Cloud storage is an emerging infrastructure that offers Platforms as a Service (PaaS). On such platforms, storage and compute power are adjusted dynamically, and therefore it is important to build a highly scalable and reliable storage that can elastically scale ondemand with minimal startup cost.In this paper, we propose ecStore -an elastic cloud storage system that supports automated data partitioning and replication, load balancing, efficient range query, and transactional access. In ecStore, data objects are distributed and replicated in a cluster of commodity computer nodes located in the cloud. Users can access data via transactions which bundle read and write operations on multiple data items stored on possibly different cluster nodes.The architecture of ecStore follows a stratum design that leverages an underlying distributed index with a replication layer in the middle and a transaction management layer on top. ecStore provides adaptive read consistency on replicated data. We also enhance the system with an effective load balancing scheme using a self-tuning replication technique that is specially designed for large-scale data. Furthermore, a multi-version optimistic concurrency control scheme matches well with the characteristics of data in cloud storages. To validate the performance of the system, we have conducted extensive experiments on various platforms including a commercial cloud (Amazon's EC2), an in-house cluster, and PlanetLab.

Support of high performance queries on large volumes of spatial data becomes increasingly important in many application domains, including geospatial problems in numerous fields, location based services, and emerging scientific applications that are increasingly data- and compute-intensive. The emergence of massive scale spatial data is due to the proliferation of cost effective and ubiquitous positioning technologies, development of high resolution imaging technologies, and contribution from a large number of community users. There are two major challenges for managing and querying massive spatial data to support spatial queries: the explosion of spatial data, and the high computational complexity of spatial queries. In this paper, we present Hadoop-GIS - a scalable and high performance spatial data warehousing system for running large scale spatial queries on Hadoop. Hadoop-GIS supports multiple types of spatial queries on MapReduce through spatial partitioning, customizable spatial query engine RESQUE, implicit parallel spatial query execution on MapReduce, and effective methods for amending query results through handling boundary objects. Hadoop-GIS utilizes global partition indexing and customizable on demand local spatial indexing to achieve efficient query processing. Hadoop-GIS is integrated into Hive to support declarative spatial queries with an integrated architecture. Our experiments have demonstrated the high efficiency of Hadoop-GIS on query response and high scalability to run on commodity clusters. Our comparative experiments have showed that performance of Hadoop-GIS is on par with parallel SDBMS and outperforms SDBMS for compute-intensive queries. Hadoop-GIS is available as a set of library for processing spatial queries, and as an integrated software package in Hive.

No abstract

Cloud computing represents a paradigm shift driven by the increasing demand of Web based applications for elastic, scalable and efficient system architectures that can efficiently support their ever-growing data volume and large-scale data analysis. A typical data management system has to deal with real-time updates by individual users, and as well as periodical large scale analytical processing, indexing, and data extraction. While such operations may take place in the same domain, the design and development of the systems have somehow evolved independently for transactional and periodical analytical processing. Such a system-level separation has resulted in problems such as data freshness as well as serious data storage redundancy. Ideally, it would be more efficient to apply ad-hoc analytical processing on the same data directly. However, to the best of our knowledge, such an approach has not been adopted in real implementation.Intrigued by such an observation, we have designed and implemented epiC, an elastic power-aware data-itensive Cloud platform for supporting both data intensive analytical operations (ref. as OLAP) and online transactions (ref. as OLTP). In this paper, we present ES 2 -the elastic data storage system of epiC, which is designed to support both functionalities within the same storage. We present the system architecture and the functions of each system component, and experimental results which demonstrate the efficiency of the system.

No abstract