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Under the constraint of finite buffer space, the proposed buffer-aware adaptive resource scheduling algorithm aims at achieving three objectives: (1) keep bit loss rate as low as possible by means of taking buffer status into account, (2) improve the total system throughput as large as possible, and (3) keep certain fairness among users by means of adjusting the overflow probability.
For this purpose, we design two optional resource scheduling schemes: resource first and user first.
Joint scheduling (JS) and independent scheduling (INS) are two resource scheduling schemes.
The use of GA decreases execution time of resource scheduling.
Besides, resource scheduling virtualization supports multi-site selection as well.
The resource distribution in post-disaster is an important part of emergency resource scheduling.
The key part of cloud computing resource management is resource scheduling.
It includes user accessing tier, technology supporting tier, resource scheduling tier and resources tier.
Thirdly, in terms of data scheduling, it designs the MapReduce late scheduling strategy that improve data processing and optimize computation resource scheduling in MapReduce cluster.
A novel bacterial foraging based hyper-heuristic resource scheduling algorithm has been designed to effectively schedule the jobs on available resources in a Grid environment.
This paper proposes a method called, Moving Average-based Fuzzy Resource Scheduling (MV-FRS) for virtualized cloud environment to optimize the scheduling of resources through virtual machines.
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