Earlier Deadline Algorithm for Virtual Machine Allocation

Document Type : Original Article

Authors

1 Communication Department Faculty of Electronic Engineering

2 Computer Department Faculty of Electronic Engineering

Abstract

The problem of allocating the virtual machines to the jobs in cloud computing systems is a complex one. The key challenge inferred is attaining better power consumption, time and cost. To allocate a job, set of power-aware dynamic allocators for Virtual Machines are presented. It takes the benefit of software Defined Networking (SDN) paradigm. Integrating Ant colony algorithm augments the power consumption with its uncertain time convergence. These approaches escalated preemptive mechanism that assists better decision in scheduling. In order to overcome all those shortcomings, the Earliest Deadline First (EDF) algorithm has been implemented which tends to solve the inefficient allocation of virtual machines. The segmentation of the dataset is done to enhance the performance of the VM which is firstly realized in this VM allocation approach. In this paper, we introduce 10 virtual machines with different allocation strategies, and compare them with a baseline that consists of using the first available server (First Fit). The allocators differ in terms of allocation policy (Best Fit/Worst Fit), allocation strategy (Single/Multi objective optimization), and joint/disjoint selection of IT and network resources. The EDF algorithm is preferred here to achieve better power consumption and it is accomplished beyond the expectations. Moreover, the experimental results highlight that joint approaches outperform disjoint ones

Keywords


        M. García-Valls, et al., "Challenges in real-time virtualization and predictable cloud computing," Journal of Systems Architecture, vol. 60, pp. 726-740, 2014.
[2]   N. M. Azmy, et al., "Adaptive power panel of cloud computing controlling cloud power consumption," in Proceedings of the 2nd Africa and Middle East Conference on Software Engineering, 2016, pp. 9-14.
[3]   C. Ghribi, et al., "Energy efficient vm scheduling for cloud data centers: Exact allocation and migration algorithms," in 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, 2013, pp. 671-678.
[4]   A. Vichare, et al., "Cloud computing using OCRP and virtual machines for dynamic allocation of resources," in 2015 International Conference on Technologies for Sustainable Development (ICTSD), 2015, pp. 1-5.
[5]   R. Bhoyar and N. Chopde, "Cloud computing: Service models, types, database and issues," International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, 2013.
[6]   T. Guérout, et al., "Mixed integer linear programming for quality of service optimization in Clouds," Future Generation Computer Systems, vol. 71, pp. 1-17, 2017.
[7]   B. Xu, et al., "Dynamic deployment of virtual machines in cloud computing using multi-objective optimization," Soft computing, vol. 19, pp. 2265-2273, 2015.
[8]   D. Boru, et al., "Energy-efficient data replication in cloud computing datacenters," Cluster computing, vol. 18, pp. 385-402, 2015.
[9]   A. Lara, et al., "Network innovation using openflow: A survey," IEEE communications surveys & tutorials, vol. 16, pp. 493-512, 2013.
[10] M. Gharbaoui, et al., "On virtualization-aware traffic engineering in OpenFlow data centers networks," in 2014 IEEE Network Operations and Management Symposium (NOMS), 2014, pp. 1-8.
[11] F. Fernandes, et al., "A virtual machine scheduler based on CPU and I/O-bound features for energy-aware in high performance computing clouds," Computers & Electrical Engineering, vol. 56, pp. 854-870, 2016.
Volume 28, ICEEM2019-Special Issue
ICEEM2019-Special Issue: 1st International Conference on Electronic Eng., Faculty of Electronic Eng., Menouf, Egypt, 7-8 Dec.
2019
Pages 326-331