FSCRank: A Failure-Sensitive Structure-Based Component Ranking Approach for Cloud Applications

Abstract

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.

A Two-Stage Fault Tolerance Method for Large-Scale Manufacturing Network

“…The critical step of LNR algorithm is to design the iterative matrix and the background node, which determines the effect and convergence of the algorithm. The design of iterative matrix has been studied in [7], [8]and [23]. In FTCloud [7], the iterative matrix only takes the network structure into consideration.…”

“…In ROCloud [8], the failure rates of the nodes are taken into consideration, which is more accurate and effective. In FSCRank [23], the buffer node is…”

“…Qiu et al improved the node ranking model based on FTCloud and proposed ROCloud [8]. Wu et al proposed the FSCRank framework to evaluate the importance of cloud applications, which introduced the buffer node to accelerate algorithm convergence [23]. Aiming at solving node ranking problem in manufacturing networks, Wu et al proposed a fault tolerance framework LIVMA based on LeaderRank algorithm [21].…”