6. In this section, we first conclude our major contributions in this work and then suggest three directions to extend this work. 6.1 Conclusions As more and more healthcare scientific applications need to process stream data, we have designed a adaptive MapReduce framework and proposed a few scheduling mechanisms to determine the active count Map and Reduce tasks in runtime. To summarize the major contribution, we have three technical aspects as follows. (1) A adaptive MapReduce scheme. This novel scheme implements a daemon-based mechanism to design each Map and Reduce task. The purpose is to revise the current MapReduce framework to process stream data in real-life healthcare scientific applications. We demonstrate the detail of the scheme by using real MapReduce case studies to justify the applicability of this scheme. (2) Runtime Map and Reduce task calculation: We have proposed theorems and one corollary to investigate the runtime Map and Reduce task count. These methods are used to increase and/or decrease the runtime Map and Reduce tasks as the workload fluctuates. (3) Adaptive MapReduce simulator: We release the adaptive MapReduce simulator online and not only can it be used as a software toolkit for the healthcare application workload purpose, but also can be applied to other stream data application scenarios of a much wider areas. 6.2 Future Work We suggest extending this work in the following two directions: (1) Extend the work theoretically towards the heterogeneous cloud systems. Currently the adaptive scheme in this paper scales Map and Reduce tasks only. Readers would equally be interested in the scalable framework if they have more knowledge of how the task scaling aligns with the compute node scaling. When the compute nodes are heterogeneous, the problem is more challenging but also very interesting. We need to extend the theoretical part of this work along this line. (2) This paper primarily focuses on a generic MapReduce Implement the scheme in Hadoop MapReduce applications. framework, which is out of the context of the widely used Hadoop MapReduce. To be able to get it deployed on a large group of application scenarios, we need to release such a Hadoop release to help users deploy their stream data scientific applications
6. In this section, we first conclude our major contributions in this work and then suggest three directions to extend this work. 6.1 Conclusions As more and more healthcare scientific applications need to process stream data, we have designed a adaptive MapReduce framework and proposed a few scheduling mechanisms to determine the active count Map and Reduce tasks in runtime. To summarize the major contribution, we have three technical aspects as follows. (1) A adaptive MapReduce scheme. This novel scheme implements a daemon-based mechanism to design each Map and Reduce task. The purpose is to revise the current MapReduce framework to process stream data in real-life healthcare scientific applications. We demonstrate the detail of the scheme by using real MapReduce case studies to justify the applicability of this scheme. (2) Runtime Map and Reduce task calculation: We have proposed theorems and one corollary to investigate the runtime Map and Reduce task count. These methods are used to increase and/or decrease the runtime Map and Reduce tasks as the workload fluctuates. (3) Adaptive MapReduce simulator: We release the adaptive MapReduce simulator online and not only can it be used as a software toolkit for the healthcare application workload purpose, but also can be applied to other stream data application scenarios of a much wider areas. 6.2 Future Work We suggest extending this work in the following two directions: (1) Extend the work theoretically towards the heterogeneous cloud systems. Currently the adaptive scheme in this paper scales Map and Reduce tasks only. Readers would equally be interested in the scalable framework if they have more knowledge of how the task scaling aligns with the compute node scaling. When the compute nodes are heterogeneous, the problem is more challenging but also very interesting. We need to extend the theoretical part of this work along this line. (2) Implement the scheme in Hadoop MapReduce applications. This paper primarily focuses on a generic MapReduce framework, which is out of the context of the widely used Hadoop MapReduce. To be able to get it deployed on a large group of application scenarios, we need to release such a Hadoop release to help users deploy their stream data scientific applications
jcao_j_healthcare.docx