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The Cobra runtime system is a resource manager which allows users to allocate processors and memory for the execution of applications using a client/server approach.
This work presents the details of a model for fully decentralised scientific workflow management designed to support the efficient execution of applications in heterogeneous and unpredictable computing environments.
In recent years, there has been increasing interest in using task-level pipelining to accelerate the overall execution of applications mainly consisting of producer/consumer tasks.
In recent years there has been a significant effort to develop middleware that facilitates the execution of applications on Grid infrastructures.
Hence, we propose a hybrid approach that combines shared-memory and message passing in a single general-purpose CMP architecture that allows efficient execution of applications developed with both parallel programming approaches.
This paper presents the design, implementation, and evaluation of an adaptive framework— Dynasa, which strives to handle security problems using adaptive fault-tolerance (i.e., checkpointing and replication) during the execution of applications according to the status of the Grid environments.
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Latter enables data amount-dependent dynamic variations in execution of application tasks.
It provides a runtime system that dynamically decides where the execution of application will take place, locally or remotely.
One important feature of HDFS is the partitioning of data and computation using thousands of hosts, and the execution of application computations in parallel in a way it is close to their data [13 16].
It intercepts the execution of application-specific methods annotated with Context and Checkpoint in subclasses of HandlerContainer and uses the handler management methods to integrate normal and exceptional behavior.
Its design goal is to permit the execution of application software inside a Web browser, which resembles the original goal of the JVM.
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