Taskscheduling for distributed programs which include streaming processing

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The purpose of our study is to execute streaming applications, for example, analyzing stream data provided from network sources and processing of audio and video stream, effectively by scheduling them to utilize computing resources like processors and storages which are potentially comprised in parallel and distributed environments like Grids or PC clusters. Distributed applications including streaming processing cannot be executed efficiently, because almost all conventional scheduling methods are not able to apply to them directly. We proposed a new task scheduling method which can schedule these streaming applications. We also use a distributed network monitoring system which developed by us for an evaluation of the proposed method. This distributed network monitoring system is one of practical streaming application. It monitors and caches IP datagram to check the contents for security. This application is cheaper than the security appliances, because it can improve that security checking is realized by utilizing PC clusters which includes idle PCs. Moreover it is very flexible compared with the security appliances, because it can append to a new function against a new security problem easily. The proposed method can schedule both streaming application and non-streaming applications well at a same time. The experimental results showed the proposed method reduced the total execution time of the practical stream application. The dispersion of the execution time was also suppressed even if the network bandwidth was dynamically changing. This characteristic is very important when this method is applied to more complicated task scheduling methods.