Allogeneic apoptotic thymocyte-stimulated dendritic cells expand functional regulatory T cells

P>Dendritic cells (DCs) play an important role in the clearance of apoptotic cells. The removal of apoptotic cells leads to peripheral tolerance, although their role is still not clear. We show that the uptake of apoptotic thymocytes by DCs converts these cells into tolerogenic DCs resistant to maturation by lipopolysaccharide, modulating the production of interleukin-12 and up-regulating the expression of transforming growth factor-beta(1) latency associated peptide. We also observed that DCs pulsed with apoptotic cells in the allogeneic context were more efficient in the expansion of regulatory T cells (Tregs), and that this expansion requires contact between DCs and the T cell. The Tregs sorted from in vitro culture suppressed the proliferation of splenocytes in vitro in a specific and non-specific manner. In the in vivo model, the transfer of CD4+ CD25- cells to Nude mice induced autoimmunity, with cell infiltrate found in the stomach, colon, liver and kidneys. The co-transfer of CD4+ CD25- and CD4+ CD25+ prevented the presence of cell infiltrates in several organs and increased the total cell count in lymph nodes. Our data indicate that apoptotic cells have an important role in peripheral tolerance via induction of tolerogenic DCs and CD4+ CD25+ Foxp3+ cells that present regulatory functions.

A novel approach for distributed application scheduling based on prediction of communication events

The evolution of commodity computing lead to the possibility of efficient usage of interconnected machines to solve computationally-intensive tasks, which were previously solvable only by using expensive supercomputers. This, however, required new methods for process scheduling and distribution, considering the network latency, communication cost, heterogeneous environments and distributed computing constraints. An efficient distribution of processes over such environments requires an adequate scheduling strategy, as the cost of inefficient process allocation is unacceptably high. Therefore, a knowledge and prediction of application behavior is essential to perform effective scheduling. In this paper, we overview the evolution of scheduling approaches, focusing on distributed environments. We also evaluate the current approaches for process behavior extraction and prediction, aiming at selecting an adequate technique for online prediction of application execution. Based on this evaluation, we propose a novel model for application behavior prediction, considering chaotic properties of such behavior and the automatic detection of critical execution points. The proposed model is applied and evaluated for process scheduling in cluster and grid computing environments. The obtained results demonstrate that prediction of the process behavior is essential for efficient scheduling in large-scale and heterogeneous distributed environments, outperforming conventional scheduling policies by a factor of 10, and even more in some cases. Furthermore, the proposed approach proves to be efficient for online predictions due to its low computational cost and good precision. (C) 2009 Elsevier B.V. All rights reserved.

Grid job scheduling using Route with Genetic Algorithm support

In 2006 the Route load balancing algorithm was proposed and compared to other techniques aiming at optimizing the process allocation in grid environments. This algorithm schedules tasks of parallel applications considering computer neighborhoods (where the distance is defined by the network latency). Route presents good results for large environments, although there are cases where neighbors do not have an enough computational capacity nor communication system capable of serving the application. In those situations the Route migrates tasks until they stabilize in a grid area with enough resources. This migration may take long time what reduces the overall performance. In order to improve such stabilization time, this paper proposes RouteGA (Route with Genetic Algorithm support) which considers historical information on parallel application behavior and also the computer capacities and load to optimize the scheduling. This information is extracted by using monitors and summarized in a knowledge base used to quantify the occupation of tasks. Afterwards, such information is used to parameterize a genetic algorithm responsible for optimizing the task allocation. Results confirm that RouteGA outperforms the load balancing carried out by the original Route, which had previously outperformed others scheduling algorithms from literature.

EFFECTIVE NETWORKS FOR REAL-TIME DISTRIBUTED PROCESSING

This paper applies the concepts and methods of complex networks to the development of models and simulations of master-slave distributed real-time systems by introducing an upper bound in the allowable delivery time of the packets with computation results. Two representative interconnection models are taken into account: Uniformly random and scale free (Barabasi-Albert), including the presence of background traffic of packets. The obtained results include the identification of the uniformly random interconnectivity scheme as being largely more efficient than the scale-free counterpart. Also, increased latency tolerance of the application provides no help under congestion.