A Multi-Objective Hybrid Cloud Resource Scheduling Method Based on Deadline and Cost Constraints | Zendy

Liyun Zuo | Zendy; Lei Shu | Zendy; Shoubin Dong | Zendy; Yuanfang Chen | Zendy; Li Yan | Zendy

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A Multi-Objective Hybrid Cloud Resource Scheduling Method Based on Deadline and Cost Constraints

Author(s) -

Liyun Zuo,

Lei Shu,

Shoubin Dong,

Yuanfang Chen,

Li Yan

Publication year - 2016

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2016.2633288

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

We propose a task-oriented multi-objective scheduling method based on ant colony optimization (MOSACO) to optimize the finite pool of public and private computing resources in a hybrid cloud computing environment according to deadline and cost constraints. MOSACO is employed to minimize task completion times and costs using time-first and cost-first single-objective optimization strategies, respectively, and to maximize user quality of service and the profit of resource providers using an entropy optimization model. The effectiveness of the MOSACO algorithm based on multiple considerations of task completion time, cost, number of deadline violations, and degree of private resource utilization is verified using simulation and three application examples. Comparisons with similar scheduling methods demonstrate that MOSACO provides the highest optimality, and that the time-first and cost-first strategies provide definite advantages for minimizing completion time and cost, respectively.

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