A Multi-Objective Hybrid Cloud Resource Scheduling Method Based on Deadline and Cost Constraints

Zuo, Lei; Shu, Lei; Dong, Shoubin; Chen, Yuanfang; Li, Yan

doi:10.1109/access.2016.2633288

Cited by 47 publications

(24 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hu et al [22] use a Genetic Algorithm for the job scheduling problem that tries to limit its number of evaluations in order to fasten its optimisation for both tasks completion and computation cost. Zuo et al [62] extend the initial definition of the scheduling problem and add deadlines to jobs completion. They also propose an ant colony algorithm to address the problem.…”

Section: Multi-objective Vm Reassignmentmentioning

confidence: 99%

VM reassignment in hybrid clouds for large decentralised companies: A multi-objective challenge

Saber

Thorburn

Murphy

et al. 2018

Future Generation Computer Systems

View full text Add to dashboard Cite

Optimising the data centres of large IT organisations is complex as (i) they are composed of various hosting departments with their own preferences and (ii) reassignment solutions can be evaluated from various independent dimensions. But in reality, the problem is even more challenging as companies can now choose from a pool of cloud services to host some of their workloads. This hybrid search space seems intractable, as each workload placement decision (seen as running in a virtual machine on a server) is required to answer many questions: can we host it internally? In which hosting department? Are the capital allocators of this hosting department ok with this placement? How much does it save us and is it safe? Is there a better option in the Cloud? Etc. In this paper, we define the multi-objective VM reassignment problem for hybrid and decentralised data centres. We also propose H2-D2, a solution that uses a multi-layer architecture and a metaheuristic algorithm to suggest reassignment solutions that are evaluated by the various hosting departments (according to their preferences). We compare H2-D2 against state-of-the-art multi-objective algorithms and find that H2-D2 outperforms them both in terms of quantity (approx 30% more than the second-best algorithm on average) and quality of solutions (19% better than the second-best on average).

show abstract

Section: Multi-objective Vm Reassignmentmentioning

confidence: 99%

VM reassignment in hybrid clouds for large decentralised companies: A multi-objective challenge

Saber

Thorburn

Murphy

et al. 2018

Future Generation Computer Systems

View full text Add to dashboard Cite

show abstract

“…For example, the Pareto optimality of cloud bursting was proposed to analyse the optimisation of the makespan and the total cost based on fixed limited resources [7]. Furthermore, the particle swarm optimisation and the entropy optimisation were proposed to find the Pareto optimality solutions based on given fixed resources for minimising the processing time and the total cost [8, 9]. However, these studies assumed unchangeable resource pools to find the Pareto optimality solutions for the scheduling and load balancing in cloud environments.…”

Section: Literatures Reviewmentioning

confidence: 99%

“…According to the variation in user requests, the cloud service optimisation model optimises the computing resources. Furthermore, previous studies focused to find the Pareto optimality according to the limited resources [2–9], but the aspirational level has not been investigated. This study proposes a two‐stage cloud service optimisation model to analyse users’ requirements and existed computing resources for finding the Pareto optimality, and the aspirational level is found in accordance with the Pareto optimality for the lower cost and lower execution time.…”

Section: Introductionmentioning

confidence: 99%

Two‐stage cloud service optimisation model for cloud service middleware platform

Kung

Kuo

Chen

et al. 2018

J. eng.

View full text Add to dashboard Cite

“…Farahabady et al [ 13 ] explored how resources in the cross-layer-cloud environment should be used to run bag-of-tasks applications. Considering the deadline and the cost limitation, Zuo et al [ 14 ] put forward a multi-objective scheduling method oriented by tasks based on ant colony optimization in a cross-layer cloud environment, with the purpose to optimize the restricted pool of edge and public cloud computing resources. Yuan et al [ 15 ] proposed a temporal task scheduling algorithm that combines the energy price of edge cloud resources with the execution price of public cloud resources.…”

Section: Related Workmentioning

confidence: 99%

A General Cross-Layer Cloud Scheduling Framework for Multiple IoT Computer Tasks

Bao

Zhu

et al. 2018

Sensors

View full text Add to dashboard Cite

The diversity of IoT services and applications brings enormous challenges to improving the performance of multiple computer tasks’ scheduling in cross-layer cloud computing systems. Unfortunately, the commonly-employed frameworks fail to adapt to the new patterns on the cross-layer cloud. To solve this issue, we design a new computer task scheduling framework for multiple IoT services in cross-layer cloud computing systems. Specifically, we first analyze the features of the cross-layer cloud and computer tasks. Then, we design the scheduling framework based on the analysis and present detailed models to illustrate the procedures of using the framework. With the proposed framework, the IoT services deployed in cross-layer cloud computing systems can dynamically select suitable algorithms and use resources more effectively to finish computer tasks with different objectives. Finally, the algorithms are given based on the framework, and extensive experiments are also given to validate its effectiveness, as well as its superiority.

show abstract

A Multi-Objective Hybrid Cloud Resource Scheduling Method Based on Deadline and Cost Constraints

Cited by 47 publications

References 34 publications

VM reassignment in hybrid clouds for large decentralised companies: A multi-objective challenge

VM reassignment in hybrid clouds for large decentralised companies: A multi-objective challenge

Two‐stage cloud service optimisation model for cloud service middleware platform

A General Cross-Layer Cloud Scheduling Framework for Multiple IoT Computer Tasks

Contact Info

Product

Resources

About