Swarm Intelligence based Fault-Tolerant Real-Time Cloud Scheduler | ||||
| Mansoura Journal for Computer and Information Sciences | ||||
| Volume 14, Issue 1, June 2018, Page 17-26 PDF (810.46 K) | ||||
| Document Type: Original Research Articles. | ||||
| DOI: 10.21608/mjcis.2018.311991 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| A. S. Abohamama; M. F. Alrahmawy; Mohamed A. Elsoud; Taher T. Hamza | ||||
| Computer Science Dept. University of Mansoura | ||||
| Abstract | ||||
| Cloud computing is a distributed computing paradigm that is deployed in many real-life applications. Many of these applications are real-time such as scientific computing, financial transactions, etc. Therefore, improving the dependability of cloud environments is extremely important to fulfill the reliability and availability requirements of different applications, especially real-time applications. Fault tolerance is the most common approach for improving the system’s dependability. In addition to traditional fault tolerance techniques such as replication, job migration, software rejuvenation, etc, fault-tolerant scheduling algorithms can play a great role toward more dependable systems. In this paper, an ACO based fault-tolerant soft real-time cloud scheduler is developed to minimize deadlines missing rate, makespan, and the imbalance in distributing the workload among the different machines. The performance of proposed scheduler has been assessed under different scenarios. Also, it has been compared to other well-known scheduling algorithms and the experimental results have shown the superiority of the proposed algorithm. | ||||
| Keywords | ||||
| Real-Time Applications; Ant Colony Optimization; Fault Tolerance; Cloud Scheduling | ||||
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