Cloud computing has emerged to provide virtual, pay-as-you-go computing and storage services over the Internet, where the usage cost directly depends on consumption. One compelling feature in Clouds is elasticity, where a user can demand, and be immediately given access to, more (or less) resources based on requirements. However, this feature introduces new challenges in developing application and services. In this paper, we focus on the challenges in data management in Cloud environments, in view of elasticity. Particularly, we consider an elastic key-value store, which is used to cache intermediate results in a service-oriented system, and accelerate future queries by reusing the stored values. Such a key-value store can clearly benefit from the elasticity offered by Clouds, by expanding the cache during query-intensive periods. However, supporting an elastic key-value store involves many challenges, including selecting an appropriate indexing scheme, data migration upon elastic resource provisioning, and optimizations to remove certain overheads in the Cloud. This paper focuses on the design of an elastic key-value store. We consider three ubiquitous methods for indexing: B+-Trees, Extendible Hashing, and Bloom Filters, and we show how these schemes can be modified to exploit elasticity in Clouds. We also evaluate various performance aspects associated with the use of these indexing schemes. Furthermore, we have developed a heuristic to request elastic compute resources for expanding the cache such that instance startup overheads are minimized in our scheme. Our evaluation studies show that the index selection depends on various application and system level parameters that we have identified. And while we confirm that B+-Trees, which pervade many of today's key-value systems, would scale well, we showcases when Extendible Hashing would outperform B+-Trees.

Computing as a utility, that is, on-demand access to computing and storage infrastructure, has emerged in the form of the Cloud. In this model of computing, elastic resource allocation, i.e., the ability to scale resource allocation for specific applications, should be optimized to manage cost versus performance. Meanwhile, the wake of the information sharing/mining age is invoking a pervasive sharing of Web services and data sets in the Cloud, and at the same time, many data-intensive scientific applications are being expressed as these services. In this paper, we explore an approach to accelerate service processing in a Cloud setting. We have developed a cooperative scheme for caching data output from services for reuse. We propose algorithms for scaling our cache system up during peak querying times, and back down to save costs. Using the Amazon EC2public Cloud, a detailed evaluation of our system has been performed, considering speed up and elastic scalability in terms resource allocation and relaxation.