Energy-Effective Service-Oriented Cloud Resource Allocation Model Based on Workload Prediction

Ahammad, Tanvir; Acharjee, Uzzal Kumar; Hasan, Md. Mahmudul

doi:10.1109/iccitechn.2018.8631953

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…To create training and testing dataset, Deep-Learning4j library was used for running neural network in adaptive selector, and then Xavier weight initialization and ReLu activation function were applied with one hidden layer of 10 nodes. Another model [82] establishes multilayer perceptron (MLP) neural network that is known by his back-propagation training procedures by calculating error and adjusting parameters of prior layers. This model predicts workloads of a certain node, at a certain interval of time intervals for training based on the previous selected workload time series.…”

Section: Supervised and Unsupervised Machine Learning Techniquesmentioning

confidence: 99%

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

et al. 2020

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Given the perpetual surging of cloud services' requests, energy consumption of cloud data centers with their related CO 2 emissions still represents major issues. Efficient use of cloud's resources becomes then the driven force ensuring both, the satisfaction of service-level agreements and the sobriety of cloud's energy consumption. This paper purports to survey for the first time, a comprehensive literature of some actual challenges facing various cloud's resources utilization scheduling approaches dealing with energy conservation. Indeed, cloud resources involve not only computing servers, but also a wide set of intra-and inter-cloud network's resources to be considered. These resources are mainly provisioned through two wellknown technologies: virtualization and/or containerization. As existing studies in the area of cloud resources provisioning are generally categorized into different groups, this research depicts a complete taxonomy of energy-efficient cloud resources scheduling. In this same perspective, we survey some recent efforts made in energy-efficient virtual and containerized resources, by emphasizing first the most used reactive then proactive techniques for managing the whole cloud resources energy efficiency scheduling.

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Section: Supervised and Unsupervised Machine Learning Techniquesmentioning

confidence: 99%

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

et al. 2020

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“…In [7] authors propose an energy-effective prediction algorithm for the cloud environment in order to identify future requirements of cloud resources based on Multilayer Perceptron (MLP) model. In [8] authors propose Wavelet Support Vector Machine (WSVM) algorithm for the prediction of data centers behaviour.…”

Section: Related Workmentioning

confidence: 99%

Proactive Network Slices Management Algorithm Based on Fuzzy Logic System and Support Vector Regression Model

Kammoun

Tabbane

Diaz

et al. 2019

Lecture Notes in Networks and Systems

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Software Defined Networks (SDN ), Network Function Virtualization (NFV ) and Network Slicing are the key technologies for future network implementation. Their aggregation allows more flexibility for the networks by provisioning network slices according to specific use cases requirements. However, in order to ensure these requirements during all the slice execution time, a management module has to be implemented. In this paper, we present our considered architecture for the management of network slices. We detail especially the network controller components. Moreover, we propose a proactive dynamic approach which forecasts the future workload behavior of network slices. Based on the actual and predicted load state, the management algorithm, which is based on a fuzzy logic system (FLS), will determine the adequate management decision for the deployed slices. Based on real network traces, an evaluation of the efficiency of our algorithm is presented.

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PSO-GA-Based Resource Allocation Strategy for Cloud-Based Software Services With Workload-Time Windows

et al. 2020

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Cloud-based software services necessitate adaptive resource allocation with the promise of dynamic resource adjustment for guaranteeing the Quality-of-Service (QoS) and reducing resource costs. However, it is challenging to achieve adaptive resource allocation for software services in complex cloud environments with dynamic workloads. To address this essential problem, we propose an adaptive resource allocation strategy for cloud-based software services with workload-time windows. Based on the QoS prediction, the proposed strategy first brings the current and future workloads into the process of calculating resource allocation plans. Next, the particle swarm optimization and genetic algorithm (PSO-GA) is proposed to make runtime decisions for exploring the objective resource allocation plan. Using the RUBiS benchmark, the extensive simulation experiments are conducted to validate the effectiveness of the proposed strategy on improving the performance of resource allocation for cloud-based software services. The simulation results show that the proposed strategy can obtain a better trade-off between the QoS and resource costs than two classic resource allocation methods. INDEX TERMS Cloud-based software services, resource allocation, QoS prediction, workload-time windows, particle swarm optimization, genetic algorithm.

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Energy-Effective Service-Oriented Cloud Resource Allocation Model Based on Workload Prediction

Cited by 3 publications

References 13 publications

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

Proactive Network Slices Management Algorithm Based on Fuzzy Logic System and Support Vector Regression Model

PSO-GA-Based Resource Allocation Strategy for Cloud-Based Software Services With Workload-Time Windows

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