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.

RePizer：a framework for prioritization of software requirements

标准的软件开发周期很大程度上取决于利益相关方的需求。软件开发全程围绕需求设计和管理。考虑到时间和资源的限制,必须分清哪些是必须首先考虑的高优先级需求。已有的需求排序架构缺少对历史数据的记录,而这些历史数据有助于从类似项目中方便地选取最适合的需求排序技术。本文中,我们提出一种名为Re Pizer的软件需求排序架构,该架构与一种选定的需求排序技术联合使用,可以基于给定标准(如开发成本),为软件需求优先级排序。Re Pizer通过从需求库提取历史数据,为软件需求工程师决策提供协助。此外,Re Pizer提供了对整个项目的全景式视角,以确保对资源的审慎使用。基于Re Pizer架构,采用已有的两种需求排序技术:计划博弈(planning game,PG)和层级分析(analytical hierarchy process,AHP),分别比较各自的预期准确度和易用程度。结果表明,采用计划博弈时,Re Pizer性能更佳。