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.

Multi-Agent Based Stochastic Dynamical Model to Measure Community Resilience

Emergency services and utilities need appropriate planning tools to analyze and improve infrastructure and community resilience to disasters.Recognized as a key metric of community resilience is the social well-being of a community during a disaster,which is made up of mental and physical social health.Other factors influencing community resilience directly or indirectly are emotional health,emergency services,and the availability of critical infrastructures services,such as food,agriculture,water,transportation,electric power,and communications system.It turns out that in computational social science literature dealing with community resilience,the role of these critical infrastructures along with some important social characteristics is not considered.To address these weaknesses,we develop a new multi-agent based stochastic dynamical model,standardized by overview,design concepts,details,and decision(ODD+D)protocol and derived from neuro-science,psychological and social sciences,to measure community resilience in terms of mental and physical well-being.Using this model,we analyze the micro-macro level dependence between the emergency services and power systems and social characteristics such as fear,risk perception,informationseeking behaviour,cooperation,flexibility,empathy,and experience,in an artificial society.Furthermore,we simulate this model in two case studies and show that a high level of flexibility,experience,and cooperation enhances community resilience.Implications for both theory and practice are discussed.