Dealing with Security Related Stress: Mindfulness on Countermeasures

Contemporary knowledge workers face information security-related stress and often struggle with responding to security threats. Employees deal with stress using different coping strategies. Some adopt avoidance coping mode to dissociate themselves with stress while some adopt approach coping mode to actively solve problems. We propose that being mindful on countermeasures of information security threats can ease stress and the negative impacts caused by stress. In addition, we also hypothesize the moderating effect of mindfulness on the relationships between security-related stress and two coping modes

Production of alkaline protease from Aspergillus oryzae isolated from seashore of Bay of Bengal

Aspergillus oryzae isolatedon  Potato dextrose agar  from soil samples of kottakoduru seashore of Bay of Bengal, Andhra Pradesh, India seashore of Bay of Bengal by spread plate method and was screened for alkaline protease production on Skim milk containing agar plates and identified by clear zones of protein hydrolysis around colonies. Different physical and chemical parameters such as pH, temperature, substrate concentration and incubation time were optimized for the better production of alkaline protease. The maximum protease activity was found at pH of 8 containing 10% wheat bran at 300C, after 72 hours of fermentation.ZnSO4was effective activator for protease activity and sodium dsulphate had shownmore than 50% inhibition of enzyme activity. Among the different oil cakes used for the production of enzyme the Sesame  oil cake proved to be suitable substrate after wheat bran for the production of protease by Aspergillus oryzae

A performance study for autoscaling big data analytics containerized applications : Scalability of Apache Spark on Kubernetes

Container technologies are rapidly changing how distributed applications are executed and managed on cloud computing resources. As containers can be deployed on a large scale, there is a tremendous need for Container Orchestration tools like Kubernetes that are highly automatic in deployment, scaling, and management. In recent times, the adoption of these container technologies like Docker has seen a rise in internal usage, commercial offering, and various application fields ranging from High-Performance Computing to Geo-distributed (Edge or IoT) applications. Big Data analytics is another field where there is a trend to run applications (e.g., Apache Spark) as containers for elastic workloads and multi-tenant service models by leveraging various container orchestration tools like Kubernetes. Despite the abundant research on the performance impact of containerizing big data applications, to the best of our knowledge, the studies that focus on specific aspects like scalability and resource management are largely unexplored, which leaves a research gap to study upon. This research studies the performance impact of autoscaling a big data analytics application on Kubernetes based on autoscaling mechanisms like Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA). These state-of-art autoscaling mechanisms available for scaling containerized applications on Kubernetes and the available big data benchmarking tools for generating workload on frameworks like Spark are identified through a literature review. Apache Spark is selected as a representative big data application due to its ecosystem and industry-wide adoption by enterprises. In particular, a series of experiments are conducted by adjusting resource parameters (such as CPU requests and limits) and autoscaling mechanisms to measure run-time metrics like execution time and CPU utilization. Our experiment results show that while Spark performs better execution time when configured to scale with VPA, it also exhibits overhead in CPU utilization. In contrast, the impact of autoscaling big data applications using HPA adds overhead in terms of both execution time and CPU utilization. The research from this thesis can be used by researchers and other cloud practitioners, using big data applications to evaluate autoscaling mechanisms and derive better performance and resource utilization

A performance study for autoscaling big data analytics containerized applications : Scalability of Apache Spark on Kubernetes

Container technologies are rapidly changing how distributed applications are executed and managed on cloud computing resources. As containers can be deployed on a large scale, there is a tremendous need for Container Orchestration tools like Kubernetes that are highly automatic in deployment, scaling, and management. In recent times, the adoption of these container technologies like Docker has seen a rise in internal usage, commercial offering, and various application fields ranging from High-Performance Computing to Geo-distributed (Edge or IoT) applications. Big Data analytics is another field where there is a trend to run applications (e.g., Apache Spark) as containers for elastic workloads and multi-tenant service models by leveraging various container orchestration tools like Kubernetes. Despite the abundant research on the performance impact of containerizing big data applications, to the best of our knowledge, the studies that focus on specific aspects like scalability and resource management are largely unexplored, which leaves a research gap to study upon. This research studies the performance impact of autoscaling a big data analytics application on Kubernetes based on autoscaling mechanisms like Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA). These state-of-art autoscaling mechanisms available for scaling containerized applications on Kubernetes and the available big data benchmarking tools for generating workload on frameworks like Spark are identified through a literature review. Apache Spark is selected as a representative big data application due to its ecosystem and industry-wide adoption by enterprises. In particular, a series of experiments are conducted by adjusting resource parameters (such as CPU requests and limits) and autoscaling mechanisms to measure run-time metrics like execution time and CPU utilization. Our experiment results show that while Spark performs better execution time when configured to scale with VPA, it also exhibits overhead in CPU utilization. In contrast, the impact of autoscaling big data applications using HPA adds overhead in terms of both execution time and CPU utilization. The research from this thesis can be used by researchers and other cloud practitioners, using big data applications to evaluate autoscaling mechanisms and derive better performance and resource utilization

Exploring the Role of Mindfulness on Easing the Negative Impacts of Information Security Stress

Information security stress has been considered as one major source of unwanted security-related behaviors. For example, based on moral disengagement theory, past studies argued and confirmed that information security stresses increase the chance for moral disengagement, which in turn, cause the violation of information security policy. Therefore, easing information security related stress is important since doing so may effectively prevent security policy violating behaviors. In this study, based on the stressor-strain-outcome three-layered model, we proposed a positive relationship between stressor and strain and between strain and avoiding behavior, and an inverted U shape relationship between strain and approaching behavior. In addition, mindfulness can mitigate the proposed positive relationships and inverted U shape relationships

Knowledge sharing, shared leadership and innovative behaviour: a cross-level analysis

Purpose The purpose of this study is to investigate the effect of knowledge sharing among team members on the development of shared leadership and innovative behaviour. Design/methodology/approach Data were collected from 64 management teams and 427 individuals working in 26 different hotels in the hospitality industry in Taiwan. Findings The results show that knowledge sharing has both direct and indirect effects on the development of shared leadership and individual innovative behaviour. Research limitations/implications Results suggest that knowledge sharing supports the occurrence of shared leadership, leading to an increase in innovative behaviour. The authors infer from the findings that encouraging a culture of knowledge sharing can have a positive impact on the creativity of teams. Originality/value This study advances knowledge of shared leadership as a mediator using a multilevel approach to test antecedents of innovative behaviour in the Taiwan hotel industry