Cloud computing has emerged as a pervasive platform for deploying scalable and highly available Internet applications. To facilitate the migration of data-driven applications to the cloud: elasticity, scalability, fault-tolerance, and self-manageability (henceforth referred to as cloud features) are fundamental requirements for database management systems (DBMS) driving such applications. Even though extremely successful in the traditional enterprise setting – the high cost of commercial relational database software, and the lack of the desired cloud features in the open source counterparts – relational databases (RDBMS) are not a competitive choice for cloud-bound applications. As a result, Key-Value stores have emerged as a preferred choice for scalable and faulttolerant data management, but lack the rich functionality, and transactional guarantees of RDBMS. We present ElasTraS, an Elastic TranSactional relational database, designed to scale out using a cluster of commodity machines while being fault-tolerant and self managing. ElasTraS is designed to support both classes of database needs for the cloud: (i) large databases partitioned across a set of nodes, and (ii) a large number of small and independent databases common in multi-tenant databases. ElasTraS borrows from the design philosophy of scalable Key-Value stores to minimize distributed synchronization and remove scalability bottlenecks, while leveraging decades of research on transaction processing, concurrency control, and recovery to support rich functionality and transactional guarantees. We present the design of ElasTraS, implementation details of our initial prototype system, and experimental results executing the TPC-C benchmark.

The emerging cloud-computing paradigm is rapidly gaining momentum as an alternative to traditional IT (information technology). However, contemporary cloud-computing offerings are primarily targeted for Web 2.0-style applications. Only recently have they begun to address the requirements of enterprise solutions, such as support for infrastructure service-level agreements. To address the challenges and deficiencies in the current state of the art, we propose a modular, extensible cloud architecture with intrinsic support for business service management and the federation of clouds. The goal is to facilitate an open, service-based online economy in which resources and services are transparently provisioned and managed across clouds on an ondemand basis at competitive costs with high-quality service. The Reservoir project is motivated by the vision of implementing an architecture that would enable providers of cloud infrastructure to dynamically partner with each other to create a seemingly infinite pool of IT resources while fully preserving their individual autonomy in making technological and business management decisions. To this end, Reservoir could leverage and extend the advantages of virtualization and embed autonomous management in the infrastructure. At the same time, the Reservoir approach aims to achieve a very ambitious goal: creating a foundation for next-generation enterprise-grade cloud computing.

A multi-tenant database system for Software as a Service (SaaS) should offer schemas that are flexible in that they can be extended for different versions of the application and dynamically modified while the system is on-line. This paper presents an experimental comparison of five techniques for implementing flexible schemas for SaaS. In three of these techniques, the database“owns”the schema in that its structure is explicitly defined in DDL. Included here is the commonly-used mapping where each tenant is given their own private tables, which we take as the baseline, and a mapping that employs Sparse Columns in Microsoft SQL Server. These techniques perform well, however they offer only limited support for schema evolution in the presence of existing data. Moreover they do not scale beyond a certain level. In the other two techniques, the application “owns ” the schema in that it is mapped into generic structures in the database. Included here are XML in DB2 and Pivot Tables in HBase. These techniques give the application complete control over schema evolution, however they can produce a significant decrease in performance. We conclude that the ideal database for SaaS has not yet been developed and offer some suggestions as to how it should be designed. Categories andSubject Descriptors

Multi-tenant data management is a form of Software as a Service (SaaS), whereby a third party service provider hosts databases as a service and provides its customers with seamless mechanisms to create, store and access their databases at the host site. One of the main problems in such a system, as we shall discuss in this paper, is scalability, namely the ability to serve an increasing number of tenants without too much query performance degradation. A promising way to handle the scalability issue is to consolidate tuples from different tenants into the same shared tables. However, this approach introduces two problems: 1) The shared tables are too sparse. 2) Indexing on shared tables is not effective. To resolve the problems, we propose a multi-tenant database system called M-Store, which provides storage and indexing services for multi-tenants. To improve the scalability of the system, we develop two techniques in M-Store: Bitmap Interpreted Tuple (BIT) and Multi-Separated Index (MSI). BIT is efficient in that it does not store NULLs from unused attributes in the shared tables and MSI provides flexibility since it only indexes each tenant’s own data on frequently accessed attributes. We extended MySQL based on our proposed design and conducted extensive experiments. The experimental results show that our proposed approach is a promising multi-tenancy storage and indexing scheme which can be easily integrated into existing DBMS.

The growing popularity of cloud computing as a platform for deploying internet scale applications has seen a large number of web applications being deployed in the cloud. These applications (or tenants) are typically characterized by small data footprints, different schemas, and variable load patterns. Scalable multitenant database management systems (DBMS) running on a cluster of commodity servers are thus critical for a cloud service provider to support a large number of small applications. Multitenant DBMSs often collocate multiple tenants ’ databases on a single server for effective resource sharing. Due to the variability in load, elastic load balancing of tenants ’ data is critical for performance and cost minimization. On demand migration of tenants ’ databases to distribute load on an elastic cluster of machines is a critical technology for elastic load balancing. Therefore, efficient live database

In most enterprises, databases are deployed on dedicated database servers. Often, these servers are underutilized much of the time. For example, in traces from almost 200 production servers from different organizations, we see an average CPU utilization of less than 4%. This unused capacity can be potentially harnessed to consolidate multiple databases on fewer machines, reducing hardware and operational costs. Virtual machine (VM) technology is one popular way to approach this problem. However, as we demonstrate in this paper, VMs fail to adequately support database consolidation, because databases place a unique and challenging set of demands on hardware resources, which are not well-suited to the assumptions made by VM-based consolidation. Instead, our system for database consolidation, named Kairos, uses novel techniques to measure the hardware requirements of database workloads, as well as models to predict the combined resource utilization of those workloads. We formalize the consolidation problem as a non-linear optimization program, aiming to minimize the number of servers and balance load, while achieving near-zero performance degradation. We compare Kairos against virtual machines, showing up to a factor of 12 × higher throughput on a TPC-C-like benchmark. We also tested the effectiveness of our approach on real-world data collected from production servers

This paper introduces a new transactional “database-as-a-service” (DBaaS) called Relational Cloud. A DBaaS promises to move much of the operational burden of provisioning, configuration, scaling, performance tuning, backup, privacy, and access control from the database users to the service operator, offering lower overall costs to users. Early DBaaS efforts include Amazon RDS and Microsoft SQL Azure, which are promising in terms of establishing the market need for such a service, but which do not address three important challenges: efficient multi-tenancy, elastic scalability, and database privacy. We argue that these three challenges must be overcome before outsourcing database software and management becomes attractive to many users, and cost-effective for service providers. The key technical features of Relational Cloud include: (1) a workload-aware approach to multi-tenancy that identifies the workloads that can be co-located on a database server, achieving higher consolidation and better performance than existing approaches; (2) the use of a graph-based data partitioning algorithm to achieve near-linear elastic scale-out even for complex transactional workloads; and (3) an adjustable security scheme that enables SQL queries to run over encrypted data, including ordering operations, aggregates, and joins. An underlying theme in the design of the components of Relational Cloud is the notion of workload awareness: by monitoring query patterns and data accesses, the system obtains information useful for various optimization and security functions, reducing the configuration effort for users and operators. 1.

The success of cloud computing as a platform for deploying webapplications has led to a deluge of applications characterized by small data footprints but unpredictable access patterns. An autonomic and scalable multitenant database management system (DBMS) is therefore an important component of the software stack for platforms supporting these applications. Elastic load balancing is a key requirement for effective resource utilization and operational cost minimization. Efficient techniques for database migration are thus essential for elasticity in a multitenant DBMS. Our vision is a DBMS where multitenancy is viewed as virtualization in the database layer, and migration is a first class notion with the same stature as scalability, availability etc. This paper serves as the first step in this direction. We analyze the various models of database multitenancy, formalize the forms of migration, evaluate the offthe-shelf migration techniques, and identify the design space and research goals for an autonomic and elastic multitenant database.

Abstract—In a cloud computing environment, resources are shared among different clients. Intelligently managing and allocating resources among various clients is important for system providers, whose business model relies on managing the infrastructure resources in a cost-effective manner while satisfying the client service level agreements (SLAs). In this paper, we address the issue of how to intelligently manage the resources in a shared cloud database system and present SmartSLA, a costaware resource management system. SmartSLA consists of two main components: the system modeling module and the resource allocation decision module. The system modeling module uses machine learning techniques to learn a model that describes the potential profit margins for each client under different resource allocations. Based on the learned model, the resource allocation decision module dynamically adjusts the resource allocations in order to achieve the optimum profits. We evaluate SmartSLA by using the TPC-W benchmark with workload characteristics derived from real-life systems. The performance results indicate that SmartSLA can successfully compute predictive models under different hardware resource allocations, such as CPU and memory, as well as database specific resources, such as the number of replicas in the database systems. The experimental results also show that SmartSLA can provide intelligent service differentiation according to factors such as variable workloads, SLA levels, resource costs, and deliver improved profit margins. Index Terms—cloud computing, virtualization, database systems, multitenant databases I.

This paper presents a new schema-sharing technique for multitenant applications. Our approach is built on top of the Extension table method and makes use of the native XML data support to store the additional data supplied by each tenant. Our proposed technique can be used to implement multitenancy on top of a standard relational database. This paper also describes an implementation of our approach using a real-world case study aiming at improving the communication channel of information related to drinking water quality parameters used by all stakeholders involved in the water treatment process in Ireland. These include the Environmental Protection Agency, the Health Service Executive, drinking water treatment plant staff, local authorities and everybody consuming water. 1.