Journal ArticleDOI
TOPSIS inspired Budget and Deadline Aware Multi-Workflow Scheduling for Cloud computing
TLDR
In this paper, a multi-workflow scheduling algorithm based on the Multi-Criteria Decision Making (MCDM) approach, TOPSIS (Technique of Order Preference by Similarity to Ideal Solution) is presented.About:
This article is published in Journal of Systems Architecture.The article was published on 2021-03-01. It has received 26 citations till now. The article focuses on the topics: Budget constraint & Scheduling (computing).read more
Citations
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Journal ArticleDOI
Task Duplication-Based Scheduling Algorithm for Budget-Constrained Workflows in Cloud Computing
TL;DR: Wang et al. as mentioned in this paper proposed a task duplication based scheduling algorithm, namely TDSA, to optimize makespan for budget-constrained workflows in cloud platforms, which aims to exploit idle slots on resources to selectively duplicate tasks' predecessors, such advancing these tasks' completion time.
Journal Article
Hybrid Scheduling Strategy for Multiple DAGs Workflow in Heterogeneous System
TL;DR: Results show that it is possible to meet different requirements of DAGs submitted at different times and to improve utilization rate of a resource and a significant "Trail Ending" principle is shown about scheduling two-DAGs.
Journal ArticleDOI
Host load prediction in cloud computing with Discrete Wavelet Transformation (DWT) and Bidirectional Gated Recurrent Unit (BiGRU) network
TL;DR: In this paper , the authors employed a Bidirectional Gated-Recurrent Unit (BiGRU), Discrete Wavelet Transformation (DWT), and an attention mechanism to improve the host load prediction accuracy.
Journal ArticleDOI
Alternative Fuel Selection Framework toward Decarbonizing Maritime Deep-Sea Shipping
TL;DR: In this article , a multi-criteria analysis is used as a decision support tool for the alternative fuel selection problem in deep-sea commercial shipping on the international waterway, and the proposed technique considers environmental, technological, and economic factors and ensures an exclusive focus on stakeholders at the firm-level decision-making capacity.
Journal ArticleDOI
Multi‐objective task scheduling in cloud computing
TL;DR: An efficient task scheduling algorithm which is based on the pollination behavior of flowers and makes use of both Pareto optimality principle and TOPSIS technique to improve the quality of the obtained solutions is proposed.
References
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Book
Multiple Attribute Decision Making: Methods and Applications
TL;DR: In this paper, the authors present a classification of MADM methods by data type and propose a ranking method based on the degree of similarity of the MADM method to the original MADM algorithm.
Journal ArticleDOI
CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms
TL;DR: The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns.
Journal ArticleDOI
Performance-effective and low-complexity task scheduling for heterogeneous computing
TL;DR: Two novel scheduling algorithms for a bounded number of heterogeneous processors with an objective to simultaneously meet high performance and fast scheduling time are presented, called the Heterogeneous Earliest-Finish-Time (HEFT) algorithm and the Critical-Path-on-a-Processor (CPOP) algorithm.
Book ChapterDOI
Methods for Multiple Attribute Decision Making
Ching-Lai Hwang,Kwangsun Yoon +1 more
TL;DR: There are some classical decision rules such as dominance, maximin and maximum which are still fit for the MADM environment but they do not require the DM’s preference information, and accordingly yield the objective (vs. subjective) solution.
Journal ArticleDOI
Runtime measurements in the cloud: observing, analyzing, and reducing variance
TL;DR: A study of the performance variance of the most widely used Cloud infrastructure (Amazon EC2) from different perspectives using established microbenchmarks to measure performance variance in CPU, I/O, and network and a multi-node MapReduce application to quantify the impact on real dataintensive applications.