A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon.

/pdf/cloud-computing-resource-scheduling-and-a-survey-of-its-2nnldbhf5x.pdf

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

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

A review of metaheuristic scheduling techniques in cloud computing

Data preprocessing is widely recognized as an important stage in anomaly detection. This paper reviews the data preprocessing techniques used by anomaly-based network intrusion detection systems (NIDS), concentrating on which aspects of the network traffic are analyzed, and what feature construction and selection methods have been used. Motivation for the paper comes from the large impact data preprocessing has on the accuracy and capability of anomaly-based NIDS. The review finds that many NIDS limit their view of network traffic to the TCP/IP packet headers. Time-based statistics can be derived from these headers to detect network scans, network worm behavior, and denial of service attacks. A number of other NIDS perform deeper inspection of request packets to detect attacks against network services and network applications. More recent approaches analyze full service responses to detect attacks targeting clients. The review covers a wide range of NIDS, highlighting which classes of attack are detectable by each of these approaches.

Data preprocessing is found to predominantly rely on expert domain knowledge for identifying the most relevant parts of network traffic and for constructing the initial candidate set of traffic features. On the other hand, automated methods have been widely used for feature extraction to reduce data dimensionality, and feature selection to find the most relevant subset of features from this candidate set. The review shows a trend toward deeper packet inspection to construct more relevant features through targeted content parsing. These context sensitive features are required to detect current attacks.

Data preprocessing for anomaly based network intrusion detection : a review

The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling

Heterogeneous distributed computing systems are the emerging for executing scientific and computationally intensive applications. Cloud computing in this context describes a paradigm to deliver the resource-like computing and storage on-demand basis using pay-per-use model. These resources are managed by data centers and dynamically provisioned to the users based on their availability, demand and quality parameters required to be satisfied. The task scheduling onto the distributed and virtual resources is a main concern which can affect the performance of the system. In the literature, a lot of work has been done by considering cost and makespan as the affecting parameters for scheduling the dependent tasks. Prior work has discussed the various challenges affecting the performance of dependent task scheduling but did not consider storage cost, failure rate-related challenges. This paper accomplishes a review of using meta-heuristics techniques for scheduling tasks in cloud computing. We presented the taxonomy and comparative review on these algorithms. Methodical analysis of task scheduling in cloud and grid computing is presented based on swarm intelligence and bio-inspired techniques. This work will enable the readers to decide suitable approach for suggesting better schemes for scheduling user’s application. Future research issues have also been suggested in this research work.

A review of task scheduling based on meta-heuristics approach in cloud computing

In the cloud computing system, the schedule of computing resources is a critical portion of cloud computing study. An effective load balancing strategy is able to markedly improve the task throughput of cloud computing. Virtual machines are selected as a fundamental processing unit of cloud computing. The resources in cloud computing will increase sharply and vary dynamically due to the utilization of virtualization technology. Therefore, implementation of load balancing in cloud computing has become complicated and it is difficult to achieve. Multi-agent genetic algorithm (MAGA) is a hybrid algorithm of GA, whose performance is far superior to that of the traditional GA. This paper demonstrates the advantage of MAGA over traditional GA, and then exploits multi-agent genetic algorithms to solve the load balancing problem in cloud computing, by designing a load balancing model on the basis of virtualization resource management. Finally, by comparing MAGA with Minimum strategy, the experiment results prove that MAGA is able to achieve better performance of load balancing.

Hybrid Genetic Algorithm for Cloud Computing Applications

This paper proposes a new network anomaly detection method in order to deal with the low detection rate and high false alarm rate problem. Ball vector machine (BVM) and extreme learning machine (ELM) is individually applied to learn three kinds of network features, then a BP neural network is utilized to simulate weights, which is used to fusion of the label. The experiments show that, the performance of this fusion method is better than single BVM or ELM classifier. Compared to the fusion method of SVM and BP neural network, the method proposed by this paper has a similar performance in detection rate and false alarm rate but with a significantly lower training time, and it is suitable for network anomaly detection with large scale dataset.

Fusion of BVM and ELM for Anomaly Detection in Computer Networks

The Binary Particle Swarm Optimization (BPSO) algorithm is introduced for face recognition. To do this, the original face images are first transformed into feature vectors by utilizing two-dimensional Discrete Cosine Transform (DCT). Secondly, the features are selected by means of the BPSO algorithm from the feature vectors, in order to obtain the most representative features of human faces. Compared to Genetic Algorithms (GA), the BPSO algorithm can achieve a higher recognition rate by a few features. The results demonstrate that the BSPO algorithm possesses a high recognition rate for various human face recognition applications, verifying it as an effective feature selection approach.

The Application of Binary Particle Swarm Algorithm in Face Recognition

The authors present an analysis which concludes that most e-health system are packaged for large-scale access through cloud-based services shared in a real-time service deployment environment. The service, which has already been deployed in the clouds for e-health construction, needs to instantly change itself in order to enhance the usability for the patients, especially for the dementia patient monitoring system. The evolution for the service in cloud-based systems can be driven fundamentally based on the service function improvement, quality of service improvement, and service collaboration improvement, which can greatly enhance the usability of the dementia patient monitoring system and dynamically enlarge the life-cycle of the current service system in clouds without replacing the reusable service components. The quality of service evolution of the dementia patient monitoring system is essential because the system reliability and instant messaging sending ability is needed in the dementia patient monitoring system. The system should be as reliable as possible for its undertaken the people's life and healthy ensure for all those who use the system.

Service Evolution in Clouds for Dementia Patient Monitoring System Usability Enhancement

The proposed research will focus on developing a novel approach to solve Software Service Evolution problems in Computing Clouds. The approach will support dynamic evolution of the software service in clouds via a set of discovered evolution patterns. An initial survey informed us that such an approach does not exist yet and is in urgent need. Evolution Requirement can be classified into evolution features; researchers can describe the whole requirement by using evolution feature typology, the typology will define the relation and dependency between each features. After the evolution feature typology has been constructed, evolution model will be created to make the evolution more specific. Aspect oriented approach can be used for enhance evolution feature-model modularity. Aspect template code generation technique will be used for model transformation in the end. Product Line Engineering contains all the essential components for driving the whole evolution process.

/pdf/evolution-feature-oriented-model-driven-product-line-190vd510ae.pdf

Evolution Feature Oriented Model Driven Product Line Engineering Approach for Synergistic and Dynamic Service Evolution in Clouds

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