Cost-Effective and Latency-Minimized Data Placement Strategy for Spatial Crowdsourcing in Multi-Cloud Environment

Wang, Pengwei; Chen, Zhe; Zhou, MengChu; Zhang, Zhaohui; Abusorrah, Abdullah; Ammari, Ahmed Chiheb

doi:10.1109/tcc.2021.3119862

Cited by 12 publications

(4 citation statements)

References 40 publications

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“…A genetic algorithm is used to further optimize the results. The proposed method has been verified through experiments and has been shown to significantly reduce system cost and latency compared to existing algorithms, with a cost reduction of up to 150 times and a latency reduction of up to twice 36 . In Cloud computing, data replication helps improve data accessibility.…”

Section: Related Workmentioning

confidence: 93%

Intelligent Cloud Selection for Communication between OpenStack-based Multiple Clouds at TEIN using HTTP Tunneling and Observer Pattern API

Sarwar

Alam

Muhammad

et al. 2023

Preprint

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This paper presents an intelligent communication mechanism and sharing resources between a Service Requesting Cloud Node (SRN) and a Service Providing Cloud Node (SPN). The proposed mechanism selects the most optimal SPN among all SPNs, deployed at TEIN (Trans Eurasia Information Network) in South Korea, Pakistan, and Malaysia. The proposed mechanism uses a Machine Learning model for selecting the most optimal SPN and HTTP tunneling with Observer Pattern API for communication. The data obtained using monitoring tools is used to train the machine learning models for selecting the most optimal node based on minimum Round-Trip Time (RTT). To this end, we utilize Extra Trees (ET), Ridge Classifier, and K-Nearest Neighbor (KNN) machine learning algorithms. The ET algorithm outperforms the other utilized algorithm with 94% accuracy. HTTP tunneling and Observer Pattern API can be used for communication between two Clouds where the Clouds support a different version of L2 tunnels, such as VXLAN or GENEVE. Access from an instance of a Cloud to the physical machine of another Cloud is provided by an HTTP tunnel using an HTTP bridge. The Observer Pattern of software engineering is implemented as an API on the target Cloud SPN to respond to the request on the HTTP bridge from the source Cloud. This way, for a specific service request, an optimal SPN is selected by ML, and communication is then established by HTTP tunneling and Observer Pattern API

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Section: Related Workmentioning

confidence: 93%

Intelligent Cloud Selection for Communication between OpenStack-based Multiple Clouds at TEIN using HTTP Tunneling and Observer Pattern API

Sarwar

Alam

Muhammad

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Though the amount is still not large, there exist some studies considering data privacy issues in data placement problems. Pengwei Wang et al 36 studied the problem of data placement in a geo‐distributed context, which mainly considers the data privacy issues brought about by cloud computing, aiming to achieve effectiveness and minimize access delays. In, 37 Danish, Syed Muhammad et al proposed a middleware based on neural networks to intelligently select storage locations for IoT programs.…”

Section: Related Workmentioning

confidence: 99%

An effective data placement strategy for IIoT applications

Shen,

Liu,

Dou

2023

Concurrency and Computation

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SummaryWith the rapid development of the Internet of things (IoT) and mobile communication technology, the amount of data related to industrial Internet of things (IIoT) applications has shown a trend of explosive growth, and hence edge‐cloud collaborative environment becomes one of the most popular paradigms to place the IIoT applications data. However, edge servers are often heterogeneous and capacity limited while having lower access delay, so there is a contradiction between capacity and latency while using edge storage. Additionally, when IIoT applications deployed crossing edge regions, the impact of data replication and data privacy should not be ignored. These factors often pose challenges to proposing an effective data placement strategy to take full advantage of edge storage. To address these challenges, an effective data placement strategy for IIoT applications is designed in this article. We first analyze the data access time and data placement cost in an edge‐cloud collaborative environment, with the consideration of data replication and data privacy. Then, we design a data placement strategy based on ‐constraint and Lagrangian relaxation, to reduce the data access time and meanwhile limit the data placement cost to an ideal level. As a result, our proposed data placement strategy can effectively reduce data access time and control data placement costs. Simulation and comparative analysis results have demonstrated the validity of our proposed strategy.

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“…Elgendy et al [8] proposed a Mobile Edge Computing solution for Unmanned Aerial Vehicles (UAVs), which uses a multi-layer resource allocation scheme, a load balancing algorithm, and integer programming to achieve cost reduction. In the context of multi-cloud spatial crowdsourcing data placement, Wang et al [9] introduced a data placement strategy with a focus on cost-effectiveness and minimal latency. Concurrently, they incorporated the interval pricing strategy and utilized a clustering algorithm to analyze the geographic distribution patterns of data centers.…”

Section: Related Workmentioning

confidence: 99%

Traffic-Aware Optimization of Task Offloading and Content Caching in the Internet of Vehicles

Wang,

Qiao

et al. 2023

Applied Sciences

Self Cite

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Emerging in-vehicle applications seek to improve travel experiences, but the rising number of vehicles results in more computational tasks and redundant content requests, leading to resource waste. Efficient compute offloading and content caching strategies are crucial for the Internet of Vehicles (IoV) to optimize performance in time latency and energy consumption. This paper proposes a joint task offloading and content caching optimization method based on forecasting traffic streams, called TOCC. First, temporal and spatial correlations are extracted from the preprocessed dataset using the Forecasting Open Source Tool (FOST) and integrated to predict the traffic stream to obtain the number of tasks in the region at the next moment. To obtain a suitable joint optimization strategy for task offloading and content caching, the multi-objective problem of minimizing delay and energy consumption is decomposed into multiple single-objective problems using an improved Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) via the Tchebycheff weight aggregation method, and a set of Pareto-optimal solutions is obtained. Finally, the experimental results verify the effectiveness of the TOCC strategy. Compared with other methods, its latency is up to 29% higher and its energy consumption is up to 83% higher.

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Cost-Effective and Latency-Minimized Data Placement Strategy for Spatial Crowdsourcing in Multi-Cloud Environment

Cited by 12 publications

References 40 publications

Intelligent Cloud Selection for Communication between OpenStack-based Multiple Clouds at TEIN using HTTP Tunneling and Observer Pattern API

Intelligent Cloud Selection for Communication between OpenStack-based Multiple Clouds at TEIN using HTTP Tunneling and Observer Pattern API

An effective data placement strategy for IIoT applications

Traffic-Aware Optimization of Task Offloading and Content Caching in the Internet of Vehicles

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