Bangalore Research Articles

Improved ECC cryptosystem-based lightweight authentication for SDN-based edge computing in IoT-enabled healthcare systems

The Internet of Things (IoT) consists of smart devices with limited resources that can identify and analyze data. In IoT-enabled healthcare systems, the security of IoT devices and the data they contain is complex. These devices in the healthcare industry, edge computing can provide low-latency information services at a reasonable cost. This work proposes a security infrastructure for Software Defined Network (SDN)-based edge computing in IoT-enabled healthcare systems consisting of three steps: Lightweight authentication, collaborative edge computing and job migration. The lightweight authentication step involves both Improved Lightweight Key Management (ILKM) and Improved Elliptic Curve Cryptography (IECC) schemes to ensure authentication among the devices and edge servers. Moreover, the patient’s data in IoT devices are scheduled to the appropriate edge server by examining the load balancing in the collaborative edge computing phase. This is done optimally using the adopted hybrid optimization model, Osprey Assisted Coati Optimization Algorithm (OACOA). Further, job migration takes place, in which the data is allocated to the edge server by comparing the capacity of edge servers and the data gets migrated to other servers by considering migration cost when the capacity of the edge server is overloaded. Finally, the efficiency of the suggested OACOA scheme is evaluated over traditional models with regard to several metrics. When considering the edge-server 30, the OACOA scheme achieves a makespan of 385, while conventional methods acquired fewer makespan ratings. Also, the OACOA approach obtained the highest security ratings (0.7143) on edge-server 20 when compared to existing schemes.

Pemanfaatan Media Sosial untuk Quality Time Keluarga pada Pekerja Stayer dan Mover

Physical absence due to distance limits quality family time for workers due to job migration. Social media now has become a means of communication to maintain ties, form family identity, and replace physical presence. This research aims to test and prove the existence of differences between stayer and mover (circular) workers in utilizing social media for quality family time. This research uses quantitative methods, explanatory in nature, with comparative analysis. The survey method was used to collect data by filling out a questionnaire via Google Form. The sample was selected using convenience sampling from workers in a number of offices in the Jabodetabek area. Data processing was carried out using SPSS version 29 on 127 questionnaires filled in by respondents. The Mann-Whitney Test was used to determine differences in the use of social media by stayer and mover (circular) workers. The research results show that there are differences between stayer and mover (circular) workers in utilizing social media for family quality time, as measured by perceptions, attitudes, behavior and level of social media use. Behavioral variables have a moderate correlation to perception variables, and strong correlations to attitude variables. The variable level of social media use has a very weak correlation with the variables of perception, attitude and behavior. This proves that compared to stayer workers, mover (circular) workers use social media more to create quality family time and overcome physical absence. It is recommended that further research add qualitative methods (becoming mixed methods) so as to enrich and strengthen the data about the different reasons for using social media through in-depth interviews. Expanding the opulation, different research areas, and samples involving different community groups can be carried out to determine differences in the use of social media by other community groups.

Biosystematic Studies, Distribution and Conservation Status of Guaiacum officinale L. in Karnataka State

Guaiacum L. is an evergreen tree indigenous to South America, West Indies and USA. It was introduced during British period. The stem is hard, abundant resin wood, ash- grey-green bark and paripinnate leaves. The flowers are blue-purple in colour and occur in axillary clusters. The fruit is a capsule. Anomocytic, Paracytic and tertacytic types of stomata present only on abaxial surface of the leaf, trichomes absent on both surface. Pollen monad, radialy sysmmetric, tricolporate. The secondary veins are joined to form a loop at the margin. Phenolic test, alkaloid test etc. gave positive results. This plant is having commercial as well as medicinal value. In Karnataka state this plant found only in two places, one in Lalbagh, Bangalore and another in Karnataka College, Dharwad. Silviculture experiments were conducted but not succeeded. In both the places flowers are many, but fruit setting is negligible. It inhibits the spreading of the species in Karnataka.

Exploring nurses' experiences: Abandoning the profession and migrating for improved opportunities

AimThis study explores nurses' experiences in migration for employment and professional abandonment in Barcelona (Spain). MethodsEmploying a mixed-design approach comprising 1) a qualitative descriptive phenomenological study, followed by 2) a subsequent cross-sectional study, 20 and 225 nurses participated in each study, respectively. Qualitative data, gathered through 4 focus group discussions, underwent inductive thematic analysis, following the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines, while quantitative data were descriptively analyzed. FindingsThree qualitative themes emerged: 1) Migration motives, such as improved job opportunities, permanent contracts, continuous training, and professional recognition; 2) Reasons for leaving or contemplating leaving the profession, including excessive workload, lack of recognition, limited development, and exhaustion; 3) Nurses' needs, encompassing more staffing, improved remuneration, permanent contracts, flexible schedules, greater autonomy, and career growth. The cross-sectional study revealed a 13.5 % professional abandonment rate at some point across all demographics and seniority levels. Migration trends varied by professional experience, with younger nurses seeking better conditions and opportunities elsewhere. ConclusionsMultifactorial causes underlie job migration and professional abandonment, necessitating comprehensive interventions to improve nurses' working and professional conditions.

Operations of data centers with onsite renewables considering greenhouse gas emissions

In a highly technology-dependent society, processing large amounts of data has become essential, resulting in a growing number of data centers. The global data center industry accounts for a significant proportion of the world’s energy consumption. A partial or total transition of the energy sources that power the operation of data centers to cleaner energy, particularly onsite renewable technologies, will help to achieve the greenhouse gas emission reduction goals established in international agreements. However, integrating such technologies is difficult due to the intermittency and availability of renewable sources. Nevertheless, temporal and geographical flexibility for processing part of the data centers’ workload provides an opportunity to efficiently integrate these renewable energy sources. In this paper, we develop an operational scheme for a network of interconnected data centers with a significant electricity supply from onsite technologies consisting of (i) an optimal job migration and scheduling policy for data centers to match their operation and the availability of renewable energy under an emissions cap-and-trade program; (ii) an energy management system to control the data centers’ onsite renewable and grid energy resources; and (iii) a robust optimization approach to account for the intermittency of the renewables and to identify the optimal migration and scheduling policies under the worst-case scenario. A case study is carried out to assess the economic and environmental impacts. The results indicate that the installation of onsite technologies and an optimal migration policy reduce the operational costs and the greenhouse gas emissions of the networks of data centers.

Urban theories and urbanization perspectives in cities across Nigeria

One of the emerging research trends from urbanization is the study of urban growth patterns. These growth patterns are primarily based on the growth of the local economy that directly affects agricultural lands. In Africa, the local economy has spurred the extinction of many agrarian sectors. Yet, few studies at the regional level have paid attention to these changes. Using diverse scientific literature on global urbanization, urban remote sensing, and economic geography, this study aimed to understand the theory behind urban developments in Nigeria. The primary data were collected in two study regions in the country’s eastern and southern parts, making it a large dataset to assess urban formation in these areas. We examined the evolution of urban development from the perspectives of infrastructural development, expansion, change in land use, and urban job migration opportunities that affect agrarian activities. White-collar job opportunities are limited in urban areas, substantially increasing the startup of small-scale businesses. We introduce the notion of natural resource urbanism, which implies urbanization that is triggered by the influx of natural resource revenue to strengthen the dwindling local economy. The infusion of a shared natural resource revenue creates a reform in the local economy and the advent of a consumption-based economy. A shared natural resource revenue drives massive infrastructural developments, but the dependence on the availability of those natural resources in the states raises concerns about its long-term sustainability for strengthening the local economy.

Scheduling Policies for Stability and Optimal Server Running Cost in Cloud Computing Platforms

We propose throughput and cost optimal job scheduling algorithms in cloud computing platforms offering Infrastructure as a Service. We first consider online job migration and propose job scheduling algorithms to minimize job migration and server running costs. We consider algorithms that assume knowledge of job-size on the arrival of jobs. We characterize the optimal cost subject to system stability. We develop a drift-plus-penalty framework based algorithm that can achieve optimal cost arbitrarily closely. Specifically, this algorithm yields a tradeoff between delay and costs. We then relax the job-size knowledge assumption and give an algorithm that uses readily offered service to the jobs. We show that this algorithm gives order-wise identical cost as the job size based algorithm. Later, we consider offline job migration that incurs migration delays. We again present throughput optimal algorithms that minimize server running cost. We illustrate the performance of the proposed algorithms and compare these to the existing algorithms via simulation.

Elastic Deep Learning in Multi-Tenant GPU Clusters

We study how to support elasticity, that is, the ability to dynamically adjust the parallelism (i.e., the number of GPUs), for deep neural network (DNN) training in a GPU cluster. Elasticity can benefit multi-tenant GPU cluster management in many ways, for example, achieving various scheduling objectives (e.g., job throughput, job completion time, GPU efficiency) according to cluster load variations, utilizing transient idle resources, and supporting performance profiling, job migration, and straggler mitigation. We propose EDL, which enables elastic deep learning with a simple API and can be easily integrated with existing deep learning frameworks such as TensorFlow and PyTorch. EDL also incorporates techniques that are necessary to reduce the overhead of parallelism adjustments, such as stop-free scaling and dynamic data pipeline. We demonstrate with experiments that EDL can indeed bring significant benefits to the above-listed applications in GPU cluster management.

Scheduling Real-Time Security Aware Tasks in Fog Networks

Fog computing brings the cloud closer to a user with the help of a micro data center ( <inline-formula><tex-math notation="LaTeX">$mdc$</tex-math></inline-formula> ), leading to lower response times for delay sensitive applications. <i>RT-SANE</i> ( <u>R</u> eal- <u>T</u> ime <u>S</u> ecurity <u>A</u> ware scheduling on the <u>N</u> etwork <u>E</u> dge) supports batch and interactive applications, taking account of their deadline and security constraints. RT-SANE chooses between an <inline-formula><tex-math notation="LaTeX">$mdc$</tex-math></inline-formula> (in proximity to a user) and a cloud data center ( <inline-formula><tex-math notation="LaTeX">$cdc$</tex-math></inline-formula> ) by taking account of network delay and security tags. Jobs submitted by a user are tagged as: private, semi-private and public, and <inline-formula><tex-math notation="LaTeX">$mdcs$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$cdcs$</tex-math></inline-formula> are classified as: trusted, semi-trusted and untrusted. RT-SANE executes private jobs on a user’s local <inline-formula><tex-math notation="LaTeX">$mdcs$</tex-math></inline-formula> or pre-trusted <inline-formula><tex-math notation="LaTeX">$cdcs$</tex-math></inline-formula> , and semi-private and public jobs on remote <inline-formula><tex-math notation="LaTeX">$mdcs$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$cdcs$</tex-math></inline-formula> . A security and performance-aware distributed orchestration architecture and protocol is made use of in RT-SANE. For evaluation, workload traces from the CERIT-SC Cloud system are used. The effect of slow executing straggler jobs on the Fog framework are also considered, involving migration of such jobs. Experiments reveal that <i>RT-SANE</i> offers a higher “success ratio” (successfully completed jobs) to comparable algorithms, including consideration of security tags.

Analysis of the urban growth pattern through spatial metrics; Ankara City

Analysis of the urban growth pattern through spatial metrics; Ankara City

Online Makespan Scheduling with Job Migration on Uniform Machines

In the classic minimum makespan scheduling problem, we are given an input sequence of n jobs with sizes. A scheduling algorithm has to assign the jobs to m parallel machines. The objective is to minimize the makespan, which is the time it takes until all jobs are processed. In this paper, we consider online scheduling algorithms without preemption. However, we allow the online algorithm to change the assignment of up to k jobs at the end for some limited number k. For m identical machines, Albers and Hellwig (Algorithmica 79(2):598–623, 2017) give tight bounds on the competitive ratio in this model. The precise ratio depends on, and increases with, m. It lies between 4/3 and approx 1.4659. They show that k = O(m) is sufficient to achieve this bound and no k = o(n) can result in a better bound. We study m uniform machines, i.e., machines with different speeds, and show that this setting is strictly harder. For sufficiently large m, there is a delta = varTheta (1) such that, for m machines with only two different machine speeds, no online algorithm can achieve a competitive ratio of less than 1.4659 + delta with k = o(n). We present a new algorithm for the uniform machine setting. Depending on the speeds of the machines, our scheduling algorithm achieves a competitive ratio that lies between 4/3 and approx 1.7992 with k = O(m). We also show that k = varOmega (m) is necessary to achieve a competitive ratio below 2. Our algorithm is based on maintaining a specific imbalance with respect to the completion times of the machines, complemented by a bicriteria approximation algorithm that minimizes the makespan and maximizes the average completion time for certain sets of machines.

Optimal Job Scheduling With Resource Packing for Heterogeneous Servers

Jobs in modern computing clusters have highly diverse processing duration and heterogeneous resource requirements. In this paper, we consider the problem of job scheduling for a computing cluster comprised of multiple servers with heterogeneous computation resources, while taking the different resource demands of the jobs into account. Our focus is to achieve a low overall job response time for the system (which is also referred to as the job flowtime) while providing fairness between small and large jobs. Since the job flowtime minimization problem under multiple (even homogeneous) servers are known to be NP-hard, we propose an approximation algorithm to tackle the original online scheduling problem by adopting the recently-proposed notion of fractional job flowtime as a surrogate objective for minimization. For the general online job arrival case with multi-dimensional resource requirements, we apply Online Convex Optimization (OCO) techniques to design the corresponding scheduling algorithm with performance guarantees. In the single-dimensional resource setting, we show that the dynamic fit of the online version of our approximate algorithm grows only sublinearly with respect to time and derive a bound for its dynamic regret when comparing to its offline counterpart. While the baseline version of our proposed scheduling algorithm assumes the possibilities of job preemption and job migration across different servers, we show that the extent of job preemption and migration can be well controlled by augmenting the objective function with the corresponding switching costs.

Improving the hybrid cloud performance through disk activity-aware data access

Cloud computing has been making significant contributions to Internet of Things, Big Data, and many other cutting-edge research areas in recent years. To deal with the cloud bursting, on-premises private clouds often extend their service capacity with off-premises public clouds, which causes the migration of jobs and their corresponding data from private clouds to public clouds. For jobs executed in public clouds, promptly transferring data they need from private clouds to public clouds is essential for their quick completion as the volume of data is often large for cloud applications. The Internet connection between private clouds and public clouds is with limited bandwidth in most cases. Therefore, it will be valuable if the underlying operating system could expedite the course of reading data from hard drives to speed up the process of moving data from private clouds to public clouds. The Apache Hadoop is considered as one of the most widely used cloud platforms in the community of cloud computing. It keeps multiple replicas of data across its cluster nodes to improve data availability. We designed and implemented a new model enabling computing nodes in Hadoop to get data in request from a cluster node with the least amount of disk activity regardless of its location to hasten the course of accessing data. Experimental results show that jobs could reduce their execution time by up to 80.83% in our model. Accordingly, our model could help accelerate the completion of job execution in both private clouds and public clouds in the environment of hybrid clouds.

A Secured and Intelligent Communication Scheme for IIoT-enabled Pervasive Edge Computing.

Industrial Internet of Things (IIoT) ensures reliable and efficient data exchanges among the industrial processes using Artificial Intelligence (AI) within the cyber-physical systems. In the IIoT ecosystem, devices of industrial applications communicate with each other with little human intervention. They need to act intelligently to safeguard the data confidentiality and devices' authenticity. The ability to gather, process, and store real-time data depends on the quality of data, network connectivity, and processing capabilities of these devices. Pervasive Edge Computing (PEC) is gaining popularity nowadays due to the resource limitations imposed on the sensor-embedded IIoT devices. PEC processes the gathered data at the network edge to reduce the response time for these devices. However, PEC faces numerous research challenges in terms of secured communication, network connectivity, and resource utilization of the edge servers. To address these challenges, we propose a secured and intelligent communication scheme for PEC in an IIoT-enabled infrastructure. In the proposed scheme, forged identities of adversaries, i.e., Sybil devices, are detected by IIoT devices and shared with edge servers to prevent upstream transmission of their malicious data. Upon Sybil attack detection, each edge server executes a parallel Artificial Bee Colony (pABC) algorithm to perform optimal network configuration of IIoT devices. Each edge server performs the job migration to their neighboring servers for load balancing and better network performance, based on their processing and storage capabilities. The experimental results justify the efficiency of our proposed scheme in terms of Sybil attack detection, the convergence curves of our pABC algorithm, delay, throughput, and control overhead of data communication using PEC for IIoT.

Using Virtual Machine live migration in trace-driven energy-aware simulation of high-throughput computing systems

Virtual Machine (VM) live migration is one of the strategic approaches that can be employed to reduce energy consumption and increase the utilisation of a large computing infrastructure. In this paper, we implement the pre-copy live migration algorithm to provide a test environment for live job migration in a high throughput computing system (HTC). We present details of our extension into the HTC-Sim simulation framework. Furthermore, we demonstrate the performance and energy impact of our responsive live migration approach in high throughput computing (HTC) systems with various migration policies. The proposed policies focus on which computer to select for migration from an HTC resource pool. We evaluate our migration policies through the use of our HTC-Sim simulation framework. Moreover, we compare the results between the policies as well as the system where migration is not considered. We demonstrate that our responsive migration could save approximately 75% of the system wasted energy due to job evictions by user interruptions where migration is not employed as a fault-tolerance mechanism.

Enhancing growth and yield potential of foxtail millet through potassium application in eastern dry zone of Karnataka

As we know that intensive cropping with high yielding varieties makes considerable demand on the soil nutrients and application of nitrogen alone is not a prescription to obtain higher yields without other nutrients supplement. The red soils of Southern Karnataka are natively rich in potassium, hence potassium is not recommended for foxtail millet in Eastern Dry Zone of Karnataka. However, long-term intensive cropping without its application resulted in low to medium status and reduced potassium supply to crop plants and consequently crop yields. By considering nutritional aspects, different levels of potassium have been applied to foxtail millet. Totally twelve treatments were tested which includes absolute control, recommended dose of fertilizers as per UAS, Bangalore package (40:40:0 kg N, P2O5 and K2O per hectare, same level of nitrogen, phosphorus and 10, 20, 30, 40 and 50 kg of potassium, 50 kg nitrogen, 45 kg phosphorus, 10, 20, 30, 40 and 50 kg of potassium. These treatments were replicated thrice using RCBD. The initial soil was low in potassium (119.84 kg ha-1), neutral in reaction (6.59), low organic carbon (0.47%), low available nitrogen (136.42 kg ha-1), phosphorus (16.62 kg ha-1) and sandy loam in texture. From the experiment it was clear that, application of 40 kg nitrogen, 40 kg phosphorus and 40 kg potassium along with 7 tonnes of FYM is performed well by recording highest plant height, number of tillers, panicle length, panicle weight, grain yield and straw yield. Application of increased levels of potassium significantly increased the yield potential of the foxtail millet.

FIXING FIVE FLAWS OF THE TAX CUTS AND JOBS ACT

The tax legislation commonly referred to as the Tax Cuts and Jobs Act (Public Law 115-97) came into effect in 2018. The new tax law was flawed in five important ways. First, the law generates large deficits that will reduce the ability of the government to fund important priorities in the future; these deficits also risk justifying a more tepid fiscal response to the next recession. Second, after more than 35 years of increasing income inequality, the tax law moves the tax system in a regressive direction. Third, while the legislation increases economic efficiency in some ways, it decreases economic efficiency in other ways, moving the tax system away from optimal design principles. Fourth, the legislation misses an opportunity to combat profit shifting by multinational companies, changing the character of the problem but leaving its scale largely undiminished; also, the law provides new incentives for the offshoring of physical investment. And fifth, the legislation introduces new sources of complexity. This paper explains these five flaws in detail, setting the analysis within the context of relevant literature and facts. Thereafter, the paper suggests both short term and more fundamental ways to reform the tax code. Nearly immediately, simple fixes can address most of the TCJA flaws. Moving forward, a true tax reform can modernize the tax system to better handle the challenges posed by economic inequality, climate change, and the global nature of business activity.

An improved migration scheme to achieve the optimal service time for the active jobs in 5G cloud environment

Among the emerging technologies, cloud computing plays a vital role in the current business world. Through its various deployment models ‘n’ number of vendors of the IT industry utilizes cloud computing to achieve their business deals. Though the usage of cloud increases day by day, there are some major challenges in the cloud computing such as load balancing issues, device utilization and energy optimization. In this paper, load balancing issues are addressed through the proposed Server Distinction based on the Corresponding Active Weights of the Jobs (SDCAW) algorithm. The proposed algorithm concentrates on the live migration of the active jobs among the virtual machines in the cloud environment. The algorithm itself contains an instruction queue where the active jobs are stored and scheduled based on the procedure. Many existing systems concentrated only on migration of non-active jobs in the cloud environment. The proposed algorithm has been compared with the existing systems and produces optimal solution for the cloud environment by minimizing the makespan of the environment.

Economic Impact of Public Expenditure in The Gambia

The impact of public expenditure on the productive sectors (agriculture, industry, and service) in The Gambia, is analyzed within the framework of a Dynamic Computable General Equilibrium (DCGE) model. The model is applied and calibrated to assess the impact of a 10% increase in public expenditure on economic growth and welfare over five years. The results indicate an increase in GDP and value-added, mainly as a result of growth in the service sector.Also, an expansion of the service sector leads to the migration of jobs to the rural areas, which will consequently enhance rural labour income. A significant finding is that general public expenditure on agriculture may not get the desired result for poverty reduction, specifically in rural areas. As a result, public agricultural spending should be targeted across various agriculture sub-sectors, such as, irrigation, among others. The Government of The Gambia (GoTG) should also prioritize investment in the service sector, given that it has immense potentials in enhancing the livelihoods of Gambians in rural areas.

Estimation of Factors Affecting Job Migration in Vietnam

This study utilized the logit regression model with data from the Vietnamese Household Living Standards Survey (VHLSS) in 2016 to discover key factors that determined the probability of job migration in Vietnam. The estimated results of the model indicated that there were seven factors affecting job migration of households including the proportion of people with high school or higher qualification, the proportion of dependents, the number of males, the rate of income from non-agricultural fields, ethnics, areas and households’ living standards compared to the previous five years. In particular, the rate of non-agricultural income had a positive impact on households’ job migration, while other factors had negative effects on households’ job migration. Households’ accommodations and living standards had made great impacts on households whose family members working far away from home and even being helpers.