Cloud computing has emerged as a preferred platform for deploying scalable web-applications. With the growing scale of these applications and the data associated with them, scalable data management systems form a crucial part of the cloud infrastructure. Key-Value stores – such as Bigtable, PNUTS, Dynamo, and their open source analogues – have been the preferred data stores for applications in the cloud. In these systems, data is represented as Key-Value pairs, and atomic access is provided only at the granularity of single keys. While these properties work well for current applications, they are insufficient for the next generation web applications – such as online gaming, social networks, collaborative editing, and many more – which emphasize collaboration. Since collaboration by definition requires consistent access to groups of keys, scalable and consistent multi key access is critical for such applications. We propose the Key Group abstraction that defines a relationship between a group of keys and is the granule for on-demand transactional access. This abstraction allows the Key Grouping protocol to collocate control for the keys in the group to allow efficient access to the group of keys. Using the Key Grouping protocol, we design and implement G-Store which uses a key-value store as an underlying substrate to provide efficient, scalable, and transactional multi key access. Our implementation using a standard key-value store and experiments using a cluster of commodity machines show that G-Store preserves the desired properties of key-value stores, while providing multi key access functionality at a very low overhead.

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.

Multitenant data infrastructures for large cloud platforms hosting hundreds of thousands of applications face the challenge of serving applications characterized by small data footprint and unpredictable load patterns. When such a platform is built on an elastic pay-per-use infrastructure, an added challenge is to minimize the system’s operating cost while guaranteeing the tenants ’ service level agreements (SLA). Elastic load balancing is therefore an important feature to enable scale-up during high load while scaling down when the load is low. Live migration, a technique to migrate tenants with minimal service interruption and no downtime, is critical to allow lightweight elastic scaling. We focus on the problem of live migration in the database layer. We propose Zephyr, a technique to efficiently migrate a live database in a shared nothing transactional database architecture. Zephyr uses phases of ondemand pull and asynchronous push of data, requires minimal synchronization, results no service unavailability and few or no aborted transactions, minimizes the data transfer overhead, provides ACID guarantees during migration, and ensures correctness in the presence of failures. We outline a prototype implementation using an open source relational database engine and an present a thorough evaluation using various transactional workloads. Zephyr’s efficiency is evident from the few tens of failed operations, 10-20% change in average transaction latency, minimal messaging, and no overhead during normal operation when migrating a live database. Categories and Subject Descriptors H.2.4 [Database Management]: Systems—Relational databases,

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

Database systems serving cloud platforms must serve large numbers of applications (or tenants). In addition to managing tenants with small data footprints, different schemas, and variable load patterns, such multitenant data platforms must minimize their operating costs by efficient resource sharing. When deployed over a pay-per-use infrastructure, elastic scaling and load balancing, enabled by low cost live migration of tenant databases, is critical to tolerate load variations while minimizing operating cost. However, existing databases—relational databases and Key-Value stores alike—lack low cost live migration techniques, thus resulting in heavy performance impact during elastic scaling. We present Albatross, a technique for live migration in a multitenant database serving OLTP style workloads where the persistent database image is stored in a network attached storage. Albatross migrates the database cache and the state of active transactions to ensure minimal impact on transaction execution while allowing transactions active during migration to continue execution. It also guarantees serializability while ensuring correctness during failures. Our evaluation using two OLTP benchmarks shows that Albatross can migrate a live tenant database with no aborted transactions, negligible impact on transaction latency and throughput both during and after migration, and an unavailability window as low as 300 ms. 1.

Cloud computing is an extremely successful paradigm of service oriented computing and has revolutionized the way computing infrastructure is abstracted and used. Three most popular cloud

The Deuteronomy system supports efficient and scalable ACID transactions in the cloud by decomposing functions of a database storage engine kernel into: (a) a transactional component (TC) that manages transactions and their “logical ” concurrency control and undo/redo recovery, but knows nothing about physical data location and (b) a data component (DC) that maintains a data cache and uses access methods to support a record-oriented interface with atomic operations, but knows nothing about transactions. The Deuteronomy TC can be applied to data anywhere (in the cloud, local, etc.) with a variety of deployments for both the TC and DC. In this paper, we describe the architecture of our TC, and the considerations that led to it. Preliminary experiments using an adapted TPC-W workload show good performance supporting ACID transactions for a wide range of DC latencies.

Scalable database management systems (DBMS)—both for update intensive application workloads as well as decision support systems for descriptive and deep analytics—are a critical part of the cloud infrastructure and play an important role in ensuring the smooth transition of applications from the traditional enterprise infrastructures to next generation cloud infrastructures. Though scalable data management has been a vision for more than three decades and much research has focussed on large scale data management in traditional enterprise setting, cloud computing brings its own set of novel challenges that must be addressed to ensure the success of data management solutions in the cloud environment. This tutorial presents an organized picture of the challenges faced by application developers and DBMS designers in developing and deploying internet scale applications. Our background study encompasses both classes of systems: (i) for supporting update heavy applications, and (ii) for ad-hoc analytics and decision support. We then focus on providing an in-depth analysis of systems for supporting update intensive web-applications and provide a survey of the state-of-theart in this domain. We crystallize the design choices made by some successful systems large scale database management systems, analyze the application demands and access patterns, and enumerate the desiderata for a cloud-bound DBMS.

Abstract. Cloud computing has emerged as an extremely successful paradigm for deploying web applications. Scalability, elasticity, pay-per-use pricing, and economies of scale from large scale operations are the major reasons for the successful and widespread adoption of cloud infrastructures. Since a majority of cloud applications are data driven, database management systems (DBMSs) powering these applications form a critical component in the cloud software stack. In this article, we present an overview of our work on instilling these above mentioned “cloud features ” in a database system designed to support a variety of applications deployed in the cloud: designing scalable database management architectures using the concepts of data fission and data fusion, enabling lightweight elasticity using low cost live database migration, and designing intelligent and autonomic controllers for system management without human intervention.

Data drives knowledge which engenders innovation. Be it personalizing search results, recommending movies or friends, determining which advertisements to display or which coupon to deliver, data is central in improving customer satisfaction and providing a competitive edge. Data, therefore, generates wealth and many modern enterprises are collecting data at the most detailed level possible, resulting in massive and ever-growing data repositories. Such massive scale of data pose a number of research challenges, called big data challenges, which form the basis for my research. My research philosophy is to build database management systems (DBMSs) designed for large scale operations that expose abstractions to simplify application design while providing tools to ease system deployment and management. Using this philosophy as the cornerstone, my research has spanned the broad area of big data management encompassing both transaction processing and analytical processing systems exercising a synergy of both theoretical and practical system-oriented research. 1 Dissertation Research: Scalable, Elastic, and Autonomic OLTP Databases DBMSs serving mission critical user facing web-applications must be scalable, fault-tolerant, and highly available to serve the growing number of users and handle the increasing amounts of data. Classical relational DBMSs (RDBMSs) support generic transactions but are expensive to scale-out to large clusters. Key-value stores can scale-out but provide transactional access to only single key-value pairs, thereby considerably increasing the complexity of application