Formal Approach to Workow Application Fragmentations Over Cloud Deployment Models

Workow management technologies have been dramatically improving their deployment architectures and systems along with the evolution and proliferation of cloud distributed computing environments.Especially,such cloud computing environments ought to be providing a suitable distributed computing paradigm to deploy very large-scale workow processes and applications with scalable on-demand services.In this paper,we focus on the distribution paradigm and its deployment formalism for such very large-scale workow applications being deployed and enacted across the multiple and heterogeneous cloud computing environments.We propose a formal approach to vertically as well as horizontally fragment very large-scale workow processes and their applications and to deploy the workow process and application fragments over three types of cloud deployment models and architectures.To concretize the formal approach,we rstly devise a series of operational situations fragmenting into cloud workow process and application components and deploying onto three different types of cloud deployment models and architectures.These concrete approaches are called the deployment-driven fragmentation mechanism to be applied to such very large-scale workow process and applications as an implementing component for cloud workow management systems.Finally,we strongly believe that our approach with the fragmentation formalisms becomes a theoretical basis of designing and implementing very large-scale and maximally distributed workow processes and applications to be deployed on cloud deployment models and architectural computing environments as well.

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.