Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   study MapReduce scheduling on nn parallel machines (servers) with different speeds v1≥v2≥⋯≥vnv1≥v2≥⋯≥vn. Each job contains two kinds of tasks: map tasks and reduce tasks. The job’s reduce tasks can only be processed after finishing all its map tasks.  assume that the map tasks are parallelized, i.e., it can be arbitrarily split and parts of the same task can be processed on different machines in parallel, while the reduce tasks are non-parallelizable.  consider both the offline and online scheduling problems. On offline version, if the reduce tasks are non-preemptive,  design an approximation algorithm whose worst case ratio is at most max{1+Δ2−1n,Δ}, where Δ=v1vn is the ratio of the fastest speed to the slost speed. If the reduce tasks are preemptive,  provide an approximation algorithm with worst case ratio of 22. On online version where jobs arriving over time,  design two heuristics for non-preemptive and preemptive reduce tasks respectively. In experiment,  verify the advantage of our algorithms comparing with the state-of-the-art.
['Yiwei Jiang', 'Yuqing Zhu', 'Weili Wu', 'Deying Li']
I am writing a research paper on a idea Makespan minimization for MapReduce systems with different servers
[{"from": "human", "value": "I am writing a research paper on a idea Makespan minimization for MapReduce systems with different servers"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   study MapReduce scheduling on nn parallel machines (servers) with different speeds v1\u2265v2\u2265\u22ef\u2265vnv1\u2265v2\u2265\u22ef\u2265vn. Each job contains two kinds of tasks: map tasks and reduce tasks. The job\u2019s reduce tasks can only be processed after finishing all its map tasks.  assume that the map tasks are parallelized, i.e., it can be arbitrarily split and parts of the same task can be processed on different machines in parallel, while the reduce tasks are non-parallelizable.  consider both the offline and online scheduling problems. On offline version, if the reduce tasks are non-preemptive,  design an approximation algorithm whose worst case ratio is at most max{1+\u03942\u22121n,\u0394}, where \u0394=v1vn is the ratio of the fastest speed to the slost speed. If the reduce tasks are preemptive,  provide an approximation algorithm with worst case ratio of 22. On online version where jobs arriving over time,  design two heuristics for non-preemptive and preemptive reduce tasks respectively. In experiment,  verify the advantage of our algorithms comparing with the state-of-the-art."}]