consistently return to a stable state.

The platform constantly monitors for failures and ensures the required number of instances are available at all times. If it detects failures, it ensures that another instance of the component is brought up instantaneously and seamlessly.

Scalable

Cloud Native Computing platforms are designed to support 1000’s of application and are required to scale to 10’s of thousands of nodes without any impact in performance.

The platform support automatic scaling within applications or across the platform based on key metrics like average memory and CPU utilization.

Automation Ready

Cloud Native Computing platforms enable full automation by providing API hooks into the platform. This allows developers to automate key actions, monitor events, make changes and respond without manual intervention.

Continuous Deployment

Cloud Native Computing platforms along with automated testing enables continuous and automated deployment of entire applications or application components without any disruption of service. The platform provides capability to perform automated deployment based on development events and roll back on failure.

Efficient

Cloud Native Computing platforms are lightweight and enables effective management and utilization of resources. It allows fine grained utilization of resources including memory, CPU, disks and network. It also allows for elastic growth and ramp down based on actual usage.

Secure

The platform has programmatic security built into it around key principles of data and process isolation, auditing and continuous monitoring, automated detection and remediation.




Containers

Container technology forms the packaging, delivery and operating layer for application components. Containers can work directly on Host OS while maintaining operational and resource isolation thereby eliminating traditional virtualization needs. It also encapsulates all dependencies for application components thereby simplifying the Host OS layer.

Distributed Application Platform

The platform provides the management and orchestration layer for your applications and handles resource management, fault tolerance, scalability, management, monitoring and security.

Technology Componentsof Cloud Native Computing

Cloud Operating System

Operating System for distributed application environments have to operate in environments with 1000s of nodes with high uptimes. This requires an OS which is container friendly, lightweight, secure, always on and highly manageable.



Organizational Prespective

Cloud Native Computing approach impacts technology as well as operational and cultural aspects of organizations. So it is critical that the success of a Cloud Native Computing approach is measured appropriately.

To drive a successful transformation strategy, it is critical to make key operational, organizational changes along with technology adoption.

There are key organizational changes that are relevant for effective Cloud Native Computing adoption.


Distributed Systems Expertise

Deep organizational understanding of distributed systems including architecturaland design patterns, scaling and fault tolerance concepts, operational complexities, networking and security concerns in distributed systems is key to adopting a Cloud Native Computing strategy.

Integrated DevOps & Development Teams

Automation is a key aspect of the Cloud Native Computing strategy and has tobe integrated deep into the development, testing and release processes. It requires teams to identify automation requirements relevant to the application, integration with the platform, understand key operational events and tooling that will aid diagnostics and troubleshooting, including detailed logging and monitoring.

An integrated team also provides application developers with an operational perspective which will inform them of the architecture and design of the system.

Platform Engineering

Platform is a robust, engineering substrate that encapsulates and handles critical cross cutting concerns thereby allowing products and services to be launched faster and managed better.



As a Cloud Native Computing organization it is key to develop a platform engineering mindset to actively try to define, develop, operate and improve the core application platform. This requires strong systems, networking, storage and distributed systems understanding to drive the features, capabilities and stability of the platform.

Platform engineering approach avoids rethinking core system features every time an application is developed. It also helps automation by providing APIhooks and toolsets into the platform to deploy, monitor, manage and operate applications.


Platform Drivers

The following are key drivers for a successful platform approach in an organization

• Management commitment and funding - Clear, long term roadmap around hiring, retaining, training • Driven by business and technology strategy for products and services - Cost and mode of operations for products and critical technology choices are key inputs • Driven by an independent team with strong systems skills and full stack awareness - Not afraid to use the best tools, enhance it, invent new ones when old ones are not sufficient • Development and DevOps teams as customers for the platform




Cloud Native Computing is a core foundation of next generation applications and businesses. It is transforming how products are developed, deployed and operated.

Cloud Native Computing defines a comprehensive strategy to develop, deploy and operate cost effective, robust, scalable and reliable applications. It identifies a core set of cloud technology platforms and practices that are key to operating in a cloud native model.

Cloud Native Computing also defines organizational capabilities that are key in transitioning to a cloud native model. Distributed Systems Architecture, DevOps and Platform Engineering are key disciplines for organizations transitioning to this model.

Summary




Saju Thomas is the practice lead for Cloud Native Computing services at ISHI Systems. He has 15 years of experience in developing, deploying and operating high performance distributed systems. He has held a variety of roles within ISHI Systems in Strategy, Product Management and Application Architecture. He has a B.S in Computer Science from Madras University.

About the Author

About ISHI Systems: Deep Expertise and Production Experience

About Us

Over years we have developed a keen understanding of the evolution of enterprise application platforms including opportunities and challenges they present. We have invested in understanding architectures, performance, use-cases, roadmap and automation of these application platforms. With this as the background, we promote Cloud Native Computing as a game changer for enterprises. It allows enterprises to achieve efficiencies and scale that only Twitter, Facebook and leading webscale companies have achieved. We have helped customers migrate to Cloud Native Computing models to achieve significant value by improving utilization, developing and operating highly scalable and reliable applications.

We are part of the Cloud Native Computing Foundation (www.cncf.io) and work with technology and user communities to drive the direction of key technologies based on our experience and customer feedback.

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Glossary Cloud Native Computing Cloud-Native Computing features systems that are container packaged, dynamically managed and micro-services oriented

Apache Mesos Mesos is a cluster manager which is modeled as a distributed systems kernel. The Mesos kernel runs on every machine and provides applications (e.g., Hadoop, Spark, Kafka, Elastic Search) with API’s for resource management and scheduling across entire datacenter and cloud environments.

Mesosphere DCOS It’s a commercial product built around an Apache Mesos core to provide enterprise features like management and monitoring, security etc.

Kubernetes Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions. Using the concepts of "labels" and "pods", it groups the containers which make up an application into logical units for easy management and discovery.

CoreOS CoreOS redefines the operating system into a smaller, more minimal Linux distribution. Traditional distros package unused software that leads to dependency conflicts and needlessly increases the attack surface.

CoreOS contains a small base and allows you to have full control over all dependencies through the use of containers. Think of them as your package manager




Cloud Native Computing Foundation

The Cloud Native Computing Foundation will create and drive the adoption of a new set of common container technologies informed by technical merit and end user value, and inspired by Internet-scale computing.

The community will advance the state-of-the-art for building cloud native applications and services. Historically only a small number of companies who have been willing to make significant investments in development and operations have been able to benefit from this model of computing. It has been out of reach for the average developer. We aspire to make the same approach that solved challenging scalability and efficiency problems for internet companies available to all developers.

CAP Theorem In theoretical computer science, the CAP theorem, also known as Brewer's theorem, states that it is impossible for a distributed computer system to simultaneously provide all three of the following guarantees:

• Consistency (all nodes see the same data at the same time) • Availability (a guarantee that every request receives a response about whether it succeeded or failed) • Partition tolerance (the system continues to operate despite arbitrary partitioning due to network failures)



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