Resource Sharing for Multi-Tenant Nosql Data Store in Cloud

Thesis (Ph.D.) - Indiana University, Informatics and Computing, 2015Multi-tenancy hosting of users in cloud NoSQL data stores is favored by cloud providers because it enables resource sharing at low operating cost. Multi-tenancy takes several forms depending on whether the back-end file system is a local file system (LFS) or a parallel file system (PFS), and on whether tenants are independent or share data across tenants In this thesis I focus on and propose solutions to two cases: independent data-local file system, and shared data-parallel file system. In the independent data-local file system case, resource contention occurs under certain conditions in Cassandra and HBase, two state-of-the-art NoSQL stores, causing performance degradation for one tenant by another. We investigate the interference and propose two approaches. The first provides a scheduling scheme that can approximate resource consumption, adapt to workload dynamics and work in a distributed fashion. The second introduces a workload-aware resource reservation approach to prevent interference. The approach relies on a performance model obtained offline and plans the reservation according to different workload resource demands. Results show the approaches together can prevent interference and adapt to dynamic workloads under multi-tenancy. In the shared data-parallel file system case, it has been shown that running a distributed NoSQL store over PFS for shared data across tenants is not cost effective. Overheads are introduced due to the unawareness of the NoSQL store of PFS. This dissertation targets the key-value store (KVS), a specific form of NoSQL stores, and proposes a lightweight KVS over a parallel file system to improve efficiency. The solution is built on an embedded KVS for high performance but uses novel data structures to support concurrent writes, giving capability that embedded KVSs are not designed for. Results show the proposed system outperforms Cassandra and Voldemort in several different workloads

HTRC Data API Performance Study

HathiTrust Research Center (HTRC) allows users to access more than 3 million volumes through a service called Data API. Data API plays an important role in HTRC infrastructure. It hides internal complexity from user, protects against malicious or inadvertent damages to data and separates underlying storage solution with interface so that underlying storage may be replaced with better solutions without affecting client code. We carried out extensive evaluations on the HTRC Data API performance over the Spring 2013. Specifically, we evaluated the rate at which data can be retrieved from the Cassandra cluster under different conditions, impact of different compression levels, and HTTP/HTTPS data transfer. The evaluation presents performance aspects of different software pieces in Data API as well as guides us to have optimal settings for Data API

HathiTrust Research Center Data Capsule v1.0: An Overview of Functionality

The first mode of access by the community of digital humanities and informatics researchers and educators to the copyrighted content of the HathiTrust digital repository will be to extracted statistical and aggregated information about the copyrighted texts. But can the HathiTrust Research Center support scientific research that allows a researcher to carry out their own analysis and extract their own information? This question is the focus of a 3-year, $606,000 grant from the Alfred P. Sloan Foundation (Plale, Prakash 2011-2014), which has resulted in a novel experimental framework that permits analytical investigation of a corpus but prohibits data from leaving the capsule. The HTRC Data Capsule is both a system architecture and set of policies that enable computational investigation over the protected content of the HT digital repository that is carried out and controlled directly by a researcher. It leverages the foundational security principles of the Data Capsules of A. Prakash of University of Michigan, which allows privileged access to sensitive data while also restricting the channels through which that data can be released. Ongoing work extends the HTRC Data Capsule to give researchers more compute power at their fingertips. The new thrust, HT-DC Cloud, extends existing security guarantees and features to allow researchers to carry out compute-heavy tasks, like LDA topic modeling, on large-scale compute resources. HTRC Data Capsule works by giving a researcher their own virtual machine that runs within the HTRC domain. The researcher can configure the VM as they would their own desktop with their own tools. After they are done, the VM switches into a "secure" mode, where network and other data channels are restricted in exchange for access to the data being protected. Results are emailed to the user. In this talk we discuss the motivations for the HTRC Data Capsule, its successes and challenges. HTRC Data Capsule runs at Indiana University. See more at http://d2i.indiana.edu/non-consumptive-researc