SEDC-Based Hardware-Level Fault Tolerance and Fault Secure Checker Design for Big Data and Cloud Computing

Abstract

Fault tolerance is of great importance for big data systems. Although several software-based application-level techniques exist for fault security in big data systems, there is a potential research space at the hardware level. Big data needs to be processed inexpensively and efficiently, for which traditional hardware architectures are, although adequate, not optimum for this purpose. In this paper, we propose a hardware-level fault tolerance scheme for big data and cloud computing that can be used with the existing software-level fault tolerance for improving the overall performance of the systems. The proposed scheme uses the concurrent error detection (CED) method to detect hardware-level faults, with the help of Scalable Error Detecting Codes (SEDC) and its checker. SEDC is an all unidirectional error detection (AUED) technique capable of detecting multiple unidirectional errors. The SEDC scheme exploits data segmentation and parallel encoding features for assigning code words. Consequently, the SEDC scheme can be scaled to any binary data length "n" with constant latency and less complexity, compared to other AUED schemes, hence making it a perfect candidate for use in big data processing hardware. We also present a novel area, delay, and power efficient, scalable fault secure checker design based on SEDC. In order to show the effectiveness of our scheme, we (1) compared the cost of hardwarebased fault tolerance with an existing software-based fault tolerance technique used in HDFS and (2) compared the performance of the proposed checker in terms of area, speed, and power dissipation with the famous Berger code and m-out-of-2m code checkers. The experimental results show that (1) the proposed SEDC-based hardware-level fault tolerance scheme significantly reduces the average cost associated with software-based fault tolerance in a big data application, and (2) the proposed fault secure checker outperforms the state-of-the-art checkers in terms of area, delay, and power dissipation.

Aggressive Fault Tolerance in Cloud Computing Using Smart Decision Agent

“…Unavailability of host, datacenter and VM may be occurred due to disk full or disk error. On the other hand memory faults and other faults are discussed in [17,18]. The software faults are software state transition faults, early and late timing faults, timing overhead, protocol incompatibilities, data fault, logical fault, numerical exception, operating system faults, link timeout fault, user defined exception and unhandled exception faults [19,20].…”

“…Trust model in cloud computing is the mostly on-demand mechanism that helps in building secured communication in cloud environment. Siddiqui et al [18] have proposed a single-bit error detection scheme based on hardware fault tolerance for huge data in cloud computing. This scheme uses concurrent error detection (CED) mechanism that is able to detect hardware faults.…”