Local and Remote Recovery of Cloud Services Using Backward Atomic Backup Recovery Technique for High Availability in Strongly Consistent Cloud Service: Recovery of Cloud Service for High Availability

Local and Remote Recovery of Cloud Services Using Backward Atomic Backup Recovery Technique for High Availability in Strongly Consistent Cloud Service: Recovery of Cloud Service for High Availability

Praveen Shivashankrappa Challagidad (Basaveshwar Engineering College, Karnataka, India) and Mahantesh N. Birje (Visvesvaraya Technological University, Karnataka, India)
DOI: 10.4018/IJAPUC.2019100102

Abstract

Data loss occurs due to crashing, correlated failure, logical failure, power outages and security threats. Several techniques (e.g. NoBackup, WARBackup and LocalRecovery) are being used to recover data locally. And, strongly consistent Cloud services (SCCS) must provide good performance and high availability. However, conventional strong consistency replication methods have the limitation of availability of replicated services when recovering huge amount of data across wide area links. There is a need for remote recovery mechanisms for high availability of service/data, because distributed nature of cloud infrastructures. To address these issues, the article proposes a hierarchical system architecture for replication across a data center, and employs the backward atomic backup recovery technique (BABRT) for local recovery and remote recovery for high availability of the cloud services/data. A mathematical model for BABRT is described. Simulation results show that BABRT reduces the storage consumption, recovery time, window of vulnerability and failure rates, compared to other recovery models.
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1. Introduction

Cloud Service Providers (CSPs) are increasing their services and infrastructures drastically because of huge scope and many benefits of cloud computing technology (Birje, Challagidad, Goudar, & Tapale, 2017; Birje & Challagidad, 2015). Hosting of number of services (such as conventional applications and strong consistency services) in cloud infrastructures is accelerating day-by-day (Gummadi, 2004), (Haq, 2017).

In conventional methods, the application states are replicated over many servers spread in different geographical locations across the world to improve the availability of the application state. Replicating state across data centers, however, makes it harder to maintain consistency across updates (Anderson, Meling, Rasmussen, Vahdat, & Marzullo, 2017; DeCandia, 2007). To have correct performance of the system for many cloud-oriented services strong consistency maintenance is essential. Strongly consistent cloud services must provide good performance and high availability. Content replication over multiple servers/data centers is one of the conventional methods used to achieve both the goals. Many services of cloud gain advantage from the simplicity and semantics of strong consistency. However, conventional strong consistency replication methods have the limitation of availability of replicated services when recovering huge amount of data across wide area links (Anderson, Meling, Rasmussen, Vahdat, & Marzullo, 2017). The necessity of strong consistency is also identified by prominent CSPs (Calder et. al., 2011; Corbett, 2012).

It is difficult to provide strong consistency for data residing in multiple sites because of dissimilar updates probably from various users, may be aimed at servers in various data centers (Anderson, Meling, Rasmussen, Vahdat, & Marzullo, 2017). Though updates are moved over the similar server, the arrays in which numerous updates are dedicated to application state should be diagonally similar across all replicas of the state. A Replicated State Machine (RSM) model (Schneider, 1990) typically achieves strong consistency, using consensus algorithm such as Paxos (Lamport, 1998; Paxos, 2001; Taft, 2009; Chandra, 2007) to direct state machine operations. In this context, an elementary requirement for strong consistency in RSMs is 2f + 1 replicas are required to tolerate f failures. Furthermore, the real-world consists of billions of items (Taft, 2009; Chandra, 2007) stored over several data centers; every datacenter consists of millions of machines need to have local recovery and remote recovery mechanisms. For these huge networks of machines, recovery from common machine failures continuously is the requirement without human intervention.

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