![Sr. no. Author Outcome
1 Mayank
Mishra et al.
[7]
author told that, the users of cloud services pay only for the amount of
resources (a pay-as-use model) used by them. Traditional data centers
are provisioned to meet the peak demand, which results in wastage of
resources during non-peak periods. To alleviate the above problem,
modern-day data centers are shifting to the cloud. The important
characteristics of cloud-based data centers are making resources
available on demand. The operation and maintenance of the data center
lies with the cloud provider.
2 Vijindra and
Sudhir
shenai. A [8]
Author, have presented an algorithm for a cloud computing
environment that could automatically allocate resources based
on energy optimization methods. Then, prove the effectiveness
of our algorithm. In the experiments and results analysis, we
find that in a practical Cloud Computing Environment, using
one whole Cloud node to calculate a single task or job will
waste a lot of energy](https://image.slidesharecdn.com/presentation-170531141006/85/Presentation-15-320.jpg)
![Sr. no. Author Outcome
3 Qiang Li and
Yike Guo [9]
proposed a model for optimization of SLA-based resource
schedule in cloud computing based on stochastic integer
programming technique. The performance evaluation has
been performed by numerical studies and simulation.
4 Xin Lu, Zilong
GU [10]
discussed that, by monitoring performance parameters of
virtual machines in real time, the overloaded is easily
detected once these parameters exceeded the threshold.
Quickly finding the nearest idle node by the ant colony
algorithm from the resources and starting the virtual
machine can bears part of the load and meets these
performance and resource requirements of the load. This
realizes the load adaptive dynamic resource scheduling in
the cloud services platform and achieves the goal of load
balancing.](https://image.slidesharecdn.com/presentation-170531141006/85/Presentation-16-320.jpg)
![Sr. no. Author Outcome
5 Liang Luo et
al. [11]
discussed about, a new VM Load Balancing Algorithm is
proposed and then implemented in Cloud Computing
environment using CloudSim toolkit, in java language. In this
algorithm, the VM assigns a varying (different) amount of the
available processing power to the individual application
services. These VMs of different processing powers, the
tasks/requests (application services) are assigned or allocated
to the most powerful VM and then to the lowest and so on.
6 Gulati et al.
[12]
describe the particular CPU and memory related utilization
metrics used by VMware's Distributed Power Management
(DPM) to trigger management actions including VM
migration and PM power-on. Recently, researchers have
started to address the issue of estimating the utilization of
micro-architectural resources such as shared processor
caches, contention for which have been shown to negatively
impact the performance of consolidated VMs](https://image.slidesharecdn.com/presentation-170531141006/85/Presentation-17-320.jpg)
![īŊ [1] Hitoshi Matsumoto, Yutaka Ezaki,â Dynamic Resource Management in
Cloud Environmentâ, July 2011, FUJITSU science & Tech journal, Volume 47,
No: 3, page no: 270-276.
īŊ [2] B. Sotomayor, R.S. Montero, I.M. Llorente, I. Foster. Capacity leasing in
cloud systems using the opennebula engine. In: Cloud Computing and
Applications; 2008, pp. 1â5.
īŊ [3] Vaquero, Luis M., et al. A break in the clouds: towards a cloud definition.
ACM SIGCOMM Computer Communication Review 2008; 50-55.
īŊ [4] Kurdi, Heba, Madeeha Enazi, and Auhood Al Faries. Evaluating Firewall
Models for Hybrid Clouds. Modelling Symposium European. IEEE,2013.
īŊ [5] Nathani, S. Chaudhary, and G. Somani. Policy based resource allocation in
IaaS cloud. Future Generation Computer Systems 2011.](https://image.slidesharecdn.com/presentation-170531141006/85/Presentation-24-320.jpg)
![īŊ [6] B. Sotomayor, K. Keahey, and I. Foster. Combining Batch Execution and
Leasing Using Virtual Machines. 17th International Symposium on High
Performance Distributed Computing (HPDCâ08:), ACM, Boston Massachussets
2008;page no: 87-96.
īŊ [7] Mayank Mishra, Anwesha Das, Purushottam Kulkarni, and Anirudha Sahoo,
âDynamic Resource Management Using Virtual Machine Migrationsâ, Sep 2012,
0163-6804/12, IEEE Communications Magazine, page no: 34-40.
īŊ [8] Vijindra and Sudhir Shenai. A, âSurvey of Scheduling Issues in Cloud
Computingâ, 2012, ICMOC-2012, 1877-7058, Elsevier Ltd, Doi:
10.1016/j.proeng.2012.06.337, page no: 2881 â 2888.
īŊ [9] Qiang Li and Yike Guo, âOptimization of Resource Scheduling in Cloud
Computingâ, 2010, 12th International Symposium on Symbolic and Numeric
Algorithms for Scientific Computing, 978-0-7695-4324-6/10, IEEE, DOI
10.1109/SYNASC.2010.8, page no: 315 â 320](https://image.slidesharecdn.com/presentation-170531141006/85/Presentation-25-320.jpg)
![īŊ [10] Xin Lu, Zilong Gu, âA Load-adapative cloud resource scheduling model based
on ant colony algorithmâ, 2011, 978-1-61284-204-2/11, Proceedings of IEEE
CCIS2011, Page no: 296-300
īŊ [11] Liang Luo, Wenjun Wu, Dichen Di, Fei Zhang, Yizhou Yan, Yaokuan Mao, âA
Resource Scheduling Algorithm of Cloud Computing based on Energy Efficient
Optimization Methodsâ, 2012, 978-1-4673-2154-9/12, IEEE.
īŊ [12] Gulati, A., Holler, A., Ji, M., Shanmuganathan, G., Waldspurger, C., Zhu, X.:
VMware distributed resource management: design, implementation, and lessons
learned. VMware Technical Journal 1(1), 45-64 (2012). URL
http://labs.vmware.com/publications/gulati-vmtj-spring2012](https://image.slidesharecdn.com/presentation-170531141006/85/Presentation-26-320.jpg)
Presentation
- 1. EFFICIENT RESOURCE MANAGEMENT IN CLOUD COMPUTING ENVIRONMENT Master of Technology In Computer Sciences and Engineering Submitted By Kirandeep Kaur Under Supervision of Dr. RAJESH . K . BAWA (HEAD OF DEPARTMENT) Department of Computer Science, Punjabi University, Patiala. Department of Computer Science, Punjabi University, Patiala â 147002 January, 2015
- 2. 1. Introduction 2. Literature review 3. Problem formulation 4. Problem statement 5. Objectives 6. Proposed work 7. References
- 3. īŊ Cloud Computing īŊ Cloud computing promises a new era of service delivery and deployment in such a way that every person can access any kind of services like storage, application, operating system and so on from anywhere any time using any device having internet connection. Cloud computing opens new possibilities approaching sustainable solutions to deploy and advance their services upon that platform. Cloud computing is an on demand service in which shared resources, information, software and other devices are provided according to the clients requirement at specific time.
- 4. īŊ The trend toward cloud computing started in the late 1980s with the concept of grid computing when, for the first time, a large number of systems were applied to a single problem, usually scientific in nature and requiring exceptionally high levels of parallel computation. Grid computing provided a virtual pool of computation resources but it's different than cloud computing. In grid computing, the focus is on moving a workload to the location of the needed computing resources, which are mostly remote and are readily available for use.
- 5. īŊ In a cloud environment, computing and extended IT and business resources, such as servers, storage, network, applications and processes, can be dynamically shaped out from the hardware infrastructure and made available to a workload.
- 6. īŊ In 1961, John McCarthy suggested that computing can be sold like a utility, but due to lack of technology this idea was not implemented. īŊ In 1999 Salesforce started delivering applications to users using a simple website. īŊ In 2002 Amazon started Amazon Web Services, providing services like storage, computation and even human intelligence. īŊ In 2006 a truly commercial service open to everybody came into existence with the launch of Elastic Compute Cloud called EC2.
- 7. īŊ In 2009 arrival of browser based cloud enterprise applications known as Google Apps. īŊ In 2010 Sales force introduced the cloud-based database at Database.com for developers.
- 8. īŊ Cloud Application īŊ Cloud Platform īŊ Cloud Infrastructure
- 9. īŊ Cloud User īŊ Cloud Provider īŊ Cloud Broker īŊ Cloud Carrier
- 10. īŊ Software as a service (SaaS) īŊ Platform as a service (PaaS) īŊ Infrastructure as a service (IaaS)
- 11. īŊ Public Cloud īŊ Private Cloud īŊ Community Cloud īŊ Hybrid Cloud
- 12. īŊ Time to Market īŊ Economic īŊ Flexibility īŊ Scalability īŊ Simplicity īŊ Rapid elasticity
- 13. īŊ Resource management is a core function required of any man-made system. It affects the three basic criteria for system evaluation: performance, functionality and cost. Inefficient resource management has a direct negative effect on performance and cost. It can also indirectly affect system functionality. Optimal resource scheduling has been a great challenge in IaaS cloud computing environment. Cloud Computing is emerging as a replacement for traditional physical hardware computing.
- 14. īŊ Infrastructure-as-a-Service (IaaS) is one of the fundamental cloud computing models, where users can request virtual resources with various capabilities whenever needed. Also users request various resources at the same time for the completion and execution of various processes. Here, comes the term Resource Management where we need to manage the resources because it is quite possible that a particular resource is requested by many processes at the same time.
- 15. Sr. no. Author Outcome 1 Mayank Mishra et al. [7] author told that, the users of cloud services pay only for the amount of resources (a pay-as-use model) used by them. Traditional data centers are provisioned to meet the peak demand, which results in wastage of resources during non-peak periods. To alleviate the above problem, modern-day data centers are shifting to the cloud. The important characteristics of cloud-based data centers are making resources available on demand. The operation and maintenance of the data center lies with the cloud provider. 2 Vijindra and Sudhir shenai. A [8] Author, have presented an algorithm for a cloud computing environment that could automatically allocate resources based on energy optimization methods. Then, prove the effectiveness of our algorithm. In the experiments and results analysis, we find that in a practical Cloud Computing Environment, using one whole Cloud node to calculate a single task or job will waste a lot of energy
- 16. Sr. no. Author Outcome 3 Qiang Li and Yike Guo [9] proposed a model for optimization of SLA-based resource schedule in cloud computing based on stochastic integer programming technique. The performance evaluation has been performed by numerical studies and simulation. 4 Xin Lu, Zilong GU [10] discussed that, by monitoring performance parameters of virtual machines in real time, the overloaded is easily detected once these parameters exceeded the threshold. Quickly finding the nearest idle node by the ant colony algorithm from the resources and starting the virtual machine can bears part of the load and meets these performance and resource requirements of the load. This realizes the load adaptive dynamic resource scheduling in the cloud services platform and achieves the goal of load balancing.
- 17. Sr. no. Author Outcome 5 Liang Luo et al. [11] discussed about, a new VM Load Balancing Algorithm is proposed and then implemented in Cloud Computing environment using CloudSim toolkit, in java language. In this algorithm, the VM assigns a varying (different) amount of the available processing power to the individual application services. These VMs of different processing powers, the tasks/requests (application services) are assigned or allocated to the most powerful VM and then to the lowest and so on. 6 Gulati et al. [12] describe the particular CPU and memory related utilization metrics used by VMware's Distributed Power Management (DPM) to trigger management actions including VM migration and PM power-on. Recently, researchers have started to address the issue of estimating the utilization of micro-architectural resources such as shared processor caches, contention for which have been shown to negatively impact the performance of consolidated VMs
- 18. īŊ âA novel flexible resource scheduling model for public clouds to avoid starvationâ īŊ Cloud computing is a pay-per-use third party based service delivery method which provides all the required features as a services. Main service offerings of cloud computing model are: īŊ IaaS: Infrastructure as a Service īŊ PaaS: Platform as a Service īŊ SaaS: Software as a Service
- 19. īŊ There are various scheduling mechanisms available in cloud computing architecture. As heizea model provides a set of Immediate, Best-Effort, Dead Line Sensitive, and Advance Reservation scheduling mechanisms. Any of these scheduling mechanism can be used as per the client needs. īŊ To show the proof of concept of our scheduling mechanism we will simulate the public cloud environment on Cloud Sim simulator. It is a java based simulator which supports eclipse IDE for development environment.
- 20. īŊ We will simulate our flexible scheduling mechanism for resource utilization like VMs, and processing elements and will show that no user request starve longer in the lack of proper resource allocation.
- 21. īŊ The existing mechanism is prone to resource starvation for Best-Effort scheduled process if resources are busy in other time constrained policies. īŊ Starvation may need in increase in service delivery time which in turn may lead to customer un- satisfaction. īŊ Some improved anti-starvation algorithm are corrective measures which still don't avoid the occurance of starvation.
- 22. īŊ To simulate public cloud environment and initiating user request for resources and giving it to scheduler to grant access for resources. īŊ Designing flexible scheduler to avoid any possibility of starvation in cloud environment. īŊ Verifying our proposed flexible scheduling mechanism on diverse range of user requests.
- 23. īŊ To deal with problem of efficient resource scheduling in cloud environment we provide a flexible scheduling mechanism based on the load on the server. As the load or the client request on the server will increase our model will switch for equal resource sharing mechanism. With the application of this scheduling model no client request will be prone to starvation even in case of heavy load from various clients.
- 24. īŊ [1] Hitoshi Matsumoto, Yutaka Ezaki,â Dynamic Resource Management in Cloud Environmentâ, July 2011, FUJITSU science & Tech journal, Volume 47, No: 3, page no: 270-276. īŊ [2] B. Sotomayor, R.S. Montero, I.M. Llorente, I. Foster. Capacity leasing in cloud systems using the opennebula engine. In: Cloud Computing and Applications; 2008, pp. 1â5. īŊ [3] Vaquero, Luis M., et al. A break in the clouds: towards a cloud definition. ACM SIGCOMM Computer Communication Review 2008; 50-55. īŊ [4] Kurdi, Heba, Madeeha Enazi, and Auhood Al Faries. Evaluating Firewall Models for Hybrid Clouds. Modelling Symposium European. IEEE,2013. īŊ [5] Nathani, S. Chaudhary, and G. Somani. Policy based resource allocation in IaaS cloud. Future Generation Computer Systems 2011.
- 25. īŊ [6] B. Sotomayor, K. Keahey, and I. Foster. Combining Batch Execution and Leasing Using Virtual Machines. 17th International Symposium on High Performance Distributed Computing (HPDCâ08:), ACM, Boston Massachussets 2008;page no: 87-96. īŊ [7] Mayank Mishra, Anwesha Das, Purushottam Kulkarni, and Anirudha Sahoo, âDynamic Resource Management Using Virtual Machine Migrationsâ, Sep 2012, 0163-6804/12, IEEE Communications Magazine, page no: 34-40. īŊ [8] Vijindra and Sudhir Shenai. A, âSurvey of Scheduling Issues in Cloud Computingâ, 2012, ICMOC-2012, 1877-7058, Elsevier Ltd, Doi: 10.1016/j.proeng.2012.06.337, page no: 2881 â 2888. īŊ [9] Qiang Li and Yike Guo, âOptimization of Resource Scheduling in Cloud Computingâ, 2010, 12th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, 978-0-7695-4324-6/10, IEEE, DOI 10.1109/SYNASC.2010.8, page no: 315 â 320
- 26. īŊ [10] Xin Lu, Zilong Gu, âA Load-adapative cloud resource scheduling model based on ant colony algorithmâ, 2011, 978-1-61284-204-2/11, Proceedings of IEEE CCIS2011, Page no: 296-300 īŊ [11] Liang Luo, Wenjun Wu, Dichen Di, Fei Zhang, Yizhou Yan, Yaokuan Mao, âA Resource Scheduling Algorithm of Cloud Computing based on Energy Efficient Optimization Methodsâ, 2012, 978-1-4673-2154-9/12, IEEE. īŊ [12] Gulati, A., Holler, A., Ji, M., Shanmuganathan, G., Waldspurger, C., Zhu, X.: VMware distributed resource management: design, implementation, and lessons learned. VMware Technical Journal 1(1), 45-64 (2012). URL http://labs.vmware.com/publications/gulati-vmtj-spring2012
- 27. ī THANK YOU