A Survey on Resource Allocation & Monitoring in Cloud Computing


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Existing research works on Resource Allocation & Monitoring In Cloud Computing

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A Survey on Resource Allocation & Monitoring in Cloud Computing

  1. 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. 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. 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. 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. 5. Cloud Computing : Overview Cloud Reference Architecture
  6. 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. 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. 8. Resource Management Resource Provisioning Process
  9. 9. Resource Management Resource Monitoring Process
  10. 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. 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. 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. 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. 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. 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. 16. Resource Allocation : Existing Mechanism Taxanomy
  17. 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. 18. Resource Monitoring: Solution Mechanism Taxanomy
  19. 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. 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. 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. 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. 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. 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. 25. Thank you