Modeling Data Redundancy and Cost-Aware Task Allocation in MEC-Enabled Internet-of-Vehicles Applications

Nguyen, Tri; Nguyen, VanDung; Pham, Van-Nam; Huynh, Luan N. T.; Hossain, Delowar; Huh, Eui‐Nam

doi:10.1109/jiot.2020.3015534

Cited by 53 publications

(24 citation statements)

References 41 publications

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“…(3) ϕ en ⟵ ϕ od ; (4) for each N i ∈ N do (5) Estimate Δ st and Δ st using equations ( 30) and ( 13); (6) if Δ st × Δ st > 0 then (7) ϕ opti � ϕ st ; (8) Estimate Δ opti using (13) and with S expec � φ − CSPcost � φ − nϕ re ; ( 9) end (10) while Δ st × Δ ed > error do (11) ϕ mid � ((ϕ st + ϕ en )/2); (12) Estimate Δ mid using ( 13) and ( 31 increases; the computation size is fixed, which restricts the execution of the services. e increased server count demands to decrease the systems service execution speed.…”

Section: Experimental Results Analysismentioning

confidence: 99%

“…e USS (η sa i ) is defined as product of service price and quality of service (η sa i � η sq i (x, T) + η tc i (x, T)) with (10).…”

Section: User Service Satisfactionmentioning

confidence: 99%

“…us, the DAG theory's importance tremendously changed over the past decade since it influences the service execution time and resource usage. erefore, this issue is formulated as NPhard [9], and many heuristic approaches resolved the same issue through resource usage consolidation [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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DAWM: Cost-Aware Asset Claim Analysis Approach on Big Data Analytic Computation Model for Cloud Data Centre

Mekala

Patan

Islam

et al. 2021

Security and Communication Networks

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The heterogeneous resource-required application tasks increase the cloud service provider (CSP) energy cost and revenue by providing demand resources. Enhancing CSP profit and preserving energy cost is a challenging task. Most of the existing approaches consider task deadline violation rate rather than performance cost and server size ratio during profit estimation, which impacts CSP revenue and causes high service cost. To address this issue, we develop two algorithms for profit maximization and adequate service reliability. First, a belief propagation-influenced cost-aware asset scheduling approach is derived based on the data analytic weight measurement (DAWM) model for effective performance and server size optimization. Second, the multiobjective heuristic user service demand (MHUSD) approach is formulated based on the CPS profit estimation model and the user service demand (USD) model with dynamic acyclic graph (DAG) phenomena for adequate service reliability. The DAWM model classifies prominent servers to preserve the server resource usage and cost during an effective resource slicing process by considering each machine execution factor (remaining energy, energy and service cost, workload execution rate, service deadline violation rate, cloud server configuration (CSC), service requirement rate, and service level agreement violation (SLAV) penalty rate). The MHUSD algorithm measures the user demand service rate and cost based on the USD and CSP profit estimation models by considering service demand weight, tenant cost, and energy cost. The simulation results show that the proposed system has accomplished the average revenue gain of 35%, cost of 51%, and profit of 39% than the state-of-the-art approaches.

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Section: Experimental Results Analysismentioning

confidence: 99%

“…e USS (η sa i ) is defined as product of service price and quality of service (η sa i � η sq i (x, T) + η tc i (x, T)) with (10).…”

Section: User Service Satisfactionmentioning

confidence: 99%

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DAWM: Cost-Aware Asset Claim Analysis Approach on Big Data Analytic Computation Model for Cloud Data Centre

Mekala

Patan

Islam

et al. 2021

Security and Communication Networks

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show abstract

“…According to (3) and ( 6), P * s,n is calculated in (7), which represents the advantage of assigning the n th task to the s th sensor. The result matrix is shown in formula (8).…”

Section: System Modelmentioning

confidence: 99%

“…The traffic conditions of different road sections are also different, and the tasks assigned by the sensors are also different. To better detect the traffic condition in the case of limited traffic sensor resources, an efficient task allocation strategy can collect traffic to a greater extent information for monitoring traffic conditions more effectively [7][8][9]. The focus of the research is to develop new task allocation schemes to maximize the network throughput and revenue of intelligent transportation wireless sensor networks (ITWSNs).…”

Section: Introductionmentioning

confidence: 99%

An Improved Adaptive Clone Genetic Algorithm for Task Allocation Optimization in ITWSNs

Zha

Xiao

et al. 2021

Journal of Sensors

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Research on intelligent transportation wireless sensor networks (ITWSNs) plays a very important role in an intelligent transportation system. ITWSNs deploy high-yield and low-energy-consumption traffic remote sensing sensor nodes with complex traffic parameter coordination on both sides of the road and use the self-organizing capabilities of each node to automatically establish the entire network. In the large-scale self-organization process, the importance of tasks undertaken by each node is different. It is not difficult to prove that the task allocation of traffic remote sensing sensors is an NP-hard problem, and an efficient task allocation strategy is necessary for the ITWSNs. This paper proposes an improved adaptive clone genetic algorithm (IACGA) to solve the problem of task allocation in ITWSNs. The algorithm uses a clonal expansion operator to speed up the convergence rate and uses an adaptive operator to improve the global search capability. To verify the performance of the IACGA for task allocation optimization in ITWSNs, the algorithm is compared with the elite genetic algorithm (EGA), the simulated annealing (SA), and the shuffled frog leaping algorithm (SFLA). The simulation results show that the execution performance of the IACGA is higher than EGA, SA, and SFLA. Moreover, the convergence speed of the IACGA is faster. In addition, the revenue of ITWSNs using IACGA is higher than those of EGA, SA, and SFLA. Therefore, the proposed algorithm can effectively improve the revenue of the entire ITWSN system.

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Data interplay: A model to optimize data usage in the Internet of Things

et al. 2023

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The Internet of Things (IoT) has been playing an important role in the technology scenario due to its high potential and impact on different society segments. Estimates suggest a trend for an increase in the number of IoT devices connected to the Internet for the next few years. Hence, the volume of data produced by IoT devices will follow this growth perspective, and there will be a demand for systems that can process, store, and promote access to large amounts of data. The data collected from sensors in typical IoT systems is stored and processed in cloud servers. However, some IoT solutions use edge devices to perform specific actions, such as processing, storage, and access, using only local infrastructure for low latency requirements. Fog computing has been used to improve IoT solutions and to transfer some of the complexity from the cloud to the edge of the network, that is, closer to devices, applications and users, working as a kind of “local and private cloud.” The cooperation of devices and applications between edge and cloud creates a need for an interplay to enable data flow among the layers of IoT systems deployed on edge and in the cloud. Thus, IoT data systems should support the data life cycle through its collection, analysis, and use. Performance efficiency is a quality factor of systems and software engineering, which measures “performance relative to the number of resources used under stated conditions.” In particular, in IoT systems that involve a large volume of data, the performance efficiency of data interplay is a relevant requirement. This work proposes a data interplay model of IoT to define and deploy the IoT data life cycle in the collection, analytics, and data use stages. This data interplay proposal aims to improve performance efficiency in IoT data life cycle operations: collection, analytics, and use among devices and applications in edge and cloud infrastructures.

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Modeling Data Redundancy and Cost-Aware Task Allocation in MEC-Enabled Internet-of-Vehicles Applications

Cited by 53 publications

References 41 publications

DAWM: Cost-Aware Asset Claim Analysis Approach on Big Data Analytic Computation Model for Cloud Data Centre

DAWM: Cost-Aware Asset Claim Analysis Approach on Big Data Analytic Computation Model for Cloud Data Centre

An Improved Adaptive Clone Genetic Algorithm for Task Allocation Optimization in ITWSNs

Data interplay: A model to optimize data usage in the Internet of Things

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