Research on Resource Allocation Strategy of PaaS Platform

Research on Resource Allocation Strategy of PaaS Platform

Hongen Peng, Yabin Xu
Copyright: © 2019 |Pages: 14
DOI: 10.4018/JITR.2019010105
(Individual Articles)
No Current Special Offers


In order to allocate elastic resource to the application of PaaS platform, the authors analyze the key technologies and the particularity of resource scheduling in PaaS platform, and design an application-oriented resource allocation model and heuristic scheduling algorithm based on an ant colony algorithm. Different from the existing resource allocation methods based on virtual machines in IaaS, the scheduling strategy is based on Application in PaaS platform. According to the analysis of the application layout, the heuristic algorithm is used to minimize the number of application migration and reduce the waiting time of the task. In order to avoid falling into the loop or local optimal solution, the authors also used a tabu search technique. The results of comparative experiments show that, this strategy has higher resource utilization and shorter task waiting time.
Article Preview

In general, there are two main purposes of resource allocation in cloud computing, one is the resource allocation scheme for the purpose that reducing the energy consumption of the Cloud Computing center (Wang, Hung, & Yang, 2014), that is, green cloud computing (Gai, Qiu, & Zhao, 2016). Another is the resource allocation algorithm based on the economics.

According to Jebalia et al. (2013), the game theory is introduced to the resource allocation of cloud computing. Some researchers used the game method and Nash equilibrium theory to solve the game problem between resources and income (Wei, Vasilakos, & Zheng, 2010).

Some research shows that due to the large scale of cloud data center, its resource allocation problem is a discrete combinatorial optimization problem (Faragardi, Rajabi, & Shojaee, 2013), which belongs to NP problem. It is difficult to get the solution of the problem in a reasonable time by using the traditional algorithm. Therefore, more and more researchers use heuristic algorithm to solve the related problems, which is the most effective way to solve the problem of resource allocation in cloud computing.

Hua et al. (2010) using ant colony algorithm, Xie, Du, and Tian (2013) using particle swarm algorithm, and Pandit, Chattopadhyay, and Chattopadhyay (2014) using the simulated annealing algorithm to find the best matching relationship between the virtual machine and the server in cloud computing which can save energy and improve the resource utilization (Rahman, Imran, & Gias, 2013).

There are also research points that the method of machine learning can be applied to the resource allocation of PaaS platform. Xu et al. (2013) put forward the decision tree method to the resource allocation of cloud. Sun et al. (2013), introducing the neural network with feedback evaluation mechanism and support vector machine model to realize the cloud resource allocation. But the machine learning method requires large number of data for training, this is difficult for the resource allocation system.

Through intensive research, the researchers study and design a resource allocation model for PaaS platform, which provides flexible resource allocation for large-scale Web applications.


3. Resource Allocation Model For Paas Platform

Figure 1.

Resource allocation model of PaaS platform


Complete Article List

Search this Journal:
Volume 16: 1 Issue (2023): Forthcoming, Available for Pre-Order
Volume 15: 6 Issues (2022): 1 Released, 5 Forthcoming
Volume 14: 4 Issues (2021)
Volume 13: 4 Issues (2020)
Volume 12: 4 Issues (2019)
Volume 11: 4 Issues (2018)
Volume 10: 4 Issues (2017)
Volume 9: 4 Issues (2016)
Volume 8: 4 Issues (2015)
Volume 7: 4 Issues (2014)
Volume 6: 4 Issues (2013)
Volume 5: 4 Issues (2012)
Volume 4: 4 Issues (2011)
Volume 3: 4 Issues (2010)
Volume 2: 4 Issues (2009)
Volume 1: 4 Issues (2008)
View Complete Journal Contents Listing