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|Title:||Deadline-constrained workflow scheduling in IaaS clouds with multi-resource packing||Authors:||Zhu, Zhaomeng
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2019||Source:||Zhu, Z. & Tang, X. (2019). Deadline-constrained workflow scheduling in IaaS clouds with multi-resource packing. Future Generation Computer Systems, 101, 880-893. https://dx.doi.org/doi.org/10.1016/j.future.2019.07.043||Project:||MOE2013-T2-2-067
|Journal:||Future Generation Computer Systems||Abstract:||Workflow is a common model to represent large computations composed of dependent tasks. Most existing workflow scheduling algorithms use computing resources in a non-multiprogrammed way, by which only one task can run on a service (machine) at a time. In this paper, we study a new workflow scheduling model on heterogeneous Infrastructure-as-a-Service (IaaS) platforms, which allows multiple tasks to run concurrently on a virtual machine (VM) according to their multi-resource demands. First, we propose a list-scheduling framework for the new multiprogrammed cloud resource model. In the order of a priority list, this framework gradually appoints tasks the best placements found on both existing and new VMs on the platform. Different task prioritization and placement comparison methods can be employed for different scheduling objectives. To fully exploit the heterogeneity of IaaS platforms, the VMs can be scaled up during the scheduling process. Then, we propose a deadline-constrained workflow scheduling algorithm (called DyDL) based on this framework to optimize the cost of workflow execution. This algorithm prioritizes tasks by their latest start times and appoints tasks the placements which can meet their latest start times and incur the minimal cost increases. Experimental results show that DyDL can achieve significantly better schedules in most test cases compared to several existing deadline-constrained workflow scheduling algorithms.||URI:||https://hdl.handle.net/10356/150516||ISSN:||0167-739X||DOI:||doi.org/10.1016/j.future.2019.07.043||Rights:||© 2019 Elsevier B.V. All rights reserved. This paper was published in Future Generation Computer Systems and is made available with permission of Elsevier B.V.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Journal Articles|
Updated on Jan 23, 2022
Updated on Jan 23, 2022
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