This document compares Google Cloud and Microsoft Azure on various features. It discusses their pricing models, infrastructure as a service and platform as a service capabilities. Some key findings are that Azure has better coverage in Asia while Google Cloud has better coverage in the US. AWS leads the cloud market currently. The document also analyzes storage performance, virtual machine pricing and types, database offerings, microservices support, load balancing options and example use cases for each provider.

1. Google Cloud vs
Microsoft Azure
Boris Simandoff:
Director V-Ray Cloud and Integration
Technologies at Chaos Group
Mihail Mateev :
Senior Solutions Architect at EPAM

2. Google Cloud vs Microsoft Azure

3. Google Cloud vs Microsoft Azure: Agenda
• Pricing
• Pricing Model
• Reselling Model
• IaaS features overview
• VMs
• Virtual Networks
• PaaS features overview
• Storage
• Messaging
• Cloud Storage
• Messaging Services
• Manages DBs
• Big Data
• Container
• Microservices
• IoT

4. Cloud Providers – Who is the Leader
• AWS leads the cloud space with the
right combination of tools in a single
platform
• Microsoft Azure is a close second just
lacking in the impactful execution that
AWS has.
• Google Cloud is the next most
promising cloud player and is gaining
the ground fast.

5. Locations
Locations and Zones for Datacenters

6. Microsoft Azure Regions
• Azure is generally
available in 34 regions
around the world, with
plans announced for 4
additional regions
• Azure is close to AWS as
coverage, but with better
support fir Asia
• Azure doesn’t support
zones

7. Google Cloud Regions
• Google has a solid
coverage in the United
States, but falls behind
in Europe and,
particularly, in Asia
(only one spot in
Taiwan)
• No coverage at all in
South America and
Africa
• Supports zones

8. Pricing
Understanding Cloud Discounting Options

9. Pricing
• On average, compute resources
represent 75-80 percent of your cloud
spend.
• it’s critical that you understand the
discount methods for each of the cloud
providers since that will be a major
driver in the price you pay.

10. Azure Pricing
• The primary approach to getting discounts on Azure is your
Microsoft Enterprise Agreement (EA)
• EAs offer discounts from 15-45 percent depending on the level of
usage
• There is used a 30 percent discount as the midpoint in our
comparisons below

11. Google Cloud Pricing
• Sustained Usage Discount (SUD) – up to 30%
• Per minute billing
• Custom machine types – 14 cores / 12.5 GB, or 6 cores /2 GB
• Preemptible VM instances – 80% discount
• Committed use discounts – up to 57% discounts
• Rightsizing recommendations
• Nearline Cloud - $0.01 GB/m – 100PB free for 6 months

12. Storage
• Object Storage
• Benchmarks are application-specific and vary based on network
load, neighboring VM activity
• Object storage (Amazon AWS Simple Storage Service (S3), Google
Cloud Storage (GCS), Microsoft Azure Storage) provides a simple
PUT/GET/HEAD/LIST interface for storing any sort of data too
large to put into a database.

13. Storage : Downloads
Metrics (beg of 2016):
• Time to first byte (TTFB) to
measure latency
• Throughput , when
downloading files from a VM
hosted by the same vendor in
the same region.

14. Storage : Downloads
How Time to first byte (TTFB) is measured:
• In the plots, the percentile (or quantile) is on the x axis, and the metric is
on the y (note the log scale
• you can see the typical response (median, 0.5) as well as the best and
worst case behaviors.

15. Storage: Downloads

16. • Time to first byte
• Single-stream API throughput
Storage: Downloads
ms GCS S3 Azure
50th 38.2 10.6 10.7
90th 51.9 14.7 16.7
99th 129.8 125.0 44.5
99.9th 238.0 432.1 106.3
MB/s GCS S3 Azure
50th 122.3 73.3 27.0
90th 106.9 59.1 23.6
99th 67.1 47.0 20.1

17. Storage : Uploads
Metrics (similar to downloads):
• Time to first byte (TTFB) to
measure latency
• Throughput , when uploading
files to a VM hosted by the
same vendor in the same
region.

18. Storage: Uploads

19. • Time to first byte
• Single-stream API throughput
Storage: Uploads
ms GCS S3 Azure
50th 106.2 16.2 8.3
90th 161.7 36.8 13.2
99th 311.4 274.8 35.6
99.9th 408.2 645.2 236.3
MB/s GCS S3 Azure
50th 59.3 23.7 20.5
90th 49.3 20.3 17.2
99th 22.1 6.1 15.6

20. Storage Summary
• Azure and Amazon provide the lowest latency, while Google
provides the highest throughput, for both uploads and downloads.
• AWS and Azure excel for smaller files, while GCE excels for larger
files
• Google's unique multi-region buckets keep costs down when
working with data from multiple datacenters in the same region
(e.g. continent)

21. Network Throughput

22. Sizes for Windows/Linux VMs in Azure
Type Sizes Description
General purpose DSv2, Dv2, DS, D, Av2, A0-7 Balanced CPU-to-memory ratio. Ideal for testing and
development, small to medium databases, and low to medium
traffic web servers.
Compute optimized Fs, F High CPU-to-memory ratio. Good for medium traffic web
servers, network appliances, batch processes, and application
servers.
Memory optimized GS, G, DSv2, DS High memory-to-core ratio. Great for relational database
servers, medium to large caches, and in-memory analytics.
Storage optimized Ls High disk throughput and IO. Ideal for Big Data, SQL, and
NoSQL databases.
GPU NV, NC Specialized virtual machines targeted for heavy graphic
rendering and video editing. Available with single or multiple
GPUs.
High performance compute H, A8-11 Our fastest and most powerful CPU virtual machines with
optional high-throughput network interfaces (RDMA).

23. VMs Instance Types (RightScale research)

24. VMs On-Demand Prices

25. VMs Discounted Price

26. Databases
Comparison of Managed Databases

27. Microsoft Azure: DocumentDB
• DocumentDB is a fully managed NoSQL database service built for
fast and predictable performance, high availability, elastic scaling,
global distribution
• Provides rich and familiar SQL query capabilities
• Consistent low latencies on JSON data - ensuring that 99% of your
reads are served under 10 milliseconds and 99% of your writes are
served under 15 milliseconds.

28. Microsoft Azure: Azure SQL Database
• SQL Database is a relational database service in the Microsoft cloud
based on the Microsoft SQL Server engine
• SQL Database supports existing SQL Server tools, libraries, and
APIs
• SQL Database service offers three service tiers:
1. Basic
2. Standard
3. Premium

29. Microsoft Azure: Azure SQL Database
• SQL Database service tiers :
Service tier Target workloads
Basic Best suited for a small database, supporting typically one single active
operation at a given time. Examples include databases used for development
or testing, or small-scale infrequently used applications.
Standard The go-to option for cloud applications with low to medium IO performance
requirements, supporting multiple concurrent queries. Examples include
workgroup or web applications.
Premium Designed for high transactional volume with high IO performance
requirements, supporting many concurrent users. Examples are databases
supporting mission critical applications.
Premium RS Designed for IO-intensive workloads that do not require the highest
availability guarantees. Examples include testing high-performance
workloads, or an analytical workload where the database is not the system of
record.

30. Microsoft Azure: Azure SQL Database
• SQL Database service tiers :
Service tier features Basic Standard Premium Premium RS
Maximum individual
database size
2 GB 250 GB 4 TB* 500 GB
Maximum total storage
in an elastic pool
117 GB 1200 GB 750 GB 750 GB
Maximum number of
databases per pool
400 400 50 50
Database backup
retention period
7 days 35 days 35 days 35 days
Customers using P11 and P15 performance levels can use
up to 4 TB of included storage at no additional charge.

31. Microsoft Azure: Microservices
There are two main options to create solutions with microservices
architecture if you do not want to implement custom orchestration
and other basic functionalities:
• Azure Service Fabric
• Azure Container Services

32. Microsoft Azure: Microservices
• Azure Container Service leverages the Docker container format
• Azure Container Services supports several open source solutions
for orchestration, microservices clustering etc. :
1. DC/OS
2. Docker Swarm
3. Kubernetes

33. Microsoft Azure: Microservices
• Azure Service Fabric provides own specific programming model
and orchestration
1. ASF is faster for implementation
2. ASF until Feb 2017 used only Windows container
3. From Feb 2017 there are Linux containers
4. ASF is more convenient for IoT solutions because of Actor model

34. Cloud Load Balancing
Azure Traffic Manager
• Allows you to control the distribution of user traffic for service
endpoints in different datacenters
• You can also use Traffic Manager with external, non-Azure
endpoints.
• Improve availability of critical applications
• Improve responsiveness for high-performance applications
• Perform service maintenance without downtime
• Combine on-premises and Cloud-based applications

35. Cloud Load Balancing
Azure Traffic Manager key benefits
• Distribution of traffic according to one of several traffic-routing
methods
• Continuous monitoring of endpoint health and automatic failover
when endpoints fail

36. Cloud Load Balancing
How clients connect using Azure Traffic Manager

37. Microsoft Azure: Use Cases
• Rolls-Royce
• Real-Madrid C.F.
• GE Healthcare
• NBC Universal
• 3M
• Mediterranean Shipping
Company (MSC)
• Marks And Spencer
• GKN
• Volvo
• Renault Nissan
• BMW

38. Microsoft Azure: Vertical Solutions
IoT Reference Architecture

39. Microsoft Azure: Vertical Solutions
IoT for Automotive Reference Architecture

40. Microsoft Azure: Vertical Solutions
Microsoft Connected Vehicle Platform

41. Google Cloud

42. Google Cloud
• Dynamic Scalability
• Network Infrastructure
• Efficiency
• Monitoring
• Logging
• Automated development pipelines

43. Google Cloud Use Cases
• HomeDepot BlackFriday
• eBay
• Spotify
• Snapchat
• Pokeman Go

44. Google Cloud Scalability

45. Google Cloud Storage
• Cloud Storage – 11 9s
• Cloud SQL
• Cloud Spanner - 5 9s
• CAP Theorem - Eric Brewer
• Cloud Datastore
• Persistent Disk
• Mem Disk

46. Google Cloud Bigdata
• Cloud Pub/Sub
• BigQuery – Data warehouse
• Dataprep
• Dataflow
• Dataproc

47. Google Cloud Network
• Load Balancer
• BGP – Border Gateway Protocol
• Single Anycast IP
• Automatic CDN
• Edge Cache Servers
• 1M r/s
• HTTP2
• Virtual Network
• Cloud DNS
• Interconnect

48. Google Cloud Management Tools
• Monitoring
• Logging
• Cloud Container Engine
• Best environment for Microservices with
maximum flexibility
• Pipeline integration from development to
production deployment

49. Google Cloud Kubernetes
• Cloud Container Engine
• Best environment for Microservices with
maximum flexibility
• Pipeline integration from development to
production deployment

50. Google Cloud vs Microsoft Azure
Q & A

51. Thank You!