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As 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 CO 2 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.

As HPC systems continue to grow to meet the requirements of tomorrow's exascale-class systems, two of the biggest challenges are power consumption and system resilience. On current systems, the dominant resilience technique is checkpoint/restart. It is believed, however, that this technique alone will not scale to the level necessary to support future systems. Therefore, alternative methods have been suggested to augment checkpoint/restart -for example process replication. In this paper we address both resilience and power together, this is in contrast to much of the competed work which does so independently. Using an analytical model that accounts for both power consumption and failures, we study the performance of checkpoint and replication-based techniques on current and future systems and use power measurements from current systems to validate our findings. Lastly, in an attempt to optimize power consumption for replication, we introduce a new protocol termed shadow replication which not only reduces energy consumption but also produces faster response times than checkpoint/restart and traditional replication when operating under system power constraints.

Level IV-case series.