Fault-Tolerant scheduling for scientific workflows in cloud environments

Executing clustered tasks has proven to be an efficient method to improve the computation of Scientific Workflows (SWf) on clouds. However, clustered tasks has a higher probability of suffering from failures than a single task. Therefore, fault tolerance in cloud computing is extremely essential while running large-scale scientific applications. In this paper, a new heuristic called Cluster based Heterogeneous Earliest Finish Time (CHEFT) algorithm to enhance the scheduling and fault tolerance mechanism for SWf in highly distributed cloud environments is proposed. To mitigate the failure of clustered tasks, this algorithm uses idle-Time of the provisioned resources to resubmit failed clustered tasks for successful execution of SWf. Experimental results show that the proposed algorithm have convincing impact on the SWf executions and also drastically reduce the resource waste compared to existing task replication techniques. A trace based simulation of five real SWf shows that this algorithm is able to sustain unexpected task failures with minimal cost and makespan. © 2017 IEEE

Cloud capacity planning and HSI based optimal resource provisioning

Cloud service providers offer spot instances through highest bidding plans that are at a very economical price compared to other pricing plans, namely on-demand and reservation. The usage of spot instance enables utilization of idle resources and provide service for cost sensitive tasks. However, this approach introduces the problem of cloud capacity allocation to different pricing plans that will have impact on the task completion time. To address these issues and improve the providers revenue, in this paper a capacity planning has been carried out based on the prediction of resource requirements for each of the different resource pricing pools. The paper also presents a solution to overcome the burden faced by the service provider due to the free issue of last hour at the time of out-of-bid situation. Simulation carried out based on capacity planning along with hybrid spot instance using Amazon EC2's price show that the resource utilization is improved across the different resource pricing pools with increased number of task completion and improved provider's revenue. © 2017 IEEE

Cluster, grid and cloud computing: A detailed comparison

Cloud computing is rapidly growing as an alternative to conventional computing. However, it is based on models like cluster computing, distributed computing, utility computing and grid computing in general. This paper presents an end-to-end comparison between Cluster Computing, Grid Computing and Cloud Computing, along with the challenges they face. This could help in better understanding these models and to know how they differ from its related concepts, all in one go. It also discusses the ongoing projects and different applications that use these computing models as a platform for execution. An insight into some of the tools which can be used in the three computing models to design and develop applications is given. This could help in bringing out the innovative ideas in the field and can be explored to the needs in the computing world. © 2011 IEEE

Prediction of resource failure uncertainty for ubiquitous applications in grid environment

In the recent years, the use of grid computing has been extended to ubiquitous computing that demands high computing capabilities. The tremendous development of mobile and embedded devices in ubiquitous computing applications and their communication requirements forms a large data set that need to be computed efficiently. However, these devices have limited resource capacity such as processing element (PE), storage, battery power, bandwidth, and so on. Hence it is required to transfer requests from ubiquitous applications to grid computing environment for computation. Nevertheless, the major issue with resource sharing lies in the uncertainties like resource failure, communication link failure, etc., during massive job arrival. Thus, it is important to consider uncertainties in the formulation of a grid scheduling problem. In this paper, we propose a model that demonstrate the uncertainty of resource failure using probabilistic Markov chain and Poisson distribution. These techniques are exploited as maximum likelihood estimator. The implementation shows prediction accuracy of uncertainty. © 2014 IEEE

Physical, structural, optical and thermoluminescence behavior of Dy2O3 doped sodium magnesium borosilicate glasses

The rare earth doped borosilicate glass materials of composition (60 − x) B2O3 − 20 SiO2 − 10 Na2O − 10 MgO − x Dy2O3 have been prepared by melt quenching method. X-ray diffraction confirms the amorphous nature of the present glasses. FTIR spectra reveal the structure of the present glasses. The density, molar volume, average molecular weight, ion concentration, polaron radius and field strength have been determined. The optical parameters like optical band gap, refractive index, dielectric constant, optical dielectric constant, molar polarizability, reflection loss, molar refractivity, metallization and Urbach energy were also calculated. The glow curve behavior of all the present glass samples irradiated with 50 Gy, 100 Gy, 500 Gy, 1 kGy, 5 kGy and 10 kGy gamma ray doses has been investigated in temperature range 50–400°C. The TL dose response shows that the sample having 0.6 mol% doping of Dy3+ was best suitable for dosimetric applications. Keywords: Borosilicate glass, XRD, FTIR, UV–visible, Thermoluminescenc