a survey on resource allocation & monitoring in cloud computing

A SURVEY ON RESOURCE A SURVEY ON RESOURCE ALLOCATION & MONITORING IN ALLOCATION & MONITORING IN

CLOUD COMPUTINGCLOUD 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)

OutlinesOutlines

i.i. Cloud Computing : OverviewCloud Computing : Overview

ii.ii. Resource Management Resource Management

iii.iii. Research ProblemResearch Problem

iv.iv. Research ObjectivesResearch Objectives

v.v. Research MethodologiesResearch Methodologies

vi.vi. Resource Allocation & Monitoring : Existing Resource Allocation & Monitoring : Existing MechanismsMechanisms

vii.vii. Research Gap Research Gap

viii.viii.ConclusionConclusion

ix.ix. ReferencesReferences

Cloud Computing : OverviewCloud 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)


Deployment Deployment ModelModel

CharacteristicsCharacteristics Service ModelService Model

Public Public PrivatePrivate HybridHybrid CommunityCommunity

Infrastructure as a ServiceInfrastructure as a Service Platform as a ServicePlatform as a Service Software as a ServiceSoftware as a Service Network as a ServiceNetwork as a Service X as a ServiceX as a Service

ProviderProvider Service ProviderService Provider Infrastructure ProviderInfrastructure Provider

On Demand Self ServiceOn Demand Self Service Resource PoolingResource Pooling Broad Network AccessBroad Network Access Rapid elasticityRapid elasticity Measured ServiceMeasured Service


Cloud Reference ArchitectureCloud Reference Architecture

Resource ManagementResource Management

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

Resource ManagementResource Management

DISCOVERY

Discovery & Provision of

Resources

DISCOVERY

Discovery & Provision of

Resources

ALLOCATION

Allocate the Resources

MONITORING

Monitoring the client / cloud subscriber & availability of

resources

RESOURCE MANAGEMENT

Elements in Resource ManagementElements in Resource Management

Resource ManagementResource ManagementResource Provisioning ProcessResource Provisioning Process

Resource ManagementResource ManagementResource Monitoring ProcessResource Monitoring Process

Research ProblemResearch Problem

RESOURCE MANAGEMENT

Exhausted Resources/Contention/Scasrcity

(Calatrava A et al, 2011; Vinothina, V et al ,2012)

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)

Objectives 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

Methodologies 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.

Resource Allocation : Existing MechanismResource Allocation : Existing Mechanism(Selected Review)(Selected Review)

Researchers Mechanism Contribution

Maurer, M., Brandic, I., & Sakellariou, R (2013)

Knowledge management (case-based & rule-based)

Decreased the most costly SLA violations, and improve performance and low energy consumption for autonomic allocation workload.

Espadas, J. et al (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



Young C.L , & Zomaya, A. Y (2011)

Scheduling Algorithm for Energy

Present energy conscious algorithms to reduce power consumption

Dechminko et al. (2011)

Service Oriented Architecture

Proposed Infrastructure Services Modelling Framework to support service provisioning

Calatrava, A. et al (2011)

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



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

Resource Allocation : Existing MechanismResource Allocation : Existing MechanismTaxanomyTaxanomy

Resource Monitoring : Existing MechanismResource Monitoring : Existing Mechanism(Selected Review )(Selected Review )


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 High-level SLAs

Sun, Y., et al (2010) IT Service Management process

Assist to achieve visualization, controllability and automation of theservice 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.

Resource Monitoring: Solution MechanismResource Monitoring: Solution MechanismTaxanomyTaxanomy

Gap AnalysisGap Analysis

Resource Management Process

Features Limitations

Resource Allocation

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

Gap AnalysisGap Analysis

Resource Management Process

Features Limitations

Resource Monitoring

Availability, Security,

No monitoring & triggering automatically the resources state (at storage level) and resource provider availability

Not much significant study on failure detection in a dynamic and cluster environment

Both

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.

Conclusion 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

ReferencesReferences(Selected) (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).


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), 1374-1381. (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).


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).

Thank you

a survey on resource allocation & monitoring in cloud computing

Education

resource managementproviding

resource contentionj

resource managementprocess

resource management

resource management

resource contention

dedicated allocation

allocation method