Shadow Replication: An Energy-Aware, Fault-Tolerant Computational Model for Green Cloud Computing
AbstractAs the demand for cloud computing continues to increase, cloud service providers face the daunting challenge to meet the negotiated SLA agreement, in terms of reliability and timely performance, while achieving cost-effectiveness. This challenge is increasingly compounded by the increasing likelihood of failure in large-scale clouds and the rising impact of energy consumption and CO2 emission on the environment. This paper proposes Shadow Replication, a novel fault-tolerance model for cloud computing, which seamlessly addresses failure at scale, while minimizing energy consumption and reducing its impact on the environment. The basic tenet of the model is to associate a suite of shadow processes to execute concurrently with the main process, but initially at a much reduced execution speed, to overcome failures as they occur. Two computationally-feasible schemes are proposed to achieve Shadow Replication. A performance evaluation framework is developed to analyze these schemes and compare their performance to traditional replication-based fault tolerance methods, focusing on the inherent tradeoff between fault tolerance, the specified SLA and profit maximization. The results show that Shadow Replication leads to significant energy reduction, and is better suited for compute-intensive execution models, where up to 30% more profit increase can be achieved due to reduced energy consumption. View Full-Text
Share & Cite This Article
Cui, X.; Mills, B.; Znati, T.; Melhem, R. Shadow Replication: An Energy-Aware, Fault-Tolerant Computational Model for Green Cloud Computing. Energies 2014, 7, 5151-5176.
Cui X, Mills B, Znati T, Melhem R. Shadow Replication: An Energy-Aware, Fault-Tolerant Computational Model for Green Cloud Computing. Energies. 2014; 7(8):5151-5176.Chicago/Turabian Style
Cui, Xiaolong; Mills, Bryan; Znati, Taieb; Melhem, Rami. 2014. "Shadow Replication: An Energy-Aware, Fault-Tolerant Computational Model for Green Cloud Computing." Energies 7, no. 8: 5151-5176.