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Dissecting Open Source Cloud Evolution: An OpenStack Case Study

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Tools and methods for analyzing the complexity of OpenStack (circa 2013). Since then, OpenStack has become much more complex.

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Dissecting Open Source Cloud Evolution: An OpenStack Case Study

  1. 1. Dissecting Open Source Cloud Evolution: An OpenStack Case Study Salman Baset, Chunqiang Tang, Byung Chul Tak, Long Wang IBM T. J. Watson Research Center June 26th, 2013
  2. 2. Open source cloud projects IaaS PaaS SaaS Broadly two types: (1) Native (listed here) (2) Adapters (e.g., Netflix on EC2) S. Baset, CQ Tang, B. Tak, L. Wang 2
  3. 3. Timeline for cloud open source 2006 2007 2008 2009 2010 2011 2012 Amazon EC2 Google App Engine 2005 2001 3
  4. 4. Two characteristics of open source cloud systems • Distributed multi-component architecture – Example: OpenStack and Cloud Foundry have more than 10 components for their IaaS controllers • Rapid development by a community of developers S. Baset, CQ Tang, B. Tak, L. Wang 4
  5. 5. Rapid development • Open source cloud projects are being developed and released at a rapid pace – OpenStack: releases every six months – Eucalyptus: every four months – OpenShift Enterprise: every four months • Compare it to – Linux kernel: 2-3 months (3.x – 3.(x+1) ) – Ubuntu distro releases: every six months • Major cloud providers are consuming OpenStack directly from development trunk – Two weeks behind the trunk S. Baset, CQ Tang, B. Tak, L. Wang 5
  6. 6. Why understand evolution? • Evolution: – A git commit or a major release • Research perspective – How logical operations (e.g., create a VM) change across major versions? • Developer perspective – What is the impact of my committed changes? • Provider perspective – Continuous deployment and delivery • How does a provider gain confidence in deploying a new release in production? • What is the impact of new changes and configuration options on logical operations? – Message flow, performance evaluation, fault injection etc S. Baset, CQ Tang, B. Tak, L. Wang 6
  7. 7. Methods for understanding evolution • Static – Source code – Documentation • Dynamic – Log analysis • Lab and/or production – Tracing message flow • With or without code instrumentation • Automatic correlation of message flow with logs • Lab and/or production – Fault injection – Performance study • Lab S. Baset, CQ Tang, B. Tak, L. Wang 7
  8. 8. Our solution • Without source code modification – Tracing – Tracing with log correlation – Fault injection • Other solutions – Google Dapper (built RPC framework leveraging callbacks) – Twitter Zipkin (attach identifiers to requests) S. Baset, CQ Tang, B. Tak, L. Wang 8
  9. 9. 9 Summary of our solution: Tracing • This simplified diagram shows one example path for one user request. • A path is the series of system events such as RECEIVE and SEND across servers captured using LD_PRELOAD technique. • Prior art: vPath constructs such causal path of system activities initiated by user requests. thread RECEIVE Monitoring Agent events caught application kernel Ex) Apache webserver thread RECEIVE Monitoring Agent events caught application kernel Ex) Application server thread RECEIVE Monitoring Agent events caught application kernel Ex) Database server Request SEND RECEIVE SEND SEND SEND RECEIVE SEND
  10. 10. 10 Summary of our solution: Tracing with queues • The path breaks if there are queues in the middle. – Apache web server inserts a message in the queue and returns – Application server retrieves the message from the queue and performs work – How do we correlate these messages? • Augment path information with unique message information – e.g., transaction ids • Run only one logical operation in the system if no unique message information thread RECEIVE Monitoring Agent events caught application kernel Ex) Apache webserver thread RECEIVE Monitoring Agent events caught application kernel Ex) Application server thread RECEIVE Monitoring Agent events caught application kernel Ex) Database server Request SEND RECEIVE SEND SEND SEND RECEIVE SEND Queue
  11. 11. 11 Summary of our solution: Log Analysis • Key idea – Combine the log information and causality (path) discovery technique Trace low-level system calls to infer causality and understand how an application executes Monitor log files and link log file entries to observed low-level system calls Link together Improved Semantics for Problem Diagnosis
  12. 12. 12 Diagram: Detecting Log Writes • During normal run, – Maintain a mapping between fd and file name string – Maintain a list of known/discovered log files • On ‘write’ system calls, – Check parameters and see if it is a ‘write’ on one of the log files. – If it is, and the data to be written contains alerting keywords such as ‘ERROR’, then this is a log write due to some errors. – This ‘write’ event will be annotated appropriately. Recv Read write SendRequest Websphere /var/log/was.log DB2 /var/log/db2/access.log DB2 /usr/local/db2/fie22xlv.log DB2 /usr/local/db2/fie23xlv.log log file name <Fragment of a Path> Parameters fd=5,offset=2048,data=“ERROR: …” 9 14 5 8 fd application
  13. 13. 13 Fault Injection for Building up Knowledge Base for Future Problem Diagnosis • Injects errors, observe application’s behavior, and build a knowledge base for future problem diagnosis – Alters a return value of a system call, e.g., mimic network communication error – It observes the logging reaction. – It repeats this for each system call and for each requests. – It accumulates the observed logging reactions as a knowledge base. • When an error message is logged in a production system, using the knowledge base to infer the probability of different root causes – Construct Bayesian Belief Network for inference • In the example figure, fault injection changes the return value of ‘Read’ event to -1. This triggers an error to be logged at the later part of the path. Recv Read write SendRequest Recv write Return value: 1024 Return value: -1 Parameter data=“ERROR: Record missing.” Newly appeared event Reaction to our error injection Altered
  14. 14. Brewing complexity: Evolution of OpenStack loc * Released Nova Cinder Glance Keystone Quantum Swift Total Austin Oct 2010 17,288 12,979 30,627 Bexar Feb 2011 27,734 3,629 16,014 47,377 Cactus Apr 2011 43,947 4,927 16,665 65,539 Diablo Sep 2011 66,395 9,961 12,451 15,591 91,947 Essex Apr 2012 87,750 15,698 11,555 17,646 149,596 Folsom Sep 2012 103,637 31,241 20,271 13,939 42,118 19,114 230,320 Grizzly Apr 2013 120,968 49,797 21,261 20,071 60,485 23,035 321,081 * CRLF and not python loc S. Baset, CQ Tang, B. Tak, L. Wang 14 Methodology wc -l `find . | grep -E '*.py' | grep -v test | grep -v 'doc'` wc -l `find . | grep -E '*.sh' | grep test | grep -v 'doc'`
  15. 15. nova database nova-api nova-scheduler nova-compute dashboard (horizon) keystone glance-api glance-registry glance database glance API (REST) AMQPdatabase keystone OpenStack logical architecture (grizzly+net+cinder) 15 keystone database REST REST AMQ P nova nova-conductor cinder-api cinder db AMQP cinder cinder-volume cinder-scheduler nova-network nova-cert nova-cells Compute nodes Volume nodes S. Baset, CQ Tang, B. Tak, L. Wang IMAGE REPO BLOCK STORAGE AUTHENTICATION COMPUTE CONTROLLER
  16. 16. nova database nova-api nova-scheduler nova-compute dashboard (horizon) keystone glance-api glance-registry glance database glance API (REST) AMQPdatabase keystone OpenStack logical architecture (grizzly+quantum+cinder) 16 keystone database REST REST AMQ P nova nova-conductor cinder-api cinder db AMQP cinder cinder-volume cinder-scheduler nova-cert nova-cells quantum-server quantum db AMQP quantum quantum-dhcp quantum-plugin quantum- metadata agent Compute nodes Volume nodes quantum-l3 agent quantum-l3 agent IMAGE REPO BLOCK STORAGE AUTHENTICATION COMPUTE CONTROLLER NETWORK CONTROLLER
  17. 17. OpenStack tracing • Understand OpenStack data and message flow for logical operations, e.g., – Create a VM – Delete a VM – List VMs – Create a volume – Add or remove volume to a VM – Create a floating IP address – Add or remove floating IP address from a VM – Create or destroy a virtual network • Understand – REST calls – Data flow – AMQP flow – Timing information 17 • Build data consistency tool • Gather data for generating performance load • Build a performance model S. Baset, CQ Tang, B. Tak, L. Wang
  18. 18. 18 Key observations from tracing OpenStack (1/2) • OpenStack is evolving very rapidly. Significant behavior changes from one release to another. • Total tables – Grizzly: 105 tables (160 with nova shadow tables), 53 in Diablo • Creating a VM (grizzly) – 139 SELECT queries, 37 INSERT queries, 74 UPDATE queries – 12 tables are touched for INSERT and UPDATE • In Diablo (Sep 2011), there were 450 SELECT, 4 INSERT, and 9 UPDATE queries – 717K read, 458K write – 655 send() calls to AMQP, 414 recv() calls • Deleting a VM – Only single record is deleted from database (rest are archived) • Request-id – Instance and request-id are stored in database (but only after updating quota) and before a request is sent to the scheduler. • Quota management – Entries are inserted in database to indicate resource allocation for a VM. Negative or NULL entries are inserted for deallocation. Each quota entry has expiration time (one day). E.g., core, fixedIP etc. • VM state and task state – networking, block_device_mapping, spawning • Keystone – Token verification is optimized in Grizzly using caches (for flavor=keystone) and PKI 18S. Baset, CQ Tang, B. Tak, L. Wang
  19. 19. 19 Key observations from tracing OpenStack (2/2) • Development of a data consistency checking tool – Orphan iptable rules (not associated with VM transaction) => security holes – Orphan data in tables due to errors in VM creation etc => audit and clean up – Orphan virsh data => audit and clean up S. Baset, CQ Tang, B. Tak, L. Wang 19S. Baset, CQ Tang, B. Tak, L. Wang
  20. 20. 20 Methodology • Run OpenStack in a machine (w/ and w/o timers disabled) • Diablo, Essex, Folsom, Grizzly • Ubuntu, RabbitMQ, MySQL • Use curl to send API request to OpenStack – flavor=keystone – Image has three parts • AMI, ram disk, kernel image – For keystone, PKI based token verification also used in grizzly – Each service’s token were created before issuing a create or delete VM call • Use our technique to capture message interaction, generate flow, run message analytics, and insert faults (on going) • curl_createserver.sh AUTHTOKEN=$1 curl -i http://9.47.240.166:8774/v2/3283d689d02c41248fc82c202e82055a/servers -X POST -H "X-Auth-Project-Id: admin" - H "User-Agent: python-novaclient" -H "Content-Type: application/json" -H "Accept: application/json" -H "X-Auth-Token: ${AUTHTOKEN}" -d '{"server": {"name": "test1", "imageRef": "de8882fb-94b3-4105-a212-c0a7fd8ab1e9", "flavorRef": "1", "max_count": 1, "min_count": 1, "networks": [{"uuid": "48de54f9-2a60-4f28-9740-d6317086c32a"}] }}' S. Baset, CQ Tang, B. Tak, L. Wang 20S. Baset, CQ Tang, B. Tak, L. Wang
  21. 21. 21 SQL queries in create, delete, list VMs and tables touched How to read: Tables touched (SQL queries) – [no of tables with INSERT or UPDATE] Diablo (Sep 2011) Essex (Apr 2012) Folsom (Sep 12 nova-network Folsom quantum Grizzly (April 12) nova-network Grizzly quantum SELECT (create) 16 (450) 17 (95) 21 (409) 26 (560) 20 (139) 37 (343) SELECT (delete) 8 (37) 10 (36) 17 (63) 23 (241) 13 (36) 31 (192) SELECT (list) 5 (31) 4 (12) 6 (24) 7 (25) 1 (1) 1 (1) INSERT (create) 4 (4) 4 (4) 8 (23) 9 (24) 10 (37) 13 (40) INSERT (delete) 0 (0) 0 (0) 1 (3) 1 (3) 3 (6) 4 (6) INSERT (list) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) UPDATE (create) 2 (9) - 5 3 (12) - 5 7 (60) - 11 7 (59) - 13 8 (74) – 13 8 (70) - 16 UPDATE (delete) 4 (6) - 4 6 (10) - 6 8 (22) - 9 8 (25) - 9 10 (31) - 11 10 (26) - 12 UPDATE (list) 0 (0) - 0 0 (0) - 0 0 (0) - 0 0 (0) - 0 0 (0) - 0 0 (0) - 0 DELETE (create) 0 (0) - 0 0 (0) - 0 0 (0) - 0 0 (0) - 0 0 (0) - 0 0 (0) - 0 DELETE (delete) 1 (1) 1 (1) 1 (1) 1 (1) 1 (1) 1 (1) Tables 53 4 (glance) 9 (keys) 39 (nova 63 4 (glance) 10 (keystone) 49 (nova) 67 5 (glance) 10 (keystone) 52 (nova) 67 (net)/83 q 16 (quantum) + folsom 6 (glance) 19 (keystone) 111 (nova) 55 shadow nova tb 136 (net)/160q 24 (quantum) + grizzly S. Baset, CQ Tang, B. Tak, L. Wang 21
  22. 22. 22 Keystone REST flow for creating a server (grizzly) 22 User Keystone nova-api glance-api Credentials Token (role) Get services and endpoints + token Services + endpoints Token + CreateInstance Verify + token Token + GetImage Verify + token image CreateInstance Success Accepted glance-registry Token + GetImage Verify + token image S. Baset, CQ Tang, B. Tak, L. Wang
  23. 23. 23 Create a VM: overview (1/4) • Which OpenStack component is issuing SELECT queries? Diablo Essex Folsom- nova- network Folsom- quantum Grizzly- nova- network Girzzly quantum Auth. keystone 422 54 358 484 82 243 API server nova-api 4 11 11 9 10 10 Agent on compute node nova- compute 4 5 13 14 0 0 Controller agent nova- conductor n/a n/a n/a n/a 15 16 Network agent on compute nova- network 13 19 17 n/a 20 n/a Scheduler nova- scheduler 1 2 1 1 4 4 Image registry server glance- registry 6 4 8 8 8 8 Network API server quantum- server n/a n/a n/a 44 n/a 62 23S. Baset, CQ Tang, B. Tak, L. Wang
  24. 24. 24 Create a VM: overview (2/4) • How many HTTP requests with respect to SELECT calls? Red indicates REST calls rcvd. Diablo Essex Folsom-nova- network Folsom- quantum Grizzly-nova- network Grizzly quantum keystone 422 54 358 484 82 243 30 GET 9 GET 17 GET 23 GET 3 GET 6 GET, 2POST nova-api 4 11 11 9 10 10 1 POST 1 POST 1 POST 1 POST 1 POST 1 POST nova-compute 4 5 13 14 0 0 nova-conductor n/a n/a n/a n/a 15 16 nova-network 13 19 17 n/a 20 n/a nova-scheduler 1 2 1 1 4 4 glance-api 0 0 0 0 0 0 2 GET, 5 HEAD 4 HEAD 8 HEAD 8 HEAD 8 HEAD 8 HEAD glance-registry 6 4 8 8 8 8 7 GET 4 GET 8 GET 8 GET 8 GET 8 GET quantum-server n/a n/a n/a 44 n/a 62 5 GET, 1 POST 9 GET, 1 POST 24 S. Baset, CQ Tang, B. Tak, L. Wang
  25. 25. Why so many SELECT queries in keystone? • In Diablo, for every keystone GET, 14 SELECT queries are issued, except for first query (16) • In Essex, for every keystone GET, 6 SELECT queries are issued • In Folsom-nova-net/quantum, for every keystone GET, 21 SELECT queries are issued, except for first query (22) • In Grizzly-nova-net, 27 SELECT queries for each request except for first (1). – Keystone tokens are also cached. So subsequent queries do not result into full keystone token authentication • If PKI token verification is used, the number of SELECT queries sent by keystone drop to 7 from 82. 25 keystone 422 54 358 484 82 243 30 GET 9 GET 17 GET 23 GET 3 GET 6 GET, 2POST S. Baset, CQ Tang, B. Tak, L. Wang
  26. 26. 26 Create a VM: overview (3/4) • What if there is no keystone? Keystone enabled Keystone disabled S. Baset, CQ Tang, B. Tak, L. Wang 26 Diablo Essex Folsom- nova- network Folsom- quantum Grizzly- nova- network Grizzly quantum SELECT 28 41 51 76 57 100 INSERT 4 4 23 24 37 38 UPDATE 6 10 60 58 74 70 Diablo Essex Folsom- nova- network Folsom- quantum Grizzly- nova- network Grizzly quantum SELECT 450 95 409 560 139 343 INSERT 4 4 23 24 37 40 UPDATE 6 10 60 58 74 70 S. Baset, CQ Tang, B. Tak, L. Wang
  27. 27. 27 Create a VM: overview (4/4) • Which components are issuing INSERT and UPDATE queries? (keystone enabled for all) INSERT Diablo Essex Folsom nova-network Folsom quantum Grizzly nova-network Grizzly quantum keystone 2 nova-api 3 (3) 3 (3) 6 (10) 6 (10) 7 (21) 7 (21) nova-compute 1 (12) 2 (12) nova-conductor 2 (13) 2 (13) nova-network 1 1 1 2 nova-scheduler 1 1 quantum-server 2 3 S. Baset, CQ Tang, B. Tak, L. Wang 27 UPDATE Diablo Essex Folsom-nova- network Folsom- quantum Grizzly nova-network Grizzly quantum nova-api 1 1 9 9 7 7 nova-compute 1 (5) 1 (6) 4 (47) 4 (47) nova-conductor 5 (59) 5 (59) nova-network 3 4 3 6 1 nova-scheduler 1 1 1 2 2 quantum-server 1
  28. 28. 28 Grizzly nova-net SELEC T 2 block_device_mapping 6 compute_node_stats 6 fixed_ips 1 floating_ips 8 images 4 instance_actions 2 instance_actions_events 1 instance_info_caches 4 networks 2 provider_fw_rules 5 quotas 4 quota_usages 2 reservations 7 role 1 security_group_rules 3 security_groups 4 virtual_interfaces S. Baset, CQ Tang, B. Tak, L. Wang 28 Grizzly nova-net INSERT 12 compute_node_stats 1 instance_actions 2 instance_actions_events 1 instance_id_mappings 1 instance_info_caches 1 instances 13 instance_system_metadata 4 reservations 1 security_group_instance_associatio n 1 virtual_interfaces Grizzly nova-net UPDATE 6 compute_nodes 44 compute_node_stats 3 fixed_ips 2 instance_actions_events 1 instance_info_caches 8 instances 8 quota_usages 2 reservations Tables touched for create VM in grizzly-nova-net S. Baset, CQ Tang, B. Tak, L. Wang
  29. 29. 29 Dataflow flow for creating a server (grizzly) (1/2) 29 nova-api nova-scheduler nova-conductor nova-compute Create server Check quota INSERT INTO reservations (instances, expires, usageid1) INSERT INTO reservations (ram, expires, usageid2) INSERT INTO reservations (core, expires, usageid3) UPDATE quota_usages (usageid1) UPDATE quota_usages (usageid2) UPDATE quota_usages (usageid3) Check if images exist INSERT INTO instances (‘instance_uuid’) INSERT INTO security_group_instance_association (‘instance_uid’) INSERT INTO instance_system_metadata (‘image_kernel_id, instance_uuid’) INSERT INTO instance_system_metadata (‘instance_type_memory_mb’) INSERT INTO instance_system_metadata (‘instance_type_swap’) INSERT INTO instance_system_metadata (‘instance_type_vcpu_weight’) INSERT INTO instance_system_metadata (‘instance_type_root_gb’) INSERT INTO instance_system_metadata (‘instance_type_id’) INSERT INTO instance_system_metadata (‘image_ramdisk_id’) INSERT INTO instance_system_metadata (‘instance_type_name’) INSERT INTO instance_system_metadata (‘instance_type_ephemeral_gb’) INSERT INTO instance_system_metadata (‘instance_type_rxtx_factor’) INSERT INTO instance_system_metadata (‘instance_type_flavorid’) INSERT INTO instance_system_metadata (‘instance_type_flavorid’) INSERT INTO instance_system_metadata (‘image_base_image_ref’) INSERT INTO instance_info_caches (‘instance_uuid) Create reservations. No request id. Default: expires after a day if not updated. Update quotas. What if nova-api dies here? Then quota updates can potentially be permanent until expired or cleanup. Create instance in the database.
  30. 30. 30 Dataflow flow for creating a server (grizzly) (2/2) 30 nova-api nova-scheduler nova-compute nova-conductor INSERT into instance_id_mappings(‘instance_uuid’) Update time in quota_usages table INSERT INTO instance_actions (instance_uuid, request_id) Send to scheduler (request_id) INSERT into instance_action_events(scheduling) nova-network INSERT into instance_actions_events(compute_run) Libvirt – create instance UPDATE instances (task_state = NULL) GET images from glance UPDATE instances (host, node) UPDATE compute_node_stats * INSERT INTO compute_node_stats UPDATE instances (task_state=networking) This request is key. It associates instance id with a request id. But occurs after quota and reservations has been updated. BAD!!! S. Baset, CQ Tang, B. Tak, L. Wang
  31. 31. 31 How many SQL queries for create VM before a request is sent to: S. Baset, CQ Tang, B. Tak, L. Wang 31 Diablo Essex Folsom-nova- network Folsom- quantum Grizzly-nova- network Grizzly quantum SELECT 202 10 27 289 98 138 INSERT 0 0 3 10 21 21 UPDATE 0 0 3 9 7 7 S. Baset, CQ Tang, B. Tak, L. Wang Diablo Essex Folsom-nova- network Folsom- quantum Grizzly-nova- network Grizzly quantum SELECT 371 52 292 290 100 140 INSERT 3 3 10 10 22 22 UPDATE 1 2 10 10 8 8 scheduler compute Diablo Essex Folsom-nova- network Folsom- quantum Grizzly-nova- network Grizzly quantum SELECT 450 95 409 560 139 343 INSERT 4 4 23 24 37 40 UPDATE 6 10 60 58 74 70
  32. 32. 32 Create VM total message bytes – read() or recv() S. Baset, CQ Tang, B. Tak, L. Wang 32 Diablo Essex Folsom nova-network Folsom quantum Grizzly nova-network keystone 154841 23090 198493 269920 41888 nova-api 65596 81836 75507 21435 22766 nova-compute 155233 (113701) 157660 (105460) 202163 (163107) 206003 (167383) 106396 (110721) nova-conductor n/a n/a n/a n/a 371614 nova-network 98101 77184 62509 n/a 103100 nova-scheduler 3380 38477 16465 19688 29674 glance-registry 36764 16632 45798 46104 30494 glance-api 17440 6326 32386 32716 11248 quantum-server n/a n/a n/a 46533 n/a quantum-dhcp n/a n/a n/a 3722 n/a Total 531355 401205 582185 650,615 717,180 S. Baset, CQ Tang, B. Tak, L. WangExcludes any image transfer
  33. 33. 33 Create VM total message bytes – write() or send() S. Baset, CQ Tang, B. Tak, L. Wang 33 Diablo Essex Folsom nova-network Folsom quantum Grizzly nova-network keystone 115606 15129 128957 174884 25364 nova-api 50704 70995 25449 20265 22693 nova-compute 99899 109136 127436 126143 (122363) 74864 (68352) nova-conductor n/a n/a n/a n/a 222228 nova-network 74106 63446 46123 n/a 57321 nova-scheduler 2964 30182 17662 21993 26997 glance-registry 23095 11006 18210 18196 20329 glance-api 8841 5038 10226 10220 8705 quantum-server n/a n/a n/a 25986 n/a quantum-dhcp n/a n/a n/a 84 n/a Total 375,447 305,156 374,499 403,507 458,501 S. Baset, CQ Tang, B. Tak, L. Wang
  34. 34. 34 Create a VM: Message exchange with RabbitMQ – send() Diablo Essex Folsom nova-network Folsom- quantum Grizzly nova-network nova-api 23 (3392) 35 (4769) 23 (8600) 11 (5254) 11 (4062) nova-compute 18 (1316) 18 (1430) 18 (3782) 1 (21) 306 (67874) nova-network 31 (1816) 45 (1018) 32 (2159) n/a 14 (1786) nova- scheduler 23 (2392) 12 (2976) 12 (7388) 12 (9737) 7 (11567) nova- conductor n/a n/a n/a n/a 317 (82717) quantum- server n/a n/a n/a 36 (4498) n/a quantum-dhcp n/a n/a n/a 4 (84) n/a S. Baset, CQ Tang, B. Tak, L. Wang 34S. Baset, CQ Tang, B. Tak, L. Wang
  35. 35. 35 Create a VM: Message exchange with RabbitMQ – recv() Diablo Essex Folsom nova-network Folsom- quantum Grizzly nova-network nova-api 16 (833) 25 (1609) 16 (833) 7 (328) 7 (328) nova-compute 14 (3442) 14 (2369) 14 (8752) 1 (9479) 230 (94463) nova-network 18 (1808) 26 (3045) 19 (7298) n/a 8 (2699) nova- scheduler 8 (2479) 8 (2918) 8 (5307) 8 (5345) 4 (3861) nova- conductor n/a n/a n/a n/a 172 (58721) quantum- server n/a n/a n/a 24 (396) n/a quantum-dhcp n/a n/a n/a 4 (3726) n/a S. Baset, CQ Tang, B. Tak, L. Wang 35S. Baset, CQ Tang, B. Tak, L. Wang
  36. 36. S. Baset, CQ Tang, B. Tak, L. Wang 36 2176 comp 172 cond 1667 gapi 139 greg 3 keys 5429 napi 12 netw 4 sche 308 comp 317 cond 17 gapi 9 greg 3 keys 19 napi 19 netw 7 sche Create a VM: send() and recv() grizzly-nova net send() recv() Single byte recv in webob library
  37. 37. Conclusions • Complexity is brewing under OpenStack. Beware! • Build distributed applications with tracing in mind • Flow diff – Through an interactive page • Ongoing and future work – Fault injection and log correlation – Leverage tool for other projects, e.g., CloudFoundry S. Baset, CQ Tang, B. Tak, L. Wang 37

Tools and methods for analyzing the complexity of OpenStack (circa 2013). Since then, OpenStack has become much more complex.

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