| 1. |
- Braik, Malik, et al.
(författare)
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Adaptive dynamic elite opposition-based Ali Baba and the forty thieves algorithm for high-dimensional feature selection
- 2024
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Ingår i: Cluster Computing. - : SPRINGER. - 1386-7857 .- 1573-7543.
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Tidskriftsartikel (refereegranskat)abstract
- High-dimensional Feature Selection Problems (HFSPs) have grown in popularity but remain challenging. When faced with such complex situations, the majority of currently employed Feature Selection (FS) methods for these problems drastically underperform in terms of effectiveness. To address HFSPs, a new Binary variant of the Ali Baba and the Forty Thieves (BAFT) algorithm known as binary adaptive elite opposition-based AFT (BAEOAFT), incorporating historical information and dimensional mutation is presented. The entire population is dynamically separated into two subpopulations in order to maintain population variety, and information and knowledge about individuals are extracted to offer adaptive and dynamic strategies in both subpopulations. Based on the individuals' history knowledge, Adaptive Tracking Distance (ATD) and Adaptive Perceptive Possibility (APP) schemes are presented for the exploration and exploitation subpopulations. A dynamic dimension mutation technique is used in the exploration subpopulation to enhance BAEOAFT's capacity in solving HFSPs. Meanwhile, the exploratory subpopulation uses Dlite Dynamic opposite Learning (EDL) to promote individual variety. Even if the exploitation group prematurely converges, the exploration subpopulation's variety can still be preserved. The proposed BAEOAFT-based FS technique was assessed by utilizing the k-nearest neighbor classifier on 20 HFSPs obtained from the UCI repository. The developed BAEOAFT achieved classification accuracy rates greater than those of its competitors and the conventional BAFT in more than 90% of the applied datasets. Additionally, BAEOAFT outperformed its rivals in terms of reduction rates while selecting the fewest number of features.
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| 2. |
- Bures, Miroslav, et al.
(författare)
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Testing the consistency of business data objects using extended static testing of CRUD matrices
- 2019
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Ingår i: Cluster Computing. - : Springer-Verlag New York. - 1386-7857 .- 1573-7543. ; 22, s. 963-976
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Tidskriftsartikel (refereegranskat)abstract
- Static testing is used to detect software defects in the earlier phases of the software development lifecycle, which makes the total costs caused by defects lower and the software development project less risky. Different types of static testing have been introduced and are used in software projects. In this paper, we focus on static testing related to data consistency in a software system. In particular, we propose extensions to contemporary static testing techniques based on CRUD matrices, employing cross-verifications between various types of CRUD matrices made by different parties at various stages of the software project. Based on performed experiments, the proposed static testing technique significantly improves the consistency of Data Cycle Test cases. Together with this trend, we observe growing potential of test cases to detect data consistency defects in the system under test, when utilizing the proposed technique.
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| 3. |
- Cajas, V., et al.
(författare)
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Migrating legacy Web applications
- 2021
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Ingår i: Cluster Computing. - : Springer Science and Business Media LLC. - 1386-7857 .- 1573-7543. ; 24:2, s. 1033-1049
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Tidskriftsartikel (refereegranskat)
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| 4. |
- Casalicchio, Emiliano
(författare)
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A study on performance measures for auto-scaling CPU-intensive containerized applications
- 2019
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Ingår i: Cluster Computing. - : Springer New York LLC. - 1386-7857 .- 1573-7543. ; 22:3, s. 995-1006
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Tidskriftsartikel (refereegranskat)abstract
- Autoscaling of containers can leverage performance measures from the different layers of the computational stack. This paper investigate the problem of selecting the most appropriate performance measure to activate auto-scaling actions aiming at guaranteeing QoS constraints. First, the correlation between absolute and relative usage measures and how a resource allocation decision can be influenced by them is analyzed in different workload scenarios. Absolute and relative measures could assume quite different values. The former account for the actual utilization of resources in the host system, while the latter account for the share that each container has of the resources used. Then, the performance of a variant of Kubernetes’ auto-scaling algorithm, that transparently uses the absolute usage measures to scale-in/out containers, is evaluated through a wide set of experiments. Finally, a detailed analysis of the state-of-the-art is presented.
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| 5. |
- Casalicchio, Emiliano, et al.
(författare)
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Energy-aware Auto-scaling Algorithms for Cassandra Virtual Data Centers
- 2017
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Ingår i: Cluster Computing. - : Springer-Verlag New York. - 1386-7857 .- 1573-7543. ; 20:3, s. 2065-2082
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Tidskriftsartikel (refereegranskat)abstract
- Apache Cassandra is an highly scalable and available NoSql datastore, largely used by enterprises of each size and for application areas that range from entertainment to big data analytics. Managed Cassandra service providers are emerging to hide the complexity of the installation, fine tuning and operation of Cassandra Virtual Data Centers (VDCs). This paper address the problem of energy efficient auto-scaling of Cassandra VDC in managed Cassandra data centers. We propose three energy-aware autoscaling algorithms: \texttt{Opt}, \texttt{LocalOpt} and \texttt{LocalOpt-H}. The first provides the optimal scaling decision orchestrating horizontal and vertical scaling and optimal placement. The other two are heuristics and provide sub-optimal solutions. Both orchestrate horizontal scaling and optimal placement. \texttt{LocalOpt} consider also vertical scaling. In this paper: we provide an analysis of the computational complexity of the optimal and of the heuristic auto-scaling algorithms; we discuss the issues in auto-scaling Cassandra VDC and we provide best practice for using auto-scaling algorithms; we evaluate the performance of the proposed algorithms under programmed SLA variation, surge of throughput (unexpected) and failures of physical nodes. We also compare the performance of energy-aware auto-scaling algorithms with the performance of two energy-blind auto-scaling algorithms, namely \texttt{BestFit} and \texttt{BestFit-H}. The main findings are: VDC allocation aiming at reducing the energy consumption or resource usage in general can heavily reduce the reliability of Cassandra in term of the consistency level offered. Horizontal scaling of Cassandra is very slow and make hard to manage surge of throughput. Vertical scaling is a valid alternative, but it is not supported by all the cloud infrastructures.
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| 6. |
- Cui, Hao, et al.
(författare)
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Multi-strategy boosted Aquila optimizer for function optimization and engineering design problems
- 2024
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Ingår i: Cluster Computing. - : SPRINGER. - 1386-7857 .- 1573-7543.
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Tidskriftsartikel (refereegranskat)abstract
- As the complexity of optimization problems continues to rise, the demand for high-performance algorithms becomes increasingly urgent. This paper addresses the challenges faced by the Aquila Optimizer (AO), a novel swarm-based intelligent optimizer simulating the predatory behaviors of Aquila in North America. While AO has shown good performance in prior studies, it grapples with issues such as poor convergence accuracy and a tendency to fall into local optima when tackling complex optimization tasks. To overcome these challenges, this paper proposes a multi-strategy boosted AO algorithm (PGAO) aimed at providing enhanced reliability for global optimization. The proposed algorithm incorporates several key strategies. Initially, a chaotic map is employed to initialize the positions of all search agents, enriching population diversity and laying a solid foundation for global exploration. Subsequently, the pinhole imaging learning strategy is introduced to identify superior candidate solutions in the opposite direction of the search domain during each iteration, accelerating convergence and increasing the probability of obtaining the global optimal solution. To achieve a more effective balance between the exploration and development phases in AO, a nonlinear switching factor is designed to replace the original fixed switching mechanism. Finally, the golden sine operator is utilized to enhance the algorithm's local exploitation trends. Through these four improvement strategies, the optimization performance of AO is significantly enhanced. The proposed PGAO algorithm's effectiveness is validated across 23 classical, 29 IEEE CEC2017, and 10 IEEE CEC2019 benchmark functions. Additionally, six real-world engineering design problems are employed to assess the practicability of PGAO. Results demonstrate that PGAO exhibits better competitiveness and application prospects compared to the basic method and various advanced algorithms. In conclusion, this study contributes to addressing the challenges of complex optimization problems, significantly improving the performance of global optimization algorithms, and holds both theoretical and practical significance.
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| 7. |
- Fakhouri, Hussam N., et al.
(författare)
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Novel hybrid success history intelligent optimizer with Gaussian transformation : application in CNN hyperparameter tuning
- 2024
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Ingår i: Cluster Computing. - : Springer. - 1386-7857 .- 1573-7543. ; 27:3, s. 3717-3739
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Tidskriftsartikel (refereegranskat)abstract
- This research proposes a novel Hybrid Success History Intelligent Optimizer with Gaussian Transformation (SHIOGT) for solving different complexity level optimization problems and for Convolutional Neural Network (CNNs) hyperparameter tuning. SHIOGT algorithm is designed to balance exploration and exploitation phases through the addition of Gaussian Transformation to the original Success History Intelligent Optimizer. The inclusion of Gaussian Transformation enhances solution diversity enables SHIO to avoid local optima. SHIOGT also demonstrates robustness and adaptability by dynamically adjusting its search strategy based on problem characteristics. Furthermore, the combination of Gaussian and SHIO facilitates faster convergence, accelerating the discovery of optimal or near-optimal solutions. Moreover, the hybridization of these two techniques brings a synergistic effect, enabling SHIOGT to overcome individual limitations and achieve superior performance in hyperparameter optimization tasks. SHIOGT was thoroughly assessed against an array of benchmark functions of varying complexities, demonstrating its ability to efficiently locate optimal or near-optimal solutions across different problem categories. Its robustness in tackling multimodal and deceptive landscapes and high-dimensional search spaces was particularly notable. SHIOGT has been benchmarked over 43 challenging optimization problems and have been compared with state-of-the art algorithm. Further, SHIOGT algorithm is applied to the domain of deep learning, with a case study focusing on hyperparameter tuning of CNNs. With the intelligent exploration–exploitation balance of SHIOGT, we hypothesized it could effectively optimize the CNN's hyperparameters. We evaluated the performance of SHIOGT across a variety of datasets, including MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100, with the aim of optimizing CNN model hyperparameters. The results show an impressive accuracy rate of 98% on the MNIST dataset. Similarly, the algorithm achieved a 92% accuracy rate on Fashion-MNIST, 76% on CIFAR-10, and 70% on CIFAR-100, underscoring its effectiveness across diverse datasets. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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| 8. |
- Jayalaxmi, P. L. S., et al.
(författare)
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MADESANT: malware detection and severity analysis in industrial environments
- 2024
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Ingår i: Cluster Computing. - : SPRINGER. - 1386-7857 .- 1573-7543.
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Tidskriftsartikel (refereegranskat)abstract
- Malware remains a persistent threat to industrial operations, causing disruptions and financial losses. Traditional malware detection approaches struggle with the increasing complexity of false positives and negatives. However, existing Intrusion Detection Systems (IDSs) often lack the capability to assess the severity of detected malware, crucial for effective threat mitigation. This paper presents a novel model, MAlware DEtection and Severity Analysis for eNcrypted Traffic (MADESANT), designed to detect and analyze malware severity in encrypted traffic data. MADESANT combines Deep Learning (DL)-based intrusion detection with Machine Learning (ML)-based severity analysis, specifically customized for the minutiae of IoT systems and assets. Notably, MADESANT introduces a cascading model integrating a Cascading Forward Back Propagation Neural Network (CFBPNN) with the J48 tree to systematically assess risk factors in network traffic. Our assessment, conducted on diverse encrypted datasets including UNSW-NB15, IoT23, and XIIoTID, highlights the remarkable efficacy of MADESANT. Impressively, it achieves a flawless 0% false positive rate in detecting binary attack instances, surpassing benchmarks set by conventional models. Additionally, MADESANT excels in accurately estimate malware severity, providing invaluable insights into the factors contributing to the risk. To further validate its efficiency, we compared MADESANT against prevalent Neural Network models like FeedForward and Recurrent Neural Networks, with MADESANT emerging as the superior choice. The experimentation encompasses both the entire dataset and subsets generated through meticulous risk factor analysis. These results underscore MADESANT's prowess in not only identifying malware but also in evaluating its potential impact, signifying a significant leap forward in industrial cybersecurity.
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| 9. |
- Liu, Yongjun, et al.
(författare)
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Depth from defocus (DFD) based on VFISTA optimization algorithm in micro/nanometer vision
- 2019
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Ingår i: Cluster Computing. - : SPRINGER. - 1386-7857 .- 1573-7543. ; 22, s. 1459-1467
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Tidskriftsartikel (refereegranskat)abstract
- In the three-dimensional (3D) morphological reconstruction of micro/nano-scale vision, the global depth from defocus algorithm (DFD) transforms the depth information of the scene into a dynamic optimization problem to solve. In order to improve the problem of dynamic optimization in the recovery process of global DFD, a variable-step-size fast iterative shrinkage-thresholding algorithm (VFISTA) is proposed. The traditional iterative shrinkage-thresholding algorithm (ISTA) is often used to solve this dynamic optimization problem in the global DFD method. The ISTA algorithm is an extension of the gradient descent method, which is close to the minimal value point of the optimization process, and the convergence speed is slow. What is more, the ISTA algorithm uses fixed step length in the iterative process, The search direction tend to be "orthogonal", prone to "saw tooth" phenomenon, so close to the minimum point when the convergence rate is slower. First, the VFISTA algorithm joins the acceleration operator on the basis of the ISTA algorithm. Further, it combines linear search method to find the optimal iteration length, and changes the limit of the ISTA algorithm step fixed. Finally, it is applied to the depth measurement of defocus scene in micro/nanometer scale vision. The experimental results show that the proposed fast depth from defocus algorithm based on VFISTA has faster convergent speed. Moreover, the precision of the measurement is obviously improved in micro/nanometer scale vision.
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| 10. |
- Liu, Ying, et al.
(författare)
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OnlineElastMan : self-trained proactive elasticity manager for cloud-based storage services
- 2017
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Ingår i: Cluster Computing. - : Springer New York LLC. - 1386-7857 .- 1573-7543. ; 20:3, s. 1977-1994
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Tidskriftsartikel (refereegranskat)abstract
- The pay-as-you-go pricing model and the illusion of unlimited resources in the Cloud initiate the idea to provision services elastically. Elastic provisioning of services allocates/de-allocates resources dynamically in response to the changes of the workload. It minimizes the service provisioning cost while maintaining the desired service level objectives (SLOs). Model-predictive control is often used in building such elasticity controllers that dynamically provision resources. However, they need to be trained, either online or offline, before making accurate scaling decisions. The training process involves tedious and significant amount of work as well as some expertise, especially when the model has many dimensions and the training granularity is fine, which is proved to be essential in order to build an accurate elasticity controller. In this paper, we present OnlineElastMan, which is a self-trained proactive elasticity manager for cloud-based storage services. It automatically evolves itself while serving the workload. Experiments using OnlineElastMan with Cassandra indicate that OnlineElastMan continuously improves its provision accuracy, i.e., minimizing provisioning cost and SLO violations, under various workload patterns.
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