scholarly journals A fault-intrusion-tolerant system and deadline-aware algorithm for scheduling scientific workflow in the cloud

2021 ◽  
Vol 7 ◽  
pp. e747
Author(s):  
Mazen Farid ◽  
Rohaya Latip ◽  
Masnida Hussin ◽  
Nor Asilah Wati Abdul Hamid
Keyword(s):  
Success Rate ◽  
Completion Time ◽  
Virtual Machines ◽  
Scientific Workflow ◽  
Scientific Workflows ◽  
Completion Rate ◽  
Task Completion ◽  
Great Cost ◽  
Workflow System ◽  
Intrusion Tolerant

Background Recent technological developments have enabled the execution of more scientific solutions on cloud platforms. Cloud-based scientific workflows are subject to various risks, such as security breaches and unauthorized access to resources. By attacking side channels or virtual machines, attackers may destroy servers, causing interruption and delay or incorrect output. Although cloud-based scientific workflows are often used for vital computational-intensive tasks, their failure can come at a great cost. Methodology To increase workflow reliability, we propose the Fault and Intrusion-tolerant Workflow Scheduling algorithm (FITSW). The proposed workflow system uses task executors consisting of many virtual machines to carry out workflow tasks. FITSW duplicates each sub-task three times, uses an intermediate data decision-making mechanism, and then employs a deadline partitioning method to determine sub-deadlines for each sub-task. This way, dynamism is achieved in task scheduling using the resource flow. The proposed technique generates or recycles task executors, keeps the workflow clean, and improves efficiency. Experiments were conducted on WorkflowSim to evaluate the effectiveness of FITSW using metrics such as task completion rate, success rate and completion time. Results The results show that FITSW not only raises the success rate by about 12%, it also improves the task completion rate by 6.2% and minimizes the completion time by about 15.6% in comparison with intrusion tolerant scientific workflow ITSW system.

scholarly journals A Correlated Model for Evaluating Performance and Energy of Cloud System Given System Reliability

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Hongli Zhang ◽  
Panpan Li ◽  
Zhigang Zhou
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Completion Time ◽  
High Performance ◽  
Virtual Machines ◽  
Memory Storage ◽  
Task Completion ◽  
Task Completion Time ◽  
Hardware Failure ◽  
And Task

The serious issue of energy consumption for high performance computing systems has attracted much attention. Performance and energy-saving have become important measures of a computing system. In the cloud computing environment, the systems usually allocate various resources (such as CPU, Memory, Storage, etc.) on multiple virtual machines (VMs) for executing tasks. Therefore, the problem of resource allocation for running VMs should have significant influence on both system performance and energy consumption. For different processor utilizations assigned to the VM, there exists the tradeoff between energy consumption and task completion time when a given task is executed by the VMs. Moreover, the hardware failure, software failure and restoration characteristics also have obvious influences on overall performance and energy. In this paper, a correlated model is built to analyze both performance and energy in the VM execution environment given the reliability restriction, and an optimization model is presented to derive the most effective solution of processor utilization for the VM. Then, the tradeoff between energy-saving and task completion time is studied and balanced when the VMs execute given tasks. Numerical examples are illustrated to build the performance-energy correlated model and evaluate the expected values of task completion time and consumed energy.

INHIBITOR: An intrusion tolerant scheduling algorithm in cloud-based scientific workflow system

2021 ◽  
Vol 114 ◽  
pp. 272-284
Author(s):  
Yawen Wang ◽  
Yunfei Guo ◽  
Wenbo Wang ◽  
Hao Liang ◽  
Shumin Huo
Keyword(s):  
Scheduling Algorithm ◽  
Scientific Workflow ◽  
Workflow System ◽  
Intrusion Tolerant

A Novel Completion-Time-Minimization Scheduling Approach of Scientific Workflows Over Heterogeneous Cloud Computing Systems

2019 ◽  
Vol 16 (4) ◽  
pp. 1-20
Author(s):  
S. Sabahat H. Bukhari ◽  
Yunni Xia
Keyword(s):  
Cloud Computing ◽  
Time Management ◽  
Completion Time ◽  
Large Scale ◽  
Scientific Workflow ◽  
Scientific Workflows ◽  
Transmission Delays ◽  
Computing Paradigm ◽  
Heterogeneous Cloud ◽  
The Impact

The cloud computing paradigm provides an ideal platform for supporting large-scale scientific-workflow-based applications over the internet. However, the scheduling and execution of scientific workflows still face various challenges such as cost and response time management, which aim at handling acquisition delays of physical servers and minimizing the overall completion time of workflows. A careful investigation into existing methods shows that most existing approaches consider static performance of physical machines (PMs) and ignore the impact of resource acquisition delays in their scheduling models. In this article, the authors present a meta-heuristic-based method to scheduling scientific workflows aiming at reducing workflow completion time through appropriately managing acquisition and transmission delays required for inter-PM communications. The authors carry out extensive case studies as well based on real-world commercial cloud sand multiple workflow templates. Experimental results clearly show that the proposed method outperforms the state-of-art ones such as ICPCP, CEGA, and JIT-C in terms of workflow completion time.

The evaluation of eye gaze using an eye tracking system in simulation training of real-time ultrasound-guided venipuncture

2021 ◽  
pp. 112972982098736
Author(s):  
Kaji Tatsuru ◽  
Yano Keisuke ◽  
Onishi Shun ◽  
Matsui Mayu ◽  
Nagano Ayaka ◽  
...  
Keyword(s):  
Medical Students ◽  
Eye Tracking ◽  
Success Rate ◽  
Real Time ◽  
Completion Time ◽  
Tracking System ◽  
Eye Gaze ◽  
Task Completion ◽  
Short Axis ◽  
The Us

Purpose: Real-time ultrasound (RTUS)-guided central venipuncture using the short-axis approach is complicated and likely to result in losing sight of the needle tip. Therefore, we focused on the eye gaze in our evaluation of the differences in eye gaze between medical students and experienced participants using an eye tracking system. Methods: Ten medical students (MS group), five residents (R group) and six pediatric surgeon fellows (F group) performed short-axis RTUS-guided venipuncture simulation using a modified vessel training system. The eye gaze was captured by the tracking system (Tobii Eye Tacker 4C) and recorded. The evaluation endpoints were the task completion time, total time and number of occurrences of the eye tracking marker outside US monitor and success rate of venipuncture. Result: There were no significant differences in the task completion time and total time of the tracking marker outside the US monitor. The number of occurrences of the eye tracking marker outside US monitor in the MS group was significantly higher than in the F group (MS group: 9.5 ± 3.4, R group: 6.0 ± 2.9, F group: 5.2 ± 1.6; p  = 0.04). The success rate of venipuncture in the R group tended to be better than in the F group. Conclusion: More experienced operators let their eye fall outside the US monitor fewer times than less experienced ones. The eye gaze was associated with the success rate of RTUS-guided venipuncture. Repeated training while considering the eye gaze seems to be pivotal for mastering RTUS-guided venipuncture.

A Novel Resource Optimization Algorithm Based on Clustering and Improved Differential Evolution Strategy Under a Cloud Environment

2021 ◽  
Vol 20 (5) ◽  
pp. 1-15
Author(s):  
Zhou Zhou ◽  
Fangmin Li ◽  
Shuiqiao Yang
Keyword(s):  
Differential Evolution ◽  
Resource Utilization ◽  
Optimization Algorithm ◽  
Completion Time ◽  
Virtual Machines ◽  
Evolution Strategy ◽  
Resource Optimization ◽  
Task Completion ◽  
Task Completion Time ◽  
Workload Imbalance

Resource optimization algorithm based on clustering and improved differential evolution strategy, as a new global optimized algorithm, has wide applications in language translation, language processing, document understanding, cloud computing, and edge computing due to high efficiency. With the development of deep learning technology and the rise of big data, the resource optimization algorithm encounters a series of challenges, such as the workload imbalance and low resource utilization. To address the preceding problems, this study proposes a novel resource optimization algorithm based on clustering and an improved differential evolution strategy (Multi-objective Task Scheduling Strategy (MTSS)). Three indexes, namely task completion time, execution cost, and workload, of virtual machines are selected and used to build the fitness function of the MTSS algorithm. At the same time, the preprocessing state is set up to cluster according to the resource and task characteristics to reduce the magnitude of their matching scale. Moreover, to solve the workload imbalance among different resource sets, local resource tasks are reallocated using the Q-value method in the MTSS strategy to achieve workload balance of global resources and improve the resource utilization rate. Experiments are carried out to evaluate the effectiveness of the proposed algorithm. Results show that the proposed algorithm outperforms other algorithms in terms of task completion time, execution cost, and workload balancing.

scholarly journals Design of Energy Aware Scheduling Algorithm for Executing Scientific Workflows in Cloud

2019 ◽  
Vol 9 (2) ◽  
pp. 1305-1311
Keyword(s):  
Energy Efficient ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Scientific Workflow ◽  
Scientific Workflows ◽  
Cloud Environment ◽  
Scientific Applications ◽  
Energy Aware ◽  
Efficient Manner ◽  
The Given

The usage of cloud computing and its resources for the execution of scientific workflow is a rapidly increasing demand. The Scientific applications are generally large in scale; even a single scientific workflow includes more number of complex tasks. Execution of these tasks can be made successful only by deploying it in the cloud virtual machines, because only cloud environment can only provide very large number of computing assets. In cloud, every processing resource is given as Virtual Machine. Any scientific workflow deployed in the cloud needs large number of virtual machines so; huge amount of computational energy is spent by the virtual machines to execute multifaceted scientific workflows. Hence there arises the need to utilize the cloud resources in an energy efficient way. Also, if the virtual machines are planned to schedule in an energy efficient manner there is an increase of makepsan of the workflow which is going to be an important parameter for completing the workflow within the deadline. So, the need for executing scientific workflows in energy efficient way with reduced makespan becomes a major issue among the researchers. It also becomes very challenging task to executing a scientific workflow in within the given deadline of a task in the given workflow. To address these issues, a new Energy Aware workflow scheduling algorithm is proposed and designed with improved makespan for the execution of different scientific applications in cloud environment.

Process-oriented ecological modeling approach and scientific workflow system

2014 ◽  
Vol 22 (3) ◽  
pp. 277
Author(s):  
Qiao Huijie ◽  
Lin Congtian ◽  
Wang Jiangning ◽  
Ji Liqiang
Keyword(s):  
Ecological Modeling ◽  
Scientific Workflow ◽  
Modeling Approach ◽  
Workflow System ◽  
Process Oriented

The Effects of Language Barriers and Time Constraints on Online Learning Performance: An Eye-Tracking Study

2021 ◽  
pp. 001872082110109
Author(s):  
Auður Anna Jónsdóttir ◽  
Ziho Kang ◽  
Tianchen Sun ◽  
Saptarshi Mandal ◽  
Ji-Eun Kim
Keyword(s):  
Online Learning ◽  
Eye Movement ◽  
Completion Time ◽  
Language Use ◽  
Time Pressure ◽  
Time Constraints ◽  
Task Completion ◽  
Esl Students ◽  
Task Completion Time ◽  
Eye Fixation

Objective The goal of this study is to model the effect of language use and time pressure on English as a first language (EFL) and English as a second language (ESL) students by measuring their eye movements in an on-screen, self-directed learning environment. Background Online learning is becoming integrated into learners’ daily lives due to the flexibility in scheduling and location that it offers. However, in many cases, the online learners often have no interaction with one another or their instructors, making it difficult to determine how the learners are reading the materials and whether they are learning effectively. Furthermore, online learning may pose challenges to those who face language barriers or are under time pressure. Method The effects of two factors, language use (EFL vs. ESL) and time constraints (high vs. low time pressure), were investigated during the presentation of online materials. The effects were analyzed based on eye movement measures (eye fixation rate—the total number of eye fixations divided by the task duration and gaze entropy) and behavioral measures (correct rate and task completion time). Results The results show that the ESL students had higher eye fixation rates and longer task completion times than the EFL students. Moreover, high time pressure resulted in high fixation rates, short task completion time, low correct rates, and high gaze entropy. Conclusion and Application The results suggest the possibility of using unobtrusive eye movement measures to develop ways to better assist those who struggle with learning in the online environment.

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