雲端運算是在分散式計算系統中一種新興的名詞，並且雲端運算已經在大型數據中心越來越流行，MapReduce是雲端運算的一個重要概念， 目前最廣為人知實現MapReduce概念的其中一套分散式運算系統為Hadoop。而Hadoop本身預設的任務排程為FSFC(先來先做)，但工作本身所具有的工作量是不一樣的，所以會導致Hadoop無法好好的利用系統的資源。在本文中，我們設計了一個工作排程的演算法，我們稱之為工作分配排程，我們的演算法主要針對於如何將資源做完善的運用， 面對不同的工作，我們的演算法可以平行處理這些工作。我們提出的工作分配排程演算法可以改善節點的資源使用率，並且改善Hadoop系統的效能。

Cloud computing was a novel noun in distributed computing system, and it becomes more and more popular in large data center. MapReduce was an important concept in cloud computing, and Hadoop was one of the familiar systems to implement MapReduce concept in cloud computing. When job ran in large data center, variety of job demanded different resources. Default job scheduler of Hadoop is First Come First Serve (FCFS), which does not concern to balance resource utilization. In this paper, we propose a job scheduler, called Job Allocation Scheduler (JAS), which aims at maximizing the resource usage. With variety of job workload, it could parallelize jobs into relative workload classification in Hadoop. The proposed scheduler could improve node usage of resource under heterogeneous environments, and it could improve the Hadoop performance under heterogeneous environments.

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