Deadline-guaranteed scheduling algorithm with improved resource utilization for cloud computing

Shin, Saemi; Kim, Yena; Lee, Sukyoung

doi:10.1109/ccnc.2015.7158082

Cited by 26 publications

(4 citation statements)

References 11 publications

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“…Itindicatestheperiodutilizedtofinishthedemandofusers,anditisasignificantparameterinSLA. Itmainlyreliesonrequestkindandresourcesinwhichtherequestisperformed.Moreover,execution timeisincreased,whiletheresourcesarenotsuitable (Shin,et al,2015): (1)…”

Section: Execution Timementioning

confidence: 99%

Taylor CFRO-Based Deep Learning Model for Service-Level Agreement-Aware VM Migration and Workload Prediction-Enabled Power Model in Cloud Computing

Pushpalatha

Ramesh²

2023

International Journal of Swarm Intelligence Research

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In this research, Taylor Chaotic Fruitfly Rider Optimization (TaylorCFRO)-based Deep Belief Network (DBN) approach is designed for workload prediction and Service level agreement (SLA)-aware Virtual Machine (VM) migration in the cloud. In this model, the round robin technique is applied for the task scheduling process. The Chaotic Fruitfly Rider Optimization driven Neural Network (CFRideNN) is also introduced in order to perform workload prediction. The DBN classifier is employed to detect SLA violations, and the DBN is trained using devised optimization model, named the TaylorCFRO technique. Accordingly, the introduced TaylorCFRO approach is newly designed by incorporating the Taylor series, Chaotic Fruitfly Optimization Algorithm (CFOA), and Rider Optimization Algorithm (ROA). The developed TaylorCFRO-based DBN scheme outperformed other workload and SLA Violation (SLAV) detection methods with violation detection rate of 0.8048, power consumption of 0.0132, SLAV of 0.0215, and load of 0.0033.

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Section: Execution Timementioning

confidence: 99%

Taylor CFRO-Based Deep Learning Model for Service-Level Agreement-Aware VM Migration and Workload Prediction-Enabled Power Model in Cloud Computing

Pushpalatha

Ramesh²

2023

International Journal of Swarm Intelligence Research

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“…Early works on cloud resource allocation focus on improving resource utilization and saving energy in a single cloud [16,17] [22].…”

Section: Related Workmentioning

confidence: 99%

Double auction and profit maximization mechanism for jobs with heterogeneous durations in cloud federations

Liang

Ling

et al. 2021

J Cloud Comp

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By sharing resources with each other, different cloud providers in a cloud federation can exploit their diversity in resource configuration and operational cost so as to improve service performance. They should consider the strategy of resource pricing, job scheduling and server provisioning altogether to maximize their own interests. On the other hand, they need to efficiently trade the resources with a suitable mechanism, typically auction, so as to guarantee the participants’ profits. Nevertheless, in consideration of the heterogeneous execution times of jobs, both the pricing strategy and trading mechanism should be delicately designed, which is obviously a challenging task. In this paper, we firstly propose a truthful, individual-rational and ex-post budget-balanced auction mechanism for selecting pairs of buyer and seller winners to trade virtual machines for different durations. Then, to maximize the individual profits, we propose a dynamic resource bidding scheme and a job scheduling strategy based on our importance model of jobs with heterogeneous execution times and resource requirements. The simulation results show that, compared with existing ones, our design can better handle varieties of both execution time and resource requirement and make the participants obtain more individual profits.

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“…Some other approaches focused on the SLA requirement of jobs, e.g., delay constraint [11] [12] [13]. A conservative backfilling algorithm was modified in [11] by utilizing the earliest deadline first (EDF) algorithm and the largest weight first (LWF) algorithm to guarantee the deadline while improving the resource utilization. However it did not consider the various types of VMs.…”

Section: Related Workmentioning

confidence: 99%

Profit-Maximizing Virtual Machine Provisioning Based on Workload Prediction in Computing Cloud

2015

KSII TIIS

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Cloud providers now face the problem of estimating the amount of computing resources required to satisfy a future workload. In this paper, a virtual machine provisioning (VMP) mechanism is designed to adapt workload fluctuation. The arrival rate of forthcoming jobs is predicted for acquiring the proper service rate by adopting an exponential smoothing (ES) method. The proper service rate is estimated to guarantee the service level agreement (SLA) constraints by using a diffusion approximation statistical model. The VMP problem is formulated as a facility location problem. Furthermore, it is characterized as the maximization of submodular function subject to the matroid constraints. A greedy-based VMP algorithm is designed to obtain the optimal virtual machine provision pattern. Simulation results illustrate that the proposed mechanism could increase the average profit efficiently without incurring significant quality of service (QoS) violations.

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Deadline-guaranteed scheduling algorithm with improved resource utilization for cloud computing

Cited by 26 publications

References 11 publications

Taylor CFRO-Based Deep Learning Model for Service-Level Agreement-Aware VM Migration and Workload Prediction-Enabled Power Model in Cloud Computing

Taylor CFRO-Based Deep Learning Model for Service-Level Agreement-Aware VM Migration and Workload Prediction-Enabled Power Model in Cloud Computing

Double auction and profit maximization mechanism for jobs with heterogeneous durations in cloud federations

Profit-Maximizing Virtual Machine Provisioning Based on Workload Prediction in Computing Cloud

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