Program Trading has gotten more popular recent years. According to thestatistics, there was about 53.6% of daily volume in the United States, and increased to 73% in 2009. With the universal of program trading, more people have begun to research program trading. The purpose of this paper is constructing a developed platform of program trading for researching or developing. In addition to developed platform, we provide the run-time environment, and three main functions: 1. The job scheduler 2. The high scalability 3. The developed platform In this paper, we use SLURM to implement an economical computing service for program trading. SLURM is a resource management software for some large clusters. However it lacked for an easy interface to the ended users. We modify Xinetd as the external interface for SLURM, and implement the program trading development platform for researching or developing. According to the result, using our scheduler and the external interface that modify from Xinetd can be effective in controlling the server resource and increase the availability.

If nodes got failed in a distributed application service, it will not only pay more cost to handle with these results missing, but also make scheduler cause additional loadings. For whole results don¡¦t recalculated cause by fault occurs, it will be recalculated data of fault nodes in backup machines. Therefore, this paper uses three methods: N + N nodes, N + 1 nodes, and N + 1 nodes with probability to experiment and analyze their pros and cons, the third way gives jobs weight before assigning them, and converts weight into probability and nice value(defined by SLURM[1]) to influence scheduler¡¦s decision of jobs¡¦ order. When fault occurs, calculating in normal nodes¡¦ results will back to control node, and then the fault node¡¦s jobs are going to be reassigned or not be reassigned to backup machine for getting complete results. Finally, we will analyze these three ways good and bad.

Per job isolation of temporary file storage in High Performance Computing (HPC) environments provide benefits in security, efficiency, and administration. HPC system administrators can use the mount_isolation Slurm task plugin to improve security by isolating temporary files where no isolation previously existed. The mount_isolation plugin also increases efficiency by removing obsolete temporary files immediately after each job terminates. This frees valuable disk space in the HPC environment to be used by other jobs. These two improvements reduce the amount of work system administrators must expend to ensure temporary files are removed in a timely manner.Previous temporary file removal solutions were removal on reboot, manual removal, or removal through a Slurm epilog script. The epilog script was the most effective of these, allowing files to be removed in a timely manner. However, HPC users can have multiple supercomputing jobs running concurrently. Temporary files generated by these concurrent or overlapping jobs are only deleted by the epilog script when all jobs run by that user on the compute node have completed. Even though the user may have only one running job, the temporary directory may still contain temporary files from many previously executed jobs, taking up valuable temporary storage on the compute node. The mount_isolation plugin isolates these temporary files on a per job basis allowing prompt removal of obsolete files regardless of job overlap.