Increase In Execution Time Research Articles

A study on k-walk generation algorithm to prevent the tottering in graph edit distance heuristic algorithms

Graph edit distance is usually used for graph similarity checking due to its low information loss and flexibility advantages. However, graph edit distance can’t be used efficiently because it is an NP-Hard problem. Many graph edit distance heuristic algorithms have been proposed to solve this problem. However, some heuristic algorithms for generating walk generate unnecessary sequences because of the tottering, which leads to many problems. Because of this, various problems arise, like a decrease in approximation accuracy and an increase in execution time. In this paper, we propose an accurate and efficient graph edit distance heuristic algorithm that prevents tottering when generating walk. When generating walk, the traversed node‘s information is saved into the queue and then proceeds to traverse the next node. Then, it is possible to prevent the tottering by comparing an existing traversed node with an enqueued one. Through this, we propose a new walk generation algorithm that prevents generating unnecessary walk and applies it to existing algorithms to prevent the tottering.

Energy-Efficient Rainfall Prediction Using Support Vector Machine on Edge Ai Platforms

The integration of AI into various sectors, including agriculture, has been advancing significantly. Implementing AI in the context of IoT and edge AI presents challenges due to resource limitations. Current climate changes affect planting strategies, pest management, and harvest timing. This study explores an SVM-based machine-learning model with multiple kernels to classify weather conditions as rainy or clear. The research includes two phases: model training on a PC-based system and model deployment on an edge AI device. The training phase includes preprocessing with PCA and fine-tuning of parameters, such as kernel types (linear, polynomial, sigmoid, and RBF), C and gamma. The development phase involves deploying the model on an ESP32, where execution time and power consumption are evaluated. The results show that the SVM model with an RBF kernel, C of 0.1, and gamma of 1 achieves a precision of 79.37%. Inference on the ESP32 yields an average execution time of 35.5 ms and a power consumption of 66 mA, showing a 202-fold reduction in power usage compared to the PC-based system and a 59-fold increase in execution time. This reduced power consumption supports the feasibility of edge AI for climate-based agricultural applications, enabling effective rainfall prediction. The findings contribute to the development of precision agriculture by providing insights into climate prediction, which can inform planting decisions, pest management, and harvest timing, thereby advancing the application of edge AI in response to global climate change.

Hybrid finite element and laplace transform method for efficient numerical solutions of fractional PDEs on graphics processing units

Fractional Partial Differential equations (FPDEs) are essential for modeling complex systems across various scientific and engineering areas. However, efficiently solving FPDEs presents significant computational challenges due to their inherent memory effects, often leading to increased execution times for numerical solutions. This study proposes a highly parallelizable hybrid computational approach that combines the Finite Element Method (FEM) for spatial discretization with Numerical Inversion of the Laplace Transform (NILT) for time-domain solutions, optimized for execution on Graphics Processing Units (GPUs). The NILT method’s high parallelizability, stemming from the independence of its series terms, combined with the robust spatial discretization provided by FEM, enables the efficient and accurate solution of FPDEs on GPUs, demonstrating substantial performance improvements over traditional CPU-based implementations. We observe a generalized pattern in execution time behavior that accounts for both the number of nodes and the number of NILT terms. Specifically, execution time scales quadratically with the number of nodes, while showing only a logarithmic marginal increase with the number of NILT terms These behaviors not only enables consistent performance assessment but also highlights potential areas for algorithm optimization. Validation against exact solutions of fractional diffusion and wave equations, employing Caputo, modified Caputo-Fabrizio, and modified Atangana-Baleanu derivatives, demonstrates the accuracy and convergence of the hybrid FEM-NILT method. Notably, the exact solutions of wave equation are novel in literature. The results highlight the method’s potential for enabling high-precision, large-scale simulations in fractional calculus applications, thereby advancing computational capabilities and efficiency in the field.

A Unified Hardware Design for Multiplication, Division, and Square Roots Using Binary Logarithms

Multiplication, division, and square root operations introduce significant challenges in digital signal processing (DSP) systems, traditionally requiring multiple operations that increase execution time and hardware complexity. This study presents a novel approach that leverages binary logarithms to perform these operations using only addition, subtraction, and shifts, enabling a unified hardware implementation—a marked departure from conventional methods that handle these operations separately. The proposed design, involving logarithm and antilogarithm calculations, exhibits an algebraically symmetrical pattern that further optimizes the processing flow. Additionally, this study introduces innovative log-domain correction terms specifically designed to minimize computation errors—a critical improvement over existing methods that often struggle with precision. Compared to standard hardware implementations, the proposed design significantly reduces hardware resource utilization and power consumption while maintaining high operational frequency.

Medical Practitioner-Centric Heterogeneous Network Powered Efficient E-Healthcare Risk Prediction on Health Big Data

From a Licensed Medical Practitioner’s (LMP) perspective, e-Healthcare Risk Prediction plays a vital role in Health Big Data. This also is a hot issue in e-healthcare because of the lack of security and privacy protections. To overcome this deficiency, this research article proposes heterogeneous network systems (HNS), an efficient and privacy-preserving e-Healthcare Risk Prediction method for e-healthcare. In comparison to the existing research contribution, the proposed HNS accomplish two steps of disease risk prediction, namely Analysis of HNS, and Heterogeneous Network (HetNet) concerning the LMP for analyzing the in-hospital involvement care by collecting and explaining the “Health Big Data” as per the view of the LMP. This will help to access the services from the hospital. In the LMP-Centric Heterogeneous Network Powered Efficient e-Healthcare Risk Prediction phase, the “Polygenic Score” is calculated for risk prediction for health big data. Through the characteristics of “non-predictive applications” and “Predictive applications,” procedural aspects are analyzed with the LMP-Centric HetNet against the Efficient e-Healthcare Risk Prediction. This will be applied to the Medical extensive data integration and clustering for handling Health Big Data. Finally, the LMP-Centric HetNet Powered Efficient e-Healthcare Risk Prediction for Health Big Data treats the LMP perspective efficiently. The proposed system increased prediction accuracy to 45.9%, and the monogenic score increased from 3% to 19%. The density accuracy range is increased from 13.9% to 39%. The increased execution time is improved from 29.95% to 36.05%. This comprehensive prediction analysis accuracy range is 73.98% efficient.

The Influence of Personality Type D and Coping Strategies on Cognitive Functioning in Students.

Academic and emotional challenges faced by medical students can affect their psychological well-being and health. Personal characteristics may also predispose one to the manifestation of distress reactions. Individuals with type D personality have an increased tendency to develop depressive reactions and somatic diseases, including the presence of cognitive dysfunction. In students, the presence of cognitive dysfunction may additionally adversely affect academic and psycho-emotional problems. The purpose of this study was to examine the influence of type D personality and coping strategies on cognitive functioning in medical students. A cross-sectional study included 258 medical students (age 19 ± 1.2 years, 79 men). All participants completed psychological questionnaires (DS-14 to identify type D personality, and The Coping Strategy Indication, CSI-to determine coping strategies), as well as extensive neuropsychological testing of cognitive functions. Among the medical students examined, the frequency of identification of type D personality was 44%. In persons with personality type D, according to psychometric testing, a decrease in the level of functional mobility of nervous processes (FMNP) was noted, which was manifested in an increase in the test completion time (p < 0.001) and an increase in the number of errors (p < 0.001) during the FMNP test, and an increase in the test completion time in the attention concentration test. In addition, in type D participants, an increase in the test execution time during the attention test was noted (p = 0.007). Personality type D was an independent risk factor for cognitive decline in students in multiple linear regression analysis, when type D was analyzed as a dichotomous construct. Assessing personal characteristics and identifying personality type D is advisable for medical students, to develop subsequent programs to increase their resistance to academic challenges, improve cognitive function, and also to prepare for future stress loads during professional activities in the field of healthcare.

EOTE-FSC: An efficient offloaded task execution for fog enabled smart cities.

Smart cities provide ease in lifestyle to their community members with the help of Information and Communication Technology (ICT). It provides better water, waste and energy management, enhances the security and safety of its citizens and offers better health facilities. Most of these applications are based on IoT-based sensor networks, that are deployed in different areas of applications according to their demand. Due to limited processing capabilities, sensor nodes cannot process multiple tasks simultaneously and need to offload some of their tasks to remotely placed cloud servers, which may cause delays. To reduce the delay, computing nodes are placed in different vicinitys acting as fog-computing nodes are used, to execute the offloaded tasks. It has been observed that the offloaded tasks are not uniformly received by fog computing nodes and some fog nodes may receive more tasks as some may receive less number of tasks. This may cause an increase in overall task execution time. Furthermore, these tasks comprise different priority levels and must be executed before their deadline. In this work, an Efficient Offloaded Task Execution for Fog enabled Smart cities (EOTE - FSC) is proposed. EOTE - FSC proposes a load balancing mechanism by modifying the greedy algorithm to efficiently distribute the offloaded tasks to its attached fog nodes to reduce the overall task execution time. This results in the successful execution of most of the tasks within their deadline. In addition, EOTE - FSC modifies the task sequencing with a deadline algorithm for the fog node to optimally execute the offloaded tasks in such a way that most of the high-priority tasks are entertained. The load balancing results of EOTE - FSC are compared with state-of-the-art well-known Round Robin, Greedy, Round Robin with longest job first, and Round Robin with shortest job first algorithms. However, fog computing results of EOTE - FSC are compared with the First Come First Serve algorithm. The results show that the EOTE - FSC effectively offloaded the tasks on fog nodes and the maximum load on the fog computing nodes is reduced up to 29%, 27.3%, 23%, and 24.4% as compared to Round Robin, Greedy, Round Robin with LJF and Round Robin with SJF algorithms respectively. However, task execution in the proposed EOTE - FSC executes a maximum number of offloaded high-priority tasks as compared to the FCFS algorithm within the same computing capacity of fog nodes.

IDaPS — Improved data-locality aware data placement strategy based on Markov clustering to enhance MapReduce performance on Hadoop

The execution of Map-Reduce applications on the Hadoop cluster poses significant challenges due to the non-consideration of data locality, i.e., assigning tasks to compute nodes where input data sets are located. Due to such non-consideration, high data transfer overheads are caused. Further, it increases latency, which may arise if input data needs to be transferred across the network, thereby significantly increasing execution time. To address this issue, an Improved DAta Placement Strategy IDaPS based on the intra-dependency among the data is proposed. IDaPS re-organizes the default data layouts in HDFS to ensure higher degree of parallelism. The efficiency of IDaPS is demonstrated in Hadoop clusters (10 and 15 nodes) by executing Hadoop Benchmark performance tests viz. WordCount, Grep on Project-Gutenberg book dataset (50 GB) and Least Square Linear Regression (LSLR) on weather dataset (10.67 GB). The results were compared with state-of-the-art algorithms viz. Hadoop Default Data Placement (HDDP), Load-Balancer and literary work RENDA. The results demonstrate that IDaPS significantly reduces execution time by 28.2% and 38.4% in 10-node and 15-node clusters while executing WordCount, and 35% and 38.1% in 10-node and 15-node clusters for Grep. Similarly, for LSLR, it reduces execution time by 32.7%.

Extraction frequent patterns in trauma dataset based on automatic generation of minimum support and feature weighting

PurposeData mining has been used to help discover Frequent patterns in health data. it is widely used to diagnose and prevent various diseases and to obtain the causes and factors affecting diseases. Therefore, the aim of the present study is to discover frequent patterns in the data of the Kashan Trauma Registry based on a new method.MethodsWe utilized real data from the Kashan Trauma Registry. After pre-processing, frequent patterns and rules were extracted based on the classical Apriori algorithm and the new method. The new method based on the weight of variables and the harmonic mean was presented for the automatic calculation of minimum support with the Python.ResultsThe results showed that the minimum support generation based on the weighting features is done dynamically and level by level, while in the classic Apriori algorithm considering that only one value is considered for the minimum support manually by the user. Also, the performance of the new method was better compared to the classical Apriori method based on the amount of memory consumption, execution time, the number of frequent patterns found and the generated rules.ConclusionsThis study found that manually determining the minimal support increases execution time and memory usage, which is not cost-effective, especially when the user does not know the dataset's content. In trauma registries and massive healthcare datasets, its ability to uncover common item groups and association rules provides valuable insights. Also, based on the patterns produced in the trauma data, the care of the elderly by their families, education to the general public about encountering patients who have an accident and how to transport them to the hospital, education to motorcyclists to observe safety points in Recommended when using a motorcycle.

Advanced Forecasting Techniques and Grid Management Strategies

Energy forecasting is crucial for addressing challenges in data-rich smart grid (SG) systems, encompassing applications such as demand-side management, load shedding, and optimal dispatch. Achieving efficient forecasting with minimal prediction error remains a significant challenge due to the inherent uncertainty in SG data. This paper provides a comprehensive, application-focused review of advanced forecasting methods for SG systems, highlighting recent advancements in probabilistic deep learning (PDL).The review extensively examines traditional point forecasting methods, including statistical, machine learning (ML), and deep learning (DL) techniques, evaluating their suitability for energy forecasting. Additionally, the importance of hybrid approaches and data preprocessing techniques in enhancing forecasting performance is discussed.A comparative case study utilizing the Victorian electricity consumption in Australia and American Electric Power (AEP) datasets is conducted to assess the performance of deterministic and probabilistic forecasting methods. The analysis reveals that DL methods, with appropriate hyper-parameter tuning, exhibit superior efficacy when dealing with larger sample sizes and nonlinear patterns. Moreover, PDL methods demonstrate at least a 60% reduction in prediction errors compared to other benchmark DL methods. However, the increased execution time for PDL methods, due to the large sample space

A Statistical Study of Arien Sequences for an H.264 Prediction Module Lower Complexity

Abstract Advanced video coding, or H.264/AVC has been proved to possess a highly complex encoder with a block motion estimation (ME) scheme, entropy coding and a prediction module. Typical applications of unmanned aerial vehicles (UAVs) require real-time video compression. As a result, the current study aims to develop an approach to decrease the computing complexity of the prediction module. The software algorithm that was developed helps basic functions to be achieved in the prediction module. In fact, the user can set up, manage, deploy, and monitor the UAVs in real time. Evaluations obtained in this paper have shown that the adopted approach can significantly improve the execution time of coding and, consequently, the network performance. When comparing the original method of the Laboratory of Electronics and Information’s software with the suggested approach, experimental results show that the proposed method had achieved an increase in execution time from 3.544–15.574 %.

Comparison of Safety Mechanisms for Human-Robot Collaboration in Assembly using a Top-View RGB-D Camera System

The paradigm shift from mass production to mass customization is leading to new requirements in today's manufacturing environment. Especially in manual manufacturing processes, these requirements are characterized by high-cost pressure, increasing variant diversity and required lot size. In contrast to full-automation settings with industrial robots, human-robot collaboration offers the potential to design adaptable hybrid solutions and generate individual levels of automation in rapidly changing production lines. The safety of the operator in collaborative robot applications is one of the key factors for facilitating their use in industrial practice. This paper addresses this issue and presents a solution for safe human-robot collaboration using a top-view RGB-D camera system that continuously monitors the shared workspace. Based on the detection of visual markers and human hand tracking, different safety strategies according to DIN ISO/TS 15066 are implemented and successively compared in a paradigmatic assembly scenario as a part of a user study with 12 test persons. The utilized safety strategies include safety-rated monitored stop, speed and separation monitoring, and active contact prevention. Compared to the incorporated safety features of collaborative robots, the results show that the acceptance and fluency of collaborative robot applications improves, but the execution time increases at the same time.

Optimization and performance investigation of 1-Toffoli gate quantum full adders for spin-torque-based n-qubit architecture

Quantum computing (QC) is suitable for reversible computing due to its inherent parallel processing ability and fast speed. It also helps to address the issue of high-power dissipation in classical computing. Moreover, QC gates are the sequence of elementary operations such as single-qubit rotation and two-qubit entanglement. Elementary quantum operations are required to be reduced for the realization of complex computing. In this paper, optimization of 1-Toffoli gate-based quantum full adders (QFAs) in terms of the number of elementary operations with the help of quantum library {Ry, Rz, sqrt{mathrm{SWAP}}} is carried out. Moreover, the performance of two different 1-Toffoli QFAs is investigated in terms of execution time, fidelity, and number of electrons required to realize the QFAs. Improvement in fidelity is 0.7% and 0.57% for QFA1 and QFA2, respectively, compared to the fidelity of 2-Toffoli QFA. A 9.97% increase in execution time is mandatory for the QFA2 compared to QFA1. The QFA2 takes 5% more number of electrons in comparison to QFA1.

MODIFIED GENETIC ALGORITHM APPROACH FOR SOLVING THE TWO-STAGE LOCATION PROBLEM

Context. Optimization of logistics processes is one of the important tasks of supply chain management in various fields, including medicine. Effective coordination in medical logistics is essential to ensure public health and prosperity. This is especially essential during global emergencies when the rapid and efficient distribution of medicines is critical. In addition, professional logistics management is critical to delivering humanitarian aid, where the timely transportation of medical supplies and resources can be lifesaving. The most advanced technologies and algorithms are being used to improve medical logistics processes. This paper considers modifying the genetic algorithm for solving the two-stage location problem in supply chain management in the distribution of medicines and medical equipment.&#x0D; Objective. The work aims to build a model and develop an algorithm for solving a two-stage location problem in the context of the medical logistics problem with further analysis of their applications and performance.&#x0D; Method. We propose to use a genetic algorithm to solve a two-stage logistics problem. The peculiarities of this algorithm are the modification of evaluation procedures and the use of mixed mutation, which allows for solving the problem effectively, considering irregularities in the statement regarding the subject – the limits on the centers’ location at several stages of the logistic process.&#x0D; Results. The paper deals with a two-stage location problem with constraints on the maximum number of centers. Considering the specific requirements of medical logistics in the transportation context of medicines and medical equipment, a mathematical model and modification of the genetic algorithm are proposed. The developed algorithm is tested on model tasks and can produce effective solutions for problems ranging in size from 25 to 1000. The solution process takes longer for larger problems with dimensions from 1001 to 2035. Additionally, the influence of increasing the maximum generations number on the time of execution is investigated. When the maximum generation value increases from 50 to 100 and from 100 to 150 generations, the algorithm’s execution time increases by 45.69% and 51.68%, respectively. 73% of the total execution time is dedicated to the evaluation procedure. The algorithm is applied to the medical logistics problem in the Dnipropetrovsk region (Ukraine). An efficient solution is obtained within an acceptable execution time.&#x0D; Conclusions. A mathematical model for a two-stage location problem in the context of medical logistics is introduced. It considers the peculiarities of the medical field. A solution algorithm based on a genetic approach is developed and applied to the medical logistics problem. The algorithm has been tested on model tasks of varying sizes, with a comprehensive analysis conducted on the correlation between the problem size and the algorithm’s running time. In addition, it is investigated how the maximum number of generations affects the algorithm’s execution time. The role of each stage in the genetic algorithm research towards the overall effectiveness of the algorithm is researched. The obtained results indicate high efficiency and wide application possibilities of the proposed mathematical model and algorithm. The developed method demonstrates high performance and reliability.

Review of Load Balancing Algorithms Inspired by Artificial Bee Colony Algorithm in the Cloud Computing

Research interest in cloud-based load balancing and task scheduling has grown rapidly over the last few years. To be more precise, load balancing of virtual machine tasks (VMs) that require nature-inspired algorithms has become an area of particular interest. It is essential to ensure load balancing between VMs as this avoids overloading and underloading VMs, aspects that can cause issues like high-power consumption, increased execution time, and elevated response times, all of which can ultimately cause a system failure. Swarm intelligence is critical when dealing with issues that are difficult to solve and must be overcome using traditional and mathematical techniques. The algorithm was developed by Karaboga in 2005 and is inspired by the foraging behaviors of an artificial bee colony. This algorithm is extremely robust, convergent, and flexible. Another researcher applied the ABC method to load balancing in order to enhance it. In-depth research on load balancing in cloud computing utilizing the ABC method is presented in this review paper. Moreover, this work also discusses some fundamental ideas on the intelligence and characteristics of the swarm. Additionally, the paper provided a detailed explanation of cloud computing, its services, and components, as well as the concept and objectives of load balancing in the cloud

Resource Sharing in Dataflow Circuits

To achieve resource-efficient hardware designs, high-level synthesis (HLS) tools share (i.e., time-multiplex) functional units among operations of the same type. This optimization is typically performed in conjunction with operation scheduling to ensure the best possible unit usage at each point in time. Dataflow circuits have emerged as an alternative HLS approach to efficiently handle irregular and control-dominated code. However, these circuits do not have a predetermined schedule—in its absence, it is challenging to determine which operations can share a functional unit without a performance penalty. More critically, although sharing seems to imply only some trivial circuitry, time-multiplexing units in dataflow circuits may cause deadlock by blocking certain data transfers and preventing operations from executing. In this paper, we present a technique to automatically identify performance-acceptable resource sharing opportunities in dataflow circuits. More importantly, we describe a sharing mechanism which achieves functionally correct and deadlock-free dataflow designs. On a set of benchmarks obtained from C code, we show that our approach effectively implements resource sharing. It results in significant area savings at a minor performance penalty compared to dataflow circuits which do not support this feature (i.e., it achieves a 64%, 2%, and 18% average reduction in DSPs, LUTs, and FFs, respectively, with an average increase in total execution time of only 2%) and matches the sharing capabilities of a state-of-the-art HLS tool.

Developing a new encryption algorithm for images transmitted through WSN systems

Wireless sensor networks (WSNs) have up until now faced many challenges because of their open, wide-ranging, and resource-limited nature, including security, efficiency, and energy consumption. In the military system, it is essential to provide high-level security to the significant data over wireless network which is proved to be unreliable public communications. To solve the above problem, high level of security with minimum complexity should be applied to be adequate to limited capabilities of transmission system. This paper presents a new algorithm named (SRS) for encrypting transmitted military images to keep them from getting hacked or broken by WSN. The SRS algorithm is designed to be lightweight, fast, and secure, taking into consideration the limited capabilities of the transmission system. It is implemented as a public key cryptosystem specifically designed for image encryption. The algorithm consists of two parts: encryption and decryption. The proposed system suggested some equations and calculations that are applied to the plain and coded images after being transmitted over the WSN. The results of testing the simulation model demonstrate the effectiveness of the system by evaluation using various metrics such as Signal-to-Noise Ratio (SNR), Mean Squared Error (MSE), and Peak Signal-to-Noise Ratio (PSNR). Overall, the paper presents a new encryption algorithm, SRS, specifically designed for securing military images transmitted over wireless sensor networks. The algorithm aims to provide a balance between security, efficiency, and energy consumption, considering the resource-limited nature of WSNs. The simulation results indicate the improvement of the proposed system by 13 %, 10 %, and 55 % in packet delivery ratio (PDR), throughput, and dropping ratio, respectively, and it shows that the suggested SRS method increased execution time by 67 % compared to RSA based algorithm

Analysis of cognitive characteristics in patients with arterial hypertension in middle and old age

Introduction. The study of the spectrum of neurocognitive disorders in patients with arterial hypertension (AH) in order to create an effective therapeutic and rehabilitation strategy is an urgent direction of modern medicine.Aim. To study neuropsychological characteristics in patients with hypertension in middle and old age.Materials and methods. 357 middle-aged and elderly patients with hypertension were examined. All patients underwent neuropsychological examination: Montreal Cognitive Function Assessment Scale (MoCA test), Schulte Table Test, Verbal Association Test, Trail Making Test (TMT), Asthenia Self-questionnaire (MFI-20), O. Kopina Reader Adaptation Test, questionnaire on the level of life exhaustion, hospital anxiety scale and depression (HADS).Results. The analysis of the results of general neuropsychological testing showed a deviation from the reference values in the majority of participants. In the Schulte test, an increase in execution time was noted in 50% of elderly patients and in 21% of middle-aged patients. In the TMT test: an increase in the execution time of part A – in 88% of elderly patients and 58% of middle-aged patients, part B – in 97 and 88% of patients, respectively. The MoCA test demonstrated pronounced cognitive impairment in 16% of middle-aged patients and in 35% of elderly patients. More than 97% of elderly and 88% of middle-aged patients showed a high level of asthenia in the MFI-20 test; life exhaustion was noted in 56 and 45%, and anxiety and depression in more than 50% of elderly and 35% of middle-aged patients, respectively.Conclusion. In the studied groups of patients with hypertension, there was a decrease in the integral index of cognitive functions, as well as changes in indicators in tests characterizing the state of control functions, attention, speed of thought processes and semantic memory, while more pronounced deviations were noted in the elderly. The described cognitive impairments were combined with a high level of psychoemotional tension, anxiety, depression and asthenia.

Legolas 2.0: Improvements and extensions to an MHD spectroscopic framework

We report on recent extensions and improvements to the Legolas code, which is an open-source, finite element-based numerical framework to solve the linearised (magneto)hydrodynamic equations for a three-dimensional force- and thermally balanced state with a nontrivial one-dimensional variation. The standard Fourier modes imposed give rise to a complex, generalised non-Hermitian eigenvalue problem which is solved to quantify all linear wave modes of the given system in either Cartesian or cylindrical geometries. The framework now supports subsystems of the eight linearised MHD equations, allowing for pure hydrodynamic setups, only one-dimensional density/temperature/velocity variations, or the option to treat specific closure relations. We discuss optimisations to the internal datastructure and eigenvalue solvers, showing a considerable performance increase in both execution time and memory usage. Additionally the code now has the capability to fully visualise eigenfunctions associated with given wave modes in multiple dimensions, which we apply to standard Kelvin-Helmholtz and Rayleigh-Taylor instabilities in hydrodynamics, thereby providing convincing links between linear stability analysis and the onset of non-linear phenomena.

Adaptive and Convex Optimization-Inspired Workflow Scheduling for Cloud Environment

Scheduling large-scale and resource-intensive workflows in cloud infrastructure is one of the main challenges for cloud service providers (CSPs). Cloud infrastructure is more efficient when virtual machines and other resources work up to their full potential. The main factor that influences the quality of cloud services is the distribution of workflow on virtual machines (VMs). Scheduling tasks to VMs depends on the type of workflow and mechanism of resource allocation. Scientific workflows include large-scale data transfer and consume intensive resources of cloud infrastructures. Therefore, scheduling of tasks from scientific workflows on VMs requires efficient and optimized workflow scheduling techniques. This paper proposes an optimised workflow scheduling approach that aims to improve the utilization of cloud resources without increasing execution time and execution cost.