By Jiang M.
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Extra resources for A 2-approximation for the preceding-and-crossing structured 2-interval pattern problem
In these frameworks, the data is staged in data/compute nodes of clusters or large-scale data centers, such as in the case of Google. The computations move to the data in order to perform data processing. Distributed file systems such as GFS and HDFS allow Google MapReduce and Hadoop to access data via distributed storage systems built on heterogeneous compute nodes, while Dryad and CGL-MapReduce support reading data from local disks. The simplicity in the programming model enables better support for quality of services such as fault tolerance and monitoring.
In parallel implementations, the input files are processed concurrently and the outputs are saved in a predefined location . For our analysis, we have implemented this application using Hadoop, CGL-MapReduce and DryadLiNQ. 2 Iterative/Complex Style Applications Parallel applications implemented using message passing runtimes can utilize various communication constructs to build diverse communication topologies . For example, a matrix multiplication application that implements Cannon's Algorithm  assumes parallel processes to be in a rectangular grid.
The OS-level virtual machines paradigm is becoming a key feature of servers, distributed systems , and grids. It simplifies the management of resources and offers a greater flexibility in resource usage. Each Virtual Machine (VM) a) provides a confined environment where non-trusted applications can be run, b) allows establishing limits in hardware-resource access and usage, through isolation techniques, c) allows adapting the runtime environment to the application instead of porting the application to the runtime environment (this enhances application portability) , d) allows using dedicated or optimized as mechanisms (scheduler, virtual-memory management, network protocol) for each application, e) enables applications and processes running within a VM to be managed as a whole.