Resource Allocation for Heterogeneous Cloud Computing

Cloud Computing and the Internet of Things (IoT) have been converging, the creation of the distributed computing system large scale, called IoT Cloud Systems. One of the main advantages of Cloud Computing is reflected in its support for self-service, on-demand resource consumption, where users can dynamically allocate the appropriate amount of infrastructure resources (e.g., computing or storage) required by an application. In this paper, we develop a method to predict the lease completion time distribution that is applicable to making sophisticated trade-off decisions in resource allocation and rescheduling. Research methods have improved the efficiency and effectiveness of heterogeneous cloud computing resources.

[1]  Thanh Thuy Nguyen,et al.  Algorithmic approach to deadlock detection for resource allocation in heterogeneous platforms , 2014, 2014 International Conference on Smart Computing.

[2]  Mladen A. Vouk,et al.  Cloud computing — Issues, research and implementations , 2008, ITI 2008 - 30th International Conference on Information Technology Interfaces.

[3]  Borja Sotomayor,et al.  Combining batch execution and leasing using virtual machines , 2008, HPDC '08.

[4]  Rajkumar Buyya,et al.  SLA-Based Resource Allocation for Software as a Service Provider (SaaS) in Cloud Computing Environments , 2011, 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.

[5]  Odej Kao,et al.  Exploiting Dynamic Resource Allocation for Efficient Parallel Data Processing in the Cloud , 2011, IEEE Transactions on Parallel and Distributed Systems.

[6]  Rajkumar Buyya,et al.  Article in Press Future Generation Computer Systems ( ) – Future Generation Computer Systems Cloud Computing and Emerging It Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility , 2022 .

[7]  Ian Foster,et al.  Provisioning computational resources using virtual machines and leases , 2010 .

[8]  Rajkumar Buyya,et al.  Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing , 2012, Future Gener. Comput. Syst..

[9]  Ha Huy Cuong Nguyen Deadlock Prevention for Resource Allocation in Heterogeneous Distributed Platforms , 2016, ICADIWT.

[10]  Ajay D. Kshemkalyani,et al.  Distributed Computing: Principles, Algorithms, and Systems , 2008 .

[11]  Dac-Nhuong Le,et al.  A New Technical Solution for Resource Allocation in Heterogeneous Distributed Platforms , 2015, ICADIWT.

[12]  Laura M. Haas,et al.  Distributed deadlock detection , 1983, TOCS.

[13]  Feng Zhao,et al.  Energy aware consolidation for cloud computing , 2008, CLUSTER 2008.

[14]  Lavanya Ramakrishnan,et al.  Performance and energy efficiency of big data applications in cloud environments: A Hadoop case study , 2014, J. Parallel Distributed Comput..

[15]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[16]  Roozbeh Farahbod,et al.  Dynamic Resource Allocation in Computing Clouds Using Distributed Multiple Criteria Decision Analysis , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.