Mobility and Battery Power Prediction Based Job Scheduling in Mobile Grid Environment

Vaithiya, S. Stephen; Bhanu, S. Mary Saira

doi:10.1007/978-3-642-24037-9_31

Cited by 9 publications

(5 citation statements)

References 6 publications

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“…Vaithiya et al [10] attempted to predict mobile resources' availability for task scheduling algorithms in a mobile grid. To address the node mobility issue in an ad-hoc mobile grid, Selvi et al [11] profiled the mobile users' regular movements over time.…”

Section: Related Workmentioning

confidence: 99%

Predicting Resource Availability in Local Mobile Crowd Computing Using Convolutional GRU

Pramanik¹,

Sinhababu²,

Nayyar³

et al. 2022

Computers, Materials &Amp; Continua

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In mobile crowd computing (MCC), people's smart mobile devices (SMDs) are utilized as computing resources. Considering the ever-growing computing capabilities of today's SMDs, a collection of them can offer significantly high-performance computing services. In a local MCC, the SMDs are typically connected to a local Wi-Fi network. Organizations and institutions can leverage the SMDs available within the campus to form local MCCs to cater to their computing needs without any financial and operational burden. Though it offers an economical and sustainable computing solution, users' mobility poses a serious issue in the QoS of MCC. To address this, before submitting a job to an SMD, we suggest estimating that particular SMD's availability in the network until the job is finished. For this, we propose a convolutional GRU-based prediction model to assess how long an SMD is likely to be available in the network from any given point of time. For experimental purposes, we collected real users' mobility data (in-time and outtime) with respect to a Wi-Fi access point. To build the prediction model, we presented a novel feature extraction method to be applied to the time-series data. The experimental results prove that the proposed convolutional GRU model outperforms the conventional GRU model.

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Section: Related Workmentioning

confidence: 99%

Predicting Resource Availability in Local Mobile Crowd Computing Using Convolutional GRU

Pramanik¹,

Sinhababu²,

Nayyar³

et al. 2022

Computers, Materials &Amp; Continua

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show abstract

“…Vaithiya and Bhanu [40] proposed a task scheduling algorithm for the mobile grid, predicting the dynamic availability of mobile resources. Selvi et al [41] dealt with the issue of node mobility in an ad-hoc mobile grid by profiling the regular movements of a user over time.…”

Section: A Resource Availability Prediction In Mobile Grid/cloud Computingmentioning

confidence: 99%

Deep Learning Based Resource Availability Prediction for Local Mobile Crowd Computing

et al. 2021

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Mobile crowd computing (MCC) that utilizes public-owned (crowd's) smart mobile devices (SMDs) collectively can give adequate computing power without any additional financial and ecological cost. However, the major challenge is to cope with the mobility (or availability) issue of SMDs. User's unpredicted mobility makes the SMDs really unstable resources. Selecting such erratic resources for job schedule would result in frequent job offloading and, in the worst case, job loss, which would affect the overall performance and the quality of service of MCC. In a Local MCC, generally, a set of users are available for a certain period regularly. Based on this information, the chances of a user being available for a certain duration from a given point of time can be predicted. In this paper, we provide an effective model to predict the availability of the users (i.e., their SMDs) in such an MCC environment. We argue that before submitting a job to an SMD, the stability of it is to be assessed for the duration of execution of the job to be assigned. If the predicted availability time is greater than the job size, then only the job should be assigned to the SMD. An accurate prediction will minimize the unnecessary job offloading or job loss due to the early departure of the designated SMD. We propose an advanced convolutional feature extraction mechanism that is applied to LSTM and GRU-based time-series prediction models for predicting SMD availability. To collect user mobility data, we considered a research lab scenario, where real mobility traces were recorded with respect to a Wi-Fi access point. We compared the prediction performances of convolutional LSTM and GRU with the basic LSTM and GRU and ARIMA in terms of MAE, RMSE, R 2 , accuracy, and perplexity. In all the measurements, the proposed convolutional LSTM exhibits considerably better prediction performance.

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“…Farooq et al 32 proposed a generic mobility model for predicting best resource that remains in a specific domain for particular time duration. Vaithiya et al 33 proposed a task-scheduling algorithm based on dynamic availability of mobile resources like BP and their physical location. Mobility is estimated by its movement type, ie, movement circle or movement track.…”

Section: Emergency Servicesmentioning

confidence: 99%

“…This is aided by mobility that affects the nodes computing power. In this chain, several studies17,32,33 consider various mobility models or mobility as parameter for task scheduling. Manvi and Birje41 presented a performance evaluation of wireless grid in which the mobility is estimated by using normal walk mobility model, daily motion of user with the direction of motion being mostly symmetric.…”

mentioning

confidence: 99%

A quantum‐inspired binary gravitational search algorithm–based job‐scheduling model for mobile computational grid

Singh

Raza

2017

Concurrency and Computation

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Owing to the advancements in low-power consumption processors and high-power storage in a small-sized battery, the cost of handheld mobile devices, eg, mobiles, tabs, or personal digital assistants, have reduced to a great extent. This has enabled people to have at least 1 smartphone in general with this number increasing exponentially. However, the increasing use of these mobile devices results in an equal increase in the underused processing capacity of these devices too. This encourages the research aiming to use this processing power by forming a mobile computational grid. Because of the inherent limitations of bandwidth, battery, and computational power, job scheduling on these devices demands an efficient scheduling approach to harness the true potential of the grid. The problem becomes even more challenging considering the dynamic nature of these mobile devices. Job scheduling being nondeterministic polynomial time-complete allows the use of evolutionary approaches by exploring and exploiting the search space efficiently. The exploration gets boosted even more with the use of quantum-computing concepts. This work proposes a quantum-inspired Newtonian approach of attraction based on gravitational search algorithm for scheduling the jobs on mobile computational grid. Simulation study has been performed to evaluate the performance of the model over various dimensions. A comparative study has been performed with quantum-genetic algorithm. Simulation result establishes the effectiveness of model under various test conditions. KEYWORDS genetic algorithm (GA), gravitational search algorithm (GSA), mobile grid computing (MGC), quantum computing (QC), quantum-inspired genetic algorithm (QGA), turnaround time (TAT)

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Mobility and Battery Power Prediction Based Job Scheduling in Mobile Grid Environment

Cited by 9 publications

References 6 publications

Predicting Resource Availability in Local Mobile Crowd Computing Using Convolutional GRU

Predicting Resource Availability in Local Mobile Crowd Computing Using Convolutional GRU

Deep Learning Based Resource Availability Prediction for Local Mobile Crowd Computing

A quantum‐inspired binary gravitational search algorithm–based job‐scheduling model for mobile computational grid

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