Application Oriented Dynamic Resource Allocation for Data Centers Using Docker Containers

Docker offers an opportunity for further improvement in data centers’ (DCs) efficiency. However, existing models and schemes fall short to be efficiently used for Docker container-based resource allocation. We design a novel application oriented Docker container (AODC)-based resource allocation framework to minimize the application deployment cost in DCs, and to support automatic scaling while the workload of cloud applications varies. We then model the AODC resource allocation problem considering features of Docker, various applications’ requirements, and available resources in cloud data centers, and propose a scalable algorithm for DCs with diverse and dynamic applications and massive physical resources.

[1]  Jie Lu,et al.  Optimal Cloud Resource Auto-Scaling for Web Applications , 2013, 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing.

[2]  Huiqun Yu,et al.  A Novel Resource Scheduling Approach in Container Based Clouds , 2014, 2014 IEEE 17th International Conference on Computational Science and Engineering.

[3]  Mohamed Faten Zhani,et al.  DREAMS: Dynamic resource allocation for MapReduce with data skew , 2015, 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM).

[4]  Baek-Young Choi,et al.  Energy efficient virtual network embedding for green data centers using data center topology and future migration , 2015, Comput. Commun..

[5]  Larry L. Peterson,et al.  Container-based operating system virtualization: a scalable, high-performance alternative to hypervisors , 2007, EuroSys '07.

[6]  Enzo Baccarelli,et al.  Minimum-energy bandwidth management for QoS live migration of virtual machines , 2015, Comput. Networks.

[7]  Daniel M. Batista,et al.  Energy-Efficient Virtual Machines Placement , 2014, 2014 Brazilian Symposium on Computer Networks and Distributed Systems.

[8]  Zhen Xiao,et al.  Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment , 2013, IEEE Transactions on Parallel and Distributed Systems.

[9]  Xiao Ma,et al.  PAPMSC: Power-Aware Performance Management Approach for Virtualized Web Servers via Stochastic Control , 2015, Journal of Grid Computing.

[10]  Xiaohua Chen,et al.  Optimization Model and Algorithm for Energy Efficient Virtual Node Embedding , 2015, IEEE Communications Letters.

[11]  Enzo Baccarelli,et al.  Energy-Efficient Adaptive Resource Management for Real-Time Vehicular Cloud Services , 2019, IEEE Transactions on Cloud Computing.