Service deployment and scheduling for improving performance of composite cloud services

Lu, Yi-Chang; Lin, Ching‐Fuh; Lai, Kuan‐Chou; Tsai, Meng-Han; Wu, Ying-Jhih; Chang, Hsi-Ya; Huang, Kuo-Chan

doi:10.1016/j.compeleceng.2018.07.018

Cited by 8 publications

(2 citation statements)

References 8 publications

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“…Finally, a good service-deployment effect is achieved from the simulation result. Reference [18] considers the problem of sharing common infrastructure component services in service deployment, and proposes a load-aware service-deployment method in a dynamic cloud environment with the goal of minimizing the cost of inter-service communication between related component services and maximizing execution parallelism. Reference [19] analyzed the problem of coordinating the deployment of HA(High Availablility) services in multiple regions, and proposed a multi-regional scheduling algorithm based on matching, which improved the service availability from ''three nine'' to ''six nine'' with a 15% cost increase.…”

Section: Related Workmentioning

confidence: 99%

Service Deployment of C4ISR Based on Genetic Simulated Annealing Algorithm

et al. 2020

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This study considers a service-deployment problem of C4ISR(Command, Control, Communication, Computer, Intelligence, Surveillance and Reconnaissance) system under service-oriented architecture. In order to make C4ISR sufficiently agile to meet the challenges brought by diverse combat missions and highly adversarial combat processes, this paper adopts a distributed deployment method to deploy the service processes that constitute C4ISR in multiple data centers. Considering the service information requirements and the location of specific data, a deployment constraint for a service is proposed. Further, in order to improve resource utilization and reduce communication overhead, this paper establishes a distributed service deployment model facing the characteristics of C4ISR. For the large search space and complex constraints of the optimal problem, a multi-objective genetic simulated annealing algorithm is proposed by designing the neighboring individual generation rules and combining the simulated annealing mechanism and the genetic algorithm to enhance the ability of both local search and global search. The simulation results show that this method can produce a high-quality C4ISR service-deployment scheme effectively.

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Section: Related Workmentioning

confidence: 99%

Service Deployment of C4ISR Based on Genetic Simulated Annealing Algorithm

et al. 2020

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“…Several works have been published in the literature to solve workflow scheduling on cloud infrastructure with total execution time reduction, energy efficiency, reliability maximization viewpoints, but less paid attention to monetary costs which may have a big conflict with users' monetary budget. For instance, a load-aware heuristic strategy for dynamic workload and service scheduling in a cloud environment has been proposed by Lu et al [9]. The main objective of the proposed algorithm was to improve execution performance; then, it has been validated by series of experiments.…”

Section: Introductionmentioning

confidence: 99%

Bi-objective scheduling algorithm for scientific workflows on cloud computing platform with makespan and monetary cost minimization approach

Shirvani

Talouki

2021

Complex Intell. Syst.

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Scheduling of scientific workflows on hybrid cloud architecture, which contains private and public clouds, is a challenging task because schedulers should be aware of task inter-dependencies, underlying heterogeneity, cost diversity, and virtual machine (VM) variable configurations during the scheduling process. On the one side, reaching a minimum total execution time or makespan is a favorable issue for users whereas the cost of utilizing quicker VMs may lead to conflict with their budget on the other side. Existing works in the literature scarcely consider VM’s monetary cost in the scheduling process but mainly focus on makespan. Therefore, in this paper, the problem of scientific workflow scheduling running on hybrid cloud architecture is formulated to a bi-objective optimization problem with makespan and monetary cost minimization viewpoint. To address this combinatorial discrete problem, this paper presents a hybrid bi-objective optimization based on simulated annealing and task duplication algorithms (BOSA-TDA) that exploits two important heuristics heterogeneous earliest finish time (HEFT) and duplication techniques to improve canonical SA. The extensive simulation results reported of running different well-known scientific workflows such as LIGO, SIPHT, Cybershake, Montage, and Epigenomics demonstrate that proposed BOSA-TDA has the amount of 12.5%, 14.5%, 17%, 13.5%, and 18.5% average improvement against other existing approaches in terms of makespan, monetary cost, speed up, SLR, and efficiency metrics, respectively.

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Survey on Optimization of Resource Scheduling in Cloud Platforms

Kasireddi

Anitha

2022

Applications of Computational Methods in Manufacturing and Product Design

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Service deployment and scheduling for improving performance of composite cloud services

Cited by 8 publications

References 8 publications

Service Deployment of C4ISR Based on Genetic Simulated Annealing Algorithm

Service Deployment of C4ISR Based on Genetic Simulated Annealing Algorithm

Bi-objective scheduling algorithm for scientific workflows on cloud computing platform with makespan and monetary cost minimization approach

Survey on Optimization of Resource Scheduling in Cloud Platforms

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