Elasca: Workload-Aware Elastic Scalability for Partition Based Database Systems

Providing the ability to increase or decrease allocated resources on demand as the transactional load varies is essential for database management systems (DBMS) deployed on today's computing platforms, such as the cloud. The need to maintain consistency of the database, at very large scales, while providing high performance and reliability makes elasticity particularly challenging. In this thesis, we exploit data partitioning as a way to provide elastic DBMS scalability. We assert that the flexibility provided by a partitioned, shared-nothing parallel DBMS can be used to implement elasticity. Our idea is to start with a small number of servers that manage all the partitions, and to elastically scale out by dynamically adding new servers and redistributing database partitions among these servers as the load varies. Implementing this approach requires (a) efficient mechanisms for addition/removal of servers and migration of partitions, and (b) policies to efficiently determine the optimal placement of partitions on the given servers as well as plans for partition migration. This thesis presents Elasca, a system that implements both these features in an existing shared-nothing DBMS (namely VoltDB) to provide automatic elastic scalability. Elasca consists of a mechanism for enabling elastic scalability, and a workload-aware optimizer for determining optimal partition placement and migration plans. Our optimizer minimizes computing resources required and balances load effectively without compromising system performance, even in the presence of variations in intensity and skew of the load. The results of our experiments show that Elasca is able to achieve performance close to a fully provisioned system while saving 35% resources on average. Furthermore, Elasca's workload-aware optimizer performs up to 79% less data movement than a greedy approach to resource minimization, and also balance load much more effectively

Inhibition of biofilm growth of Gram-positive and Gram-negative bacteria on tuned polyurethane nanofibers

112-121Biofilm formation is a process of bacterial attachment whereby they fasten irreversibly to a biomaterial surface and lead to unwanted phenotypic changes. The chief concern is its formation and to prevent the harmful changes that follow the accumulation of bacteria on implants, so the scientific community has made efforts. In this study, we attempted to fabricate a novel tissue engineering candidate to prevent the biofilm formation desired by ideal biomaterials. We prepared the micro/nanofibers of polyurethane (PU) incorporated with hydrophilic β-cyclodextrin (CD) by electrospinning technique. Further on, these as-spun fibers were in fused with an antibacterial agent. As an antibacterial agent, silver nanoparticles (Ag NPs) were adsorbed on scaffolds. Among the varied methods of its adsorption, adsorption by sonication and hydrothermal process were chosen. Characterization studies performed were scanning electron microscopy (SEM) and water contact angle analysis. The uniform morphology of nanofibers was seen in SEM micrographs which mimics the extracellular matrix. The hydrophilicity test showed the increased hydrophilicity of scaffolds with a decrease in contact angle in CD and Ag NPs incorporated fibre scaffolds. The Ag release assay showed slow release in the case of the fibers where Ag was adsorbed by hydrothermal treatment compared to adsorption by sonication. The antibacterial tests show inhibition of bacteria to different degrees by the fibers. The highest zones were seen in the case of samples with Ag NPs adsorption by sonication. The in vitro MTT assay presented that these scaffolds were non-toxic to the cells and could be employed in biological applications