Self-adapting parallel metric-space search engine for variable query loads

This research focuses on automatically adapting a search engine size in response to fluctuations in query workload. Deploying a search engine in an Infrastructure as a Service (IaaS) cloud facilitates allocating or deallocating computer resources to or from the engine. Our solution is to contribute an adaptive search engine that will repeatedly re-evaluate its load and, when appropriate, switch over to a dierent number of active processors. We focus on three aspects and break them out into three sub-problems as follows: Continually determining the Number of Processors (CNP), New Grouping Problem (NGP) and Regrouping Order Problem (ROP). CNP means that (in the light of the changes in the query workload in the search engine) there is a problem of determining the ideal number of processors p active at any given time to use in the search engine and we call this problem CNP. NGP happens when changes in the number of processors are determined and it must also be determined which groups of search data will be distributed across the processors. ROP is how to redistribute this data onto processors while keeping the engine responsive and while also minimising the switchover time and the incurred network load. We propose solutions for these sub-problems. For NGP we propose an algorithm for incrementally adjusting the index to t the varying number of virtual machines. For ROP we present an ecient method for redistributing data among processors while keeping the search engine responsive. Regarding the solution for CNP, we propose an algorithm determining the new size of the search engine by re-evaluating its load. We tested the solution performance using a custom-build prototype search engine deployed in the Amazon EC2 cloud. Our experiments show that when we compare our NGP solution with computing the index from scratch, the incremental algorithm speeds up the index computation 2{10 times while maintaining a similar search performance. The chosen redistribution method is 25% to 50% faster than other methods and reduces the network load around by 30%. For CNP we present a deterministic algorithm that shows a good ability to determine a new size of search engine. When combined, these algorithms give an adapting algorithm that is able to adjust the search engine size with a variable workload.

Effects of engine cooling water temperature on performance and emission characteristics of a CI engine operated with biofuel blend

The temperature of the coolant is known to have significant influence on engine performance and emissions. Whereas existing literature describes the effects of coolant temperature in engines using fossil derived fuels, very few studies have investigated these effects when biofuel is used. In this study, Jatropha oil was blended separately with ethanol and butanol. It was found that the 80% jatropha oil + 20% butanol blend was the most suitable alternative, as its properties were closest to that of fossil diesel. The coolant temperature was varied between 50°C and 95°C. The combustion process enhanced for both diesel and biofuel blend, when the coolant temperature was increased. The carbon dioxide emissions for both diesel and biofuel blend were observed to increase with temperature. The carbon monoxide, oxygen and lambda values were observed to decrease with temperature. When the engine was operated using diesel, nitrogen oxides emissions correlated in an opposite manner to smoke opacity; however, nitrogen oxides emissions and smoke opacity correlated in an identical manner for biofuel blend. Brake specific fuel consumption was observed to decrease as the temperature was increased and was higher on average when the biofuel was used. The study concludes that both biofuel blend and fossil diesel produced identical correlations between coolant temperature and engine performance. The trends of nitrogen oxides and smoke emissions with cooling temperatures were not identical to fossil diesel when biofuel blend was used in the engine.