Existing research works on Resource Allocation & Monitoring In Cloud Computing

1. A SURVEY ON RESOURCE
ALLOCATION & MONITORING IN
CLOUD COMPUTING
By:
Mohd Hairy Mohamaddiah, Azizol Abdullah, Shamala Subramaniam & Masnida
Hussin
Department of Communication Technology and Network
Faculty Of Computer Science & Information Technology
Universiti Putra Malaysia (UPM)

2. Outlines
i.
ii.
iii.
iv.
v.
vi.
Cloud Computing : Overview
Resource Management
Research Problem
Research Objectives
Research Methodologies
Resource Allocation & Monitoring : Existing
Mechanisms
vii. Research Gap
viii.Conclusion
ix. References

3. Cloud Computing : Overview
Model enabling ubiquitous, convenient, on-demand network
access to a shared pool of configurable computing resources
(e.g., networks, servers, storage, applications, and services) that
can be rapidly provisioned and released with minimal
management effort or service provider interaction (NIST,2009)

4. Cloud Computing : Overview




Public Deployment
Private
Model
Hybrid
Community
Characteristics





On Demand Self Service
Resource Pooling
Broad Network Access
Rapid elasticity
Measured Service
Provider
 Service Provider
 Infrastructure Provider
Service Model





Infrastructure as a Service
Platform as a Service
Software as a Service
Network as a Service
X as a Service

5. Cloud Computing : Overview
Cloud Reference Architecture

6. Resource Management
Process that manage physical resources such as
CPU cores, disk space, and network bandwidth.
This resources must be sliced and shared
between virtual machines running potentially
heterogeneous workloads.

7. Resource Management
Elements in Resource Management
RESOURCE
MANAGEMENT
MONITORING
Monitoring the
client / cloud
subscriber &
availability of
resources
ALLOCATION
Allocate the
Resources
DISCOVERY
DISCOVERY
Discovery &
Discovery &
Provision of
Provision of
Resources
Resources

8. Resource Management
Resource Provisioning Process

9. Resource Management
Resource Monitoring Process

10. Research Problem
Exhausted
Resources/Contention/Scasrcity
(Calatrava A et al, 2011; Vinothina, V et al
,2012)
RESOURCE MANAGEMENT
Providing Guaranteed
resources On Time
(Dechminko et al,2011)
Limited Usage, Resource Contention
(Calatrava A et al, 2011; Iyer R et al
2009)
Energy efficiency becoming low in
Resource Management (Wang et al, 2012)

11. Objectives
 To conduct a study in resource allocation and monitoring
in the cloud computing environment.
 To describe cloud computing and its properties, research
issues in resource management mainly in resource
allocation and monitoring
 To study current solutions approach for resource
allocation and monitoring

12. Methodologies
 Provide a cloud computing taxonomy covers the cloud
definitions, characteristics and deployment models.
 Analyze
the
literatures
and
discuss
about
resource
management, the process and the elements.
 Concentrate
literatures
on
resource
allocation
and
monitoring.
 Derived the problems, challenge and the approach solution
for resource allocation and monitoring in the cloud.

13. Resource Allocation : Existing Mechanism
(Selected Review)
Researchers
Mechanism
Contribution
Maurer, M., Brandic, I.,
&
Sakellariou,
R
(2013)
Knowledge management (casebased & rule-based)
Decreased the most costly
SLA violations, and improve
performance and low energy
consumption for autonomic
allocation workload.
Espadas,
(2013)
Tenant
Isolation
concept
(algorithm) tenant isolation, VM
Instance allocation and load
balancing
Establish measurement model
for underutilized resources
(CPU & Memory)
Li, J. et al (2012)
Optimization, Scheduling
present
a
resource
optimization mechanism in
heterogeneous
IaaS
federated multi-cloud systems,
which enables pre-emptable
task scheduling
Javadi, B. et al (2012)
Scheduling
Proposed
a
preemption
policies to improve the QoS for
the user request by facilitating
lease preemption to resolve
resource contention
J.
et
al

14. Resource Allocation : Existing Mechanism
(Selected Review)
Researchers
Mechanism
Contribution
Young
C.L
,
&
Zomaya, A. Y (2011)
Scheduling Algorithm for
Energy
Present energy conscious algorithms to
reduce power consumption
Dechminko
(2011)
al.
Service
Architecture
Oriented
Proposed
Infrastructure
Services
Modelling Framework to support
service provisioning
al
Model
Integration
&
Meta
Scheduling
analysis
Integrates cloud and grid resources to
allocate
resources
for
scientific
applications
Urgaonkar, R. et al
(2010)
Optimization
Proposed Online admission control,
routing and resource allocation for
virtualized data center
Hu, Y. et al (2009)
Scheduling (First Come
First Server (FCFS))
Provide an allocation method to meet
the SLAs for shared and dedicated
allocation by using FCFS algorithms
Calatrava,
(2011)
et
A.
et

15. Resource Allocation : Existing Mechanism
(Selected Review)
Researchers
Mechanism
Contribution
Hu, Y. et al (2009)
Scheduling (First Come
First Server (FCFS))
Provide an allocation method to meet
the SLAs for shared and dedicated
allocation by using FCFS algorithms

16. Resource Allocation : Existing Mechanism
Taxanomy

17. Resource Monitoring : Existing Mechanism
(Selected Review )
Researchers
Mechanism
Contribution
Dabrowski, C &
Hunt, F. (2011)
Fault Detection Mechanism
via Discrete Time Markov
Chain
Detecting & Fixing problem on time in
cloud facilities
Zhy, Y. & Xu, W.
(2010)
Event triggering / High
availability
Monitor current state of resources
Emeakaroha, V.C.
et al (2010)
Fault Detection (SLA
Threats)
Introduce a framework for mappings of
the Low-level resource Metrics to Highlevel SLAs
Sun, Y., et al (2010)
IT Service Management
process
Assist to achieve visualization,
controllability and automation of the
service availability and performance
management, to ensure QoS and
reduce operation cost of deployment of
cloud.
Iyer, R. , et al.
(2009)
State estimation via
monitoring scheme of
cache space and memory
bandwidth.
The estimation helps to reduce the
overhead of VPA and works well with
data center consolidation scenario in
data center.

18. Resource Monitoring: Solution Mechanism
Taxanomy

19. Gap Analysis
Resource
Management
Process
Features
Limitations
Agility,
Elastic
No Infrastructure and Service Agility to adapt
and formulate changes
Reliability
No reliability checking mechanism for actual
task executions in allocation of resources
Predictive,
Scalable
Prediction model used to lower the power
consumption only can be adapted in private
cloud
Resource Allocation

20. Gap Analysis
Resource
Management
Process
Features
Limitations
Availability,
Security,
No monitoring & triggering automatically the
resources state (at storage level) and resource
provider availability
Resource
Monitoring
Both
Not much significant study on failure detection
in a dynamic and cluster environment
Single
Framework,
Scalability
There is also no single framework for the whole
autonomous resource management process
being carried out in order to provide services to
cloud subscriber.

21. Conclusion
•
Previous studies have shown the importance of resource
management in cloud computing comprising discovery, monitoring
and allocation resources.
•
Currently
and in the future there will be / are multiple
heterogeneous workload will be outsourced to cloud resources. The
importance of an efficient framework for the process is a high
demand especially to fulfill the agile of user requests.
• We had summarized different methods (algorithms technique) and
theory which being used to formulate framework and model, derived
to provide a better resource allocation and monitoring process in
terms of a better performance, competitive and efficiency to meet the
required SLA, improved the resource performance and lowered the
power consumption

22. References
(Selected)
1. Espadas, J., Molina, A., Jiménez, G., Molina, M., Ramírez, R., & Concha, D. : A tenant-based resource
allocation model for scaling software-as-a-service applications over cloud computing infrastructures.
Future Generation Computer Systems, 29(1), 273-286. doi: 10.1016/j.future.2011.10.013.(2013).
2. Maurer, M., Brandic, I., & Sakellariou, R. : Adaptive resource configuration for Cloud infrastructure
management. Future Generation Computer Systems, 29(2), 472–487. doi:10.1016/j.future.2012.07.004.
(2013).
3. Li, J., Qiu, M., Ming, Z., Quan, G., Qin, X., & Gu, Z.: Online optimization for scheduling preemptable tasks
on IaaS cloud systems. Journal of Parallel and Distributed Computing, 72(5), 666–677.
doi:10.1016/j.jpdc.2012.02.002.(2012).
4. Javadi, B., Abawajy, J., & Buyya, R. : Failure-aware resource provisioning for hybrid Cloud infrastructure.
Journal of Parallel and Distributed Computing, 72(10), 1318–1331. doi:10.1016/j.jpdc.2012.06.012.(2012).
5. Wang, X., Du, Z., & Chen, Y. : An adaptive model-free resource and power management approach for
multi-tier cloud environments. Journal of Systems and Software, 85(5), 1135-1146. doi:
10.1016/j.jss.2011.12.043.(2012).
6. Vinothina, V. , Sridaran R. & Ganapathi, P. : A Survey on Resource Allocation Strategies in Cloud
Computing. International Journal Of Advanced Computer Science and Applications, 3(6), 97–104. (2012).

23. References
(Selected)
6. Demchenko, Y.; Van der Ham, J.; Yakovenko, V.; De Laat, C.; Ghijsen, M.; Cristea, M., On-demand
provisioning of Cloud and Grid based infrastructure services for collaborative projects and groups,.
Collaboration Technologies and Systems (CTS), 2011 International Conference on , vol., no., pp.134,142,
23-27 May 2011.doi: 10.1109/CTS.2011.5928675.
7. Calatrava, A.; Molto, G.; Hernandez, V. : Combining Grid and Cloud Resources for Hybrid Scientific
Computing Executions," Cloud Computing Technology and Science (CloudCom), 2011 IEEE Third
International Conference on , vol., no., pp.494-501. (2011).
8. Young Choon Lee, & Zomaya, A. Y. : Energy conscious scheduling for distributed computing systems
under different operating conditions. Parallel and Distributed Systems, IEEE Transactions on, 22(8), 13741381. (2011).
9. Dabrowski, C., & Hunt, F. : Identifying Failure Scenarios in Complex System by Perturbing Markov Chain
Analysis Models. In : Proceedings of the 2011 Pressure Vessels & Piping Division (PVPD) Conference .
PVP2011-57683, 1–24. (2011).
10. Urgaonkar, R., Kozat, U. C., Igarashi, K., & Neely, M. J. : Dynamic Resource Allocation and Power
Management in Virtualized Data Centers (pp. 479–486). (2010).
11. Sun, Y., Xiao, Z., & Bao, D. : An architecture model of management and monitoring on Cloud services
resources. 2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE),
V3–207–V3–211. doi:10.1109/ICACTE.2010.5579654.(2010).

24. References
(Selected)
12. Hu, Y., Wong, J., Iszlai, G., & Litoiu, M. : Resource provisioning for cloud computing. CASCON '09
Proceedings of the 2009 Conference of the Center for Advanced Studies on Collaborative Research, 101–
111. (2009).
13. Iyer, R., Illikkal, R., Tickoo, O., Zhao, L., Apparao, P., & Newell, D. : VM3: Measuring, modeling and
managing
VM
shared
resources.
Computer
Networks,
53(17),
2873-2887.
doi:
10.1016/j.comnet.2009.04.015.(2009).

25. Thank you