Abstract
Cloud computing enables the accommodation of an increasing number of applications in shared infrastructures. The routing for the incoming jobs in the cloud has become a real challenge due to the heterogeneity in both workload and machine hardware and the changes of load conditions over time. The present paper design and investigate the adaptive dynamic allocation algorithms that take decisions based on on-line and up-to-date measurements, and make fast online decisions to achieve both desirable QoS levels and high resource utilization. The Task allocation platform (TAP) is implemented as a practical system to accommodate the allocation algorithms and perform online measurement. The paper studies the potential of our proposed algorithms to deal with multi-class tasks in heterogeneous cloud environments and the experimental evaluations are also presented.
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Wang, L. (2018). Adaptive Allocation of Multi-class Tasks in the Cloud. In: Czachórski, T., Gelenbe, E., Grochla, K., Lent, R. (eds) Computer and Information Sciences. ISCIS 2018. Communications in Computer and Information Science, vol 935. Springer, Cham. https://doi.org/10.1007/978-3-030-00840-6_2
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