Executive Resources Research Articles

A deep reinforcement learning-based optimization approach for containerized microservice scheduling in Hybrid Fog/Cloud environments

The deployment of microservices in Hybrid Fog/Cloud (HFC) environments for Internet of Things (IoT) applications presents a significant challenge in efficiently scheduling containerized services across distributed resources. While existing studies have explored microservice scheduling, a comprehensive approach that considers resource constraints, workflow dependencies, and dynamic hybrid environments remains elusive. This paper introduces a novel Deep Reinforcement Learning-based Algorithm (DRLA) for containerized microservice scheduling in HFC environments. DRLA utilizes a multi-constrained Binary Quadratic Program (BQP) model to optimize execution time, resource consumption, and occupancy rates while considering microservice dependencies and resource capabilities. The algorithm leverages two Deep Reinforcement Learning (DRL) agents, DQN and REINFORCE, to learn and adapt to the dynamic nature of the HFC federation. Experimental evaluations using five real-world Business Process (BP) use cases demonstrate that DRLA outperforms existing scheduling approaches such as default Kubernetes, Reward Sharing Deep Q-Learning (RSDQL), and Deep Reinforcement Learning (DRL) schedulers. Compared to existing schedulers, ours delivers optimal or near-optimal solutions, demonstrating significant improvements in key performance values. Indeed, DRLA achieves average optimality gaps of just 0.16% and 0.21%, significantly outperforming the Kubernetes scheduler, which exhibits a gap of 2.88%. It is worth noting that other similar algorithms see a far higher increase in optimality gaps. This highlights DRLA’s excellent performance and ability to schedule containerized microservices in hybrid fog/cloud environments, resulting in near-optimal solutions for a variety of use cases.

Multisegment Overlap–Save Method for Coherent Dedispersion

Dispersion occurs due to the interstellar medium, which functions as a prism and causes different time delays in radio waves of varying frequencies. The coherent dedispersion technique is often used in pulsar and fast radio burst observations to mitigate this phenomenon. The widely recognized Overlap–Save approach enables this dedispersion algorithm to efficiently perform long linear convolution. However, with the present implementation, the necessary filter length for dedispersion can reach 100 million points or more. The corresponding fast Fourier transform (FFT) points should be larger than this value and a GPU cluster can be used to tackle this demanding process. This study presents the Multisegment Overlap–Save Method (MS-OSM) to effectively address this problem. Our algorithm divides signal bands into separate short segments based on frequency component delays during dedispersion. By using the short segments shuffling technique with the Overlap-Save structure, MS-OSM can greatly reduce the FFT and inverse FFT points required down to fewer than 65,536 points. To evaluate the performance of MS-OSM, a synthetic pulsar signal is created and verified using standard software tools like DSPSR and PRESTO. The results show that MS-OSM maintains the same resolution while reducing execution time and resource usage. The validation of MS-OSM is taken by processing real pulsar observation data.

Testing the impact of hatha yoga on task switching: a randomized controlled trial.

Switching attention between or within tasks is part of the implementation and maintenance of executive control processes and plays an indispensable role in our daily lives: It allows us to perform on distinct tasks and with variable objects, enabling us to adapt to and respond in dynamically changing environments. Here, we tested if yoga could benefit switching of attention between distinct objects of one’s focus (e.g., through practicing switching between one’s own body, feelings, and different postures) in particular and executive control in general. We therefore conducted a randomized controlled trial with 98 participants and a waitlisted control group. In the intervention group, healthy yoga novices practiced Hatha yoga 3x a week, for 8 weeks. We conducted two experiments: A purely behavioral task investigating changes in behavioral costs during switching between attentional control sets (74 participants analyzed), and a modality-switching task focusing on electrophysiology (EEG data of 47 participants analyzed). At the electrophysiological level, frequency-tagging indicated no interventional effect on participants’ ability to switch between the auditory and visual modalities. However, increases in task-related frontocentral theta activity, resulting from the intervention, indicated an ability to increasingly deploy executive resources to the prioritized task when needed. At the behavioral level, our intervention resulted in more efficient holding of target representations in working memory, indicated by decreased mixing costs. Again, however, intervention effects on switching costs were missing. We, thus, conclude that Hatha yoga has a positive influence on executive control, potentially through improvements in working memory rather than directly on switching.Clinical trial registrationclinicaltrials.gov, identifier [NCT05232422].

Increasing mind wandering with accelerated intermittent theta burst stimulation over the left dorsolateral prefrontal cortex

Mind wandering (MW) is the intentional or unintentional experience of attending to internal task-unrelated thoughts while being occupied with an external task. Even though maintaining task focus is assumed to require executive functions (EF), it is not clear how and to what extent MW and EF interact. Research has found that activity in the dorsolateral prefrontal cortex (DLPFC) is associated with EF and MW. To understand the causal role of the DLPFC in relation to MW and EF, researchers have turned to non-invasive brain stimulation. Thus far, most studies have used transcranial direct current stimulation, but the results have been inconclusive. To further elucidate the relationship between the DLPFC, EF and MW, we conducted a pre-registered, sham-controlled, triple-blinded within-subject experiment by combining intermittent theta burst stimulation (iTBS) interleaved with a recently developed MW-EF task. In contrast to our expectations, participants reported significantly more MW following real iTBS as compared to sham stimulation. However, at the same time, psychomotor precision and EF improved, indicating that participants were able to engage in resource-intensive MW while simultaneously performing well on the task. We argue that iTBS enhanced the underlying executive resources that could be used to increase both MW and task performance in line with the resource-control view of MW. This finding opens exciting avenues for studying the complex interplay between MW and EF and provides empirical support for the utility of iTBS in improving executive performance during a demanding cognitive task.

Controlling user access with scripting to mitigate cyber-attacks

With the growing threats in the dynamic digital landscape, organizations are grappling with the difficult task of managing cyber-attacks by restricting user accessibility to most communication systems. This study aimed to develop a Linux script as a proactive response to the growing of cyber-attacks in organizational settings to prevent attacking vectors based on user negligence. The primary objective was to devise inventive solutions through script automation to restrict user access to the organizational network within predefined time frames, thereby mitigating potential cyber threats. The criteria used to define predefined time frames for user access, include the restriction of users on off duty times, The scripting seeks to address cyber-attacks targeted such as cross-site scripting (XSS) and SQL injection. To achieve the goal of this paper, the necessary steps and logic required to develop the scripts were taken. For instance, there are several suitable languages such as Bash, Python, or Perl, that can be used to write script. The paper selected Bash because it excels at scripting existing command-line tools and utilities, making it a great choice for automating tasks by using Vim text editor tool. The final scripts were run, some errors were identified, and modified as needed to ensure it worked correctly. This phase was necessary to improve the script's performance, readability, and maintainability by refining code, using efficient algorithms, by incorporating best practices. Finally, the various user inputs and scenarios were deployed to put the script into production for user access management. The adopted script metrics deployed such as Script Execution Time, Resources Utilization, Success Rate and Error Rate helped in evaluating the performance, efficiency, and effectiveness of the scripts. The implementation of the script achieved an executive time of 0.032 s, success rate was 1 and error rate was 0. The practical implementation in organizations can be scaled up or down depending on the organization's size and infrastructure. The compatibility of the developed Linux script with different operating systems, hardware platforms and network architectures will have no impact in adapting to various organizational environments. The script did not show any challenges, but real-world deployment may exhibit some potential challenges and limitations that may be associated with implementing the solution, such as resource constraints and legacy system compatibility.

K-medoid clustering containerized allocation algorithm for cloud computing environment

Load balancing is critical for container-based cloud computing environments for several reasons. A lack of appropriate load balancing techniques could result in a decrease in performance and possible service interruptions as some nodes get overloaded, while others are left underutilized. Cloud service providers can reduce latency and boost system performance by strategically placing containers using clustering algorithms. These techniques aid in efficiently using resources and load balancing by clustering related containers together according to their shared attributes. Clustering strategies are effective in allocating and controlling resources to meet the demands of a changing workload. Algorithms for clustering combine related workloads or containers into clusters, improving performance isolation and maximizing resource usage. One popular methodology for data clustering is the K-Medoid Clustering Algorithm. It is especially helpful when working with categorical data or when the dataset contains outliers. K-medoids is an unsupervised clustering approach where the core of the cluster is made up of data points known as “medoids.” A medoid is a location in the cluster whose total distance to every object in the cluster—also known as its dissimilarity—is as small as possible. Any appropriate distance function may be used, such as the Manhattan distance, the Euclidean distance, or another one. Thus, by choosing K medoids from our data sample, the K-medoids method splits the data into K clusters. This work presents the K-Medoid clustering technique for containers, which can enhance load balancing, decrease resource execution times, and increase resource utilization rates all at the same time. The results of the experiment show that the proposed method outperforms MACO and FCFS in terms of throughput by about 70% when number of cloudlets increased. The relative improvement of execution time of the proposed K-medoid algorithm w.r.t FCFS is about 50%.

Deep learning and optimization enabled multi-objective for task scheduling in cloud computing

ABSTRACT In cloud computing (CC), task scheduling allocates the task to best suitable resource for execution. This article proposes a model for task scheduling utilizing the multi-objective optimization and deep learning (DL) model. Initially, the multi-objective task scheduling is carried out by the incoming user utilizing the proposed hybrid fractional flamingo beetle optimization (FFBO) which is formed by integrating dung beetle optimization (DBO), flamingo search algorithm (FSA) and fractional calculus (FC). Here, the fitness function depends on reliability, cost, predicted energy, and makespan, the predicted energy is forecasted by a deep residual network (DRN). Thereafter, task scheduling is accomplished based on DL using the proposed deep feedforward neural network fused long short-term memory (DFNN-LSTM), which is the combination of DFNN and LSTM. Moreover, when scheduling the workflow, the task parameters and the virtual machine’s (VM) live parameters are taken into consideration. Task parameters are earliest finish time (EFT), earliest start time (EST), task length, task priority, and actual task running time, whereas VM parameters include memory utilization, bandwidth utilization, capacity, and central processing unit (CPU). The proposed model DFNN-LSTM+FFBO has achieved superior makespan, energy, and resource utilization of 0.188, 0.950J, and 0.238, respectively.

Organized Optimization Integration Validation Model for Internet of Things (IoT)-Based Real-Time Applications

Emerging technology like the Internet of Things (IoT) has great potential for use in real time in many areas, including healthcare, agriculture, logistics, manufacturing, and environmental surveillance. Many obstacles exist alongside the most popular IoT applications and services. The quality of representation, modeling, and resource projection is enhanced through interactive devices/interfaces when IoT is integrated with real-time applications. The architecture has become the most significant obstacle due to the absence of standards for IoT technology. Essential considerations while building IoT architecture include safety, capacity, privacy, data processing, variation, and resource management. High levels of complexity minimization necessitate active application pursuits with variable execution times and resource management demands. This article introduces the Organized Optimization Integration Validation Model (O2IVM) to address these issues. This model exploits k-means clustering to identify complexities over different IoT application integrations. The harmonized service levels are grouped as a single entity to prevent additional complexity demands. In this clustering, the centroids avoid lags of validation due to non-optimized classifications. Organized integration cases are managed using centroid deviation knowledge to reduce complexity lags. This clustering balances integration levels, non-complex processing, and time-lagging integrations from different real-time levels. Therefore, the cluster is dissolved and reformed for further integration-level improvements. The volatile (non-clustered/grouped) integrations are utilized in the consecutive centroid changes for learning. The proposed model’s performance is validated using the metrics of execution time, complexity, and time lag.

Optimizing Database Performance in Complex Event Processing through Indexing Strategies

Complex event processing (CEP) systems have gained significant importance in various domains, such as finance, logistics, and security, where the real-time analysis of event streams is crucial. However, as the volume and complexity of event data continue to grow, optimizing the performance of CEP systems becomes a critical challenge. This paper investigates the impact of indexing strategies on the performance of databases handling complex event processing. We propose a novel indexing technique, called Hierarchical Temporal Indexing (HTI), specifically designed for the efficient processing of complex event queries. HTI leverages the temporal nature of event data and employs a multi-level indexing approach to optimize query execution. By combining temporal indexing with spatial- and attribute-based indexing, HTI aims to accelerate the retrieval and processing of relevant events, thereby improving overall query performance. In this study, we evaluate the effectiveness of HTI by implementing complex event queries on various CEP systems with different indexing strategies. We conduct a comprehensive performance analysis, measuring the query execution times and resource utilization (CPU, memory, etc.), and analyzing the execution plans and query optimization techniques employed by each system. Our experimental results demonstrate that the proposed HTI indexing strategy outperforms traditional indexing approaches, particularly for complex event queries involving temporal constraints and multi-dimensional event attributes. We provide insights into the strengths and weaknesses of each indexing strategy, identifying the factors that influence performance, such as data volume, query complexity, and event characteristics. Furthermore, we discuss the implications of our findings for the design and optimization of CEP systems, offering recommendations for indexing strategy selection based on the specific requirements and workload characteristics. Finally, we outline the potential limitations of our study and suggest future research directions in this domain.

When the Heart Meets the Mind: Exploring the Brain-Heart Interaction during Time Perception.

Recent studies suggest that time estimation relies on bodily rhythms and interoceptive signals. We provide the first direct electrophysiological evidence suggesting an association between the brain's processing of heartbeat and duration judgment. We examined heartbeat-evoked potential (HEP) and contingent negative variation (CNV) during an auditory duration-reproduction task and a control reaction-time task spanning 4, 8, and 12 s intervals, in both male and female participants. Interoceptive awareness was assessed with the Self-Awareness Questionnaire (SAQ) and interoceptive accuracy through the heartbeat-counting task (HCT). Results revealed that SAQ scores, but not the HCT, correlated with mean reproduced durations with higher SAQ scores associating with longer and more accurate duration reproductions. Notably, the HEP amplitude changes during the encoding phase of the timing task, particularly within 130-270 ms (HEP1) and 470-520 ms (HEP2) after the R-peak, demonstrated interval-specific modulations that did not emerge in the control task. A significant ramp-like increase in HEP2 amplitudes occurred during the duration-encoding phase of the timing but not during the control task. This increase within the reproduction phase of the timing task correlated significantly with the reproduced durations for the 8 s and the 4 s intervals. The larger the increase in HEP2, the greater the under-reproduction of the estimated duration. CNV components during the encoding phase of the timing task were more negative than those in the reaction-time task, suggesting greater executive resources orientation toward time. We conclude that interoceptive awareness (SAQ) and cortical responses to heartbeats (HEP) predict duration reproductions, emphasizing the embodied nature of time.

IoT-driven blockchain to manage the healthcare supply chain and protect medical records

Healthcare supply chain domain and medical records’ management face numerous challenges that come with new demands, such as customer dissatisfaction, rising healthcare costs, tracking and traceability of drugs, and security and privacy related to the sensitive information managed in such a domain. Executing processes related to healthcare domain in a trusted, secure, efficient, accessible and traceable manner is challenging due to the fragmented nature of the healthcare supply chain, which is prone to systemic errors and redundant efforts that may compromise patient safety and negatively impact health outcomes. To cope with such issues, blockchain technology, combined with the Internet of Things (IoT), can offers a reliable way to track and trace products and protect medical data though a peer-to-peer distributed, secure, and shared ledger. Hence, this paper proposes an IoT-driven blockchain-based architecture to manage the healthcare supply chain and protect medical records from tampering and access violation. Hyperledger Fabric, which is a permissioned blockchain, has been adopted due to the sensitive and private nature of the collected data. The envisioned network has been implemented and performance has been evaluated in terms of execution time, resources consumption and throughput.

Mixed-integer linear programming for project scheduling under various resource constraints

Project scheduling is an important management task in many companies across different industries. Generally, projects require resources, such as personnel or funds, whose availabilities are limited, giving rise to the challenging problem of resource-constrained project scheduling. In this paper, we consider the scheduling of a project consisting of precedence-related activities that require time and two types of resources for execution: storage resources representing, e.g., the project budget; and renewable resources representing, e.g., personnel or equipment. Storage resources are consumed by activities at their start or produced upon their completion, while renewable resources are allocated to activities at their start and released upon their completion. The resource-constrained project scheduling problem with consumption and production of resources (RCPSP-CPR) consists of determining a minimum-makespan schedule such that all precedence relations are respected, the demand for each renewable resource never exceeds its capacity, and the stock level of each storage resource never falls below a prescribed minimum. Due to the consideration of storage resources, the feasibility variant of this problem is NP-complete. We propose a novel compact mixed-integer linear programming (MILP) model based on a novel type of sequencing variables. These variables enable us to identify which activities are processed in parallel and whether a sequencing of activities is necessary to respect the resource capacities. Our computational results indicate that our novel model significantly outperforms state-of-the-art MILP models for all considered scarcity settings of the storage resources. Additionally, our results indicate a superior performance for instances of the well-known resource-constrained project scheduling problem (RCPSP).

Efficiency of large integer multiplication algorithms: A comparative study of traditional methods and Karatsuba's algorithm

The large integer multiplication is the basis of many computer science algorithms, ranging from cryptography to complex calculations in various scientific fields. Contemporary society excessively depends on complex computing tasks. Hence, the need for good algorithms is becoming increasingly apparent as well. This text gives the reader an in-depth knowledge of the multiplication algorithms of large integers by contrasting traditional algorithms with the new Algorithm developed by Karatsuba. This research methodology involves a comparative analysis of the components using an advanced analysis framework that primarily focuses on execution times, efficiency metrics, and resource utilization. Incontrovertibly, the experimental results confirm the Karatsuba algorithm's undoubted hastiness compared to the conventional approaches. This study extends our grasp of the evolution of algorithms in computational optimization, enabling people to get unique and relevant findings that will benefit numerous areas where large integer multiplications are involved. In addition to these findings, the study also highlights the importance of algorithm selection in ensuring computational efficiency and accuracy in large integer multiplications across various applications.

Enhancing the quality of banking technology platforms through a hybrid AI testing approach

In the dynamic landscape of the banking sector, marked by increased operational complexity and regulatory scrutiny, the pursuit of innovation demands a strategic orientation. While offering advantages in cost, quality and speed, this strategic approach to innovation should mandate a cautious consideration of the risks inherent in the integration of emerging technologies. With the continuous advancement of artificial intelligence (AI), particularly generative AI (GenAI), as a method for emerging banking technologies, its wider adoption in the financial industry demands careful consideration of associated risk implications. This paper advocates for a software testing approach that delves into the complexities of banking technology platforms by leveraging generative algorithms to attain extensive test coverage and simultaneously employs rule-based analytics to refine the generated datasets, optimising coverage for faster execution and efficient resource utilisation. Such an approach is in line with a risk-averse innovation strategy, as it balances out the smart creativity of novel GenAI methods with a rule-based discriminative potential of more established symbolic AI methods. This paper serves as a position paper, contributing to the discourse on the integration of innovative testing methodologies in the banking domain.

The value of health service-based research to health service organisations: a qualitative study with senior health service executives

BackgroundResearch evidence has demonstrably improved health care practices and patient outcomes. However, systemic translation of evidence into practice is far from optimal. The reasons are complex, but often because research is not well aligned with health service priorities. The aim of this study was to explore the experiences and perspectives of senior health service executives on two issues: (1) the alignment between local research activity and the needs and priorities of their health services, and (2) the extent to which research is or can be integrated as part of usual health care practice.MethodsIn this qualitative study, semi-structured interviews were conducted with senior health leaders from four large health service organisations that are members of Sydney Health Partners (SHP), one of Australia’s nationally accredited research translation centres committed to accelerating the translation of research findings into evidence-based health care. The interviews were conducted between November 2022 and January 2023, and were either audio-recorded and transcribed verbatim or recorded in the interviewer field notes. A thematic analysis of the interview data was conducted by two researchers, using the framework method to identify common themes.ResultsSeventeen health executives were interviewed, including chief executives, directors of medical services, nursing, allied health, research, and others in executive leadership roles. Responses to issue (1) included themes on re-balancing curiosity- and priority-driven research; providing more support for research activity within health organisations; and helping health professionals and researchers discuss researchable priorities. Responses to issue (2) included identification of elements considered essential for embedding research in health care; and the need to break down silos between research and health care, as well as within health organisations.ConclusionsHealth service leaders value research but want more research that aligns with their needs and priorities. Discussions with researchers about those priorities may need some facilitation. Making research a more integrated part of health care will require strong and broad executive leadership, resources and infrastructure, and investing in capacity- and capability-building across health clinicians, managers and executive staff.

How mind wandering influences motor control: The modulating role of movement difficulty

It has been found that mind wandering can impair motor control. However, it remains unclear whether the impact of mind wandering on motor control is modulated by movement difficulty and its associated neural mechanisms. To address this issue, we manipulated movement difficulty using handedness and finger dexterity separately in two signal-response tasks with identical experiment designs, in which right-handed participants performed key-pressing and key-releasing movements with the specified fingers, and they had to intermittently report whether their attention was “On task” or “Off task.” Key-releasing with the right index finger (RI) had a faster reaction time and stronger contralateral delta-theta (1–7 Hz) functional connectivity than with the left index (LI) in Experiment 1, and mind wandering only reduced the contralateral delta-theta functional connectivity and midfrontal delta-theta activity for key-releasing with RI. Key-pressing with right index and middle fingers (RIR) had a faster reaction time and stronger midfrontal delta-theta activity than with right index and ring fingers (RIR) in Experiment 2, and mind wandering only reduced the midfrontal delta-theta activity for key-pressing with RIM. Theta oscillations are vital in motor control. These findings suggest that mind wandering only impairs the motor control of relatively simple movements without affecting the difficult ones. It supports the notion that mind wandering competes for executive resources with the primary task. Moreover, the quantity of executive resources recruited for a task and how these resources are allocated is contingent upon the task difficulty, which may determine whether mind wandering would interfere with motor control.

Harnessing idle edge resources for execution of cloud services: A comprehensive review

AbstractThis article presents a comprehensive investigation into voluntary resource sharing at the edge of the internet for cloud service execution. Through a systematic review, 460 scientific articles published between 2019 and 2023 were analyzed, compiling the primary recent contributions in the field. The study presents a taxonomy of the results and highlights the state of the art. Despite the growing interest in the topic in recent years due to the expansion of Edge and Fog Computing, numerous challenges and open research gaps were identified for the effective utilization of idle resources for cloud service execution at the edge. This paper delves into these challenges, emphasizing the need for extensive research in areas such as resource management, interoperability, incentive model establishment, and security and privacy concerns.

Semantic Stroop interference is modulated by the availability of executive resources: Insights from delta-plot analyses and cognitive load manipulation

We investigated whether, during visual word recognition, semantic processing is modulated by attentional control mechanisms directed at matching semantic information with task-relevant goals. In previous research, we analyzed the semantic Stroop interference as a function of response latency (delta-plot analyses) and found that this phenomenon mainly occurs in the slowest responses. Here, we investigated whether this pattern is due to reduced ability to proactively maintain the task goal in these slowest trials. In two pairs of experiments, participants completed two semantic Stroop tasks: a classic semantic Stroop task (Experiment 1A and 2A) and a semantic Stroop task combined with an n-back task (Experiment 1B and 2B). The two pairs of experiments only differed in the trial pace, which was slightly faster in Experiments 2A and 2B than in Experiments 1A and 1B. By taxing the executive control system, the n-back task was expected to hinder proactive control. Delta-plot analyses of the semantic Stroop task replicated the enhanced effect in the slowest responses, but only under sufficient time pressure. Combining the semantic Stroop task with the n-back task produced a change in the distributional profile of semantic Stroop interference, which we ascribe to a general difficulty in the use of proactive control. Our findings suggest that semantic Stroop interference is, to some extent, dependent on the available executive resources, while also being sensitive to subtle variations in task conditions.

Cloud Security Using Fine-Grained Efficient Information Flow Tracking

This study provides a comprehensive review and comparative analysis of existing Information Flow Tracking (IFT) tools which underscores the imperative for mitigating data leakage in complex cloud systems. Traditional methods impose significant overhead on Cloud Service Providers (CSPs) and management activities, prompting the exploration of alternatives such as IFT. By augmenting consumer data subsets with security tags and deploying a network of monitors, IFT facilitates the detection and prevention of data leaks among cloud tenants. The research here has focused on preventing misuse, such as the exfiltration and/or extrusion of sensitive data in the cloud as well as the role of anonymization. The CloudMonitor framework was envisioned and developed to study and design mechanisms for transparent and efficient IFT (eIFT). The framework enables the experimentation, analysis, and validation of innovative methods for providing greater control to cloud service consumers (CSCs) over their data. Moreover, eIFT enables enhanced visibility to assess data conveyances by third-party services toward avoiding security risks (e.g., data exfiltration). Our implementation and validation of the framework uses both a centralized and dynamic IFT approach to achieve these goals. We measured the balance between dynamism and granularity of the data being tracked versus efficiency. To establish a security and performance baseline for better defense in depth, this work focuses primarily on unique Dynamic IFT tracking capabilities using e.g., Infrastructure as a Service (IaaS). Consumers and service providers can negotiate specific security enforcement standards using our framework. Thus, this study orchestrates and assesses, using a series of real-world experiments, how distinct monitoring capabilities combine to provide a comparatively higher level of security. Input/output performance was evaluated for execution time and resource utilization using several experiments. The results show that the performance is unaffected by the magnitude of the input/output data that is tracked. In other words, as the volume of data increases, we notice that the execution time grows linearly. However, this increase occurs at a rate that is notably slower than what would be anticipated in a strictly proportional relationship. The system achieves an average CPU and memory consumption overhead profile of 8% and 37% while completing less than one second for all of the validation test runs. The results establish a performance efficiency baseline for a better measure and understanding of the cost of preserving confidentiality, integrity, and availability (CIA) for cloud Consumers and Providers (C&P). Consumers can scrutinize the benefits (i.e., security) and tradeoffs (memory usage, bandwidth, CPU usage, and throughput) and the cost of ensuring CIA can be established, monitored, and controlled. This work provides the primary use-cases, formula for enforcing the rules of data isolation, data tracking policy framework, and the basis for managing confidential data flow and data leak prevention using the CloudMonitor framework.

An examination of the effects of eye-tracking on behavior in psychology experiments.

Eye-tracking is emerging as a tool for researchers to better understand cognition and behavior. However, it is possible that experiment participants adjust their behavior when they know their eyes are being tracked. This potential change would be considered a type of Hawthorne effect, in which participants alter their behavior in response to being watched and could potentially compromise the outcomes and conclusions of experimental studies that use eye tracking. We examined whether eye-tracking produced Hawthorne effects in six commonly used psychological scales and five behavioral tasks. The dependent measures were selected because they are widely used and cited and because they involved measures of sensitive topics, including gambling behavior, racial bias, undesirable personality characteristics, or because they require working memory or executive attention resources, which might be affected by Hawthorne effects. The only task where Hawthorne effects manifested was the mixed gambles task, in which participants accepted or rejected gambles involving a 50/50 chance of gaining or losing different monetary amounts. Participants in the eye-tracking condition accepted fewer gambles that were low in expected value, and they also took longer to respond for these low-value gambles. These results suggest that eye-tracking is not likely to produce Hawthorne effects in most common psychology laboratory tasks, except for those involving risky decisions where the probability of the outcomes from each choice are known.