Online multi-resource scheduling for minimum task completion time in cloud servers

We design a simple and efficient online scheme for scheduling cloud tasks requesting multiple resources, such as CPU and memory. The proposed scheme reduces the queuing delay of the cloud tasks by accounting for their execution time lengths. We also derive bounds on the average queuing delays, and evaluate the performance of our proposed scheme and compare it with those achievable under existing schemes by relying on real Google data traces. Using this data, we show that our scheme outperforms the other schemes in terms of resource utilizations as well as average task queuing delays. 2014 IEEE.Scopus2-s2.0-8490451210