Cloud computing environments facilitate applications by providing virtualized resources that can be provisioned dynamically. However, users are charged on a pay-per-use basis. User applications may incur large data retrieval and execution costs when they are scheduled taking into account only the ‘execution time’. In addition to optimizing execution time, the cost arising from data transfers between resources as well as execution costs must also be taken into account. In this paper, we present a particle swarm optimization (PSO) based heuristic to schedule applications to cloud resources that takes into account both computation cost and data transmission cost. We experiment with a workflow application by varying its computation and communication costs. We compare the cost savings when using PSO and existing ‘Best Resource Selection’ (BRS) algorithm. Our results show that PSO can achieve: a) as much as 3 times cost savings as compared to BRS, and b) good distribution of workload onto resources.

A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments

Machine Learning (ML) has been enjoying an unprecedented surge in applications that solve problems and enable automation in diverse domains. Primarily, this is due to the explosion in the availability of data, significant improvements in ML techniques, and advancement in computing capabilities. Undoubtedly, ML has been applied to various mundane and complex problems arising in network operation and management. There are various surveys on ML for specific areas in networking or for specific network technologies. This survey is original, since it jointly presents the application of diverse ML techniques in various key areas of networking across different network technologies. In this way, readers will benefit from a comprehensive discussion on the different learning paradigms and ML techniques applied to fundamental problems in networking, including traffic prediction, routing and classification, congestion control, resource and fault management, QoS and QoE management, and network security. Furthermore, this survey delineates the limitations, give insights, research challenges and future opportunities to advance ML in networking. Therefore, this is a timely contribution of the implications of ML for networking, that is pushing the barriers of autonomic network operation and management.

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A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

As a promising machine learning tool to handle the accurate pattern recognition from complex raw data, deep learning (DL) is becoming a powerful method to add intelligence to wireless networks with large-scale topology and complex radio conditions. DL uses many neural network layers to achieve a brain-like acute feature extraction from high-dimensional raw data. It can be used to find the network dynamics (such as hotspots, interference distribution, congestion points, traffic bottlenecks, spectrum availability, etc.) based on the analysis of a large amount of network parameters (such as delay, loss rate, link signal-to-noise ratio, etc.). Therefore, DL can analyze extremely complex wireless networks with many nodes and dynamic link quality. This paper performs a comprehensive survey of the applications of DL algorithms for different network layers, including physical layer modulation/coding, data link layer access control/resource allocation, and routing layer path search, and traffic balancing. The use of DL to enhance other network functions, such as network security, sensing data compression, etc., is also discussed. Moreover, the challenging unsolved research issues in this field are discussed in detail, which represent the future research trends of DL-based wireless networks. This paper can help the readers to deeply understand the state-of-the-art of the DL-based wireless network designs, and select interesting unsolved issues to pursue in their research.

Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey

Network traffic classification has become more important with the rapid growth of Internet and online applications. Numerous studies have been done on this topic which have led to many different approaches. Most of these approaches use predefined features extracted by an expert in order to classify network traffic. In contrast, in this study, we propose a deep learning-based approach which integrates both feature extraction and classification phases into one system. Our proposed scheme, called “Deep Packet,” can handle both traffic characterization in which the network traffic is categorized into major classes (e.g., FTP and P2P) and application identification in which identifying end-user applications (e.g., BitTorrent and Skype) is desired. Contrary to most of the current methods, Deep Packet can identify encrypted traffic and also distinguishes between VPN and non-VPN network traffic. The Deep Packet framework employs two deep neural network structures, namely stacked autoencoder (SAE) and convolution neural network (CNN) in order to classify network traffic. Our experiments show that the best result is achieved when Deep Packet uses CNN as its classification model where it achieves recall of 0.98 in application identification task and 0.94 in traffic categorization task. To the best of our knowledge, Deep Packet outperforms all of the proposed classification methods on UNB ISCX VPN-nonVPN dataset.

Deep packet: a novel approach for encrypted traffic classification using deep learning

https://cyberleninka.org/article/n/468779.pdf

A review of metaheuristic scheduling techniques in cloud computing

Grid computing is a high performance computing environment to solve larger scale computational demands. Grid computing contains resource management, task scheduling, security problems, information management and so on. Task scheduling is a fundamental issue in achieving high performance in grid computing systems. However, it is a big challenge for efficient scheduling algorithm design and implementation. In this paper, a heuristic approach based on particle swarm optimization algorithm is adopted to solving task scheduling problem in grid environment. Each particle is represented a possible solution, and the position vector is transformed from the continuous variable to the discrete variable. This approach aims to generate an optimal schedule so as to get the minimum completion time while completing the tasks. The results of simulated experiments show that the particle swarm optimization algorithm is able to get the better schedule than genetic algorithm.

A Task Scheduling Algorithm Based on PSO for Grid Computing

Traffic classification, a branch of passive network measurement, becomes more and more important for network management. As traditional traffic classification techniques like port-based and payload-based techniques become ineffective for complicated internet applications which use dynamic port number and encryption techniques to avoid detection, machine learning based techniques gained more and more attentions in the past few years. But there are few studies that focus on applying neural computation techniques for traffic classification. In this paper, we use a distributed host based traffic collection platform (DHTCP) to gather traffic samples with accurate application information on user hosts. Then probabilistic neural network was used to traffic classification. Web and P2P traffics were studied since they are the most predominant internet traffic types. experimental results show that probabilistic neural network is an effective machine learning technique for traffic identification

Traffic classification using probabilistic neural networks

Improving matrix approximation for recommendation via a clustering-based reconstructive method

Nowadays, the cloud computing owners lack management and monitoring tools to ensure the performance, robustness, dependability, and security. To address this limitation, this paper described our experience with a lightweight monitoring framework using some extra development work. This framework performed end-to-end measurements at virtual machine instances and software in the public cloud. It monitors quality of service parameters of the IaaS and SaaS layer without modifying the implementation of the monitored object. In addition, we discussed the manager-agent and module-centralized architecture in details. All the modules make up the entire proposed framework to improve the performance of applications in public clouds.

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Toward a lightweight framework for monitoring public clouds

The invention discloses a multi-feature mobile terminal malicious software detecting method based on network flow and a multi-feature mobile terminal malicious software detecting system based on the network flow. The method comprises the following steps of: extracting features capable of effectively representing mobile terminal malicious software network behaviors from network flow data; classifying the extracted features capable of effectively representing the mobile terminal malicious software network behaviors according to different feature types; building detecting models adapting to the classified features, wherein each kind of types has one corresponding unique detecting model; and selecting the corresponding detecting model for each kind of features, and outputting the corresponding detecting result. By aiming at different feature types of the mobile terminal network flow, the detecting models adapting to different feature types are designed; a user can select the required model by the self according to requirements; the detecting accuracy can be improved to a certain degree through different detecting models designed by aiming at different feature types; and the individualized requirements of the user are met.

Multi-feature mobile terminal malicious software detecting method based on network flow and multi-feature mobile terminal malicious software detecting system based on network flow

Runyuan Sun

Papers

A Task Scheduling Algorithm Based on PSO for Grid Computing

Traffic classification using probabilistic neural networks

Improving matrix approximation for recommendation via a clustering-based reconstructive method

Toward a lightweight framework for monitoring public clouds

Multi-feature mobile terminal malicious software detecting method based on network flow and multi-feature mobile terminal malicious software detecting system based on network flow