Self-adaptive resource allocation for cloud-based software services based on iterative QoS prediction model

Chen, Xing; Wang, Haijiang; Ma, Yun; Zheng, Xianghan; Guo, Longkun

doi:10.1016/j.future.2019.12.005

Cited by 107 publications

(44 citation statements)

References 18 publications

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“…Bandwidth is the amount of information that can be transmitted in a second by means of communication. It depends on the bit handling capacity, the speed of information handling by electronic circuits [ 25 ].…”

Section: Analysis and Resultsmentioning

confidence: 99%

RETRACTED CHAPTER: Design of a Network with VANET Sporadic Cloud Computing Applied to Traffic Accident Prevention

Viloria

Lezama

Varela

et al. 2020

Communications in Computer and Information Science

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The study analyzes the bandwidth available in a segment of route in the VANET network, since this value directly affects sporadic cloud computing. For this purpose, the bandwidth was tested on a highly complex urban scenario, where a number of mobile nodes were used with random conditions both in mobility and in resources of transmission. The results of the tests show that the stability of the bandwidth available in each region is proportional to the number of real mobile nodes in the region. However, a considerable bandwidth is also reached with a smaller number of mobile nodes, but there is no stability in the region, thus causing the network to collapse. The VANET network simulation tool was NS-3, since it is currently one of the most commonly used free software that allows configure the simulation parameters in a vehicular environment. The urban simulation scenario is the historic center of the City of Bogotá, Colombia, which was created with SUMO for obtaining the mobility traces.

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Section: Analysis and Resultsmentioning

confidence: 99%

RETRACTED CHAPTER: Design of a Network with VANET Sporadic Cloud Computing Applied to Traffic Accident Prevention

Viloria

Lezama

Varela

et al. 2020

Communications in Computer and Information Science

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“…Although this model can make on-demand resource adjustments for the applications under changeable workloads, it did not well consider the relationship between the QoS and VM rental costs nor take into account future workload changes with complex fluctuations. Besides, Chen et al [17] proposed an adaptive resource management framework for cloud-based software services. This framework first trained the QoS prediction model by using historical data.…”

Section: A Literature Reviewmentioning

confidence: 99%

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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“…The quality offered by cloud-based software service differs with time due to the variations in its runtime environment. Generally, the environmental variations can be split into extrinsic or intrinsic variations, as per the factors that initiate [23]. The external factors primarily indicates the workloads (L), the changes of which are provided in this technical work, and the internal ones to the allocated resources (VM).…”

Section: System Modelmentioning

confidence: 99%

“…2 architecture of the novel self-adaptive resource allocation technique. The core concept here is to integrate a method known as feedback control loop used in control theory with the machine learning technique, and later facilitating this algorithm to employ feedbacks coordinating with the training data and hence mitigating the burden of learning from inadequate previous events data [23] [29]. Consequently, this yields a sufficiently reasonable QoS and helps in the accuracy improvement of machine learning techniques.…”

Section: Adaptive Resource Allocation Technique Based On Feedback Loopmentioning

confidence: 99%

An Intelligent Machine Learning and Self Adaptive Resource Allocation Framework for Cloud Computing Environment

Hasan¹,

Balamurugan²,

Almamun³

et al. 2020

EAI Endorsed Transactions on Cloud Systems

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Resource allocation is one of the major concern in cloud computing model. When several problems exists in rendering a useful resource allocator. In this research , a self adaptive resource allocation frame work based on machine learning is proposed for modelling and analysing the problem of multi-dimensional cloud resource. This novel self-adaptive resource allocation architecture consists of three stages, QoS prediction model, Improved Bat Algorithm (IBA) and Energy Efficient Model (EEM). The first one, the QoS prediction model, which depends on the same scale of system's past events data, can attain a comparable accuracy with regard to QoS prediction. Secondly, an Energy Efficient Model, which is based on Modified Clonal Selection Algorithm (MCSA) is introduced for minimizing the energy depletion. Thirdly, a runtime decision-making algorithm that depends on improved bat algorithm can rapidly decide on a suitable function for resource allocation

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Self-adaptive resource allocation for cloud-based software services based on iterative QoS prediction model

Cited by 107 publications

References 18 publications

RETRACTED CHAPTER: Design of a Network with VANET Sporadic Cloud Computing Applied to Traffic Accident Prevention

RETRACTED CHAPTER: Design of a Network with VANET Sporadic Cloud Computing Applied to Traffic Accident Prevention

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

An Intelligent Machine Learning and Self Adaptive Resource Allocation Framework for Cloud Computing Environment

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