A comprehensive survey on container resource allocation approaches in cloud computing: State-of-the-art and research challenges

The allocation of resources in the cloud environment is efficient and vital, as it directly impacts versatility and operational expenses. Containers, like virtualization technology, are gaining popularity due to their low overhead when compared to traditional virtual machines and portability. The resource allocation methodologies in the containerized cloud are intended to dynamically or statically allocate the available pool of resources such as CPU, memory, disk, and so on to users. Despite the enormous popularity of containers in cloud computing, no systematic survey of container scheduling techniques exists. In this survey, an outline of the present works on resource allocation in the containerized cloud correlative is discussed. In this work, 64 research papers are reviewed for a better understanding of resource allocation, management, and scheduling. Further, to add extra worth to this research work, the performance of the collected papers is investigated in terms of various performance measures. Along with this, the weakness of the existing resource allocation algorithms is provided, which makes the researchers to investigate with novel algorithms or techniques.

Optimal Management and Control of Smart Thermal-Energy Grids

This work deals with the development of novel algorithms and methodologies for the optimal management and control of thermal and electrical energy units operating in a networked configuration. The aim of the work is to foster the creation of a smart thermal-energy grid (smart-TEG), by providing supporting tools for the modeling of subsystems and their optimal control and coordination. A hierarchical scheme is proposed to optimally address the management and control issues of the smart-TEG. Different methods are adopted to deal with the features of the specific generation units involved, e.g., multi-rate MPC approaches, or linear parameter-varying strategies for managing subsystem nonlinearity. An advanced scheme based on ensemble model is also conceived for a network of homogeneous units operating in parallel. Moreover, a distributed optimization algorithm for the high-level unit commitment problem is proposed to provide a robust, flexible and scalable scheme.

Exploiting the S-Iteration Process for Solving Power Flow Problems: Novel Algorithms and Comprehensive Analysis

In recent studies, the competitiveness of the Newton-S-Iteration-Process (Newton-SIP) techniques to efficiently solve the Power Flow (PF) problems in both well and ill-conditioned systems has been highlighted, concluding that these methods may be suitable for industrial applications. This paper aims to tackle some of the open topics brought for this kind of techniques. Different PF techniques are proposed based on the most recently developed Newton-SIP methods. In addition, convergence analysis and a comparative study of four different Newton-SIP methods PF techniques are presented. To check the features of considered PF techniques, several numerical experiments are carried out. Results show that the considered Newton-SIP techniques can achieve up to an eighth order of convergence and typically are more efficient and robust than the Newton–Raphson (NR) technique. Finally, it is shown that the overall performance of the considered PF techniques is strongly influenced by the values of parameters involved in the iterative procedure.

Latest Development in Physics-Based Modeling of Coiled Tubing Plasticity and Fatigue

Summary Coiled tubing (CT) integrity is critical for well intervention operations in the field. To monitor and manage tubing integrity, the industry has developed a number of computer models over the past decades. Among them, low-cycle fatigue (LCF) modeling plays a paramount role in safeguarding tubing integrity. LCF modeling of CT strings dates back to the 1980s. Recently, novel algorithms have contributed to developments in physics-based modeling of tubing fatigue and plasticity. When CT trips into and out of the well, it goes through bending/straightening cycles under high differential pressure. Such tough conditions lead to low- or ultralow-cycle fatigue, limiting CT useful life. The model proposed in this study is derived from a previous one and is based on rigorously derived material parameters to compute the evolution of state variables from a wide range of loading conditions. Through newly formulated plasticity and strain parameters, a physics-based damage model predicts CT fatigue life, along with diametral growth and wall thinning. The revised modeling approach gives results for CT damage accumulation, diametral growth, and wall thinning under realistic field conditions, with experimental validation. For 20 different CT alloys, it was observed that the model improved in accuracy overall by approximately 18.8% and consistency by 14.0%, for constant pressure data sets of more than 4,500 data points. The modeling results provide insights into the nonlinear nature of fatigue damage accumulation. This study allowed developing recommendations to guide future analytical modeling and experimental investigations, summarize theoretical findings in physics-based LCF modeling, and provide practical guidelines for CT string management in the field. The study provides a fundamental understanding of CT LCF and introduces novel algorithms in plasticity and damage.

Performance-efficient distributed transfer and transformation of big spatial histopathology datasets in the cloud

Whole Slide Image (WSI) datasets are giga-pixel resolution, unstructured histopathology datasets that consist of extremely big files (each can be as large as multiple GBs in compressed format). These datasets have utility in a wide range of diagnostic and investigative pathology applications. However, the datasets present unique challenges: The size of the files, propriety data formats, and lack of efficient parallel data access libraries limit the scalability of these applications. Commercial clouds provide dynamic, cost-effective, scalable infrastructure to process these datasets, however, we lack the tools and algorithms that will transfer/transform them onto the cloud seamlessly, providing faster speeds and scalable formats. In this study, we present novel algorithms that transfer these datasets onto the cloud while at the same time transforming them into symmetric scalable formats. Our algorithms use intelligent file size distribution, and pipelining transfer and transformation tasks without introducing extra overhead to the underlying system. The algorithms, tested in the Amazon Web Services (AWS) cloud, outperform the widely used transfer tools and algorithms, and also outperform our previous work. The data access to the transformed datasets provides better performance compared to the related work. The transformed symmetric datasets are fed into three different analytics applications: a distributed implementation of a content-based image retrieval (CBIR) application for prostate carcinoma datasets, a deep convolutional neural network application for classification of breast cancer datasets, and to show that the algorithms can work with any spatial dataset, a Canny Edge Detection application on satellite image datasets. Although different in nature, all of the applications can easily work with our new symmetric data format and performance results show near-linear speed-ups as the number of processors increases.

Algorithms for computing sums of powers of arithmetic progressions by using Eulerian numbers

The sums of powers of arithmetic progressions is of the form a^p+(a+d)^p +(a+2d)^p+\cdots+(a+(n-1)d)^p, where n\geq 1, p is a non-negative integer, and a and d are complex numbers with d\neq 0. This sum can be computed using classical Eulerian numbers \cite{worpitzky1883studien} and general Eulerian numbers \cite{xiong2013general}. This paper gives a new method using classical Eulerian numbers to compute this sum. The existing formula that uses general Eulerian numbers are more algorithmically complex due to more numbers to compute. This paper presents and focuses on two novel algorithms involving both types of Eulerian numbers. This paper gives a comparison to Xiong \textit{et al.}’s result with general Eulerian numbers \cite{xiong2013general}. Moreover, an analysis of theoretical time complexities is presented to show our algorithm is less complex. Various values of p are analyzed in the proposed algorithms to add significance to the results. The experimental results show the proposed algorithm remains around 70\% faster as p increases.

Diagnosing Systems through Approximated Information

This article presents a novel approach to diagnose faults in injection molding machines. A novel data-driven approach is presented to learn an approximation of dependencies between variables using Spearman correlation. It is further shown, how the approximation of the dependencies are used to create propositional logic rules for fault diagnosis. The article presents two novel algorithms: 1) to estimate dependencies from process data and 2) to create propositional logic diagnosis rules from those connections and perform consistency-based fault diagnosis. The presented approach was validated using three experiments. The first two show that the presented approach works well for injection molding machines and a simulation of a four-tank system. The limits of the presented method are shown with the third experiment containing sets of highly correlated signals.

The Ability of Near Infrared (NIR) Spectroscopy to Predict Functional Properties in Foods: Challenges and Opportunities

Near infrared (NIR) spectroscopy is considered one of the main routine analytical methods used by the food industry. This technique is utilised to determine proximate chemical compositions (e.g., protein, dry matter, fat and fibre) of a wide range of food ingredients and products. Novel algorithms and new instrumentation are allowing the development of new applications of NIR spectroscopy in the field of food science and technology. Specifically, several studies have reported the use of NIR spectroscopy to evaluate or measure functional properties in both food ingredients and products in addition to their chemical composition. This mini-review highlights and discussed the applications, challenges and opportunities that NIR spectroscopy offers to target the quantification and measurement of food functionality in dairy and cereals.

Advanced Computational Methods for Oncological Image Analysis

The Special Issue “Advanced Computational Methods for Oncological Image Analysis”, published for the Journal of Imaging, covered original research papers about state-of-the-art and novel algorithms and methodologies, as well as applications of computational methods for oncological image analysis, ranging from radiogenomics to deep learning [...]