An efficient learning automata based task offloading in mobile cloud computing environments

Krishna, P. Venkata; Misra, Sudip; Saritha, V.; Raju, Dasari Naga; Obaidat, Mohammad S.

doi:10.1109/icc.2017.7997139

Cited by 14 publications

(2 citation statements)

References 18 publications

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“…In the scheme, we distribute the responsibilities of distance estimation and data collection to edge and users mobile phone respectively. The distribution of responsibility overcomes the high latency of cloud computing and low computational power of mobile phone while reducing the probability of private information leakage [19] [20].…”

Section: Introductionmentioning

confidence: 99%

DISTERNING: Distance Estimation Using Machine Learning Approach for COVID-19 Contact Tracing and Beyond

Zhang

Wang

et al. 2022

IEEE J. Select. Areas Commun.

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Since the coronavirus disease 19 (COVID-19) outbreak, the epidemiological analysis has raised a strong requirement for more effective and accurate contact tracing solution. However, the existing contact tracing solutions either lacked the evaluation of tracing proximity or the features used for the tracing proximity evaluation were susceptible to certain negative environmental factors (e.g., body shielding). In this article, we propose a novel distance estimation algorithm based on machine learning for contact tracing: DISTERNING, where we leverage machine learning algorithms including Learning Vector Quantization, Regression, and Deep Feed-forward (DFF) Neural Network, data processing methods, and digital filters to process the Bluetooth signal information collected by the mobile phone for contact distance estimation. A contact tracing scheme based on edge computing is also proposed for algorithm deployment due to the requirements of the computational power. Compared with the existing contact tracing solutions, our algorithm considers the factors that have significant negative influence on the Bluetooth signal for distance estimation in reality. The evaluation results show that when the collected Bluetooth signal is influenced by real-world negative environmental factors, employing our proposed algorithm DISTERNING can keep the accuracy of the estimated distance reliable. The output distance can be combined with some medical models to conduct infection risk assessments.

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Section: Introductionmentioning

confidence: 99%

DISTERNING: Distance Estimation Using Machine Learning Approach for COVID-19 Contact Tracing and Beyond

Zhang

Wang

et al. 2022

IEEE J. Select. Areas Commun.

View full text Add to dashboard Cite

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“…However, allocating more tasks to single resource leads to performance degrades and it takes more time to execute the tasks which leads to user dissatisfaction. Resource scheduling mechanism allocates the tasks to suitable resources, and hence the applications can effectively utilize the resources which ultimately lead to scaling advantage [10].…”

Section: Introductionmentioning

confidence: 99%

Research of Task Management and Resource Allocation in Cloud Computing

2019

IJITEE

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This paper presents the task management framework in which tasks are identified, analysed and grouped based on the user QoS requirements. As a next step, cost and time based scheduling algorithm is employed to select the suitable pair of resources and tasks. The performance of the proposed algorithm is tested with execution time and cost allocated to the resources. Two existing algorithms are considered along with proposed algorithm for performance comparison. The results proved the efficiency of the proposed algorithm.

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Energy‐aware dynamic task offloading and collective task execution in mobile cloud computing

Saritha

Dasari²

2019

Int J Communication

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SummaryThere is a good opportunity for enlightening the services of the mobile devices by introducing computational offloading using cloud technology. Offloading is a process for managing the complexity of the mobile environment by migrating computational load to the cloud. The mobile devices oblige the quick response for the offloading requests; it is dependent on network connectivity. The cloud services take long set‐up time irrespective of network connectivity. In this paper, new system architecture for the dynamic task offloading in the mobile cloud environment is proposed. The architecture includes the offloading algorithm that concentrates on energy consumption of the tasks both in the local and remote environment. The proposed algorithm formulates a collective task execution model for minimizing the energy consumption. The architecture concentrates on the network model by considering the task completion time in three different network scenarios. The experimental results show the efficiency of the suggested architecture in reducing the energy consumption and completion time of the tasks.

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An efficient learning automata based task offloading in mobile cloud computing environments

Cited by 14 publications

References 18 publications

DISTERNING: Distance Estimation Using Machine Learning Approach for COVID-19 Contact Tracing and Beyond

DISTERNING: Distance Estimation Using Machine Learning Approach for COVID-19 Contact Tracing and Beyond

Research of Task Management and Resource Allocation in Cloud Computing

Energy‐aware dynamic task offloading and collective task execution in mobile cloud computing

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