adaptive sla-awarecloud federations
DESCRIPTION
TRANSCRIPT
Adaptive SLA-aware Cloud Federations
Attila Kertész (SZTAKI), Ivona Brandic (TUW), Gabor Kecskemeti (SZTAKI), Marc Oriol (UPC),
S-Cube Industry Workshop, Thales, France Feb. 24, 2012.
www.s-cube-network.eu
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Introduction
• Highly dynamic service environments require a novel infrastructure to handle on demand deployment and decommission of service instances.
• Cloud Computing allows:
• Outsourcing these dynamic environments
• Constructing extensible service-based applications
• Utilizes the latest achievements of Grid Computing, Service-oriented computing, business-processes and virtualization
• Virtual Appliances encapsulate metadata with a complete system (OS, libraries and applications)
• Infrastructure cloud systems (IaaS) allow to instantiate VA’s on their virtualized resources: Virtual Machines
• Several public and private IaaS systems co-exist
• Only a “Federated Cloud” could offer the different capabilities as a whole
Repository
VA VA VA
Infrastructure as a Service Cloud
Host VMM
Host VMM
Host VMM
Host VMM
Host VMM
Host VMM
Host VMM
Host VMM
2. Delivery
IaaS utilization steps
VA Host
VMM
VA
Insta
ntiation
VM
Virtual
Appliance
Service Libs
+
OS
Support
Environment
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1. Upload
3. Deployment
4. Access
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Federated Cloud Management (FCM)
• An autonomic resource management solution
• Provides an entry point to a cloud federation
• Provides transparent service execution for users
• Following challenges are considered:
• Varying load of user requests
• Enabling virtualized management of applications
• Establishing interoperability and provider selection
• Minimizing Cloud usage costs
• Builds on meta-brokering, cloud brokering and automated on-demand service deployment
• Layered architecture
• Meta-broker
• Cloud Brokers
• Cloud infrastructure providers
Cloud
Cloud
Cloud Cloud FCM
A. Cs. Marosi, G. Kecskemeti, A. Kertesz, P. Kacsuk, FCM: an Architecture for Integrating IaaS
Cloud Systems, In Cloud Computing 2011, IARIA, pp. 7-12, Rome, Italy, 2011.
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FCM Architecture: overview
CloudBroker
Clouda
FCM
Repository
Cloudb
• Top-level brokering
• Autonomously manage
the interconnected
cloud infrastructures
• Forms a federation with
the help of Cloud
Brokers
Generic Meta-Broker Service
CloudBroker
1
G. Kecskemeti, A. Kertesz, A. Marosi, P. Kacsuk, Interoperable Resource Management for establishing Federated
Clouds, In Achieving Federated and Self-Manageable Cloud Infrastructures: Theory and Practice, IGI Global (USA), 2011.
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FCM Architecture: overview
CloudBroker
Clouda
FCM
Repository
Cloudb
• Manages VA
distribution among the
various cloud
infrastructures
• Automated federation-
wide repository content
management
• Offers current VA
availability and
estimates its future
deployment
Generic Meta-Broker Service
CloudBroker
2
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FCM Architecture: overview
CloudBroker
Clouda
FCM
Repository
Cloudb
• Interacts with a single
IaaS system
• Manages resources
• Schedules service calls
Generic Meta-Broker Service
CloudBroker
3 3
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Generic Meta-Broker Service
FCM
Repository
VAx..VAy
Cloudc
Generic Meta-Broker Service
CloudBroker
IS Agent
Meta-Broker
Core
MatchMaker Invoker
Information
Collector
BPDL list
Service call
(+requierments) Call result
Cloudb
CloudBroker
Clouda
CB
• BPDL – Broker Property
Description Language
• Cloud Brokers are
described
• Basic and aggregated
dynamic properties
• Estimated availability time
for a specific VA in the
native repository
• Average VA deployment
time
• Scheduling filters and
ranks Cloud Brokers
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Cloud Broker
CloudBroker a
Q1
Clouda
VMQx
Clouda
VMQy
VAx VAy
VMx1
VMx2
VMxn
Clouda
…
VMy1
VMy2
VMym
…
Clouda VM Handler
Native R
epository
Generic Meta-Broker Service
FCM
Repository
VAx..VAy
• Dynamic requirements
may be specified with a
service call
• Treated as a new VA type
• Some IaaS systems offer
predefined classes of
resources
• Resource class selected
with at least the required
resources
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Cloud Broker: Scheduling of service calls
• The Cloud Broker performs scheduling of service calls to
resources (VMs)
• Based on the monitoring information gathered
• May decide to start new resources based on:
• The number of running VM’s to handle the service call
• The number of waiting service calls in the Service call queue
• The average execution time of service calls
• The average deployment time of VA’s
• SLA constraints
• VM decommission
• Takes into account the “billing period”
Service monitoring with SALMon
• Service Level Agreement Monitor (SALMon): designed for
monitoring QoS of software services
• Capable of passive monitoring and testing purposes
• Supports any type of service technology (SOAP-based WS,
RESTful services, etc.)
• It is itself an SBA with two main components:
• Monitor: retrieves values of quality metrics with the help of Measure
Instruments
• Analyzer: evaluates conditions over these metrics
• It is suitable for monitoring running services in Cloud infra-
structures
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The SALMon service monitoring framework
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Integrated Monitoring Approach for Seamless Service Provisioning (IMA4SSP)
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IMA4SSP details
• We have integrated SALMon to enable performance-driven SLA-based service executions in Cloud federations
• Metrics related to service methods may be defined to monitor service operations
• Metric values are regularly refreshed in the Generic Service Registry (GSR) of the architecture
• Finally information stored in the GSR are used by the GMBS for Cloud infrastructure selection based on service reliability
A. Kertesz, G. Kecskemeti, M. Oriol, A. Marosi, X. Franch, J. Marco, Integrated Monitoring Approach for
Seamless Service Provisioning in Federated Clouds, Proc. of PDP '12, IEEE CS, 2012.
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Enhanced monitoring with M3S
• SALMon reports
service metrics to
a DB regularly
checked by the
meta-broker
• The Minimal Metric
Montoring Service
(M3S) is used to
monitor:
• Availability;
• Computing
capability;
• and data transfer
reliability.
GMBS Users
CloudBroker
Amazon
CloudBroker
OpenNebula
SALMon
VM
1. Test
metrics
3. Fetch
reliability
info
2. Send
results
SALMon
DDBB
M3S
Miminizing monitoring costs
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• Keeping the monitoring VMs in the Cloud can be costly
• To reduce these costs, the IS Agent component of GMBS has been extended to initiate the deployment and decommission of these VMs
• The monitored metric values have timestamps
• When the retrieved metric value of a service is outdated, the IS Agent contacts the responsible Cloud Broker to initiates a M3S and SALMon VM deployment
• When metric values with new timestamps are read from the registry, the IS Agent contacts the CB again, to decommission the monitoring VMs
Autonomous behavior
• Inter-Cloud management for optimized resource usage and SLA violation prevention
• Predefined set of reactive actions in the Knowledge management system requiring local/global intervention in the system
• Adaptation actions are triggered by a rule-based system, based on monitored metrics
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Action Involved Component Integration
Reschedule calls Meta-Broker Global
Rearrange VM queues Cloud-Broker Global
Extend/Shrink VM Queue Cloud-Broker Local
Rearrange VA storage FCM repository Global
Self-Initiated Deployment Service instances Local
Hybrid knowledge management in FCM
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Hybrid approach for incorporting a Knowledge Management System to FCM: global and local
Allows global control over local decisions
Global KM could stop the application of a locally optimal action to avoid an autonomic chain reaction
Enables the execution of more fine-grained actions postponing adaptation action exhaustion
G. Kecskemeti, M. Maurer, I. Brandic, A. Kertesz, Zs. Nemeth, and S. Dustdar,
Facilitating self-adaptable Inter-Cloud management, Proc. of PDP '12, IEEE CS, 2012.
VM
qu
eu
e
rea
rra
ng
em
ents
a
nd
q
ue
ue
e
xte
nsio
n/s
hrinkin
g
Monitored metrics C
all
reschedulin
g
rea
rrang
ing
VA
sto
rag
e
- Service call queue length in every Cloud-Broker
- VM queue length for every appliance in every Cloud-Broker
- Call throughput
- Average waiting time for particular service
- Average waiting time of a queue
- Number of service (s) instances in an IaaS system (x): vms(x,s)
- Call/VM ratio
- overall infrastructure load
- Global storage cost
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Conclusions
• We have designed a Federated Cloud Management solution that acts as an entry point to cloud federations
• Meta-brokering, cloud brokering and on-demand service deployment
• We have shown how Cloud Brokers manage the number and location of VMs for the various service requests
• We have extended FCM with enhanced SLA monitoring capabilities with the SALMon framework in collaboration with Universitat Politecnica de Catalunya (UPC)
• We have developed a method to enable SLA-aware self-adaptable Inter-Cloud management with a rule-based knowledge model in collaboration with the Technical University of Vienna (TUW)
Relation to S-Cube learning packages
• The presented work is related to the following packages:
• JRA-1.2: Service Level Agreement based Service infrastructures in the context of multi layered adaptation
• JRA-2.3: SLA-based Service Virtualization in distributed, heterogenious environments
• The following research topics are covered:
• T-JRA-2.3.1: Infrastructure Mechanisms for the Run-Time Adaptation of
Services
• T-JRA-1.2.2: Integrated Adaptation Principles, Techniques and Methodologies
• T-JRA-1.2.3: Comprehensive, Context-Aware Monitoring
Thank You for your attention!
Questions?
https://www.lpds.sztaki.hu/CloudResearch
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