Traditional network functions such as firewalls and Intrusion Detection Systems (IDS) are implemented in costly dedicated hardware, making the networks expensive to manage and inflexible to changes. Network function v...
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Traditional network functions such as firewalls and Intrusion Detection Systems (IDS) are implemented in costly dedicated hardware, making the networks expensive to manage and inflexible to changes. Network function virtualization enables flexible and inexpensive operation of network functions, by implementing virtual network functions (VNFs) as software in virtual machines (VMs) that run in commodity servers. However, VNFs are vulnerable to various faults such as software and hardware failures. Without efficient and effective fault tolerant mechanisms, the benefits of deploying VNFs in networks can be traded-off. In this paper, we investigate the problem of fault tolerant VNF placement in cloud networks, by proactively deploying VNFs in stand-by VM instances when necessary. It is challenging because VNFs are usually stateful. This means that stand-by instances require continuous state updates from active instances during their operation, and the fault tolerant methods need to carefully handle such states. Specifically, the placement of active/stand-by VNF instances, the request routing paths to active instances, and state transfer paths to stand-by instances need to be jointly considered. To tackle this challenge, we devise an efficient heuristic algorithm for the fault tolerant VNF placement. We also propose two bicriteria approximation algorithms with provable approximation ratios for the problem without compute or bandwidth constraints. We then consider the dynamic fault recovery problem given that some placed active instances of VNFs may go faulty, for which we propose an approximation algorithm that dynamically switches traffic processing from faulty VNFs to stand-by instances. Simulations with realistic settings show that our algorithms can significantly improve the request admission rate compared to conventional approaches. We finally evaluate the performance of the proposed algorithm for the dynamic fault recovery problem in a real test-bed consisting of bo
Given a set of n elements, each of which is colored one of c colors, we must determine an element of the plurality (most frequently occurring) color by pairwise equal/unequal color comparisons of elements. We prove th...
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Given a set of n elements, each of which is colored one of c colors, we must determine an element of the plurality (most frequently occurring) color by pairwise equal/unequal color comparisons of elements. We prove that (c - 1)(n - c)/2 color comparisons are necessary in the worst case to determine the plurality color and give an algorithm requiring (0.775c + 5.9)n + O(c(2)) color comparisons for c >= 9.
A large number of metaheuristics inspired by natural and social phenomena have been proposed in the last few decades, each trying to be more powerful and innovative than others. However, there is a lack of accessible ...
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A large number of metaheuristics inspired by natural and social phenomena have been proposed in the last few decades, each trying to be more powerful and innovative than others. However, there is a lack of accessible tools to analyse, contrast and visualise the behaviour of metaheuristics when solving optimisation problems. When the metaphors are stripped away, are these algorithms different in their behaviour? To help to answer this question, we propose a data-driven, graph-based model, search trajectory networks (STNs) in order to analyse, visualise and directly contrast the behaviour of different types of metaheuristics. One strength of our approach is that it does not require any additional sampling or algorithmic methods. Instead, the models are constructed from data gathered while the metaheuristics are solving the optimisation problems. We present our methodology, and consider in detail two case studies covering both continuous and combinatorial optimisation. In terms of metaheuristics, our case studies cover the main current paradigms: evolutionary, swarm, and stochastic local search approaches. (c) 2021 Elsevier B.V. All rights reserved. Superscript/Subscript Available
Frequently used items mining is a significant subject of data mining studies. In the last ten years, due to innovative development, the quantity of data has grown exponentially. For frequent Itemset (FIs) mining appli...
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Frequently used items mining is a significant subject of data mining studies. In the last ten years, due to innovative development, the quantity of data has grown exponentially. For frequent Itemset (FIs) mining applications, it imposes new challenges. Misconceived information may be found in recent algorithms, including both threshold and size based algorithms. Threshold value plays a central role in generating frequent itemsets from the given dataset. Selecting a support threshold value is very complicated for those unaware of the dataset's characteristics. The performance of algorithms for finding FIs without the support threshold is, however, deficient due to heavy computation. Therefore, we have proposed a method to discover FIs without the support threshold, called Top-k frequent itemsets mining (TKFIM). It uses class equivalence and set-theory concepts for mining FIs. The proposed procedure does not miss any FIs;thus, accurate frequent patterns are mined. Furthermore, the results are compared with state-of-the-art techniques such as Top-k miner and Build Once and Mine Once (BOMO). It is found that the proposed TKFIM has outperformed the results of these approaches in terms of execution and performance, achieving 92.70, 35.87, 28.53, and 81.27 percent gain on Top-k miner using Chess, Mushroom, and Connect and T1014D100K datasets, respectively. Similarly, it has achieved a performance gain of 97.14, 100, 78.10, 99.70 percent on BOMO using Chess, Mushroom, Connect, and T1014D100K datasets, respectively. Therefore, it is argued that the proposed procedure may be adopted on a large dataset for better performance.
Deep learning object detection algorithms have proven their capacity to identify a variety of objects from images and videos in near real-time speed. The construction industry can potentially benefit from this machine...
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Deep learning object detection algorithms have proven their capacity to identify a variety of objects from images and videos in near real-time speed. The construction industry can potentially benefit from this machine intelligence by linking algorithms with construction videos to automatically analyze productivity and monitor activities from a safety perspective. However, an effective image data set of construction machines for training deep learning object detection algorithms is not currently available due to the limited accessibility of construction images, the time-and-labor-intensiveness of manual annotations, and the knowledge base required in terms of both construction and deep learning. This research presents a case study on developing an image data set specifically for construction machines named the Alberta Construction Image Data Set (ACID). In the case of ACID, 10,000 images belonging to 10 types of construction machines are manually collected and annotated with machine types and their corresponding positions on the images. To validate the feasibility of ACID, we train the data set using four existing deep learning object detection algorithms, including YOLO-v3, Inception-SSD, R-FCN-ResNet101, and Faster-RCNN-ResNet101. The mean average precision (mAP) is 83.0% for Inception-SSD, 87.8% for YOLO-v3, 88.8% for R-FCN-ResNet101, and 89.2% for Faster-RCNN-ResNet101. The average detection speed of the four algorithms is 16.7 frames per second (fps), which satisfies the needs of most studies in the field of automation in construction.
In a competitive digital age where data volumes are increasing with time, the ability to extract meaningful knowledge from high-dimensional data using machine learning (ML) and data mining (DM) techniques and making d...
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In a competitive digital age where data volumes are increasing with time, the ability to extract meaningful knowledge from high-dimensional data using machine learning (ML) and data mining (DM) techniques and making decisions based on the extracted knowledge is becoming increasingly important in all business domains. Nevertheless, high-dimensional data remains a major challenge for classification algorithms due to its high computational cost and storage requirements. The 2016 Demographic and Health Survey of Ethiopia (EDHS 2016) used as the data source for this study which is publicly available contains several features that may not be relevant to the prediction task. In this paper, we developed a hybrid multidimensional metrics framework for predictive modeling for both model performance evaluation and feature selection to overcome the feature selection challenges and select the best model among the available models in DM and ML. The proposed hybrid metrics were used to measure the efficiency of the predictive models. Experimental results show that the decision tree algorithm is the most efficient model. The higher score of HMM (m, r) = 0.47 illustrates the overall significant model that encompasses almost all the user’s requirements, unlike the classical metrics that use a criterion to select the most appropriate model. On the other hand, the ANNs were found to be the most computationally intensive for our prediction task. Moreover, the type of data and the class size of the dataset (unbalanced data) have a significant impact on the efficiency of the model, especially on the computational cost, and the interpretability of the parameters of the model would be hampered. And the efficiency of the predictive model could be improved with other feature selection algorithms (especially hybrid metrics) considering the experts of the knowledge domain, as the understanding of the business domain has a significant impact.
Given a set of n elements, each of which is colored one of c >= 2 colors, we have to determine an element of the plurality ( most frequently occurring) color by pairwise equal/unequal color comparisons of elements....
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Given a set of n elements, each of which is colored one of c >= 2 colors, we have to determine an element of the plurality ( most frequently occurring) color by pairwise equal/unequal color comparisons of elements. We derive lower bounds for the expected number of color comparisons when the c(n) colorings are equally probable. We prove a general lower bound of c/3n - O(root n) for c >= 2;we prove the stronger particular bounds of 7/6n - O(root n) for c = 3, 54/35n - O(root n) for c = 4, 607/315n - O(root n) for c = 5, 1592/693n - O(root n) for c = 6, 7985/3003n - O(root n) for c = 7, and 19402/6435n - O(root n) for c = 8.
We study the mixed-criticality scheduling problem, where the goal is to schedule jobs with different criticality levels on a single machine. As shown by D & uuml;rr et al. (2018), the problem can be treated as a s...
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We study the mixed-criticality scheduling problem, where the goal is to schedule jobs with different criticality levels on a single machine. As shown by D & uuml;rr et al. (2018), the problem can be treated as a specific 1dimensional triangle scheduling problem. In that paper a new Greedy algorithm was defined, and the authors proved that its approximation ratio lies between 1.05 and 3/2. In this paper we present a quadratic integer programming model, which can be used to computationally analyze the algorithm for inputs with small sizes. The model simulates the behavior of the algorithm and it compares the makespan with the optimal one. Using this model, we found sequences extendable to longer series, giving a lower bound of 1.27 for the Greedy algorithm. Also, the optimum on problem instances consisting of intervals of natural numbers is analyzed and a closed formula is determined. In this way, we detected two input classes where, in one of them, Greedy is far from optimal (we think that this could be the worst case), and in the other one it is optimal.
The minimum, maximum and average computing times of the classical Euclidean algorithm are derived. With positive integer inputs of lengths m and n, and with output (greatest common divisor) of length k, m≧n≧km≧n≧k...
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The minimum, maximum and average computing times of the classical Euclidean algorithm are derived. With positive integer inputs of lengths m and n, and with output (greatest common divisor) of length k, m≧n≧k
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