Cloud computing has attracted a growing number of enterprises to move their business to the cloud because of the associated operational and cost benefits. Improving availability is one of the major concerns of cloud application owners because modern applications generally comprise a large number of components and failures are common at scale. Fault tolerance enables an application to continue operating properly when failure occurs, but fault tolerance strategy is typically employed for the most important components because of financial concerns. Therefore, identifying important components has become a critical research issue. To address this problem, we propose a failure-sensitive structure-based component ranking approach (FSCRank), which integrates component failure impact and application structure information into component importance evaluation. An iterative ranking algorithm is developed according to the structural characteristics of cloud applications. The experimental results show that FSCRank outperforms the other two structure-based ranking algorithms for cloud applications. In addition, factors that affect application availability optimization are analyzed and summarized. The experimental results suggest that the availability of cloud applications can be greatly improved by implementing fault tolerance strategy for the important components identified by FSCRank.

Multiple-input multiple- output (MIMO) and orthogonal frequency division multiplexing (OFDM) are two key techniques for broadband wireless mobile communications and channel state information (CSI) is critical for the realization and performance of MIMO-OFDM systems in doubly-selective fading channels. Channel estimation based on two-dimensional discrete-time Fourier transform interpolation (2D-DFTI) is a promising solution to obtain accurate CSI for MIMO-OFDM systems in theory because of both its robustness and high computational efficiency, however, its performance will degrade significantly in practical MIMO-OFDM systems due to the two-dimensional Gibbs phenomenon caused by virtual subcarriers and burst transmission. In this paper, we propose a novel channel estimation method based on the two-dimensional enhanced DFT interpolation (2D-EDFTI), i.e., the frequency-domain EDFTI (FD-EDFTI) concatenated with the time-domain EDFTI (TD-EDFTI), for practical burst-mode MIMO-OFDM systems with virtual subcarriers, which can increase the channel estimation accuracy effectively by mitigating the Gibbs phenomenon in frequency-domain and time-domain, respectively, while keeping good robustness and high computational efficiency. In addition to computer simulations, we further implement the 2D-EDFTI channel estimator into our real-time FPGA testbed of 44 MIMO-OFDM transmission via spatial multiplexing, together with different MIMO detectors. Both computer simulations and RF experiments demonstrate the superior performance of 2D-EDFTI channel estimation in doubly-selective fading channels, therefore, high-throughput MIMO-OFDM transmission based on different MIMO detection algorithms can always be well supported. Also, it can be applied to other MIMO-OFDM transmission schemes straightforwardly.

This paper proposes a component ranking method to identify important components which have great impact on the system reliability. This method, which is opposite to an existing method, believes components which frequently invoke other components have more impact than others and employs component invocation structures and invocation frequencies for making important component ranking. It can strongly support for improving the reliability of software systems, especially large-scale systems. Extensive experiments are provided to validate this method and draw performance comparison.