Managing Multi-Cloud Data Dependability Faults

Introduction

Cloud computing is considered a great paradigm that enables access to large scale, on demand, flexible computing infrastructure. Lower infrastructure cost and better application performance are some of the benefits of utilizing a cloud computing model. With the appearance of the new generation of multi-cloud computing, clients can benefit from a diversity of services. However, data security has become an important requirement for clients when dealing with clouds that may fail due to faults in the software or hardware, or attacks from malicious insiders (Tiwari and Joshi 2018). . Hence, building a highly dependable and reliable cloud system has become a critical research problem (Williams and Griffin 2018). It means a cloud service provider needs to make sure that two of the dependability requirements are satisfied: (1) its clients’ outsourced data are stored securely and, (2) data queries and retrievals are executed correctly (in terms of reliability) and privately (in terms of three security attributes: Confidentiality, Integrity and Availability – CIA) (AlZain, Soh et al. 2013) (AlZain, Soh et al. 2011) (AlZain, Pardede et al. 2012) (AlZain, Soh et al. 2013) (AlZain, Li et al. 2015). Here, we term any data fault as data dependability fault that leads to security beaches and unmet reliability.

On the other hand, it also means businesses are willing to outsource their data to a third party (cloud service provider) only if they trust their data are not accessible and visible to the service provider and other non-authorized parties (Hore, Mehrotra et al. 2004). However, one of the main obstacles here for ensuring multi-cloud data dependability is Byzantine faults.

Byzantine fault tolerance (BFT) has received growing attention from the academic research community but not many systems use it in practice. While a great deal of recent research has focused on comparing the standard practical Byzantine fault tolerance protocol (PBFT) (Castro and Liskov 1999) and improving its performance with the development of Zyzzyva (Kotla, Alvisi et al. 2007) and Aardvark (Clement, Wong et al. 2009), very few studies on the BFT in a multi-cloud computing environment have addressed the detection of Byzantine failure to ensure data dependability within the cloud. In this chapter, we extend the original definition of Byzantine faults (Lamport, Shostak et al. 1982) to include the CIA dimensions (data Confidentiality, data Integrity and service Availability) (AlZain, Pardede et al. 2012), and cloud data intrusion by malicious insiders. Needless to say, data dependability is a major issue whenever users rely on third-party services because of the possibility of Byzantine failure in the cloud. Therefore, building a highly dependable multi-cloud system has become a critical research problem. To that end we enhance our previous investigation on how BFT and, secure cloud data storage and sharing techniques can be deployed to manage multi-cloud data dependability faults (AlZain, Li et al. 2016).

The remainder of this chapter is organized as follows. We first present the related work followed by overviews our conceptualization of multi-cloud data dependability faults as well as fault tolerant state machines. Then we detail three crucial operations in the proposed fault-tolerant architecture, and propose fault-tolerant method in managing multi-cloud data security faults. Subsequently we give a qualitative evaluation of the proposed architecture, and conclude this chapter with our research contribution.