Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment

Cloud computing infrastructure is suitable for meeting computational needs of large task sizes. Optimal scheduling of tasks in cloud computing environment has been proved to be an NP-complete problem, hence the need for the application of heuristic methods. Several heuristic algorithms have been developed and used in addressing this problem, but choosing the appropriate algorithm for solving task assignment problem of a particular nature is difficult since the methods are developed under different assumptions. Therefore, six rule based heuristic algorithms are implemented and used to schedule autonomous tasks in homogeneous and heterogeneous environments with the aim of comparing their performance in terms of cost, degree of imbalance, makespan and throughput. First Come First Serve (FCFS), Minimum Completion Time (MCT), Minimum Execution Time (MET), Maxmin, Min-min and Sufferage are the heuristic algorithms considered for the performance comparison and analysis of task scheduling in cloud computing

Preparation and Characterization of Isosorbide Mononitrate Hydrogels Obtained by Free-Radical Polymerization for Site-Specific Delivery

Purpose: To prepare and characterize acrylic acid and ethyl cellulose hydrogels of isosorbide mononitrate for site-specific delivery.Methods: Free radical polymerization method was employed using benzoyl peroxide as initiator and N, N’-Methylenebisacrylamide (MBA) crosslinked copolymer of ethyl cellulose and acrylic acid. Benzyl peroxide and N, N'-Methylenebisacrylamide in varying amounts were dissolved in acrylic acid. The two solutions were mixed together to a final weight of 100 g. Hydrogels were evaluated for sol-gel characteristics, diffusion coefficient, and porosity. Hydrogel formation was examined by FTIR while drug loading efficiency study was carried out using 1 % (w/v) drug solution.Results: Swelling and drug release decreased with increasing acrylic acid and MBA concentrations due to high degree of crosslinking. Increasing acrylic acid content of hydrogel produced a decrease in drug release from 29.89 to 25.79 %, 75.37 to 67.87 % and 84.91 to 75.85 % at pH 1.2, 6.5 and 7.5, respectively. Remarkably, high swelling was observed at higher pH. Gel fraction and porosity results showed that acrylic acid and crosslinker raised gel fraction but reduced porosity, while ethyl cellulose exhibited a reverse effect. FTIR confirmed graft copolymer formation.Conclusion: Isosorbide mononitrate hydrogels prepared with crosslinked copolymer of ethyl cellulose and acrylic acid can be suitably formulated for targeted delivery of the drug to the small intestine.Keywords: N, N'-Methylenebisacrylamide, Ethyl cellulose, Acrylic acid, Isosorbide mononitrate, Free radical polymerization, Graft copolymer, Site-specific delivery, Hydrogel, pH-sensitiv

Nepali migrant workers and the need for pre-departure training on mental health: a qualitative study

Every year around 1,000 Nepali migrant workers die abroad. Every one in three females and one in ten males commit suicide, reflecting a high mental health risk among Nepali migrant workers. This study aims to identify triggers of mental ill-health among Nepali migrant workers and their perception on need of mental health components in the pre-departure orientation programme. We conducted five focus group discussions (FGD) and seven in-depth interviews with Nepali migrant workers and eight semi-structured interviews with stakeholders working for migrants. Participants were invited at Kathmandu’s international airport on return from abroad, at hotels or bus stations near the airport, through organisations working for migrants, and participants’ network. All FGD and interviews were conducted in Kathmandu and audio recorded, transcribed and translated into English. Data were analyzed thematically. High expectations from families back home, an unfair treatment at work, poor arrangements of accommodation, loneliness and poor social life abroad were frequently reported factors for poor mental health. Access to mental health services abroad by Nepali migrant was also poor. We found little on mental health in the pre-departure orientation. We need to improve our knowledge of mental health risks to provide better, more focused and more up-to-date pre-departure training to new migrant workers leaving Nepal

An appraisal of meta-heuristic resource allocation techniques for IaaS cloud

Background/Objectives: This appraisal investigates the meta-heuristics resource allocation techniques for maximizing financial gains and minimizing the financial expenses of cloud users for IaaS in cloud computing environment. Methods/Statistical Analysis: Overall, a total of ninety-one studies from 1954 to 2015 have been reviewed in this paper. However, twenty-three studies are selected that focused on the meta-heuristic algorithms for their research. The selected papers are categorized into eight groups according to the optimization algorithms used. Findings: From the analytical study, we pointed out the various issues addressed (optimal and dynamically resource allocation, energy and QoS aware resource allocation, VM allocation and placement) through resource allocation meta-heuristics algorithms.Whereas, the improvement shows better performance concerns minimizing the execution and response time, energy consumption and cost while enhancing the efficiency and QoS in this environment. The comparison parameters (makespan 35%,execution time 13%, response time 26%, cost 22%, utilization21% and other 13% including energy, throughput etc) and also the experimental tools (CloudSim 43%, GridSim 5%, Simjava 9%, Matlab 9% and others 13%) used for evaluation of the various techniques for resource allocation in IaaS cloud computing. Applications/Improvements: The comprehensive review and systematic comparison of meta-heuristic resource allocation algorithms described in this appraisal will help researchers to analyze different techniques for future research directions

Secure scientific applications scheduling technique for cloud computing environment using global league championship algorithm

Cloud computing system is a huge cluster of interconnected servers residing in a datacenter and dynamically provisioned to clients on-demand via a front-end interface. Scientific applications scheduling in the cloud computing environment is identified as NP-hard problem due to the dynamic nature of heterogeneous resources. Recently, a number of metaheuristics optimization schemes have been applied to address the challenges of applications scheduling in the cloud system, without much emphasis on the issue of secure global scheduling. In this paper, scientific applications scheduling techniques using the Global League Championship Algorithm (GBLCA) optimization technique is first presented for global task scheduling in the cloud environment. The experiment is carried out using Cloud-Sim simulator. The experimental results show that, the proposed GBLCA technique produced remarkable performance improvement rate on the makespan that ranges between 14.44 to 46.41. It also shows significant reduction in the time taken to securely schedule applications as parametrically measured in terms of the response time. In view of the experimental results, the proposed technique provides better-quality scheduling solution that is suitable for scientific applications task execution in the Cloud Computing environment than the MinMin, MaxMin, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) scheduling techniques

Recent advancements in resource allocation techniques for cloud computing environment: a systematic review

There are two actors in cloud computing environment cloud providers and cloud users. On one hand cloud providers hold enormous computing resources in the cloud large data centers that rent the resources out to the cloud users on a pay-per-use basis to maximize the profit by achieving high resource utilization. On the other hand cloud users who have applications with loads variation and lease the resources from the providers they run their applications within minimum expenses. One of the most critical issues of cloud computing is resource management in infrastructure as a service (IaaS). Resource management related problems include resource allocation, resource adaptation, resource brokering, resource discovery, resource mapping, resource modeling, resource provisioning and resource scheduling. In this review we investigated resource allocation schemes and algorithms used by different researchers and categorized these approaches according to the problems addressed schemes and the parameters used in evaluating different approaches. Based on different studies considered, it is observed that different schemes did not consider some important parameters and enhancement is required to improve the performance of the existing schemes. This review contributes to the existing body of research and will help the researchers to gain more insight into resource allocation techniques for IaaS in cloud computing in the future

RFCNN: Traffic Accident Severity Prediction Based on Decision Level Fusion of Machine and Deep Learning Model

Traffic accidents on highways are a leading cause of death despite the development of traffic safety measures. The burden of casualties and damage caused by road accidents is very high for developing countries. Many factors are associated with traffic accidents, some of which are more significant than others in determining the severity of accidents. Data mining techniques can help in predicting influential factors related to crash severity. In this study, significant factors that are strongly correlated with the accident severity on highways are identified by Random Forest. Top features affecting accidental severity include distance, temperature, wind+Chill, humidity, visibility, and wind direction. This study presents an ensemble of machine learning and deep learning models by combining Random Forest and Convolutional Neural Network called RFCNN for the prediction of road accident severity. The performance of the proposed approach is compared with several base learner classifiers. The data used in the analysis include accident records of the USA from February 2016 to June 2020. Obtained results demonstrate that the RFCNN enhanced the decision-making process and outperformed other models with 0.991 accuracy, 0.974 precision, 0.986 recall, and 0.980 F-score using the 20 most significant features in predicting the severity of accidents

Towards automated eye diagnosis: an improved retinal vessel segmentation framework using ensemble block matching 3D filter

Automated detection of vision threatening eye disease based on high resolution retinal fundus images requires accurate segmentation of the blood vessels. In this regard, detection and segmentation of finer vessels, which are obscured by a considerable degree of noise and poor illumination, is particularly challenging. These noises include (systematic) additive noise and multiplicative (speckle) noise, which arise due to various practical limitations of the fundus imaging systems. To address this inherent issue, we present an efficient unsupervised vessel segmentation strategy as a step towards accurate classification of eye diseases from the noisy fundus images. To that end, an ensemble block matching 3D (BM3D) speckle filter is proposed for removal of unwanted noise leading to improved detection. The BM3D-speckle filter, despite its ability to recover finer details (i.e., vessels in fundus images), yields a pattern of checkerboard artifacts in the aftermath of multiplicative (speckle) noise removal. These artifacts are generally ignored in the case of satellite images; however, in the case of fundus images, these artifacts have a degenerating effect on the segmentation or detection of fine vessels. To counter that, an ensemble of BM3D-speckle filter is proposed to suppress these artifacts while further sharpening the recovered vessels. This is subsequently used to devise an improved unsupervised segmentation strategy that can detect fine vessels even in the presence of dominant noise and yields an overall much improved accuracy. Testing was carried out on three publicly available databases namely Structured Analysis of the Retina (STARE), Digital Retinal Images for Vessel Extraction (DRIVE) and CHASE_DB1. We have achieved a sensitivity of 82.88, 81.41 and 82.03 on DRIVE, SATARE, and CHASE_DB1, respectively. The accuracy is also boosted to 95.41, 95.70 and 95.61 on DRIVE, SATARE, and CHASE_DB1, respectively. The performance of the proposed methods on images with pathologies was observed to be more convincing than the performance of similar state-of-the-art methods