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.

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

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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

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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

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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!

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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

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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

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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

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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!!!

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Cloud layers
6
App 1
App 2
Let’s have a look at the way Cloud applications are composed/
deployed

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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)

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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

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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

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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

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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)

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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

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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

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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

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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

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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

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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

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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

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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

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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
…

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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.

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• 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

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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

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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

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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

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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)

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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

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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

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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. 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. 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. 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