Scheduling Cloud Workloads Using Carry-On Weighted Round Robin

Cloud Computing represents a paradigm shift in computing. It advocates the use of computing resources as a service rather than as a product. The numerous advantages which the Cloud offers has led to many users adopting it at a phenomenal rate. Providing service to this ever growing number of users in a fast and effective manner is a major challenge. Numerous researchers have proposed various approaches to scheduling user workloads, notable among which are the First-Come-First-Serve and Weight Round Robin (WRR), and have obtained varied levels of successes. Unfairness and excess allocation delay are some of the shortcomings of these approach. There is also the assumption that all Cloud users’ workloads belong to a single class of requirement. This work proposes an efficient and fair Cloud workload scheduling algorithm called Adaptive Carry-On Weighted Round Robin (ACWRR), and also takes into consideration multiple workloads classes. Experimental simulations were conducted with ACWRR benchmarked against WRR. Results show that ACWRR performs better than WRR by at least 13% in terms of system latency and 38% for makespan.