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Home / programming language / Online Java Projects / Cost-Efficient Tasks and Data Co-Scheduling with AffordHadoop
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Cost-Efficient Tasks and Data Co-Scheduling with AffordHadoop

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SKU: PROJ8034 Categories: 2018-2019 Projects, Cloud Computing Projects, Online Java Projects 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, Matlab Project, PHP Project, Students Projects
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Cost-Efficient Tasks and Data Co-Scheduling with AffordHadoop

Abstract-With today’s massive jobs spanning thousands of tasks each, cost-optimality has become more important than ever.Modern distributed data processing paradigms can be significantly more sensitive to cost than makespan, especially for long jobs deployed in commercial clouds. This paper posits that minimized dollar costs can not be achieved unless data and tasks are scheduled simultaneously. In this paper, we introduce the problem of cost-efficient co-scheduling for highly data-intensive jobs in cloud, such as MapReduce. We show that while the problem is polynomial in some cases, its general problem is NP-Hard. We propose to tackle the problem by using integer programming techniques coupled with heuristic reduction and optimization to enable a near-realtime solution. AffordHadoop, a pluggable co-scheduler for Hadoop, is implemented as an example of such a co-scheduler. AffordHadoop can save up to 48% of the overall dollar costs when compared to existing schedulers and provides significant flexibility in fine-tuning the cost-performance tradeoff.

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