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3-D Cloud Monitoring:
Enabling Effective Cloud Infrastructure and
Application Management
Dario Bruneo, Francesco Longo,
An...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
Outline
• Managing and monitoring Cloud en...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
Managing cloud environments
• Appropriate ...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
Managing cloud environments (cont’d.)
• Ho...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
Cloud monitoring
• The decision on which m...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
Cloud monitoring
• The decision on which m...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
Cloud monitoring
• The decision on which m...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
Cloud monitoring
• The decision on which m...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
Cloud layers
6
App 1
App 2
Let’s have a lo...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
Cloud layers
6
App 1
App 2
DBDB AS
AS LBAS...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
Cloud layers
6
App 1
App 2
DBDB AS
AS LBAS...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
Cloud layers
6
App 1
App 2
DBDB AS
AS LBAS...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
Cloud layers
6
App 1
App 2
DBDB AS
AS LBAS...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
Cloud layers
6
App 1
App 2
DBDB AS
AS LBAS...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
Data collection
• How to integrate data co...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
Data management
• Just the collection of d...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
3-D Cloud monitoring model
• We propose us...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
3-D Cloud monitoring model (cont’d.)
(1) t...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
Monitoring slices
• The space of the colle...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
Monitoring slices (cont’d.)
• We use the i...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
Monitoring slices (cont’d.)
• We use the i...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
Monitoring slices (cont’d.)
• We use the i...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
Context of Interest
• A Context of Interes...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
Toward a complete formalism
• We proposed ...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
• The goal of using the CoI is to define th...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
The WordPress case study
• WordPress is a ...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
Synthetic traffic generation
• The load on ...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
Synthetic traffic generation (cont’d.)
• We...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
Testbed arrangement
• Different cloud
depl...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
Testbed arrangement (cont’d.)
• We first id...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
Results
• Baseline configuration
• during t...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
Results
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0
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40
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00:00 00:10 0...
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Results
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Summary
• We proposed a new 3-D monitoring...
IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo
/25
thank you!
Dario Bruneo
dbruneo@unime.it
D...
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3-D Cloud Monitoring: Enabling Effective Cloud Infrastructure and Application Management (CNSM 2014)

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Presentation by Dario Bruneo for the 2014 Conference on Network and Service Management (CNSM 2014) in Rio de Janeiro, Brazil.

Reference:
C. Cassales Marquezan, D. Bruneo, F. Longo, F. Wessling, A. Metzger and A. Puliafito, “3-D Cloud Monitoring: Enabling Effective Cloud Infrastructure and Application Management.”, Proceedings of the 10th International Conference on Network and Service Management (CNSM 2014), Rio de Janeiro, Brazil, 2014

Abstract:
A cloud environment is a complex environment composed of many different entities and layers. Each of these cloud entities may be furnished with mechanisms offering various management actions. For any given situation, different management actions may be applicable and often simultaneously. Enforcing isolated management actions or combining contradictory management actions may negatively affect cloud application quality and cloud infrastructure performance. This means that correctly selecting and effectively combining these management actions for a given situation becomes an important challenge in cloud computing. In this paper, we address the problem of identifying situations where more than one management action can be performed. The key contributions of our paper are: (1) a three dimensional (3-D) monitoring model for analyzing cloud monitoring information; (2) the concept and formalization of Context of Interest (CoI) that specifies how to retrieve meaningful information from the 3-D model to support the coordination of management actions between cloud infrastructure and application. We conducted experiments in a real testbed using Openstack and the WordPress Web site application. Our results show that analyzing cloud monitoring information using the 3-D model and the CoI can support a more effective identification of management actions to be taken.

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3-D Cloud Monitoring: Enabling Effective Cloud Infrastructure and Application Management (CNSM 2014)

  1. 1. 3-D Cloud Monitoring: Enabling Effective Cloud Infrastructure and Application Management Dario Bruneo, Francesco Longo, Antonio Puliafito Dipartimento di Ingegneria DICIEAMA Università degli Studi di Messina Messina, Italy IEEE / IFIP International Conference on Network and Service Management (CNSM) - Nov 17-21 2014 - Rio de Janeiro (Brazil) Clarissa Cassales Marquezan, Florian Wessling,Andreas Metzger Paluno University of Duisburg-Essen Essen, Germany
  2. 2. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 Outline • Managing and monitoring Cloud environments • Cloud layers • Data collection and management • The proposed 3-D monitoring model • Context of Interest • A WordPress-based quantitative analysis 2
  3. 3. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 Managing cloud environments • Appropriate cloud management 
 is essential in order to respect 
 stringent SLAs in terms of: • quality of service • operational costs • availability/reliability • Popular management actions include: 3 • multi-cloud resource management • load balancing • elasticity • migration of virtual resources • infrastructure resource management
  4. 4. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 Managing cloud environments (cont’d.) • However, a cloud environment is a complex environment composed of many different entities and layers 4 Source: ThinkStock • these entities typically offer dedicated management actions that can be used to enable dynamic adaptation at runtime • various management actions can be applied simultaneously • this might negatively affect both cloud application QoS and cloud infrastructure performance!
  5. 5. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 Cloud monitoring • The decision on which management action(s) are appropriate for a given situation depends on monitoring and analysis support able to consider the complex dependencies between cloud entities and their management mechanisms 5
  6. 6. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 Cloud monitoring • The decision on which management action(s) are appropriate for a given situation depends on monitoring and analysis support able to consider the complex dependencies between cloud entities and their management mechanisms 5 Application monitoring
  7. 7. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 Cloud monitoring • The decision on which management action(s) are appropriate for a given situation depends on monitoring and analysis support able to consider the complex dependencies between cloud entities and their management mechanisms 5 Application monitoring Infrastructure monitoring
  8. 8. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 Cloud monitoring • The decision on which management action(s) are appropriate for a given situation depends on monitoring and analysis support able to consider the complex dependencies between cloud entities and their management mechanisms 5 Unified monitoring!!!
  9. 9. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 Cloud layers 6 App 1 App 2 Let’s have a look at the way Cloud applications are composed/ deployed
  10. 10. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 Cloud layers 6 App 1 App 2 DBDB AS AS LBAS Let’s have a look at the way Cloud applications are composed/ deployed • each application is composed by a set of entities, such as servers and software components (e.g., web servers, database servers)
  11. 11. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 Cloud layers 6 App 1 App 2 DBDB AS AS LBAS VM VM VM VM VM Let’s have a look at the way Cloud applications are composed/ deployed • each application is composed by a set of entities, such as servers and software components (e.g., web servers, database servers) • such entities are deployed using single or multipleVMs
  12. 12. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 Cloud layers 6 App 1 App 2 DBDB AS AS LBAS VM VM VM VM VM PM PM PM Let’s have a look at the way Cloud applications are composed/ deployed • each application is composed by a set of entities, such as servers and software components (e.g., web servers, database servers) • such entities are deployed using single or multipleVMs • VMs are allocated in different PMs according to provider-specific algorithms
  13. 13. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 Cloud layers 6 App 1 App 2 DBDB AS AS LBAS VM VM VM VM VM PM PM PM Physical Layer Virtualization Layer Application Architecture Layer Application Business Logic Layer Let’s have a look at the way Cloud applications are composed/ deployed • each application is composed by a set of entities, such as servers and software components (e.g., web servers, database servers) • such entities are deployed using single or multipleVMs • VMs are allocated in different PMs according to provider-specific algorithms
  14. 14. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 Cloud layers 6 App 1 App 2 DBDB AS AS LBAS VM VM VM VM VM MonitoringBus PM PM PM Physical Layer Virtualization Layer Application Architecture Layer Application Business Logic Layer Let’s have a look at the way Cloud applications are composed/ deployed • each application is composed by a set of entities, such as servers and software components (e.g., web servers, database servers) • such entities are deployed using single or multipleVMs • VMs are allocated in different PMs according to provider-specific algorithms Data coming from different layers have to be opportunely collected (e.g., integrated tools)
  15. 15. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 Data collection • How to integrate data coming from different layers? 7 Pollster 1 libvirt Pollster 2 libvirt Pollster n libvirt App Pollster CW Probe Plugin 1 Plugin 2 Plugin m Internal monitoring tools DB External monitoring tools DB Ceilometer Compute App VM AMQP queue Ceilometer Controller AMQP queue DB dispatcher CEP dispatcher Mongo DB
  16. 16. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 Data management • Just the collection of data is not enough • It is still necessary the definition of a monitoring model that can lead to 8 • successful data analyses • easy runtime detection of situations where multiple management actions could be applied
  17. 17. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 3-D Cloud monitoring model • We propose using three dimensions to define a cloud monitoring model to support an elaborated 
 analysis of the measured 
 information • We assume: • entities can be monitored and their 
 measurements can be correlated • the existence of mappings among the entities across layers • the Monitoring as a Service model is used 9
  18. 18. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 3-D Cloud monitoring model (cont’d.) (1) the PM on top of which the metric was collected (2) the application to which the metric is related (3) the kind of metric and its corresponding cloud layer 
 10 Application Architecture Monitoring Volume Physical Machines Cloud Metrics PLVLAALABLL Applications Cloud Environment Monitoring Volume A specific metric within the space of the collected data can be identified by three different characteristics: The Monitoring Volume can be sub-divided and reduced into monitoring sub-volumes
  19. 19. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 Monitoring slices • The space of the collected metrics can be cut through with the use of planes to identify subsets of the space that are of interest for a given situation 11 Such a monitoring slice groups all the metrics in all the layers that are collected for application appi, on top of all the PMs that host virtual resources associated with appi. Such a monitoring slice involves all the metrics in all the layers that are related to pmj Physical Machines Cloud Metrics appi Monitoring Slice PLVLAALABLL Applications app i Physical Machines Cloud Metrics pmj Monitoring Slice PLVLAALABLL pmj Applications
  20. 20. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 Monitoring slices (cont’d.) • We use the intersection among monitoring slices from different planes to detect situations that will eventually lead to the activation of multiple management actions throughout the different cloud entities 12 Physical Machines Cloud Metrics pmj Monitoring Slice PLVLAALABLL pmj Applications
  21. 21. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 Monitoring slices (cont’d.) • We use the intersection among monitoring slices from different planes to detect situations that will eventually lead to the activation of multiple management actions throughout the different cloud entities 12 appi Monitoring Slice app i Physical Machines Cloud Metrics pmj Monitoring Slice PLVLAALABLL pmj Applications The intersection of the two slices (highlighted in red) represents the set of the metrics in the different cloud layers that are related to application appi and measured in pmj
  22. 22. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 Monitoring slices (cont’d.) • We use the intersection among monitoring slices from different planes to detect situations that will eventually lead to the activation of multiple management actions throughout the different cloud entities 12 appi Monitoring Slice app i Physical Machines Cloud Metrics pmj Monitoring Slice PLVLAALABLL pmj Applications The intersection of the two slices (highlighted in red) represents the set of the metrics in the different cloud layers that are related to application appi and measured in pmj • not all the monitoring data in an intersection is relevant for the 
 identification of such situations • for this reason we introduce the concept of Context of Interest
  23. 23. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 Context of Interest • A Context of Interest (CoI) is the simultaneous observation of metrics in the intersection of monitoring slices that are relevant for taking management actions in different layers of the cloud environment in response to dynamic changes inside such an environment 13 • this concept does not determine how information is collected in the cloud environment • it rather establishes how such information will be analyzed CPU Mem …
  24. 24. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 Toward a complete formalism • We proposed a complete formalism for the CoI concept (described in the paper) • Goals: • creating automatic configuration tools based on information models • investigating the use of correlation techniques to automatically devise CoI 14 {Oz} obtained from the intersection between the application monitoring slice SA related to application app and a physical machine cut set CPM, with 1  z  |CPM| satisfying the following constraints: 9 c0 = (app, pm0 ,m0 ), c00 = (app, pm00 ,m00 ) 2 CoIA : c0 2 Oz0 ,c00 2 Oz00 , z0 6= z00 OR layer(m0 ) 6= layer(m00 ) where the function layer returns the metric set corresponding to a metric m. The constraints guarantee that there will exist at least 2 metrics associated with the same application that belong either to different PMs or layers of the cloud environment. This is necessary to characterize situations where management actions for the same application can be coordinated. Being these actions either in entities hosted in different physical machines or in different layers of the cloud environment but all associated with the same application. Definition 16 (Physical Machine Context of Interest) A physical machine context of interest CoIPM is a set of observ- ing data sets {Oz} obtained from the intersection between the onding to the Application Architecture Layer. Let us r a function f : V ⇥ T ! R [ {null} where T ✓ R+ set of time instants. Then, f(i, j,k,t) is the value ted with the metric mk related to the components ication appi deployed in the PM pmj, measured at stant t. If the application appi does not have any nent actually deployed in pmj or the measure mk does ly to the specific application, the null value is returned. nition 10 (Application Monitoring Slice) An tion monitoring slice SA is a plane {(app, pmj,mk)} cloud monitoring volume V obtained by considering points in the volume associated to a specific app 2 A . nition 11 (Physical Machine Monitoring Slice) sical machine monitoring slice SPM is a plane pm,mk)} in the cloud monitoring volume V obtained sidering all the points in the volume associated to a pm 2 P. 3 graphically depicts two monitoring slices. The tion between the application monitoring slice ted to appi and the physical machine monitoring slice ted to pmj is represented by a dashed line. nition 12 (Observing Data Set) An observing t O over a subset of metrics Mo ✓ M is a subset pm,mo)} (with mo 2 Mo) of the monitoring volume V d by properly selecting the points in the intersection n a physical machine monitoring slice SPM (associated hysical machine pm) and an application monitoring SA (associated to an application app) that correspond ics in Mo. that belong either to different PMs or layers of the clou environment. This is necessary to characterize situation where management actions for the same application can b coordinated. Being these actions either in entities hosted i different physical machines or in different layers of the clou environment but all associated with the same application. Definition 16 (Physical Machine Context of Interest) physical machine context of interest CoIPM is a set of observ ing data sets {Oz} obtained from the intersection between th physical machine monitoring slice SPM related to the physic machine pm and an application cut set CA, with 1  z  |CA satisfying the following constraints: 9 c0 = (app0 , pm,m0 ), c00 = (app0 , pm,m00 ) 2 CoIPM : c0 2 Oz0 ,c00 2 Oz00 , z0 6= z00 OR layer(m0 ) 6= layer(m00 ). The constraints, in this case, guarantee that there will exist least 2 metrics associated with the same PM that belong eithe to different applications or layers of the cloud environmen The objective of these constraints is to characterize situation to coordinate management actions inside the same physic machine that affect either different applications or entities i different layers of the cloud for the specific physical machine Definition 17 (Context of Interest) A context of intere CoI is either an application context of interest or a physic machine context of interest. Multiple CoIs can be define for the same cloud monitoring volume. In addition, the sam type of metric can appear in one or more CoIs.
  25. 25. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 • The goal of using the CoI is to define the set of metrics that expose situations in which multiple management 
 actions would be applicable Management Actions 15 if ((abllm_response is high) AND (aalm_mysql_conn is high) AND (abllm_wb_perrors is high) AND (aalm_wb_pages is low) AND (vlm_cpu is high) AND (vlm_mem is high)) then Apply management action (4) ... if ((aalm_mysql_conn is high) AND (abllm_wb_perrors is low) AND (vlm_cpu is high) AND (vlm_mem is high)) then Apply management action (1) Fig. 4. Using CoI for specification of management action rules. TABLE I EXAMPLE OF A CoI FOR THE WORDPRESS APPLICATION. Layer Tuple Description MPL (app1, pm2, plm cpu) Physical CPU Usage MPL (app1, pm2, plm mem) Physical Memory Usage MVL (app1, pm2,vlm cpu) Virtual CPU Usage MVL (app1, pm2,vlm mem) Virtual Memory Usage MAAL (app1, pm2,aalm mysql conn) MySQL Num. of Connections MAAL (app1, pm2,aalm wb pages) Apache Pages per Second MABLL (app1, pm2,abllm wb perrors) WordPress Pages Errors MABLL (app1, pm2,abllm response) WordPress Response Time be used for the WordPress application in a cloud environment. WordPress is a CMS (Content Management System) for per- sonal blogs and Web sites. It allows a simple creation of posts and pages together with a simple comment management. The WordPress system architecture is composed of the Apache2 Web server, the MySQL DBMS, the Memchached tool, and the code implementing the functionalities of the CMS itself. Different cloud deployment configurations can be used for this application. We assume the simplest deployment alternative where all components of the WordPress application are de- ployed inside the same VM. Using the model proposed in [9], we first identify the possible changes that can be suffered by the entities in the cloud environment hosting this specific I lyz mo is t and fro usi ma not all the actions should 
 be actually enforced • one is not the most appropriate for the situation • all of them applied together will only reinforce the problem experienced 
 by the application
  26. 26. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 The WordPress case study • WordPress is a CMS (Content Management System) for personal blogs and Web sites • It allows a simple creation of posts and pages together with a simple comment management 16 • The WordPress system architecture is composed of • the Apache2 Web server • the MySQL DBMS • the Memchached tool • the code implementing the functionalities of the CMS itself
  27. 27. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 Synthetic traffic generation • The load on the WordPress Web site is synthetically generated using the Apache Jmeter tool • It allows to record the actions executed by users when interacting with the Web site • These actions are saved in terms of the 
 HTTP requests the users performed and 
 then Jmeter reproduces such requests 
 according to a certain traffic pattern 17
  28. 28. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 Synthetic traffic generation (cont’d.) • We consider five categories of users, who perform a different set of operations in a specific order. • The users continuously perform their set of actions waiting for a random time between one set and the next one • We defined three different scenarios (low, medium, and high) in order to stress the system • For each scenario, we change the number of users in each category that are concurrently performing a set of operations in the WordPress Web site 18 ances with different ctions, e.g., t1.micro all (with 150 connec- nfigured the MySQL ncurrent connections. er tool that emulates discussed in the next ons are also depicted monitoring tools. We m the Physical layer Ceilometer to collect er (vitual CPU and yCheckPoint extract layer, respectively, ond provided by the ncurrent connections Application Business a random time between one set and the next one. We defined three different scenarios (low, medium, and high) in order to stress the system. For each scenario, we change the number of users in each category that are concurrently performing a set of operations in the WordPress Web site. TABLE II CATEGORIES OF USERS AND POPULATION CHARACTERIZATION FOR EACH OF THE THREE SCENARIOS DESCRIBED IN EACH COLUMN. User Type and Description Low Load Medium Load High Load Admin creating new post 1 1 1 Guest reading latest post 1 15 30 Guest reading random post 1 15 30 Guest reading latest post 1 15 30 and leaving a comment Guest reading random post 1 15 30 and leaving a comment
  29. 29. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 Testbed arrangement • Different cloud deployment configurations can be used for this application • We assume the simplest deployment alternative where all components of the WordPress application are deployed inside the sameVM 19 ! 6 VCPU ! 16 GB Memory ! 20 GB virtual storage ! Scientific Linux 6.5 ! Apache2 with php5 and php-pecl-memcached plugin ! MySql-Server 5.1.73 ! Memcached 1.1.4 ! Wordpress 3.8.1 ! Wordpress plugins: ○ Advanced Random Posts Widget 1.51 ○ Configure Login Timeout 1.0 ○ Disable Check Comment Flood 1.0 ○ Memcached Object Cache 2.0.2 ! CPU: 2 x Dual-Core AMD Opteron 2218 HE @ 2.8 GHz (2 cores each) ! RAM: 8 GB ! Storage: 73 GB SATA disk ! Scientific Linux 6.5 ! Openstack (Havana) ● CPU: Intel Xeon E5506 @ 2.13 GHz (8 cores each) ● RAM: 36 GB ! Storage: 6 x 500 GB SATA disks ● Scientific Linux 6.5 ● Openstack (Havana) MySql Memcached Apache WordPress OpenStack controller OpenStack compute Cloud Environment Client Side Jmeter PM1 PM2 PM3 ● Jmeter 2.11 VM1 Key Ceilometer (Havana) Nagios (3.5.1) AWStats (7.3.1) Jmeter (2.11) analysis plugin Mycheckpoint (231-1)
  30. 30. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 Testbed arrangement (cont’d.) • We first identify the possible changes that can be suffered by the entities in the cloud environment hosting this specific deployment of the WordPress Application • increase virtual resources • request a newVM and move some components of the application to the newVM • change the Apache Web server configuration • change the MySQL DBMS configuration 20 TABLE I EXAMPLE OF A CoI FOR THE WORDPRESS APPLICATION. Layer Tuple Description MPL (app1, pm2, plm cpu) Physical CPU Usage MPL (app1, pm2, plm mem) Physical Memory Usage MVL (app1, pm2,vlm cpu) Virtual CPU Usage MVL (app1, pm2,vlm mem) Virtual Memory Usage MAAL (app1, pm2,aalm mysql conn) MySQL Num. of Connections MAAL (app1, pm2,aalm wb pages) Apache Pages per Second MABLL (app1, pm2,abllm wb perrors) WordPress Pages Errors MABLL (app1, pm2,abllm response) WordPress Response Time if ((abllm_response is hi AND (aalm_mysql_conn AND (abllm_wb_perrors AND (aalm_wb_pages is AND (vlm_cpu is high) AND (vlm_mem is high) then Apply management ac ... if ((aalm_mysql_conn is h AND (abllm_wb_perrors AND (vlm_cpu is high) AND (vlm_mem is high) then Apply management CoI
  31. 31. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 Results • Baseline configuration • during the high load scenario, the system is overloaded and both the number of errors and the response time increase considerably • typically the metrics from Physical and Virtualization layers would be observed in order to activate the management action • A natural reaction to such a situation would be the activation of traditional auto-scaling policies 21 0 20 40 60 80 100 00:00 00:10 00:20 00:30 00:40 00:50 01:00 01:10 01:20 01:30 CPU% Time [h:m] PM VM 0 20 40 60 80 100 00:00 00:10 00:20 00:30 00:40 00:50 01:00 01:10 01:20 01:30 Mem(used)% Time [h:m] PM VM 0 20 40 60 80 100 00:00 00:10 00:20 00:30 00:40 00:50 01:00 01:10 01:20 01:30 MySqlconnections(#) Time [h:m] 0 10 20 30 40 50 00:00 00:10 00:20 00:30 00:40 00:50 01:00 01:10 01:20 01:30 Apachepages/sec Time [h:m] 0 20 40 60 80 100 00:00 00:10 00:20 00:30 00:40 00:50 01:00 01:10 01:20 01:30 WPerrors% Time [h:m] 0 20 40 60 80 100 00:00 00:10 00:20 00:30 00:40 00:50 01:00 01:10 01:20 01:30 Responsetime(sec) Time [h:m] low medium high Cloudlayers Load intensity
  32. 32. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 Results 22 0 20 40 60 80 100 00:00 00:10 00:20 00:30 00:40 00:50 01:00 01:10 01:20 01:30 CPU% Time [h:m] PM VM 0 20 40 60 80 100 00:00 00:10 00:20 00:30 00:40 00:50 01:00 01:10 01:20 01:30 Mem(used)% Time [h:m] PM VM 0 20 40 60 80 100 00:00 00:10 00:20 00:30 00:40 00:50 01:00 01:10 01:20 01:30 MySqlconnections(#) Time [h:m] 0 10 20 30 40 50 00:00 00:10 00:20 00:30 00:40 00:50 01:00 01:10 01:20 01:30 Apachepages/sec Time [h:m] 0 20 40 60 80 100 00:00 00:10 00:20 00:30 00:40 00:50 01:00 01:10 01:20 01:30 WPerrors% Time [h:m] 0 20 40 60 80 100 00:00 00:10 00:20 00:30 00:40 00:50 01:00 01:10 01:20 01:30 Responsetime(sec) Time [h:m] low medium high • #Virtual CPUs: 6 ➜ 8 • there are no significant improvements in terms of quality of service • this experiment demonstrates that the management action consisting of increasing the number of virtual CPUs associated withVM1 does not solve the situation • the knowledge of information coming from different layers allows for a different management action to be considered as a possible solution to the overload situation
  33. 33. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 Results 23 0 20 40 60 80 100 00:00 00:10 00:20 00:30 00:40 00:50 01:00 01:10 01:20 01:30 CPU% Time [h:m] PM VM 0 20 40 60 80 100 00:00 00:10 00:20 00:30 00:40 00:50 01:00 01:10 01:20 01:30 Mem(used)% Time [h:m] PM VM 0 20 40 60 80 100 00:00 00:10 00:20 00:30 00:40 00:50 01:00 01:10 01:20 01:30 MySqlconnections(#) Time [h:m] 0 10 20 30 40 50 00:00 00:10 00:20 00:30 00:40 00:50 01:00 01:10 01:20 01:30 Apachepages/sec Time [h:m] 0 20 40 60 80 100 00:00 00:10 00:20 00:30 00:40 00:50 01:00 01:10 01:20 01:30 WPerrors% Time [h:m] 0 20 40 60 80 100 00:00 00:10 00:20 00:30 00:40 00:50 01:00 01:10 01:20 01:30 Responsetime(sec) Time [h:m] low medium high • maximum MySQL concurrent connections: 50 ➜ 100 • the actual problem is related to the maximum number of concurrent connections at the MySQL DBMS • the quality of service experienced by the users has been greatly improved and the errors have been eliminated • such a management action has the additional advantage of not influencing other applications in otherVMs on top of the same PM
  34. 34. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 Summary • We proposed a new 3-D monitoring model by formalizing the innovative concept of CoI • to analyze the huge quantity of cloud monitoring information and to identify situations in which multiple adaptations are possible, coordinating them and selecting the most appropriate • Future work will deal with: • automation of the deployment of the monitoring tools based on the CoI (e.g., using OpenStack Heat) • formalization of the adaptation actions and their relationship with the 3-D monitoring model and with the concept of CoI (Situation of Interest) • implementation of more complex scenarios with multipleVMs, PMs, and applications in order to demonstrate the advantages of our approach in a more realistic context 24
  35. 35. IEEE / IFIP CNSM’14 - Nov 17-21 2014 - Rio de Janeiro (Brazil) Dario Bruneo /25 thank you! Dario Bruneo dbruneo@unime.it Dipartimento di Ingegneria DICIEAMA Università degli Studi di Messina Messina, Italy 25 http://www.cloudwave-fp7.euhttp://mdslab.unime.it “Monitoring in a Cloud environment”  free webinar 26 November 2014, 12.00 – 13.00 CET http://bit.ly/ZVFyV1

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