Optimization of Maritime Communication Workflow Execution with a Task-Oriented Scheduling Framework in Cloud Computing

Ahmad, Zulfiqar; Acarer, Tayfun; Kim, Wooseong

doi:10.3390/jmse11112133

Cited by 4 publications

(3 citation statements)

References 35 publications

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“…These cloud computing services are made available via cloud data centers. To meet day-to-day needs such as bulk amounts of data, cloud-related environments must provide high-performance servers and fast storage devices [19], [20], [21], [22], [23], [24]. The resources are considered a major source of power consumption, like air conditioning and cooling down various equipment [25].…”

Section: Introductionmentioning

confidence: 99%

Predictive Energy Management for Docker Containers in Cloud Computing: A Time Series Analysis Approach

Algarni,

Shah,

Jehangiri

et al. 2024

IEEE Access

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Cloud computing infrastructure is designed to deploy and assess service-oriented applications, primarily via cloud datacenters. These datacenters are integral to energy utilization in cloud environments, with energy consumption closely tied to resource utilization. It is important to monitor and predict power consumption in these datacenters, especially for high-demand services. Container-based virtualization, particularly using Docker containers, has gained significant attention due to its lightweight nature. However, predicting energy usage at a fine-grained level for container-based applications is a challenging task. In this study, we employ three time series analysis algorithms-AR, ARIMA, and ETS-to predict the energy usage of Docker containers over the next hour. Utilizing collected time-series power consumption data, our study contributes to enhancing power predictions for Docker containers within cloud infrastructures. Our prediction results focus on four Docker containers, each running multiple applications as Docker subprocesses. Power data for individual applications was aggregated to determine total container power consumption. Comparing the performance of ARIMA, ETS, and AR algorithms in predicting Docker container instance power, we found varying outcomes across containers. Through assessing MAPE across different time series model window lengths, we identified superior performance among the models. Specifically, ETS consistently demonstrated the lowest MAPE values for containers like 'polinx-container' and 'alpines-container', indicating higher prediction accuracy compared to ARIMA and AR models. The ARIMA model outperformed the ETS and AR models for the 'progrium container'. These findings underscore the necessity of selecting appropriate time series models tailored to specific Docker container configurations and workload scenarios for precise energy consumption forecasts.INDEX TERMS Docker container; timer series analysis, energy consumption; cloud computing; monitoring.

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Section: Introductionmentioning

confidence: 99%

Predictive Energy Management for Docker Containers in Cloud Computing: A Time Series Analysis Approach

Algarni,

Shah,

Jehangiri

et al. 2024

IEEE Access

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“…The development of intelligent ships promotes innovation in the field of ship sensing technology, with maritime radio signal sensing emerging as a new research direction [1]. Ship navigation and maritime traffic management rely heavily on various radio signals, among which ship-borne Very High Frequency (VHF) Radios are most commonly used for navigation and traffic control purposes [2]. However, due to the utilization of analog communication technology in maritime VHF communication and the requirement for ships sailing in the same area to use a shared public channel for interactive communication, effectively identifying ships using VHF radios has always been a technical challenge within the realm of ship perception [3].…”

Section: Introductionmentioning

confidence: 99%

Identification of Shipborne VHF Radio Based on Deep Learning with Feature Extraction

Chen,

Liu

2024

JMSE

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In the feature identification of maritime VHF radio communication signals, shipborne VHF communication technology follows the same international technical standards formulated by IMO, uses analog communication technology and uses the same communication channel in the same area, and cannot effectively achieve signal feature identification by adding feature elements in the process of signal modulation. How to effectively identify the ship using VHF radio has always been a technical difficulty in the field of ship perception. In this paper, based on the convolutional neural network, combined with the feasibility of CAM feature extraction and BiLSTM feature extraction in non-cooperative signal recognition, a deep learning recognition model of shipborne VHF radio communication signals is established, and the deep learning approach is employed to discern the features of VHF signals, thereby accomplishing the identification and classification of transmitting VHF radio stations. Several experiments are designed according to the characteristics of ship communication scenes at sea. The experimental data show that the method proposed in this paper can provide a new feasible path for ship target perception in terms of radio signal characteristics and identification.

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“…To avoid mishaps and guarantee the general safety of maritime operations, communication systems, such as satellite communication and Very High Frequency/Ultra High Frequency (VHF/UHF) radio are required to be reliable [6]. The Automatic Identification System (AIS) greatly enhances maritime safety by giving vessels real-time information on the positions, courses, and speeds of other adjacent ships [7], [8], [9], [10]. This helps with navigation and collision avoidance, enabling ships to modify their paths to avoid mishaps and guarantee safe travel.…”

Section: Introductionmentioning

confidence: 99%

An Edge Computing-Based Preventive Framework With Machine Learning- Integration for Anomaly Detection and Risk Management in Maritime Wireless Communications

Algarni,

Acarer,

Ahmad

2024

IEEE Access

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The safety of maritime environments in context with effective and secure wireless communication networks is required for ships, coastal stations, and maritime authorities. The dynamic nature of marine environments, where ships traverse vast and unpredictable expanses of oceans and seas, presents big challenges to safety and risk management. Wireless communication technology is widely employed in maritime activities for communication via ocean networks and underwater wireless sensor networks (UWSNs). Maintaining the safety of the maritime environment, effective anomaly detection, prompt risk mitigation, and real-time communication becomes more difficult due to its dynamic nature. International trade and transportation are facilitated by the maritime industry. In addition to protecting lives and averting environmental disasters, maritime safety is important for maintaining the effectiveness and dependability of shipping routes. To handle the intricacies of maritime safety, this work proposes a novel preventive framework for anomaly detection and risk management in Maritime Wireless Communications (MWC). The proposed framework is based on edge computing and machine learning models. The framework makes use of edge computing technology to process data locally, lowering latency and enabling real-time communication in maritime environments. A proactive safety approach has been adopted to ensure the wellbeing of seafarers, safeguard vessels, and protect the marine environment. As maritime cybersecurity threats continue to evolve, the proposed research aims to enhance the cybersecurity posture of MWC. The framework will incorporate measures to detect and respond to potential cyber threats, ensuring the integrity and security of communication channels under international maritime cybersecurity standards. The proposed anomaly detection framework incorporates machine learning models such as Long Short-Term Memory (LSTM) and Isolation Forests (IF). The proposed framework also places a strong emphasis on preventative safety measures, including cybersecurity safeguards to protect communication channels in the constantly changing digital marine operations environment. To demonstrate the effectiveness of the proposed framework, the experiments were performed based on a publicly available dataset and implemented in the context of marine communications. The results show significant accuracy as well as high precision, recall, and F1-score metrics generated by the LSTM and IF models. The results highlight that the proposed framework can detect anomalies and potential threats in real-time marine communications.INDEX TERMS Machine learning; intelligent systems; sustainable navigation, autonomous vessels, ship safety management systems; maritime shipping and satellite technology; sensing and communication in maritime; automatic identification system; edge computing, prevention of ship accidents.

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Optimization of Maritime Communication Workflow Execution with a Task-Oriented Scheduling Framework in Cloud Computing

Cited by 4 publications

References 35 publications

Predictive Energy Management for Docker Containers in Cloud Computing: A Time Series Analysis Approach

Predictive Energy Management for Docker Containers in Cloud Computing: A Time Series Analysis Approach

Identification of Shipborne VHF Radio Based on Deep Learning with Feature Extraction

An Edge Computing-Based Preventive Framework With Machine Learning- Integration for Anomaly Detection and Risk Management in Maritime Wireless Communications

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