The Two-Point Correlation of 2QZ Quasars and 2SLAQ LRGs: From a Quasar Fueling Perspective

Public data from the 2dF quasar survey (2QZ) and 2dF/SDSS LRG & QSO (2SLAQ), with their vast reservoirs of spectroscopically located and identified sources, afford us the chance to more accurately study their real space correlations in the hopes of identifying the physical processes that trigger quasar activity. We have used these two public databases to measure the projected cross correlation, $\omega_p$, between quasars and luminous red galaxies. We find the projected two-point correlation to have a fitted clustering radius of $r_0, = 5.3 \pm 0.6$ and a slope, $\gamma =1.83 \pm 0.42$ on scales from 0.7-27$h^{-1}$Mpc. We attempt to understand this strong correlation by separating the LRG sample into 2 populations of blue and red galaxies. We measure at the cross correlation with each population. We find that these quasars have a stronger correlation amplitude with the bluer, more recently starforming population in our sample than the redder passively evolving population, which has a correlation that is much more noisy and seems to flatten on scales $< 5h^{-1}$Mpc. We compare this result to published work on hierarchical models. The stronger correlation of bright quasars with LRGs that have undergone a recent burst of starformation suggests that the physical mechanisms that produce both activities are related and that minor mergers or tidal effects may be important triggers of bright quasar activity and/or that bright quasars are less highly biased than faint quasars.Comment: Accepted for publication in Ap

LDM: Lineage-Aware Data Management in Multi-tier Storage Systems

We design and develop LDM, a novel data management solution to cater the needs of applications exhibiting the lineage property, i.e. in which the current writes are future reads. In such a class of applications, slow writes significantly hurt the over-all performance of jobs, i.e. current writes determine the fate of next reads. We believe that in a large scale shared production cluster, the issues associated due to data management can be mitigated at a way higher layer in the hierarchy of the I/O path, even before requests to data access are made. Contrary to the current solutions to data management which are mostly reactive and/or based on heuristics, LDM is both deterministic and pro-active. We develop block-graphs, which enable LDM to capture the complete time-based data-task dependency associations, therefore use it to perform life-cycle management through tiering of data blocks. LDM amalgamates the information from the entire data center ecosystem, right from the application code, to file system mappings, the compute and storage devices topology, etc. to make oracle-like deterministic data management decisions. With trace-driven experiments, LDM is able to achieve 29–52% reduction in over-all data center workload execution time. Moreover, by deploying LDM with extensive pre-processing creates efficient data consumption pipelines, which also reduces write and read delays significantly