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In a multi-tenant business environment, tenants share the same applications and databases to store their data. Due to the widespread use of a multi-tenant environment, the service providers face difficult challenges daily. These challenges are condensed in how to guarantee the quality of service provided to tenants, which are documented in a formal document known as a Service Level Agreement (SLA). In addition, SLA should consider the irregular patterns of workload of tenants which may affect the level of guarantee. In this research, an Enhanced Multi-Tenant Database Management System (EMT DBMS) is proposed. In addition, an Enhanced Multi-tenant Migration Algorithm called EMT-M is presented, which aims to migrate the violated tenants depending on both the number of SLA violations and the variance rate. Experimental results prove that the proposed EMT-M algorithm is ideal for migrating violated tenants, as it reduces the number of SLA violations compared to previous migration algorithms.

With the rapid adoption of multi-tenant databases, the cloud provider consolidates multiple tenants’ database on server machines, where the tenants share a common application and database instances. To ensure the quality of service (QoS) for the leased resources, both sides (i.e., the user and the provider) create a Service Level Agreement (SLA). Higher SLA violations result in high SLA contractual penalties and increase the possibility of losing the tenant. In addition, the unusual workload patterns of each tenant transactions require seamless adjustments due to the sudden burden changes and variability. As a result, to satisfy simultaneously availability and performance tenant requirements, it is necessary to perform reliable tenant migration and replication to distribute the workload to a flexible set of sites and avoid SLA violations. In this research, a cluster-based multi-tenant database management system (CB-MT DBMS) is proposed, which takes the migration and replication decisions in advance by monitoring and acting before the violation of the SLA occurs. In addition, a dynamic proactive multi-tenant database migration and replication MTDB-MR algorithm is proposed to reduce collisions and inconsistencies between migration and replication decisions for a group of violated tenants. Experimental results show that the proposed MTDB-MR algorithm is the ideal candidate for migration and replication of the violated multi-tenant databases, as it minimizes the total number of SLA violations, the number of multi-tenant clients SLA violations, client sites average response time and total execution time of each multi-tenant client site as compared to the previous algorithms.

In a multi-tenant environment, the companies referred to as tenants share a common application and Relational Database Management System (RDBMS) instances to store their data. However, with the rapid adoption of multi-tenant databases, the cloud provider faces two challenges: Tenants have irregular workload patterns. Also, tenants require a strict guarantee of their rental services’ quality and performance, known as a Service Level Agreement (SLA). In this research, a Multi-Tenant Database Management System (MT DBMS) is presented. A multi-tenant migration algorithm called MT-M is presented, which migrates the violated tenants on an elastic cluster of machines to mitigate the SLA Violations. Experiment results show that the proposed MT-M algorithm is ideal for the migration of the violated multi-tenant databases, reducing SLA violations’ total number compared to the previous migration algorithms.

User authentication is a crucial requirement for cloud service providers to prove that the outsourced data and services are safe from imposters. Keystroke dynamics is a promising behavioral biometrics for strengthening user authentication, however, current keystroke based solutions designed for certain datasets, for example, a fixed length text typed on a traditional personal computer keyboard and their authentication performances were not acceptable for other input devices nor free length text. Moreover, they suffer from a high dimensional feature space that degrades the authentication accuracy and performance.

Along with the widespread interest in cloud computing, there are still concerns that hinder the proliferation and the adoption of cloud services. One of the main concerns is data security in cloud storage environments. Numerous research problems belonging to cloud storage security have been studied intensively before. However, addressing the three dimensions of outsourced data security (i.e., confidentiality, availability, and integrity) as a cloud service is still a challenge in cloud storage. As there is always a tradeoff between maintaining security and obtaining efficiency, it is difficult but nevertheless essential to explore how to efficiently address security challenges over dynamic cloud data. This paper proposes an integrated security model for data storage in the cloud that provides authentication, access control, auditing and data management services. A strong authentication sub-system that is based on the traditional password and the user keystroke dynamics is presented. A dynamic access control system is proposed to ensure data confidentiality in cloud computing. A public auditing system is proposed to delegate the integrity verification of outsourced data in the cloud storage to a third party auditor while maintaining the data privacy. Experimental results demonstrate the effectiveness and efficiency of the proposed model.

Traditional authentication techniques such as static passwords are vulnerable to replay and guessing attacks. Recently, many studies have been conducted on keystroke dynamics as a promising behavioral biometrics for strengthening user authentication, however, current keystroke based solutions suffer from a numerous number of features with an insufficient number of samples which lead to a high verification error rate and high verification time. In this paper, a keystroke dynamics based authentication system is proposed for cloud environments that supports fixed and free text samples. The proposed system utilizes the ReliefF dimensionality reduction method, as a preprocessing step, to minimize the feature space dimensionality. The proposed system applies clustering to users' profile templates to reduce the verification time. The proposed system is applied to two different benchmark datasets. Experimental results prove the effectiveness and efficiency of the proposed system.

With the rapid and wide adoption of cloud computing, data outsourcing in cloud storage is gaining attention due to its cost effectiveness, reliability and availability. However, data outsourcing introduces new data security and privacy issues, therefore access control and cryptography are essential ingredients in a cloud computing environment to assure the confidentiality of the outsourced data. Existing access control systems suffer from manual user role and role permission assignments that impose online and computational burdens on the data owner in large scale cloud systems. In this paper, a hierarchical attribute driven role based access control system is proposed, such that the user role assignments can be automatically constructed using policies applied on the attributes of users and roles. The proposed access control system consequently solves the scalability and key management problems in cloud storage systems.

Cloud Storage Service (CSS) is a vital service of cloud computing which relieves the burden of storage management, cost and maintenance. However, Cloud storage introduces new security and privacy challenges that make data owners worry about their data. It is essential to have an auditing service to verify the integrity of outsourced data and to prove to data owners that their data is correctly stored in the Cloud. Recently, many researchers have focused on validating the integrity of outsourced data and proposed various schemes to audit the data stored on CSS. However, most of those schemes deal with static and single copy data files and do not consider data dynamic operations on replicated data. Furthermore, they do not have the facility to repair corrupt data. In this paper, we address these challenging issues and propose a public auditing scheme for multiple-copy outsourced data in CSS. Our scheme achieves better reliability, availability, and scalability by supporting replication and data recovery.

Summary With the proliferation of cloud storage services, data integrity verification becomes increasingly significant in order to guarantee the availability and correctness of the outsourced data. Recently, many auditing schemes have been proposed to verify data integrity without possessing or downloading the outsourced data files. However, such schemes in existence assume that there is a single data owner that can update the file and compute the integrity tags. They do not consider the efficiency of user revocation when auditing multi-owner data in the cloud. In addition, existing schemes only support dynamic data operations over fixed-size and single-copy data blocks. Thus, every small update may need updating the tags for all file blocks in all replicas, which in turn causes higher storage and communication overheads. This paper proposes a public and dynamic auditing scheme that supports fully dynamic data operations over variable-size data blocks for replicated and multi-owner cloud storage. Moreover, the proposed scheme can also support efficient user revocation. By supporting batch auditing, the proposed scheme can handle multiple auditing tasks simultaneously and divide an auditing task into multiple sub-tasks in order to increase the detection probability. Experimental results prove the effectiveness and efficiency of the proposed system. Copyright © 2016 John Wiley & Sons, Ltd.