A Heuristics-Based Cost Model for Scientic Workow Scheduling in Clou

Scientic Workow Applications(SWFAs)can deliver collaborative tools useful to researchers in executing large and complex scientic processes.Particularly,Scientic Workow Scheduling(SWFS)accelerates the computational procedures between the available computational resources and the dependent workow jobs based on the researchers’requirements.However,cost optimization is one of the SWFS challenges in handling massive and complicated tasks and requires determining an approximate(near-optimal)solution within polynomial computational time.Motivated by this,current work proposes a novel SWFS cost optimization model effective in solving this challenge.The proposed model contains three main stages:(i)scientic workow application,(ii)targeted computational environment,and(iii)cost optimization criteria.The model has been used to optimize completion time(makespan)and overall computational cost of SWFS in cloud computing for all considered scenarios in this research context.This will ultimately reduce the cost for service consumers.At the same time,reducing the cost has a positive impact on the protability of service providers towards utilizing all computational resources to achieve a competitive advantage over other cloud service providers.To evaluate the effectiveness of this proposed model,an empirical comparison was conducted by employing three core types of heuristic approaches,including Single-based(i.e.,Genetic Algorithm(GA),Particle Swarm Optimization(PSO),and Invasive Weed Optimization(IWO)),Hybrid-based(i.e.,Hybrid-based Heuristics Algorithms(HIWO)),and Hyper-based(i.e.,Dynamic Hyper-Heuristic Algorithm(DHHA)).Additionally,a simulation-based implementation was used for SIPHT SWFA by considering three different sizes of datasets.The proposed model provides an efcient platform to optimally schedule workow tasks by handing data-intensiveness and computational-intensiveness of SWFAs.The results reveal that the proposed cost optimization model attained an optimal Job completion time(makespan)and total computational cost for small and large sizes of the considered dataset.In contrast,hybrid and hyper-based approaches consistently achieved better results for the medium-sized dataset.

Towards Interaction,Communication and Integration of Ethnic Groups with Commonality:A Comparison of Inner Mongolia,Guangxi and Ningxia Autonomous Region

One crucial measure of strengthening and advancing the Party's work on ethnic affairs in the new era is to promote interaction,communication and integration of various ethnic groups,so as to enhance commonality.Entering the new era,Inner Mongolia,Guangxi and Ningxia have achieved remarkable results in encouraging interaction,communication and integration of various ethnic groups.By comparatively analyzing successes of the three autonomous regions through the perspective of promoting commonality,this article summarizes the following strategic revelations:1)we should comprehensively implement the Party's ethnic policies and ensure theactual implementation of mutual interaction,mutual communication and mutual integration of ethnic groups;2)we should proactively promote the substantial progress of the economy,so as to provide solid material foundation for mutual interaction,mutual communication and mutual integration of ethnic groups;3)we should facilitate activities that promote ethnic unity and progress,so that the promotion of interaction,communication and mutual integration of ethnic groups can be seen,be felt and gain actual outcomes;4)we should take measures that suit to local conditions and explore all-round embedding mechanisms in order to foster the ethos for mutual interaction,mutual communication and mutual integration of ethnic groups;5)we should make every effort to forge a strong sense of community for the Chinese nation and orient precisely to the cause of encouraging mutual interaction,mutual communication and mutual integration of ethnic groups.

Learning Gaussian mixture with automatic model selection:A comparative study on three Bayesian related approaches

Three Bayesian related approaches,namely,variational Bayesian(VB),minimum message length(MML)and Bayesian Ying-Yang(BYY)harmony learning,have been applied to automatically determining an appropriate number of components during learning Gaussian mixture model(GMM).This paper aims to provide a comparative investigation on these approaches with not only a Jeffreys prior but also a conjugate Dirichlet-Normal-Wishart(DNW)prior on GMM.In addition to adopting the existing algorithms either directly or with some modifications,the algorithm for VB with Jeffreys prior and the algorithm for BYY with DNW prior are developed in this paper to fill the missing gap.The performances of automatic model selection are evaluated through extensive experiments,with several empirical findings:1)Considering priors merely on the mixing weights,each of three approaches makes biased mistakes,while considering priors on all the parameters of GMM makes each approach reduce its bias and also improve its performance.2)As Jeffreys prior is replaced by the DNW prior,all the three approaches improve their performances.Moreover,Jeffreys prior makes MML slightly better than VB,while the DNW prior makes VB better than MML.3)As the hyperparameters of DNW prior are further optimized by each of its own learning principle,BYY improves its performances while VB and MML deteriorate their performances when there are too many free hyper-parameters.Actually,VB and MML lack a good guide for optimizing the hyper-parameters of DNW prior.4)BYY considerably outperforms both VB and MML for any type of priors and whether hyper-parameters are optimized.Being different from VB and MML that rely on appropriate priors to perform model selection,BYY does not highly depend on the type of priors.It has model selection ability even without priors and performs already very well with Jeffreys prior,and incrementally improves as Jeffreys prior is replaced by the DNW prior.Finally,all algorithms are applied on the Berkeley segmentation database of real world images.Again,BYY considerably outperforms both VB and MML,especially in detecting the objects of interest from a confusing background.