Recently, with the expansion of communications and generated data, the need for processing this high volume of data in minimum time and maximum speed has increased. Also, performing this volume of computing operations requires high processing and storage resources leading to hardware cost increment. In such systems, one of the most critical challenges is the task scheduling problem, which tries to find the optimal allocation for maximum resource usage and reduce the response time. Therefore, the purpose of this study is to design an infrastructure for smart home energy management with minimum hardware cost using cloud and fog computing and to propose a latency-aware scheduling algorithm based on virtual machine matching using meta-heuristics. Among heuristic methods, Tabu search makes it a common practice because of its high expansion in various optimization issues, as well as memory and high-speed features. Thereby, a novel algorithm based on the Tabu search is proposed that is improved using approximate nearest neighbor (ANN) and fruit fly optimization (FOA) algorithms. Finally, to validate the proposed method, a case study is simulated and the proposed algorithm is implemented considering target factors of execution time, latency, allocated memory, and cost function to illustrate the performance of the algorithm. The comparison results show that the proposed algorithm outperforms the Tabu search, genetic algorithm, PSO, and simulated annealing methods.

A latency-aware task scheduling algorithm for allocating virtual machines in a cost-effective and time-sensitive fog-cloud architecture

Despite its technological benefits, the Internet of Things (IoT) has cyber weaknesses due to vulnerabilities in the wireless medium. Machine Larning (ML)-based methods are widely used against cyber threats in IoT networks with promising performance. An Advanced Persistent Threat (APT) is prominent for cybercriminals to compromise networks, and it is crucial to long-term and harmful characteristics. However, it is difficult to apply ML-based approaches to identify APT attacks to obtain a promising detection performance due to an extremely small percentage among normal traffic. There are limited surveys that fully investigate APT attacks in IoT networks due to the lack of public datasets with all types of APT attacks. It is worth bridging the state of the art in network attack detection with APT attack detection in a comprehensive review article. This survey article reviews the security challenges in IoT networks and presents well-known attacks, APT attacks, and threat models in IoT systems. Meanwhile, signature-based, anomaly-based, and hybrid intrusion detection systems are summarized for IoT networks. The article highlights statistical insights regarding frequently applied ML-based methods against network intrusion. Finally, open issues and challenges for common network intrusion and APT attacks are presented for future research.

Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats

Job scheduling for big data analytical applications in clouds: A taxonomy study

Federated learning empowered mobility-aware proactive content offloading framework for fog radio access networks

TS-REPLICA: A novel replica placement algorithm based on the entropy weight TOPSIS method in spark for multimedia data analysis

With the rapid development and the widespread use of cloud computing in various applications, the number of users distributed in different regions has grown exponentially. Therefore, the Geo-distributed cloud systems have become a research hotspot and big data processing technology has also emerged. Nowadays, the most widely used big data processing framework is Spark. However, massive amounts of data are generated every moment, and the processing procedure becomes more and more complex, the execution efficiency of Spark has been greatly affected. In the Spark frame of geo-distributed cloud systems, aiming at the data placement problem, the data placement strategy based on RDD dynamic weight is introduced. The target node is selected with a strong computation capacity to place the data. Aiming at the problems of multi-task scheduling, a task will be scheduled to a node whose computation capacity can satisfy the requirement of this task. And then considering job classification and computing node performance, the optimized task scheduling strategy is in traduced. Experiments show that our algorithms can effectively adjust the weight of node data placement according to the actual task execution information, and shorten the task execution time.

Adaptive priority-based data placement and multi-task scheduling in geo-distributed cloud systems

Cost-aware automatic scaling and workload-aware replica management for edge-cloud environment

Efficient cooperative cache management for latency-aware data intelligent processing in edge environment

The Internet of Things (IoT) is the integration of all information and Internet technology in the information age, which can realize the collection and transmission of intelligent information. A large number of sensors are producing and collecting data involving various industries every day. The amount of stream data generated is huge, and a large number of abnormal data are also generated in the process. Due to the demands of business and life quality improvement, the application of IoT technology to real-time monitoring and correction of massive stream data, especially the correction of abnormal data, is a very valuable research direction, and also the key to ensure the credibility and fidelity of IoT data. This article proposes a recurrent neural network model based on long- and short-term memory network (LSTM) and LSTM+. LSTM+ model not only reduces the regression error compared with the traditional LSTM model, but also can detect abnormal data collected by IoT terminal nodes, and can correct the abnormal data in real time, so as to ensure that the network prediction can have good stability and robustness.

Jun Liu

Papers

Adaptive priority-based data placement and multi-task scheduling in geo-distributed cloud systems

Cost-aware automatic scaling and workload-aware replica management for edge-cloud environment

Efficient cooperative cache management for latency-aware data intelligent processing in edge environment

Improved LSTM-Based Abnormal Stream Data Detection and Correction System for Internet of Things

Deeply learning a discriminative spatial–temporal feature for robot action understanding