The DAQ/HLT system of the ATLAS experiment at CERN, Switzerland, is being commissioned for first collisions in 2009. Presently, the system is composed of an already very large farm of computers that accounts for about one-third of its final event processing capacity. Event selection is conducted in two steps after the hardware-based Level-1 Trigger: a Level-2 Trigger processes detector data based on regions of interest (RoI) and an Event Filter operates on the full event data assembled by the Event Building system. The detector read out is fully commissioned and can be operated at its full design capacity. This places the responsibility on the High-Level Triggers system to select only events of highest physics interest that will finally reach the offline reconstruction farms. This paper brings an overview of the current ATLAS DAQ/HLT implementation and performance based on studies originated from its operation with simulated, cosmic particles and first-beam data. Its built-in event processing parallelism is presented and discussed.
Distributed database servers are very popular as they provide data availability, reliability, replication, and partition for both homogeneous as well as heterogeneous software and hardware. In this paper, we analyze the previous works on load balancing of database servers. Further we also propose an algorithm for controlling job distribution at the database servers in different node partitions. We also formulate a methodology for load status evaluation of database servers to balance their loads for effective load status management. The load status of the database servers depends on three important parameters namely processor, RAM, and bandwidth. On the basis of load status, the clients'/users' requests can then be directed to another database in a distributed environment in order to balance the load effectively to meet user demands in an unobtrusive manner.
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