Biblio

Provenance metadata captures history of derivation of an entity, such as a dataset obtained through numerous data transformations. It is of great importance for science, among other fields, as it enables reproducibility and greater intelligibility of research results. With the avalanche of provenance produced by today's society, there is a pressing need for storing and low-latency querying of large provenance graphs. To address this need, in this paper we present a scalable approach to storing and querying provenance graphs using a popular NoSQL column family database system called DataStax Enterprise (DSE). Specifically, we i) propose a storage scheme, including two novel indices that enable efficient traversal of provenance graphs along causality lines, ii) present an algorithm for building our proposed indices for a given provenance graph, iii) implement our algorithm and conduct a performance study in which we store and query a provenance graph with over five million vertices using a DSE cluster running in AWS cloud. Our performance study results further validate scalability and performance efficiency of our approach.

Different applications concurrently running on modern MPSoCs can interfere with each other when they use shared resources. This interference can cause side channels, i.e., sources of unintended information flow between applications. To prevent such side channels, we propose a hybrid mapping methodology that attempts to ensure spatial isolation, i.e., a mutually-exclusive allocation of resources to applications in the MPSoC. At design time and as a first step, we compute compact and connected application mappings (called shapes). In a second step, run-time management uses this information to map multiple spatially segregated shapes to the architecture. We present and evaluate a (fast) heuristic and an (exact) SAT-based mapper, demonstrating the viability of the approach.

Different applications concurrently running on modern MPSoCs can interfere with each other when they use shared resources. This interference can cause side channels, i.e., sources of unintended information flow between applications. To prevent such side channels, we propose a hybrid mapping methodology that attempts to ensure spatial isolation, i.e., a mutually-exclusive allocation of resources to applications in the MPSoC. At design time and as a first step, we compute compact and connected application mappings (called shapes). In a second step, run-time management uses this information to map multiple spatially segregated shapes to the architecture. We present and evaluate a (fast) heuristic and an (exact) SAT-based mapper, demonstrating the viability of the approach.