Task scheduling in genetic sequencing tool

This paper proposes a task scheduler to control the demand of sending gaps encountered during the process of genome sequencing processing considering computational resources available. Gaps are spaces without representation in the genome sequencing process. This activity generates many competing tasks that consume a lot of computational resources, mainly memory. The goal of the scheduler is to prevent more required computational resources besides those which can be alive supplied, because in this case, a performance degradation of the system will occur and it may cause a delay in the processing time of the tasks. The motivation for this work is to improve the efficiency of the implementation of the closure of gaps in genome sequencing. For the evaluation of the proposal, a scheduler for gaps with scheduling policies based on monitoring of computing resources has been implemented.Keywords: bioinformatics, scheduling tasks, genetic sequencing

Memory Conscious Task Partition and Scheduling in Grid Environments

While resource management and task scheduling are identified challenges of Grid computing, current Grid scheduling systems mainly focus on CPU and network availability. Recent performance improvement of CPU and computer network has made memory usage a significant factor of overall performance. In this study, we consider memory availability as a performance factor and introduce memory conscious task partition and scheduling. Three task partition policies are discussed. They are CPU-based, memory-based, and CPU-memory combined partition. We first investigate the three task partition policies on dedicated resources and verify the effectiveness of the CPU-memory combined partition algorithm in finding an optimal solution. We then extend the task partition policies in non-dedicated environments with the consideration of resource sharing. Analytical and experimental results show that the CPU-memory combined scheduling approach outperforms either the CPU-based or memory-based scheduling approach considerably for memory-intensive applications in Grid environments. 1