# Replication-Based Dynamic Energy-Aware Resource Provisioning for Scientific Workflows
## 1 Introduction  
The cloud environment provides end users with distributed computing services [1]. The computing services are provided to the end users in a subscription-based atmosphere [2]. The cloud environment provides computing services in the shape of applications, platforms, and computing resources, termed \"software as a service\" (SaaS) [3], \"platform as a service\" (PaaS) [4] and \"infrastructure as a service\" (IaaS) [5], respectively. IaaS is a flexible cloud environment model in which distributed computing resources such as storage, memory, bandwidth, and processing power are provided to clients on a pay-per-use basis [6, 7]. The three main subcategories of cloud computing services are SaaS, PaaS, and IaaS. SaaS is a method of distributing computer programs in which the vendor hosts the program and makes it accessible to users online. Customers who use SaaS do not have to worry about maintaining software or managing the underlying infrastructure. Instead, users merely use a web browser or a thin client to access the software. Systems for managing client relationships (CRM), corporate resource planning (ERP), and email services are examples of SaaS [3]. PaaS offers a platform for creating, deploying, and managing services and applications. Customers who use PaaS do not have to worry about constructing and maintaining the platform or managing the underlying infrastructure. They can instead concentrate on creating and implementing their applications. Google App Engine, Microsoft Azure, and Heroku are some examples of PaaS [4]. Over the internet, IaaS offers virtualized computer resources. Clients can rent computing resources including virtual machines, storage, and networking through IaaS. They are in charge of managing the software and applications that are used by the infrastructure. IaaS examples include \"Google Cloud Platform\", \"Microsoft Azure\", and \"Amazon Web Services\" (AWS) [5]. The IaaS cloud service model is currently the most suitable cloud environment model for the execution of highly data- and computing-intensive applications [8]. The best examples of this in the context of data- and computing-intensiveness are real-world scientific workflows (SWs). Real-world SWs are those such as Montage in astronomy, CyberShake in earthquake science, and Epigenomics in biology; they are scientific applications developed by or for scientists for analyses in their scientific research [9; 10; 11]. For their implementation, SW applications need high-performance computing resources and large volumes of storage, which can be obtained from the IaaS cloud service model. Scientists in most scientific fields, such as astronomy, bioinformatics and high-energy physics, conduct research that requires and creates terabytes of data obtained from physical equipment. Their workflow activities are connected based on computational and data interdependence [12; 13]. SWs are highly complex applications and are represented in terms of directed acyclic graphs (DAGs) [14]. Tasks are represented by nodes in a DAG, and computational or data dependencies among nodes are represented by the edges [15; 16; 17]. The resource-provisioning process for the execution of SWs with minimum energy consumption and without failure of tasks is a challenging process due to the variety of workflow tasks and complex dependencies among them [10; 18; 19]. Since workflow tasks need massive amounts of computation and storage with diverse resource requirements, the best solution is to deploy the workflow tasks on an IaaS cloud service [20; 21; 22]. However, keeping in mind the high volume and variety of SWs, the resources of an IaaS cloud environment require managing energy efficiently and without failure [23; 24; 25]. Therefore, in order to address the issues of energy consumption and task failure for real-world SWs, this research work proposed a replication-based dynamic energy-aware resource-provisioning (R-DEAR) strategy for SWs in an IaaS cloud environment. The main contributions of the proposed work are as follows:  
1.