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Home / programming language / Online Java Projects / Hybrid Job-Driven Scheduling for Virtual MapReduce Clusters
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Java Project - Parallel & Distributed, final year projects.
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Hybrid Job-Driven Scheduling for Virtual MapReduce Clusters

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SKU: PROJ7459 Categories: 2017 Projects, Final Year Projects, Online Java Projects, Parallel and Distributed Projects, programming language Tags: Android Project, Asp.Net Project, C# Project, Computer Engineering Final Year Projects, Computer Science Final Year Projects, Electronics Projects Engineering Students Final Year, Engineering Student Project Ideas, Final Semester Projects, Final Year Project Center, Final Year Projects, ieee ECE Projects, ieee EEE Projects, ieee Final Year Projects, IEEE Projects, ieee Projects Networking, Image Processing Projects, J2EE Project, Java Project, Java Project Code, Matlab Project, PHP Project, Students Projects
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Hybrid Job-Driven Scheduling for Virtual MapReduce Clusters

Abstract— It is cost-efficient for a tenant with a limited budget to establish a virtual MapReduce cluster by renting multiple virtual private servers (VPSs) from a VPS provider. To provide an appropriate scheduling scheme for this type of computing environment, a hybrid job-driven scheduling scheme (JoSS for short) from a tenant’s perspective. JoSS provides not only job-level scheduling, but also map-task level scheduling and reduce-task level scheduling. JoSS classifies MapReduce jobs based on job scale and job type and designs an appropriate scheduling policy to schedule each class of jobs. The goal is to improve data locality for both map tasks and reduce tasks, avoid job starvation, and improve job execution performance. Two variations of JoSS are further introduced to separately achieve a better map-data locality and a faster task assignment. We conduct extensive experiments to
evaluate and compare the two variations with cur rent scheduling algorithms supported by Hadoop. The results show that the two variations outperform the other tested algorithms in terms of map-data locality, reduce-data locality, and network overhead without incurring significant overhead. < final year projects >

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