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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