SlideShare a Scribd company logo
ADVISE: a Framework for Evaluating 
Cloud Service Elasticity Behavior 
Georgiana Copil1, Demetris Trihinas2, Hong−Linh Truong1, Daniel Moldovan1, 
George Pallis2, Schahram Dustdar1, Marios Dikaiakos2 
1 
Distributed Systems Group, Vienna University of Technology 
2 
Computer Science Department, University of Cyprus 
12th International Conference on Service Oriented Computing
Overview 
 Motivation 
 Evaluating Cloud Service Behavior 
– Learning process 
– Determining expected elasticity behavior 
 Experiments 
 Conclusions and Future Work 
ICSOC 2014, 5 November, Paris 2
Motivation – Cloud service runtime evolution 
Complex 
Cloud 
Service 
Elastic 
Cloud 
Service 
Deployment (running) 
process 
ICSOC 2014, 5 November, Paris 3 
Elasticity 
control 
process 
Elasticity Control 
Processes 
What would be 
the elasticity 
behavior? 
Elasticity 
requirements 
Elasticity controller
Motivation – Cloud service runtime evolution 
Elasticity control 
process enforced 
Which will be the behavior? 
now 
ICSOC 2014, 5 November, Paris 4
Motivation – Cloud service runtime evolution 
Which will be the behavior? 
ICSOC 2014, 5 November, Paris 5 
Possible requirements 
violations 
Elasticity control 
process enforced 
Expected impact 
Expected cool-off 
period 
now 
Which elasticity control process is most appropriate? 
How a control process will affect metrics, e.g., throughput, of the 
overall service and individually on each part of the cloud service?
Motivation – Cloud service behavior 
 Cloud service behavior is complex and can depend on: 
– The structure of the cloud service 
– The runtime resources used 
– The workload of the cloud service 
– The control processes enforced, e.g., by the controller 
 Capturing & using these types of information for 
evaluating elasticity behavior 
ICSOC 2014, 5 November, Paris 6 
Service 
Topology 1 
Unit 1 
Unit 2 
Topology 2 
Unit 3 
Unit 4 
푉푀푥1 푉푀 푉푀푥2 푥3 푉푀푥푛
Approach 
 Input: 
– Cloud service structure 
– Monitoring information of different service parts (e.g., service 
units, service topologies) 
– Elasticity control process 퐸퐶푃푖 
 Expected output: 
– Metrics evolution, in time, for different service parts and 퐸퐶푃푠 
 Main mechanism: 
– Creating behavior clusters 
– Computing closest behavior centroids 
ICSOC 2014, 5 November, Paris 7
Gathering information 
Relevant timeseries 
퐸퐶푃푖 enforcement 
Metric measurement 
Select relevant timeseries where 퐸퐶푃푖 was enforced before 
ICSOC 2014, 5 November, Paris 8
Clustering elasticity behaviors 
 Transform relevant timeseries to multi-dimensional 
points 
푡1 푡2 … 푡푛 Time 
Metric 
푚푥 
퐶푙푢푠푡푒푟푐 푚푥 퐶1푚푥 
ICSOC 2014, 5 November, Paris 9 
푀푒푡푟푖푐푉푎푙 (푡1) 
푀푒푡푟푖푐푉푎푙 (푡2) 
푀푒푡푟푖푐푉푎푙 (푡푛) 
푀푒푡푟푖푐푉푎푙 (푡3) 
… 
푀푒푡푟푖푐푉푎푙 (푡4) 
Behavior Point 
BP 
퐶푙푢푠푡푒푟1푚 K-means 푥 
퐶푙푢푠푡푒푟2 푚푥 
퐶2푚푥 
퐶푐 푚푥
Computing expected behavior 
퐵푃푚푥 
Current values 
퐶푙푢푠푡푒푟1 푚푥 
퐶1푚푥 
퐵푃푚푦 
퐶푙푢푠푡푒푟2 푚푥 
퐶2푚푥 
퐶푙푢푠푡푒푟1 푚푦 
퐶1푚푦 
퐶푙푢푠푡푒푟푝 푚푦 
퐶푝 푚푦 
퐶푙푢푠푡푒푟1푚푥 퐶푙푢푠푡푒푟2 푚푥 
퐶푙푢푠푡푒푟푟 푚푥 
퐶푙푢푠푡푒푟1 푚푦 
a b - 
퐶푙푢푠푡푒푟푝 푚푦 
c - d 
Co-occurrence matrix 
ICSOC 2014, 5 November, Paris 10 
Compute centroids 
closest to the 퐵푃푖 
퐶1푚푥 
퐶푝 푚푦 
Transform 
to timeseries 
푚 푚푥 푦 
퐶푙푢푠푡푒푟푟 푚푥 
퐶푟 푚푥
Experiment Settings [1/3] 
 Setting: 
– M2M service 
– Video Service 
ICSOC 2014, 5 November, Paris 11
Experiment Settings [2/3] 
 Setting: 
– Running on public Flexiant cloud FCO 
– MELA & JCatascopia for monitoring cloud services 
– Randomly apply ECPs of random type for collecting behavioral 
information 
– “Interesting” metrics 
ICSOC 2014, 5 November, Paris 12
Experiment Settings [3/3] 
ICSOC 2014, 5 November, Paris 13
Experiments – Video Service 
Video Service – effect of 퐸퐶푃1 on Application Server 
퐸퐶푃1 - scale in application server tier – select instance to remove, 
stop the video streaming service, remove instance from load 
balancer, stop JCatascopia monitoring agent, delete instance 
ICSOC 2014, 5 November, Paris 14
Experiments – M2M Service [1/2] 
M2M Service – effect of 퐸퐶푃7 on the entire cloud service 
퐸퐶푃7 - scale in data node service unit – copy data from the instance 
to be removed, remove recursively virtual machine 
ICSOC 2014, 5 November, Paris 15
Experiments – M2M Service [2/2] 
M2M Service – effect on Data End Controller of enforcing 퐸퐶푃8 
퐸퐶푃8 - scale out data node service unit – create new network 
interface, create new instance, assign token to node, set cluster 
controller 
ICSOC 2014, 5 November, Paris 16
The more random the workload, of the service, 
the more difficult to estimate the behavior Lower abstraction layer 
Experiments – 
Quality of Results 
푉푎푟푖푎푛푐푒푚 
= 
=> better estimations 
푛푏퐸푠푡푖푚푎푡푖표푛푠 푒푠푡푖푚푎푡푖표푛푆푖푧푒(푒푠푡푖푚푎푡푒푑푀푒푡푟푖푐푚 − 표푏푠푒푟푣푒푑푀푒푡푟푖푐푚)2 
ICSOC 2014, 5 November, Paris 17 
푛푏퐸푠푡푖푚푎푡푖표푛푠 − 1 
Complex, 
unpredictable 
metrics => very low 
degree of accuracy
Conclusions and Future Work 
 Conclusions 
– When controlling a complex cloud service, we need to consider 
the impact elasticity control processes have on different service 
parts 
– ADVISE is indeed able to "advise" elasticity controllers about 
cloud service behavior 
 Future work 
– Integrating with rSYBL (https://github.com/tuwiendsg/rSYBL) 
– Adapting the control mechanisms of rSYBL to use such 
information 
 ADVISE 
– More experiments available at http://tuwiendsg.github.io/ADVISE 
– Prototype https://github.com/tuwiendsg/ADVISE 
ICSOC 2014, 5 November, Paris 18
Thank you! 
Georgiana Copil 
e.copil@dsg.tuwien.ac.at 
http://dsg.tuwien.ac.at/staff/ecopil/ 
Distributed Systems Group 
Vienna University of Technology 
Austria 
ICSOC 2014, 5 November, Paris 19

More Related Content

What's hot

The Power Of Event Chapter 2
The Power Of Event  Chapter 2The Power Of Event  Chapter 2
The Power Of Event Chapter 2
Woojin Joe
 
QUELLE - a Framework for Accelerating the Development of Elastic Systems
QUELLE - a Framework for Accelerating the Development of Elastic SystemsQUELLE - a Framework for Accelerating the Development of Elastic Systems
QUELLE - a Framework for Accelerating the Development of Elastic Systems
Daniel Moldovan
 
The Power Of Event Chapter 5
The Power Of Event Chapter 5The Power Of Event Chapter 5
The Power Of Event Chapter 5
Woojin Joe
 
The Power Of Event Chapter 1
The Power Of Event Chapter 1The Power Of Event Chapter 1
The Power Of Event Chapter 1
Woojin Joe
 
Load Balancing in Cloud
Load Balancing in CloudLoad Balancing in Cloud
Load Balancing in Cloud
Mphasis
 
Shaheer
ShaheerShaheer
Shaheer
hfay118
 
Wei's notes on MapReduce Scheduling
Wei's notes on MapReduce SchedulingWei's notes on MapReduce Scheduling
Wei's notes on MapReduce Scheduling
Lu Wei
 
Self-adaptation Challenges for Cloud-based Applications (Feedback Computing 2...
Self-adaptation Challenges for Cloud-based Applications (Feedback Computing 2...Self-adaptation Challenges for Cloud-based Applications (Feedback Computing 2...
Self-adaptation Challenges for Cloud-based Applications (Feedback Computing 2...
Soodeh Farokhi
 
Quality of Service Control Mechanisms in Cloud Computing Environments
Quality of Service Control Mechanisms in Cloud Computing EnvironmentsQuality of Service Control Mechanisms in Cloud Computing Environments
Quality of Service Control Mechanisms in Cloud Computing Environments
Soodeh Farokhi
 
Hierarchical SLA-based Service Selection for Multi-Cloud Environments
Hierarchical SLA-based Service Selection for Multi-Cloud EnvironmentsHierarchical SLA-based Service Selection for Multi-Cloud Environments
Hierarchical SLA-based Service Selection for Multi-Cloud Environments
Soodeh Farokhi
 
Coordinating CPU and Memory Elasticity Controllers to Meet Service Response T...
Coordinating CPU and Memory Elasticity Controllers toMeet Service Response T...Coordinating CPU and Memory Elasticity Controllers toMeet Service Response T...
Coordinating CPU and Memory Elasticity Controllers to Meet Service Response T...
Soodeh Farokhi
 
Coordination-aware Elasticity
Coordination-aware ElasticityCoordination-aware Elasticity
Coordination-aware Elasticity
Hong-Linh Truong
 
Reactive by example (DevOpsDaysTLV 2019)
Reactive by example (DevOpsDaysTLV 2019)Reactive by example (DevOpsDaysTLV 2019)
Reactive by example (DevOpsDaysTLV 2019)
Eran Harel
 
Process Management-Process Migration
Process Management-Process MigrationProcess Management-Process Migration
Process Management-Process Migration
MNM Jain Engineering College
 

What's hot (14)

The Power Of Event Chapter 2
The Power Of Event  Chapter 2The Power Of Event  Chapter 2
The Power Of Event Chapter 2
 
QUELLE - a Framework for Accelerating the Development of Elastic Systems
QUELLE - a Framework for Accelerating the Development of Elastic SystemsQUELLE - a Framework for Accelerating the Development of Elastic Systems
QUELLE - a Framework for Accelerating the Development of Elastic Systems
 
The Power Of Event Chapter 5
The Power Of Event Chapter 5The Power Of Event Chapter 5
The Power Of Event Chapter 5
 
The Power Of Event Chapter 1
The Power Of Event Chapter 1The Power Of Event Chapter 1
The Power Of Event Chapter 1
 
Load Balancing in Cloud
Load Balancing in CloudLoad Balancing in Cloud
Load Balancing in Cloud
 
Shaheer
ShaheerShaheer
Shaheer
 
Wei's notes on MapReduce Scheduling
Wei's notes on MapReduce SchedulingWei's notes on MapReduce Scheduling
Wei's notes on MapReduce Scheduling
 
Self-adaptation Challenges for Cloud-based Applications (Feedback Computing 2...
Self-adaptation Challenges for Cloud-based Applications (Feedback Computing 2...Self-adaptation Challenges for Cloud-based Applications (Feedback Computing 2...
Self-adaptation Challenges for Cloud-based Applications (Feedback Computing 2...
 
Quality of Service Control Mechanisms in Cloud Computing Environments
Quality of Service Control Mechanisms in Cloud Computing EnvironmentsQuality of Service Control Mechanisms in Cloud Computing Environments
Quality of Service Control Mechanisms in Cloud Computing Environments
 
Hierarchical SLA-based Service Selection for Multi-Cloud Environments
Hierarchical SLA-based Service Selection for Multi-Cloud EnvironmentsHierarchical SLA-based Service Selection for Multi-Cloud Environments
Hierarchical SLA-based Service Selection for Multi-Cloud Environments
 
Coordinating CPU and Memory Elasticity Controllers to Meet Service Response T...
Coordinating CPU and Memory Elasticity Controllers toMeet Service Response T...Coordinating CPU and Memory Elasticity Controllers toMeet Service Response T...
Coordinating CPU and Memory Elasticity Controllers to Meet Service Response T...
 
Coordination-aware Elasticity
Coordination-aware ElasticityCoordination-aware Elasticity
Coordination-aware Elasticity
 
Reactive by example (DevOpsDaysTLV 2019)
Reactive by example (DevOpsDaysTLV 2019)Reactive by example (DevOpsDaysTLV 2019)
Reactive by example (DevOpsDaysTLV 2019)
 
Process Management-Process Migration
Process Management-Process MigrationProcess Management-Process Migration
Process Management-Process Migration
 

Similar to ADVISE - a Framework for Evaluating Cloud Service Elasticity Behavior - Best paper award

Configurable Monitoring For Multi-Domain Networks
Configurable Monitoring For Multi-Domain NetworksConfigurable Monitoring For Multi-Domain Networks
Configurable Monitoring For Multi-Domain Networks
IJMER
 
A Class-based Adaptive QoS Control Scheme Adopting Optimization Technique ove...
A Class-based Adaptive QoS Control Scheme Adopting Optimization Technique ove...A Class-based Adaptive QoS Control Scheme Adopting Optimization Technique ove...
A Class-based Adaptive QoS Control Scheme Adopting Optimization Technique ove...
IJCNCJournal
 
A CLASS-BASED ADAPTIVE QOS CONTROL SCHEME ADOPTING OPTIMIZATION TECHNIQUE OVE...
A CLASS-BASED ADAPTIVE QOS CONTROL SCHEME ADOPTING OPTIMIZATION TECHNIQUE OVE...A CLASS-BASED ADAPTIVE QOS CONTROL SCHEME ADOPTING OPTIMIZATION TECHNIQUE OVE...
A CLASS-BASED ADAPTIVE QOS CONTROL SCHEME ADOPTING OPTIMIZATION TECHNIQUE OVE...
IJCNCJournal
 
IRJET- A Literature Survey on Scaling Approaches for VNF in NFV Monitoring
IRJET- A Literature Survey on Scaling Approaches for VNF in NFV MonitoringIRJET- A Literature Survey on Scaling Approaches for VNF in NFV Monitoring
IRJET- A Literature Survey on Scaling Approaches for VNF in NFV Monitoring
IRJET Journal
 
Cloud Computing Task Scheduling Algorithm Based on Modified Genetic Algorithm
Cloud Computing Task Scheduling Algorithm Based on Modified Genetic AlgorithmCloud Computing Task Scheduling Algorithm Based on Modified Genetic Algorithm
Cloud Computing Task Scheduling Algorithm Based on Modified Genetic Algorithm
IRJET Journal
 
Efficient Configuration of Monitoring Slices for Cloud Platform Administrators
Efficient Configuration of Monitoring Slices for Cloud Platform AdministratorsEfficient Configuration of Monitoring Slices for Cloud Platform Administrators
Efficient Configuration of Monitoring Slices for Cloud Platform Administrators
Marcio Barbosa de Carvalho
 
The Difference Impact on QoS Parameters between the IPSEC and L2TP
The Difference Impact on QoS Parameters between the IPSEC and L2TPThe Difference Impact on QoS Parameters between the IPSEC and L2TP
The Difference Impact on QoS Parameters between the IPSEC and L2TP
AM Publications
 
Classification of Software Defined Network Traffic to provide Quality of Service
Classification of Software Defined Network Traffic to provide Quality of ServiceClassification of Software Defined Network Traffic to provide Quality of Service
Classification of Software Defined Network Traffic to provide Quality of Service
IRJET Journal
 
Thesis - Differentiated Optical QoS Service
Thesis - Differentiated Optical QoS ServiceThesis - Differentiated Optical QoS Service
Thesis - Differentiated Optical QoS Service
Lui Spatz Izarra
 
Performance Simulation and Analysis for LTESystem Using Human Behavior Queue ...
Performance Simulation and Analysis for LTESystem Using Human Behavior Queue ...Performance Simulation and Analysis for LTESystem Using Human Behavior Queue ...
Performance Simulation and Analysis for LTESystem Using Human Behavior Queue ...
ijwmn
 
IRJET- An Adaptive Scheduling based VM with Random Key Authentication on Clou...
IRJET- An Adaptive Scheduling based VM with Random Key Authentication on Clou...IRJET- An Adaptive Scheduling based VM with Random Key Authentication on Clou...
IRJET- An Adaptive Scheduling based VM with Random Key Authentication on Clou...
IRJET Journal
 
Final Year IEEE Project 2013-2014 - Web Services Project Title and Abstract
Final Year IEEE Project 2013-2014  - Web Services Project Title and AbstractFinal Year IEEE Project 2013-2014  - Web Services Project Title and Abstract
Final Year IEEE Project 2013-2014 - Web Services Project Title and Abstract
elysiumtechnologies
 
LOAD BALANCING IN CLOUD COMPUTING
LOAD BALANCING IN CLOUD COMPUTINGLOAD BALANCING IN CLOUD COMPUTING
LOAD BALANCING IN CLOUD COMPUTING
IRJET Journal
 
A Review: Metaheuristic Technique in Cloud Computing
A Review: Metaheuristic Technique in Cloud ComputingA Review: Metaheuristic Technique in Cloud Computing
A Review: Metaheuristic Technique in Cloud Computing
IRJET Journal
 
IOT model to Unified Communication Events in SDN
IOT model to Unified Communication  Events in SDNIOT model to Unified Communication  Events in SDN
IOT model to Unified Communication Events in SDN
Chandrashekhar Rao
 
Programming Elasticity in the Cloud
Programming Elasticity in the CloudProgramming Elasticity in the Cloud
Programming Elasticity in the Cloud
Hong-Linh Truong
 
An Analysis Of Cloud ReliabilityApproaches Based on Cloud Components And Reli...
An Analysis Of Cloud ReliabilityApproaches Based on Cloud Components And Reli...An Analysis Of Cloud ReliabilityApproaches Based on Cloud Components And Reli...
An Analysis Of Cloud ReliabilityApproaches Based on Cloud Components And Reli...
ijafrc
 
MELA: Monitoring and Analyzing Elasticity of Cloud Services -- CloudCom 2013
MELA: Monitoring and Analyzing Elasticity of Cloud Services -- CloudCom 2013MELA: Monitoring and Analyzing Elasticity of Cloud Services -- CloudCom 2013
MELA: Monitoring and Analyzing Elasticity of Cloud Services -- CloudCom 2013
Daniel Moldovan
 
Cloud network management model a novel approach to manage cloud traffic
Cloud network management model   a novel approach to manage cloud trafficCloud network management model   a novel approach to manage cloud traffic
Cloud network management model a novel approach to manage cloud traffic
ijccsa
 
2014 IEEE JAVA CLOUD COMPUTING PROJECT A stochastic model to investigate data...
2014 IEEE JAVA CLOUD COMPUTING PROJECT A stochastic model to investigate data...2014 IEEE JAVA CLOUD COMPUTING PROJECT A stochastic model to investigate data...
2014 IEEE JAVA CLOUD COMPUTING PROJECT A stochastic model to investigate data...
IEEEFINALYEARSTUDENTPROJECT
 

Similar to ADVISE - a Framework for Evaluating Cloud Service Elasticity Behavior - Best paper award (20)

Configurable Monitoring For Multi-Domain Networks
Configurable Monitoring For Multi-Domain NetworksConfigurable Monitoring For Multi-Domain Networks
Configurable Monitoring For Multi-Domain Networks
 
A Class-based Adaptive QoS Control Scheme Adopting Optimization Technique ove...
A Class-based Adaptive QoS Control Scheme Adopting Optimization Technique ove...A Class-based Adaptive QoS Control Scheme Adopting Optimization Technique ove...
A Class-based Adaptive QoS Control Scheme Adopting Optimization Technique ove...
 
A CLASS-BASED ADAPTIVE QOS CONTROL SCHEME ADOPTING OPTIMIZATION TECHNIQUE OVE...
A CLASS-BASED ADAPTIVE QOS CONTROL SCHEME ADOPTING OPTIMIZATION TECHNIQUE OVE...A CLASS-BASED ADAPTIVE QOS CONTROL SCHEME ADOPTING OPTIMIZATION TECHNIQUE OVE...
A CLASS-BASED ADAPTIVE QOS CONTROL SCHEME ADOPTING OPTIMIZATION TECHNIQUE OVE...
 
IRJET- A Literature Survey on Scaling Approaches for VNF in NFV Monitoring
IRJET- A Literature Survey on Scaling Approaches for VNF in NFV MonitoringIRJET- A Literature Survey on Scaling Approaches for VNF in NFV Monitoring
IRJET- A Literature Survey on Scaling Approaches for VNF in NFV Monitoring
 
Cloud Computing Task Scheduling Algorithm Based on Modified Genetic Algorithm
Cloud Computing Task Scheduling Algorithm Based on Modified Genetic AlgorithmCloud Computing Task Scheduling Algorithm Based on Modified Genetic Algorithm
Cloud Computing Task Scheduling Algorithm Based on Modified Genetic Algorithm
 
Efficient Configuration of Monitoring Slices for Cloud Platform Administrators
Efficient Configuration of Monitoring Slices for Cloud Platform AdministratorsEfficient Configuration of Monitoring Slices for Cloud Platform Administrators
Efficient Configuration of Monitoring Slices for Cloud Platform Administrators
 
The Difference Impact on QoS Parameters between the IPSEC and L2TP
The Difference Impact on QoS Parameters between the IPSEC and L2TPThe Difference Impact on QoS Parameters between the IPSEC and L2TP
The Difference Impact on QoS Parameters between the IPSEC and L2TP
 
Classification of Software Defined Network Traffic to provide Quality of Service
Classification of Software Defined Network Traffic to provide Quality of ServiceClassification of Software Defined Network Traffic to provide Quality of Service
Classification of Software Defined Network Traffic to provide Quality of Service
 
Thesis - Differentiated Optical QoS Service
Thesis - Differentiated Optical QoS ServiceThesis - Differentiated Optical QoS Service
Thesis - Differentiated Optical QoS Service
 
Performance Simulation and Analysis for LTESystem Using Human Behavior Queue ...
Performance Simulation and Analysis for LTESystem Using Human Behavior Queue ...Performance Simulation and Analysis for LTESystem Using Human Behavior Queue ...
Performance Simulation and Analysis for LTESystem Using Human Behavior Queue ...
 
IRJET- An Adaptive Scheduling based VM with Random Key Authentication on Clou...
IRJET- An Adaptive Scheduling based VM with Random Key Authentication on Clou...IRJET- An Adaptive Scheduling based VM with Random Key Authentication on Clou...
IRJET- An Adaptive Scheduling based VM with Random Key Authentication on Clou...
 
Final Year IEEE Project 2013-2014 - Web Services Project Title and Abstract
Final Year IEEE Project 2013-2014  - Web Services Project Title and AbstractFinal Year IEEE Project 2013-2014  - Web Services Project Title and Abstract
Final Year IEEE Project 2013-2014 - Web Services Project Title and Abstract
 
LOAD BALANCING IN CLOUD COMPUTING
LOAD BALANCING IN CLOUD COMPUTINGLOAD BALANCING IN CLOUD COMPUTING
LOAD BALANCING IN CLOUD COMPUTING
 
A Review: Metaheuristic Technique in Cloud Computing
A Review: Metaheuristic Technique in Cloud ComputingA Review: Metaheuristic Technique in Cloud Computing
A Review: Metaheuristic Technique in Cloud Computing
 
IOT model to Unified Communication Events in SDN
IOT model to Unified Communication  Events in SDNIOT model to Unified Communication  Events in SDN
IOT model to Unified Communication Events in SDN
 
Programming Elasticity in the Cloud
Programming Elasticity in the CloudProgramming Elasticity in the Cloud
Programming Elasticity in the Cloud
 
An Analysis Of Cloud ReliabilityApproaches Based on Cloud Components And Reli...
An Analysis Of Cloud ReliabilityApproaches Based on Cloud Components And Reli...An Analysis Of Cloud ReliabilityApproaches Based on Cloud Components And Reli...
An Analysis Of Cloud ReliabilityApproaches Based on Cloud Components And Reli...
 
MELA: Monitoring and Analyzing Elasticity of Cloud Services -- CloudCom 2013
MELA: Monitoring and Analyzing Elasticity of Cloud Services -- CloudCom 2013MELA: Monitoring and Analyzing Elasticity of Cloud Services -- CloudCom 2013
MELA: Monitoring and Analyzing Elasticity of Cloud Services -- CloudCom 2013
 
Cloud network management model a novel approach to manage cloud traffic
Cloud network management model   a novel approach to manage cloud trafficCloud network management model   a novel approach to manage cloud traffic
Cloud network management model a novel approach to manage cloud traffic
 
2014 IEEE JAVA CLOUD COMPUTING PROJECT A stochastic model to investigate data...
2014 IEEE JAVA CLOUD COMPUTING PROJECT A stochastic model to investigate data...2014 IEEE JAVA CLOUD COMPUTING PROJECT A stochastic model to investigate data...
2014 IEEE JAVA CLOUD COMPUTING PROJECT A stochastic model to investigate data...
 

Recently uploaded

Parkinson Disease & Anti-Parkinsonian Drugs.pptx
Parkinson Disease & Anti-Parkinsonian Drugs.pptxParkinson Disease & Anti-Parkinsonian Drugs.pptx
Parkinson Disease & Anti-Parkinsonian Drugs.pptx
AnujVishwakarma34
 
RDBMS Lecture Notes Unit4 chapter12 VIEW
RDBMS Lecture Notes Unit4 chapter12 VIEWRDBMS Lecture Notes Unit4 chapter12 VIEW
RDBMS Lecture Notes Unit4 chapter12 VIEW
Murugan Solaiyappan
 
Our Guide to the July 2024 USPS® Rate Change
Our Guide to the July 2024 USPS® Rate ChangeOur Guide to the July 2024 USPS® Rate Change
Our Guide to the July 2024 USPS® Rate Change
Postal Advocate Inc.
 
Imagination in Computer Science Research
Imagination in Computer Science ResearchImagination in Computer Science Research
Imagination in Computer Science Research
Abhik Roychoudhury
 
MathematicsGrade7-Presentation-July-12024.pptx
MathematicsGrade7-Presentation-July-12024.pptxMathematicsGrade7-Presentation-July-12024.pptx
MathematicsGrade7-Presentation-July-12024.pptx
nolicaliso1
 
PRESS RELEASE - UNIVERSITY OF GHANA, JULY 16, 2024.pdf
PRESS RELEASE - UNIVERSITY OF GHANA, JULY 16, 2024.pdfPRESS RELEASE - UNIVERSITY OF GHANA, JULY 16, 2024.pdf
PRESS RELEASE - UNIVERSITY OF GHANA, JULY 16, 2024.pdf
nservice241
 
Node JS Interview Question PDF By ScholarHat
Node JS Interview Question PDF By ScholarHatNode JS Interview Question PDF By ScholarHat
Node JS Interview Question PDF By ScholarHat
Scholarhat
 
BÀI TẬP BỔ TRỢ 4 KỸ NĂNG TIẾNG ANH LỚP 12 - GLOBAL SUCCESS - FORM MỚI 2025 - ...
BÀI TẬP BỔ TRỢ 4 KỸ NĂNG TIẾNG ANH LỚP 12 - GLOBAL SUCCESS - FORM MỚI 2025 - ...BÀI TẬP BỔ TRỢ 4 KỸ NĂNG TIẾNG ANH LỚP 12 - GLOBAL SUCCESS - FORM MỚI 2025 - ...
BÀI TẬP BỔ TRỢ 4 KỸ NĂNG TIẾNG ANH LỚP 12 - GLOBAL SUCCESS - FORM MỚI 2025 - ...
Nguyen Thanh Tu Collection
 
How to Manage Shipping Connectors & Shipping Methods in Odoo 17
How to Manage Shipping Connectors & Shipping Methods in Odoo 17How to Manage Shipping Connectors & Shipping Methods in Odoo 17
How to Manage Shipping Connectors & Shipping Methods in Odoo 17
Celine George
 
FIRST AID PRESENTATION ON INDUSTRIAL SAFETY by dr lal.ppt
FIRST AID PRESENTATION ON INDUSTRIAL SAFETY by dr lal.pptFIRST AID PRESENTATION ON INDUSTRIAL SAFETY by dr lal.ppt
FIRST AID PRESENTATION ON INDUSTRIAL SAFETY by dr lal.ppt
ashutoshklal29
 
Brigada Eskwela 2024 PowerPoint Update for SY 2024-2025
Brigada Eskwela 2024 PowerPoint Update for SY 2024-2025Brigada Eskwela 2024 PowerPoint Update for SY 2024-2025
Brigada Eskwela 2024 PowerPoint Update for SY 2024-2025
ALBERTHISOLER1
 
How to Manage Access Rights & User Types in Odoo 17
How to Manage Access Rights & User Types in Odoo 17How to Manage Access Rights & User Types in Odoo 17
How to Manage Access Rights & User Types in Odoo 17
Celine George
 
Codeavour 5.0 International Impact Report - The Biggest International AI, Cod...
Codeavour 5.0 International Impact Report - The Biggest International AI, Cod...Codeavour 5.0 International Impact Report - The Biggest International AI, Cod...
Codeavour 5.0 International Impact Report - The Biggest International AI, Cod...
Codeavour International
 
3. Maturity_indices_of_fruits_and_vegetable.pptx
3. Maturity_indices_of_fruits_and_vegetable.pptx3. Maturity_indices_of_fruits_and_vegetable.pptx
3. Maturity_indices_of_fruits_and_vegetable.pptx
UmeshTimilsina1
 
Mail Server Configuration Using App passwords in Odoo 17
Mail Server Configuration Using App passwords in Odoo 17Mail Server Configuration Using App passwords in Odoo 17
Mail Server Configuration Using App passwords in Odoo 17
Celine George
 
A beginner’s guide to project reviews - everything you wanted to know but wer...
A beginner’s guide to project reviews - everything you wanted to know but wer...A beginner’s guide to project reviews - everything you wanted to know but wer...
A beginner’s guide to project reviews - everything you wanted to know but wer...
Association for Project Management
 
Genetics Teaching Plan: Dr.Kshirsagar R.V.
Genetics Teaching Plan: Dr.Kshirsagar R.V.Genetics Teaching Plan: Dr.Kshirsagar R.V.
Genetics Teaching Plan: Dr.Kshirsagar R.V.
DrRavindrakshirsagar1
 
QCE – Unpacking the syllabus Implications for Senior School practices and ass...
QCE – Unpacking the syllabus Implications for Senior School practices and ass...QCE – Unpacking the syllabus Implications for Senior School practices and ass...
QCE – Unpacking the syllabus Implications for Senior School practices and ass...
mansk2
 
How to Manage Line Discount in Odoo 17 POS
How to Manage Line Discount in Odoo 17 POSHow to Manage Line Discount in Odoo 17 POS
How to Manage Line Discount in Odoo 17 POS
Celine George
 
C# Interview Questions PDF By ScholarHat.pdf
C# Interview Questions PDF By ScholarHat.pdfC# Interview Questions PDF By ScholarHat.pdf
C# Interview Questions PDF By ScholarHat.pdf
Scholarhat
 

Recently uploaded (20)

Parkinson Disease & Anti-Parkinsonian Drugs.pptx
Parkinson Disease & Anti-Parkinsonian Drugs.pptxParkinson Disease & Anti-Parkinsonian Drugs.pptx
Parkinson Disease & Anti-Parkinsonian Drugs.pptx
 
RDBMS Lecture Notes Unit4 chapter12 VIEW
RDBMS Lecture Notes Unit4 chapter12 VIEWRDBMS Lecture Notes Unit4 chapter12 VIEW
RDBMS Lecture Notes Unit4 chapter12 VIEW
 
Our Guide to the July 2024 USPS® Rate Change
Our Guide to the July 2024 USPS® Rate ChangeOur Guide to the July 2024 USPS® Rate Change
Our Guide to the July 2024 USPS® Rate Change
 
Imagination in Computer Science Research
Imagination in Computer Science ResearchImagination in Computer Science Research
Imagination in Computer Science Research
 
MathematicsGrade7-Presentation-July-12024.pptx
MathematicsGrade7-Presentation-July-12024.pptxMathematicsGrade7-Presentation-July-12024.pptx
MathematicsGrade7-Presentation-July-12024.pptx
 
PRESS RELEASE - UNIVERSITY OF GHANA, JULY 16, 2024.pdf
PRESS RELEASE - UNIVERSITY OF GHANA, JULY 16, 2024.pdfPRESS RELEASE - UNIVERSITY OF GHANA, JULY 16, 2024.pdf
PRESS RELEASE - UNIVERSITY OF GHANA, JULY 16, 2024.pdf
 
Node JS Interview Question PDF By ScholarHat
Node JS Interview Question PDF By ScholarHatNode JS Interview Question PDF By ScholarHat
Node JS Interview Question PDF By ScholarHat
 
BÀI TẬP BỔ TRỢ 4 KỸ NĂNG TIẾNG ANH LỚP 12 - GLOBAL SUCCESS - FORM MỚI 2025 - ...
BÀI TẬP BỔ TRỢ 4 KỸ NĂNG TIẾNG ANH LỚP 12 - GLOBAL SUCCESS - FORM MỚI 2025 - ...BÀI TẬP BỔ TRỢ 4 KỸ NĂNG TIẾNG ANH LỚP 12 - GLOBAL SUCCESS - FORM MỚI 2025 - ...
BÀI TẬP BỔ TRỢ 4 KỸ NĂNG TIẾNG ANH LỚP 12 - GLOBAL SUCCESS - FORM MỚI 2025 - ...
 
How to Manage Shipping Connectors & Shipping Methods in Odoo 17
How to Manage Shipping Connectors & Shipping Methods in Odoo 17How to Manage Shipping Connectors & Shipping Methods in Odoo 17
How to Manage Shipping Connectors & Shipping Methods in Odoo 17
 
FIRST AID PRESENTATION ON INDUSTRIAL SAFETY by dr lal.ppt
FIRST AID PRESENTATION ON INDUSTRIAL SAFETY by dr lal.pptFIRST AID PRESENTATION ON INDUSTRIAL SAFETY by dr lal.ppt
FIRST AID PRESENTATION ON INDUSTRIAL SAFETY by dr lal.ppt
 
Brigada Eskwela 2024 PowerPoint Update for SY 2024-2025
Brigada Eskwela 2024 PowerPoint Update for SY 2024-2025Brigada Eskwela 2024 PowerPoint Update for SY 2024-2025
Brigada Eskwela 2024 PowerPoint Update for SY 2024-2025
 
How to Manage Access Rights & User Types in Odoo 17
How to Manage Access Rights & User Types in Odoo 17How to Manage Access Rights & User Types in Odoo 17
How to Manage Access Rights & User Types in Odoo 17
 
Codeavour 5.0 International Impact Report - The Biggest International AI, Cod...
Codeavour 5.0 International Impact Report - The Biggest International AI, Cod...Codeavour 5.0 International Impact Report - The Biggest International AI, Cod...
Codeavour 5.0 International Impact Report - The Biggest International AI, Cod...
 
3. Maturity_indices_of_fruits_and_vegetable.pptx
3. Maturity_indices_of_fruits_and_vegetable.pptx3. Maturity_indices_of_fruits_and_vegetable.pptx
3. Maturity_indices_of_fruits_and_vegetable.pptx
 
Mail Server Configuration Using App passwords in Odoo 17
Mail Server Configuration Using App passwords in Odoo 17Mail Server Configuration Using App passwords in Odoo 17
Mail Server Configuration Using App passwords in Odoo 17
 
A beginner’s guide to project reviews - everything you wanted to know but wer...
A beginner’s guide to project reviews - everything you wanted to know but wer...A beginner’s guide to project reviews - everything you wanted to know but wer...
A beginner’s guide to project reviews - everything you wanted to know but wer...
 
Genetics Teaching Plan: Dr.Kshirsagar R.V.
Genetics Teaching Plan: Dr.Kshirsagar R.V.Genetics Teaching Plan: Dr.Kshirsagar R.V.
Genetics Teaching Plan: Dr.Kshirsagar R.V.
 
QCE – Unpacking the syllabus Implications for Senior School practices and ass...
QCE – Unpacking the syllabus Implications for Senior School practices and ass...QCE – Unpacking the syllabus Implications for Senior School practices and ass...
QCE – Unpacking the syllabus Implications for Senior School practices and ass...
 
How to Manage Line Discount in Odoo 17 POS
How to Manage Line Discount in Odoo 17 POSHow to Manage Line Discount in Odoo 17 POS
How to Manage Line Discount in Odoo 17 POS
 
C# Interview Questions PDF By ScholarHat.pdf
C# Interview Questions PDF By ScholarHat.pdfC# Interview Questions PDF By ScholarHat.pdf
C# Interview Questions PDF By ScholarHat.pdf
 

ADVISE - a Framework for Evaluating Cloud Service Elasticity Behavior - Best paper award

  • 1. ADVISE: a Framework for Evaluating Cloud Service Elasticity Behavior Georgiana Copil1, Demetris Trihinas2, Hong−Linh Truong1, Daniel Moldovan1, George Pallis2, Schahram Dustdar1, Marios Dikaiakos2 1 Distributed Systems Group, Vienna University of Technology 2 Computer Science Department, University of Cyprus 12th International Conference on Service Oriented Computing
  • 2. Overview  Motivation  Evaluating Cloud Service Behavior – Learning process – Determining expected elasticity behavior  Experiments  Conclusions and Future Work ICSOC 2014, 5 November, Paris 2
  • 3. Motivation – Cloud service runtime evolution Complex Cloud Service Elastic Cloud Service Deployment (running) process ICSOC 2014, 5 November, Paris 3 Elasticity control process Elasticity Control Processes What would be the elasticity behavior? Elasticity requirements Elasticity controller
  • 4. Motivation – Cloud service runtime evolution Elasticity control process enforced Which will be the behavior? now ICSOC 2014, 5 November, Paris 4
  • 5. Motivation – Cloud service runtime evolution Which will be the behavior? ICSOC 2014, 5 November, Paris 5 Possible requirements violations Elasticity control process enforced Expected impact Expected cool-off period now Which elasticity control process is most appropriate? How a control process will affect metrics, e.g., throughput, of the overall service and individually on each part of the cloud service?
  • 6. Motivation – Cloud service behavior  Cloud service behavior is complex and can depend on: – The structure of the cloud service – The runtime resources used – The workload of the cloud service – The control processes enforced, e.g., by the controller  Capturing & using these types of information for evaluating elasticity behavior ICSOC 2014, 5 November, Paris 6 Service Topology 1 Unit 1 Unit 2 Topology 2 Unit 3 Unit 4 푉푀푥1 푉푀 푉푀푥2 푥3 푉푀푥푛
  • 7. Approach  Input: – Cloud service structure – Monitoring information of different service parts (e.g., service units, service topologies) – Elasticity control process 퐸퐶푃푖  Expected output: – Metrics evolution, in time, for different service parts and 퐸퐶푃푠  Main mechanism: – Creating behavior clusters – Computing closest behavior centroids ICSOC 2014, 5 November, Paris 7
  • 8. Gathering information Relevant timeseries 퐸퐶푃푖 enforcement Metric measurement Select relevant timeseries where 퐸퐶푃푖 was enforced before ICSOC 2014, 5 November, Paris 8
  • 9. Clustering elasticity behaviors  Transform relevant timeseries to multi-dimensional points 푡1 푡2 … 푡푛 Time Metric 푚푥 퐶푙푢푠푡푒푟푐 푚푥 퐶1푚푥 ICSOC 2014, 5 November, Paris 9 푀푒푡푟푖푐푉푎푙 (푡1) 푀푒푡푟푖푐푉푎푙 (푡2) 푀푒푡푟푖푐푉푎푙 (푡푛) 푀푒푡푟푖푐푉푎푙 (푡3) … 푀푒푡푟푖푐푉푎푙 (푡4) Behavior Point BP 퐶푙푢푠푡푒푟1푚 K-means 푥 퐶푙푢푠푡푒푟2 푚푥 퐶2푚푥 퐶푐 푚푥
  • 10. Computing expected behavior 퐵푃푚푥 Current values 퐶푙푢푠푡푒푟1 푚푥 퐶1푚푥 퐵푃푚푦 퐶푙푢푠푡푒푟2 푚푥 퐶2푚푥 퐶푙푢푠푡푒푟1 푚푦 퐶1푚푦 퐶푙푢푠푡푒푟푝 푚푦 퐶푝 푚푦 퐶푙푢푠푡푒푟1푚푥 퐶푙푢푠푡푒푟2 푚푥 퐶푙푢푠푡푒푟푟 푚푥 퐶푙푢푠푡푒푟1 푚푦 a b - 퐶푙푢푠푡푒푟푝 푚푦 c - d Co-occurrence matrix ICSOC 2014, 5 November, Paris 10 Compute centroids closest to the 퐵푃푖 퐶1푚푥 퐶푝 푚푦 Transform to timeseries 푚 푚푥 푦 퐶푙푢푠푡푒푟푟 푚푥 퐶푟 푚푥
  • 11. Experiment Settings [1/3]  Setting: – M2M service – Video Service ICSOC 2014, 5 November, Paris 11
  • 12. Experiment Settings [2/3]  Setting: – Running on public Flexiant cloud FCO – MELA & JCatascopia for monitoring cloud services – Randomly apply ECPs of random type for collecting behavioral information – “Interesting” metrics ICSOC 2014, 5 November, Paris 12
  • 13. Experiment Settings [3/3] ICSOC 2014, 5 November, Paris 13
  • 14. Experiments – Video Service Video Service – effect of 퐸퐶푃1 on Application Server 퐸퐶푃1 - scale in application server tier – select instance to remove, stop the video streaming service, remove instance from load balancer, stop JCatascopia monitoring agent, delete instance ICSOC 2014, 5 November, Paris 14
  • 15. Experiments – M2M Service [1/2] M2M Service – effect of 퐸퐶푃7 on the entire cloud service 퐸퐶푃7 - scale in data node service unit – copy data from the instance to be removed, remove recursively virtual machine ICSOC 2014, 5 November, Paris 15
  • 16. Experiments – M2M Service [2/2] M2M Service – effect on Data End Controller of enforcing 퐸퐶푃8 퐸퐶푃8 - scale out data node service unit – create new network interface, create new instance, assign token to node, set cluster controller ICSOC 2014, 5 November, Paris 16
  • 17. The more random the workload, of the service, the more difficult to estimate the behavior Lower abstraction layer Experiments – Quality of Results 푉푎푟푖푎푛푐푒푚 = => better estimations 푛푏퐸푠푡푖푚푎푡푖표푛푠 푒푠푡푖푚푎푡푖표푛푆푖푧푒(푒푠푡푖푚푎푡푒푑푀푒푡푟푖푐푚 − 표푏푠푒푟푣푒푑푀푒푡푟푖푐푚)2 ICSOC 2014, 5 November, Paris 17 푛푏퐸푠푡푖푚푎푡푖표푛푠 − 1 Complex, unpredictable metrics => very low degree of accuracy
  • 18. Conclusions and Future Work  Conclusions – When controlling a complex cloud service, we need to consider the impact elasticity control processes have on different service parts – ADVISE is indeed able to "advise" elasticity controllers about cloud service behavior  Future work – Integrating with rSYBL (https://github.com/tuwiendsg/rSYBL) – Adapting the control mechanisms of rSYBL to use such information  ADVISE – More experiments available at http://tuwiendsg.github.io/ADVISE – Prototype https://github.com/tuwiendsg/ADVISE ICSOC 2014, 5 November, Paris 18
  • 19. Thank you! Georgiana Copil e.copil@dsg.tuwien.ac.at http://dsg.tuwien.ac.at/staff/ecopil/ Distributed Systems Group Vienna University of Technology Austria ICSOC 2014, 5 November, Paris 19

Editor's Notes

  1. First
  2. First
  3. First