Cloud Computing design patterns blueprints

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1

2 Cloud Computing design patterns blueprints Principal Cloud Engineer Kronos Incorporated Rohit Bhardwaj

3 Agenda Why build for Cloud Design? Dockers Cloud design patterns Sharing, Scaling and Elasticity Patterns Reliability, Resiliency and Recovery Patterns Cloud Security 12 factor application guidelines

4 A

5

6 A

7 Supportability

8 Testability

9 Vendor lockin

10 A

11 A

12

13

14

15 initial über

16

17 1000 services challenges Trip tracking Mapping and Routing Realtime Market place + Matching (Supply vs Demand) Reputation tracking (Ratings) Global Payment Processing Data Warehousing + Modeling Supply + Demand prediction Fraud Detection

18 availability

19 operational cost per trip

20 response time

21

22

23

24

25

26

27 twitter

28 scalability challenge

29 Traditional Solutions

30 3 TIER ATCHITECTURE

31 MULTI-TIERED APP

32

33 Dockers Docker allows you to package an application with all of its dependencies into a standardized unit for software development.

34

35 Dockers demo To deploy a single Cassandra node to this docker host, run this command: docker run --name=n1 -d cassandra You need the IP of this first container to pass as a parameter to subsequent containers docker inspect -f '{{.NetworkSettings.IPAddress }}' n1 To add a second node to the Cassandra cluster, run this command: docker run --name=n2 -d cassandra -seeds IP docker exec -it n1 nodetool status

36

37 PRINCIPLES OF CLOUD APPLICATION ARCHITECTURE

38 Cloud computing scenarios

39 Cloud offerings

40 Cloud App Architectures

41 Cloud Application Management

42 Principle 1: Resilient to Failure

43 Vulnerabilities Hardware failure of a compute network, or storage physical appliance A software failure in an application component Cloud service, or a network failure due to an unresponsive component or a transient network connectivity problem

44 APPLICATION MUST WORK! Degraded performance is fine Degraded functionality

45 Loose coupling Components to be substituted without impact

46 Manage failure and latency Component not statically bind to instance Another component can take over

47 What if critical data is not written to database How to handle the Mobile devices? Offline storing of data Automatic sync

48 Principle 2: Resilient to Latency

49

50 Issue: Infrastructure failures causing delays Network congestion or network partition Request contention in non-scalable infrastructure services I/O bandwidth saturation in storage systems Software failures in shared services Denial-of-service attacks that trigger service failure or resource exhaustion

51 Developers: Queue requests

52 Retry mechanism

53 Handle failure gracefully

54 Issue: Latency between New York and San Francisco 13.8 ms 27.6 ms (Round trip) As per AT&T: 70 ms Distance, as the crow flies, between New York and San Francisco = 2,565 miles; speed of light = 186,000 miles/second.

55 Service A depends on B which depends on C

56 Solution 1: Co-locate

57 Solution 2: Reducing the number of network requests REST API design Ask for Zip and phone in same REST request Consolidate small details into one form

58 Bandwidth Aware

59 Reduce chattiness

60 Principle 3: Secure KPMG data life cycle

61 Data is not secure!

62 Solution 1: Secure data in transit

63 Solution 2: Control API Access

64 Threat modeling

65

66 Principle 4: Location Independent

67 Solution 1: No hardcoded config: End point addresses

68 Solution 2: Register service

69

70 Solution 3: Application components should be stateless

71 Solution 4: Applications should use established protocols HTTPS Dockers

72 Solution 5: API management through proxy

73

74 Principle 5: Elastically Scalable

75 Principle 6: SOA/Composeability Discoverability enables an application to dynamically locate components

76 Principle 7: Designed for Manageability

77

78 Principle 8: Infrastructure Independent

79 Cost and Resource Consumption Aware

80 Minimizing Transport Cost Minimize payload sizes. Provide APIs that return just the data the consumer needs ( partial response ). Consider data compression, but balance against CPU costs for encoding and decoding.

81 Minimize data transfers Cache immutable data. Replace or avoid chatty protocols.

82 Netflix case study

83

84

85 Component microservices

86 Chaos Gorilla

87 Cassandra maintenance

88 Isolated Regions

89 Failure Modes and Effects

90 Stateless Micro-Service Architecture

91 Cloud Application Design Patterns

92 Circuit Breaker

93 A request to a remote service timed out. The request queue for the remote service is full, indicating that the remote service is unable to handle additional requests.

94 Fallback Strategy Fail transparently. Generates a custom response Fail silently. Returns a null value for the request. Fail fast. Generates a 5xx response code

95 Circuit Breaker demo

96

97 CIRCUIT BREAKER DESIGN Benefits PATTERN Improved resilience to failure To provide visibility into component failures (though visibility into tripped breakers) Where to use this pattern To eliminate timeout delays due to network or VM instance failures To prevent errors cascading and complicating upstream error handling (fail transparently, fail silently)

98 Request Queuing Design Pattern

99

100 REQUEST QUEUING DESIGN PATTERN Benefits Improved fault tolerance to ensure high availability Improved performance for highly requested APIs Where to use this pattern To manage application failover (fault tolerance) To reduce end point load on a heavily requested API(s) To apply alternate strategies to auto-scaling via throttling

101 Request Collapsing Design Pattern

102

103 Object Change Notification Pattern

104 Benefits Netflix change movie to different formats Improved resilience to failure Increased scalability More efficient resource utilization Where to use this pattern Where compute intensive processing can be parallelized To eliminate single points of failure To reduce tight coupling between components to allow for implementations to be replaced

105 Service Discovery Pattern

106 Netflix Eureka architecture

107 Service benefits Provide a way for components to direct requests to available instances. Support dynamic registration and de-registration of service instances. Maintain awareness of the state of any given instance.

108 Serverless Architectures

109 AWS Lambda

110 Open Tracing

111 Open Tracing

112 AWS Device Farm

113 Microservices Design Pattern

114

115

116 MICROSERVICES DESIGN PATTERN Benefits Reduces overhead of deploying updates Reduces side effects across co-resident services Enables elasticity Greater visibility into source of issues Improved developer productivity Where to use this pattern In large systems that need to scale elastically and cost effectively To eliminate single points of failure To improve performance To support frequent updates to a critical production system

117 Stateless Services Design pattern Reliability Scalability Visibility Simplicity: Easy debug

118 Configuration Service pattern

119 Current app Config changes are made at startup? Redeploy / Startup

120 External Configuration Store

121 Runtime Reconfiguration: No downtime New Nodes receive new config changes Application can subscribe to configs

122 Authorization Pattern

123 Operational Strategies

124

125 Big data characteristics

126 Database Sharding design

127 replication

128

129 CAP Theorem BASE (Basically Available, Soft-State, Eventual Consistency) data store

130 lambda architecture spark and Cassandra

131 lambda architecture spark and Cassandra

132

133 Spring Cloud

134 The Twelve Factors

135 Factor 1: Codebase One codebase tracked in revision control, many deploys

136 Factor 2: Dependencies Explicitly declare and isolate dependencies Repeatable deployment

137 Factor 3: Config Store config in the environment, no properties files Resource handles to the database, Memcached, and other backing services Credentials to external services such as Amazon S3 or Twitter Per-deploy values such as the canonical hostname for the deploy

138 Spring Picking up Env Vars... import org.springframework.context.environmentaware; import public class HelloController implements EnvironmentAware { private String public void setenvironment(environment environment) { this.name = environment.getproperty("who"); }...

139 Factor 4: Backing Services

140 Factor 5: Build, release, run Strictly separate build and run stages

141 Strictly separate stages

142 Factor 6: Processes: Design for failure Execute the app as one or more stateless processes Share nothing No Sticky sessions

143 Factor 7: Port binding Export services via port binding Completely self-contained and does not rely on runtime injection of a webserver into the execution environment to create a web-facing service.

144 Export Services via Port Bindings Apps are deployed into containers Multiple containers per host Platform to handle port assignments and mappings

145

146

147

148 Factor 8: Concurrency the unix process model for running service daemons.

149 Factor 9: Disposability Maximize robustness with fast startup and graceful shutdown Processes are disposable, meaning they can be started or stopped at a moment s notice.

150 Factor 10: Dev/prod parity Keep development, staging, and production as similar as possible

151 Faster commits (Quick CI)

152 Factor 11: Logs Treat logs as event streams Routing or storage of its output stream Finding specific events in the past. Large-scale graphing of trends (such as requests per minute). Active alerting according to user-defined heuristics (such as an alert when the quantity of errors per minute exceeds a certain threshold).

153 Spring Using ENV to config logging In application.yml logging: level: org.springframework: ${SPRING_LOG_LEVEL:INFO} hello: ${LOG_LEVEL:INFO}

154

155

156

157

158

159 Factor 12: Admin processes Run admin/management tasks as one-off processes

160 The Twelve Factors

161 Must haves for Cloud Dev Ops automation is must API Support: consuming and supporting APIs Microservices HTML 5 Applications: Run Apps in any device Blue-Green Development: New functionality deployment validate and gradually grow it App scaling and Elasticity Pass with Cloud Foundry Big Data: Near real time analytics

162 ASSESSING APPLICATION CLOUD MATURITY

163 References Cloud Computing Design Patterns, by Robert Cope; Amin Naserpour; Thomas Erl The 12 factors: architecting_cloud_aware_applications.pdf

164

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