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Multi-tenancy hosting of users in cloud NoSQL data stores is favored by cloudproviders because it enables resource sharing at low operating cost.Multi-tenancy takes several forms depending on whether the back-end file systemis a local file system (LFS) or a parallel file system (PFS), and on whethertenants are independent or share data across tenants. In this thesis I focus onand propose solutions to two cases: independent data-local file system, andshared data-parallel file system.  In the independent data-local file system case, resource contention occursunder certain conditions in Cassandra and HBase, two state-of-the-art NoSQLstores, causing performance degradation for one tenant by another. Weinvestigate the interference and propose two approaches. The first provides ascheduling scheme that can approximate resource consumption, adapt to workloaddynamics and work in a distributed fashion. The second introduces aworkload-aware resource reservation approach to prevent interference. Theapproach relies on a performance model obtained offline and plans thereservation according to different workload resource demands. Results show theapproaches together can prevent interference and adapt to dynamic workloadsunder multi-tenancy.  In the shared data-parallel file system case, it has been shown that runninga distributed NoSQL store over PFS for shared data across tenants is not costeffective. Overheads are introduced due to the unawareness of the NoSQL storeof PFS. This dissertation targets the key-value store (KVS), a specific form ofNoSQL stores, and proposes a lightweight KVS over a parallel file system toimprove efficiency. The solution is built on an embedded KVS for highperformance but uses novel data structures to support concurrent writes.Results show the proposed system outperforms Cassandra and Voldemort in severaldifferent workloads.

Resource Sharing for Multi-Tenant NoSQL Data Store in Cloud