Automated deployment of a microservice-based monitoring infrastructure. Augusto Ciuffoletti. 6 ottobre 2015
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1 Automated deployment of a microservice-based monitoring infrastructure 6 ottobre
2 Introducing two topics The title: Automated deployment of a microservice-based monitoring infrastructure Microservices Monitoring on demand Let us see how the two stories merge...
3 Introducing two topics The title: Automated deployment of a microservice-based monitoring infrastructure Microservices Monitoring on demand Let us see how the two stories merge...
4 Introducing two topics The title: Automated deployment of a microservice-based monitoring infrastructure Microservices Monitoring on demand Let us see how the two stories merge...
5 Introducing two topics The title: Automated deployment of a microservice-based monitoring infrastructure Microservices Monitoring on demand Let us see how the two stories merge...
6 Number one: Microservices A design paradigm for distributed system a book in O Reilly "animal series" by S Newman Principles: each component in the system is designed to provide one small, well defined service each component is a stand alone entity that interacts with others across a network with a well defined interface
7 Number one: Microservices A design paradigm for distributed system a book in O Reilly "animal series" by S Newman Principles: each component in the system is designed to provide one small, well defined service each component is a stand alone entity that interacts with others across a network with a well defined interface
8 Number one: Microservices A design paradigm for distributed system a book in O Reilly "animal series" by S Newman Principles: each component in the system is designed to provide one small, well defined service each component is a stand alone entity that interacts with others across a network with a well defined interface
9 Number one: Microservices A design paradigm for distributed system a book in O Reilly "animal series" by S Newman Principles: each component in the system is designed to provide one small, well defined service each component is a stand alone entity that interacts with others across a network with a well defined interface
10 Number one: Microservices A design paradigm for distributed system a book in O Reilly "animal series" by S Newman Principles: each component in the system is designed to provide one small, well defined service each component is a stand alone entity that interacts with others across a network with a well defined interface
11 Reasons to adopt the microservices paradigm simplifies maintenance e.g., upgrade one single component agility in deployment e.g., to scale up or down each component may use a different technology e.g., for technical or performance reasons simplifies development e.g., each component developed by a distinct team robustness e.g., if one component fails there is a chance that the system still works
12 Reasons to adopt the microservices paradigm simplifies maintenance e.g., upgrade one single component agility in deployment e.g., to scale up or down each component may use a different technology e.g., for technical or performance reasons simplifies development e.g., each component developed by a distinct team robustness e.g., if one component fails there is a chance that the system still works
13 Reasons to adopt the microservices paradigm simplifies maintenance e.g., upgrade one single component agility in deployment e.g., to scale up or down each component may use a different technology e.g., for technical or performance reasons simplifies development e.g., each component developed by a distinct team robustness e.g., if one component fails there is a chance that the system still works
14 Reasons to adopt the microservices paradigm simplifies maintenance e.g., upgrade one single component agility in deployment e.g., to scale up or down each component may use a different technology e.g., for technical or performance reasons simplifies development e.g., each component developed by a distinct team robustness e.g., if one component fails there is a chance that the system still works
15 Reasons to adopt the microservices paradigm simplifies maintenance e.g., upgrade one single component agility in deployment e.g., to scale up or down each component may use a different technology e.g., for technical or performance reasons simplifies development e.g., each component developed by a distinct team robustness e.g., if one component fails there is a chance that the system still works
16 Reasons to adopt the microservices paradigm simplifies maintenance e.g., upgrade one single component agility in deployment e.g., to scale up or down each component may use a different technology e.g., for technical or performance reasons simplifies development e.g., each component developed by a distinct team robustness e.g., if one component fails there is a chance that the system still works
17 Reasons to adopt the microservices paradigm simplifies maintenance e.g., upgrade one single component agility in deployment e.g., to scale up or down each component may use a different technology e.g., for technical or performance reasons simplifies development e.g., each component developed by a distinct team robustness e.g., if one component fails there is a chance that the system still works
18 Reasons to adopt the microservices paradigm simplifies maintenance e.g., upgrade one single component agility in deployment e.g., to scale up or down each component may use a different technology e.g., for technical or performance reasons simplifies development e.g., each component developed by a distinct team robustness e.g., if one component fails there is a chance that the system still works
19 Reasons to adopt the microservices paradigm simplifies maintenance e.g., upgrade one single component agility in deployment e.g., to scale up or down each component may use a different technology e.g., for technical or performance reasons simplifies development e.g., each component developed by a distinct team robustness e.g., if one component fails there is a chance that the system still works
20 Reasons to adopt the microservices paradigm simplifies maintenance e.g., upgrade one single component agility in deployment e.g., to scale up or down each component may use a different technology e.g., for technical or performance reasons simplifies development e.g., each component developed by a distinct team robustness e.g., if one component fails there is a chance that the system still works
21 Number two: Cloud monitoring A cloud user wants to have a functional feedback from cloud sourced resources: not necessarily to verify service quality control a scalable resource, provide feedback to the users, trigger compensating actions NIST indicates monitoring as one of the distinctive features of cloud computing
22 Number two: Cloud monitoring A cloud user wants to have a functional feedback from cloud sourced resources: not necessarily to verify service quality control a scalable resource, provide feedback to the users, trigger compensating actions NIST indicates monitoring as one of the distinctive features of cloud computing
23 Number two: Cloud monitoring A cloud user wants to have a functional feedback from cloud sourced resources: not necessarily to verify service quality control a scalable resource, provide feedback to the users, trigger compensating actions NIST indicates monitoring as one of the distinctive features of cloud computing
24 Number two: Cloud monitoring A cloud user wants to have a functional feedback from cloud sourced resources: not necessarily to verify service quality control a scalable resource, provide feedback to the users, trigger compensating actions NIST indicates monitoring as one of the distinctive features of cloud computing
25 Number two: Cloud monitoring A cloud user wants to have a functional feedback from cloud sourced resources: not necessarily to verify service quality control a scalable resource, provide feedback to the users, trigger compensating actions NIST indicates monitoring as one of the distinctive features of cloud computing
26 Number two: Cloud monitoring A cloud user wants to have a functional feedback from cloud sourced resources: not necessarily to verify service quality control a scalable resource, provide feedback to the users, trigger compensating actions NIST indicates monitoring as one of the distinctive features of cloud computing
27 Our option: on demand monitoring Provide monitoring as part of the service Give the user wide possibilities to configure a monitoring infrastructure Which metrics are captured and how data are preprocessed and retrieved Scale from simple to complex infrastructures Do not overkill the problem in simple cases Cope with complex infrastructures Resource agnostic Basic functionalities and unlimited pluggable extensions
28 Our option: on demand monitoring Provide monitoring as part of the service Give the user wide possibilities to configure a monitoring infrastructure Which metrics are captured and how data are preprocessed and retrieved Scale from simple to complex infrastructures Do not overkill the problem in simple cases Cope with complex infrastructures Resource agnostic Basic functionalities and unlimited pluggable extensions
29 Our option: on demand monitoring Provide monitoring as part of the service Give the user wide possibilities to configure a monitoring infrastructure Which metrics are captured and how data are preprocessed and retrieved Scale from simple to complex infrastructures Do not overkill the problem in simple cases Cope with complex infrastructures Resource agnostic Basic functionalities and unlimited pluggable extensions
30 Our option: on demand monitoring Provide monitoring as part of the service Give the user wide possibilities to configure a monitoring infrastructure Which metrics are captured and how data are preprocessed and retrieved Scale from simple to complex infrastructures Do not overkill the problem in simple cases Cope with complex infrastructures Resource agnostic Basic functionalities and unlimited pluggable extensions
31 Our option: on demand monitoring Provide monitoring as part of the service Give the user wide possibilities to configure a monitoring infrastructure Which metrics are captured and how data are preprocessed and retrieved Scale from simple to complex infrastructures Do not overkill the problem in simple cases Cope with complex infrastructures Resource agnostic Basic functionalities and unlimited pluggable extensions
32 Our option: on demand monitoring Provide monitoring as part of the service Give the user wide possibilities to configure a monitoring infrastructure Which metrics are captured and how data are preprocessed and retrieved Scale from simple to complex infrastructures Do not overkill the problem in simple cases Cope with complex infrastructures Resource agnostic Basic functionalities and unlimited pluggable extensions
33 Our option: on demand monitoring Provide monitoring as part of the service Give the user wide possibilities to configure a monitoring infrastructure Which metrics are captured and how data are preprocessed and retrieved Scale from simple to complex infrastructures Do not overkill the problem in simple cases Cope with complex infrastructures Resource agnostic Basic functionalities and unlimited pluggable extensions
34 Our option: on demand monitoring Provide monitoring as part of the service Give the user wide possibilities to configure a monitoring infrastructure Which metrics are captured and how data are preprocessed and retrieved Scale from simple to complex infrastructures Do not overkill the problem in simple cases Cope with complex infrastructures Resource agnostic Basic functionalities and unlimited pluggable extensions
35 Find a match Monitoring is by nature split into small components (remember Nagios) monitoring probes are small components, possibly embedded monitoring data crosses a pipe of processors (anonymization, aggregation etc) data is finally published using an endpoint reachable from the outside (database, web service) Each component is supported by a specific technology e.g., network monitoring vs storage monitoring The on demand nature requires agility in deployment the cloud user that obtains a new resource may want to monitor it There is a match between microservices and on demand monitoring
36 Find a match Monitoring is by nature split into small components (remember Nagios) monitoring probes are small components, possibly embedded monitoring data crosses a pipe of processors (anonymization, aggregation etc) data is finally published using an endpoint reachable from the outside (database, web service) Each component is supported by a specific technology e.g., network monitoring vs storage monitoring The on demand nature requires agility in deployment the cloud user that obtains a new resource may want to monitor it There is a match between microservices and on demand monitoring
37 Find a match Monitoring is by nature split into small components (remember Nagios) monitoring probes are small components, possibly embedded monitoring data crosses a pipe of processors (anonymization, aggregation etc) data is finally published using an endpoint reachable from the outside (database, web service) Each component is supported by a specific technology e.g., network monitoring vs storage monitoring The on demand nature requires agility in deployment the cloud user that obtains a new resource may want to monitor it There is a match between microservices and on demand monitoring
38 Find a match Monitoring is by nature split into small components (remember Nagios) monitoring probes are small components, possibly embedded monitoring data crosses a pipe of processors (anonymization, aggregation etc) data is finally published using an endpoint reachable from the outside (database, web service) Each component is supported by a specific technology e.g., network monitoring vs storage monitoring The on demand nature requires agility in deployment the cloud user that obtains a new resource may want to monitor it There is a match between microservices and on demand monitoring
39 Find a match Monitoring is by nature split into small components (remember Nagios) monitoring probes are small components, possibly embedded monitoring data crosses a pipe of processors (anonymization, aggregation etc) data is finally published using an endpoint reachable from the outside (database, web service) Each component is supported by a specific technology e.g., network monitoring vs storage monitoring The on demand nature requires agility in deployment the cloud user that obtains a new resource may want to monitor it There is a match between microservices and on demand monitoring
40 Find a match Monitoring is by nature split into small components (remember Nagios) monitoring probes are small components, possibly embedded monitoring data crosses a pipe of processors (anonymization, aggregation etc) data is finally published using an endpoint reachable from the outside (database, web service) Each component is supported by a specific technology e.g., network monitoring vs storage monitoring The on demand nature requires agility in deployment the cloud user that obtains a new resource may want to monitor it There is a match between microservices and on demand monitoring
41 Find a match Monitoring is by nature split into small components (remember Nagios) monitoring probes are small components, possibly embedded monitoring data crosses a pipe of processors (anonymization, aggregation etc) data is finally published using an endpoint reachable from the outside (database, web service) Each component is supported by a specific technology e.g., network monitoring vs storage monitoring The on demand nature requires agility in deployment the cloud user that obtains a new resource may want to monitor it There is a match between microservices and on demand monitoring
42 Find a match Monitoring is by nature split into small components (remember Nagios) monitoring probes are small components, possibly embedded monitoring data crosses a pipe of processors (anonymization, aggregation etc) data is finally published using an endpoint reachable from the outside (database, web service) Each component is supported by a specific technology e.g., network monitoring vs storage monitoring The on demand nature requires agility in deployment the cloud user that obtains a new resource may want to monitor it There is a match between microservices and on demand monitoring
43 Find a match Monitoring is by nature split into small components (remember Nagios) monitoring probes are small components, possibly embedded monitoring data crosses a pipe of processors (anonymization, aggregation etc) data is finally published using an endpoint reachable from the outside (database, web service) Each component is supported by a specific technology e.g., network monitoring vs storage monitoring The on demand nature requires agility in deployment the cloud user that obtains a new resource may want to monitor it There is a match between microservices and on demand monitoring
44 Summarizing: a IaaS provisioning IaaS is just for the example: could be PaaS or anything else
45 A monitoring infrastructure Adding a monitoring infrastructure: probes that collect monitoring data (collectors) a device that processes monitoring data (sensor)
46 A monitoring infrastructure Adding a monitoring infrastructure: probes that collect monitoring data (collectors) a device that processes monitoring data (sensor)
47 A monitoring infrastructure Adding a monitoring infrastructure: probes that collect monitoring data (collectors) a device that processes monitoring data (sensor)
48 A cloud interface we need to design an interface an open standard exists: OCCI
49 A cloud interface we need to design an interface an open standard exists: OCCI
50 Open Cloud Computing Interface basics The interface is REST, therefore web-oriented The items accessed across the interface are entities One type of entity is the resource Another is the link, that connects two resources A type is characterized by standard features of the instances attributes whose values define instances actions that model dynamic change The interface is extensible: a type can be subtyped, thus adding new attributes to the standard ones an instance can be modified using mixins
51 Open Cloud Computing Interface basics The interface is REST, therefore web-oriented The items accessed across the interface are entities One type of entity is the resource Another is the link, that connects two resources A type is characterized by standard features of the instances attributes whose values define instances actions that model dynamic change The interface is extensible: a type can be subtyped, thus adding new attributes to the standard ones an instance can be modified using mixins
52 Open Cloud Computing Interface basics The interface is REST, therefore web-oriented The items accessed across the interface are entities One type of entity is the resource Another is the link, that connects two resources A type is characterized by standard features of the instances attributes whose values define instances actions that model dynamic change The interface is extensible: a type can be subtyped, thus adding new attributes to the standard ones an instance can be modified using mixins
53 Open Cloud Computing Interface basics The interface is REST, therefore web-oriented The items accessed across the interface are entities One type of entity is the resource Another is the link, that connects two resources A type is characterized by standard features of the instances attributes whose values define instances actions that model dynamic change The interface is extensible: a type can be subtyped, thus adding new attributes to the standard ones an instance can be modified using mixins
54 Open Cloud Computing Interface basics The interface is REST, therefore web-oriented The items accessed across the interface are entities One type of entity is the resource Another is the link, that connects two resources A type is characterized by standard features of the instances attributes whose values define instances actions that model dynamic change The interface is extensible: a type can be subtyped, thus adding new attributes to the standard ones an instance can be modified using mixins
55 Open Cloud Computing Interface basics The interface is REST, therefore web-oriented The items accessed across the interface are entities One type of entity is the resource Another is the link, that connects two resources A type is characterized by standard features of the instances attributes whose values define instances actions that model dynamic change The interface is extensible: a type can be subtyped, thus adding new attributes to the standard ones an instance can be modified using mixins
56 Open Cloud Computing Interface basics The interface is REST, therefore web-oriented The items accessed across the interface are entities One type of entity is the resource Another is the link, that connects two resources A type is characterized by standard features of the instances attributes whose values define instances actions that model dynamic change The interface is extensible: a type can be subtyped, thus adding new attributes to the standard ones an instance can be modified using mixins
57 Open Cloud Computing Interface basics The interface is REST, therefore web-oriented The items accessed across the interface are entities One type of entity is the resource Another is the link, that connects two resources A type is characterized by standard features of the instances attributes whose values define instances actions that model dynamic change The interface is extensible: a type can be subtyped, thus adding new attributes to the standard ones an instance can be modified using mixins
58 Open Cloud Computing Interface basics The interface is REST, therefore web-oriented The items accessed across the interface are entities One type of entity is the resource Another is the link, that connects two resources A type is characterized by standard features of the instances attributes whose values define instances actions that model dynamic change The interface is extensible: a type can be subtyped, thus adding new attributes to the standard ones an instance can be modified using mixins
59 Open Cloud Computing Interface basics The interface is REST, therefore web-oriented The items accessed across the interface are entities One type of entity is the resource Another is the link, that connects two resources A type is characterized by standard features of the instances attributes whose values define instances actions that model dynamic change The interface is extensible: a type can be subtyped, thus adding new attributes to the standard ones an instance can be modified using mixins
60 An OCCI model for monitoring A Sensor is a subtype of the Resource type A Collector is a subtype of the Link type Add Mixins to specify the type of activity Legenda: the sensor (red) is an OCCI resource the collectors (blue) are OCCI links computing boxes and the network are OCCI resources too
61 An OCCI model for monitoring A Sensor is a subtype of the Resource type A Collector is a subtype of the Link type Add Mixins to specify the type of activity Legenda: the sensor (red) is an OCCI resource the collectors (blue) are OCCI links computing boxes and the network are OCCI resources too
62 An OCCI model for monitoring A Sensor is a subtype of the Resource type A Collector is a subtype of the Link type Add Mixins to specify the type of activity Legenda: the sensor (red) is an OCCI resource the collectors (blue) are OCCI links computing boxes and the network are OCCI resources too
63 An OCCI model for monitoring A Sensor is a subtype of the Resource type A Collector is a subtype of the Link type Add Mixins to specify the type of activity Legenda: the sensor (red) is an OCCI resource the collectors (blue) are OCCI links computing boxes and the network are OCCI resources too
64 An OCCI model for monitoring A Sensor is a subtype of the Resource type A Collector is a subtype of the Link type Add Mixins to specify the type of activity Legenda: the sensor (red) is an OCCI resource the collectors (blue) are OCCI links computing boxes and the network are OCCI resources too
65 An OCCI model for monitoring A Sensor is a subtype of the Resource type A Collector is a subtype of the Link type Add Mixins to specify the type of activity Legenda: the sensor (red) is an OCCI resource the collectors (blue) are OCCI links computing boxes and the network are OCCI resources too
66 An OCCI model for monitoring A Sensor is a subtype of the Resource type A Collector is a subtype of the Link type Add Mixins to specify the type of activity Legenda: the sensor (red) is an OCCI resource the collectors (blue) are OCCI links computing boxes and the network are OCCI resources too
67 An OCCI model for monitoring A Sensor is a subtype of the Resource type A Collector is a subtype of the Link type Add Mixins to specify the type of activity Legenda: the sensor (red) is an OCCI resource the collectors (blue) are OCCI links computing boxes and the network are OCCI resources too
68 How do we do that? We want to study the big arrow in the figure How do we implement a monitoring infrastructure starting form OCCI entities
69 How do we do that? We want to study the big arrow in the figure How do we implement a monitoring infrastructure starting form OCCI entities
70 Vintage programming but... For an early prototype we selected seasoned technologies: Java/Unix sockets/unix Pipes not bound to specific programming tools better solutions do exist As a virtualization platform we selected Docker demo is easely reproduceable/shareable efficient to run on a single laptop upgradeable to a real deployment
71 Vintage programming but... For an early prototype we selected seasoned technologies: Java/Unix sockets/unix Pipes not bound to specific programming tools better solutions do exist As a virtualization platform we selected Docker demo is easely reproduceable/shareable efficient to run on a single laptop upgradeable to a real deployment
72 Vintage programming but... For an early prototype we selected seasoned technologies: Java/Unix sockets/unix Pipes not bound to specific programming tools better solutions do exist As a virtualization platform we selected Docker demo is easely reproduceable/shareable efficient to run on a single laptop upgradeable to a real deployment
73 Vintage programming but... For an early prototype we selected seasoned technologies: Java/Unix sockets/unix Pipes not bound to specific programming tools better solutions do exist As a virtualization platform we selected Docker demo is easely reproduceable/shareable efficient to run on a single laptop upgradeable to a real deployment
74 Vintage programming but... For an early prototype we selected seasoned technologies: Java/Unix sockets/unix Pipes not bound to specific programming tools better solutions do exist As a virtualization platform we selected Docker demo is easely reproduceable/shareable efficient to run on a single laptop upgradeable to a real deployment
75 Vintage programming but... For an early prototype we selected seasoned technologies: Java/Unix sockets/unix Pipes not bound to specific programming tools better solutions do exist As a virtualization platform we selected Docker demo is easely reproduceable/shareable efficient to run on a single laptop upgradeable to a real deployment
76 Vintage programming but... For an early prototype we selected seasoned technologies: Java/Unix sockets/unix Pipes not bound to specific programming tools better solutions do exist As a virtualization platform we selected Docker demo is easely reproduceable/shareable efficient to run on a single laptop upgradeable to a real deployment
77 Deploying a demo infrastructure The minimal architecture (4 dockers): one HTTP server to host the OCCI entities descriptions one dashboard to display monitoring data one monitored compute resource one sensor resource In OCCI view One compute resource One sensor resource One collector link
78 Deploying a demo infrastructure The minimal architecture (4 dockers): one HTTP server to host the OCCI entities descriptions one dashboard to display monitoring data one monitored compute resource one sensor resource In OCCI view One compute resource One sensor resource One collector link
79 Deploying a demo infrastructure The minimal architecture (4 dockers): one HTTP server to host the OCCI entities descriptions one dashboard to display monitoring data one monitored compute resource one sensor resource In OCCI view One compute resource One sensor resource One collector link
80 Deploying a demo infrastructure The minimal architecture (4 dockers): one HTTP server to host the OCCI entities descriptions one dashboard to display monitoring data one monitored compute resource one sensor resource In OCCI view One compute resource One sensor resource One collector link
81 Deploying a demo infrastructure The minimal architecture (4 dockers): one HTTP server to host the OCCI entities descriptions one dashboard to display monitoring data one monitored compute resource one sensor resource In OCCI view One compute resource One sensor resource One collector link
82 Deploying a demo infrastructure The minimal architecture (4 dockers): one HTTP server to host the OCCI entities descriptions one dashboard to display monitoring data one monitored compute resource one sensor resource In OCCI view One compute resource One sensor resource One collector link
83 Deploying a demo infrastructure The minimal architecture (4 dockers): one HTTP server to host the OCCI entities descriptions one dashboard to display monitoring data one monitored compute resource one sensor resource In OCCI view One compute resource One sensor resource One collector link
84 Deploying a demo infrastructure The minimal architecture (4 dockers): one HTTP server to host the OCCI entities descriptions one dashboard to display monitoring data one monitored compute resource one sensor resource In OCCI view One compute resource One sensor resource One collector link
85 Deploying a demo infrastructure The minimal architecture (4 dockers): one HTTP server to host the OCCI entities descriptions one dashboard to display monitoring data one monitored compute resource one sensor resource In OCCI view One compute resource One sensor resource One collector link
86 The web server The web server is deployed (with OCCI docs) this is part of the cloud infrastructure
87 The web server The web server is deployed (with OCCI docs) this is part of the cloud infrastructure
88 The dashboard The dashboard is deployed (UDP to receive data) this is on user premises
89 The dashboard The dashboard is deployed (UDP to receive data) this is on user premises
90 The computing resource The virtual computing resource is deployed the RMI interface is for configuration
91 The computing resource The virtual computing resource is deployed the RMI interface is for configuration
92 The sensor It is the resource that implements the monitoring uploads the collector across the RMI interface
93 The sensor It is the resource that implements the monitoring uploads the collector across the RMI interface
94 GO! The dashboard receives the probe measurements......filtered by the sensor
95 GO! The dashboard receives the probe measurements......filtered by the sensor
96 Conclusions Microservices are appropriate for distributed monitoring The OCCI API is appropriate as the interface A prototype is available on dockerhub (look for occimon-live) BYOD demo on stage tonight at 17:45
97 Conclusions Microservices are appropriate for distributed monitoring The OCCI API is appropriate as the interface A prototype is available on dockerhub (look for occimon-live) BYOD demo on stage tonight at 17:45
98 Conclusions Microservices are appropriate for distributed monitoring The OCCI API is appropriate as the interface A prototype is available on dockerhub (look for occimon-live) BYOD demo on stage tonight at 17:45
99 Conclusions Microservices are appropriate for distributed monitoring The OCCI API is appropriate as the interface A prototype is available on dockerhub (look for occimon-live) BYOD demo on stage tonight at 17:45
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