A Levelized Multiple Workflow Heterogeneous Earliest Finish Time Allocation Model for Infrastructure as a Service (IaaS) Cloud Environment  
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1 Industrial Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia; [EMAIL_ADDRESS]  
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2 Workday Inc., Pleasanton, CA 94588, USA  
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3 Department of Commerce, Aligarh Muslim University, Aligarh 202002, India  
ing has experienced rapid development with great attention from related scholars in many fields. Cloud computing platforms can integrate various resources such as computational machines, storage, and applications, and by using high-level virtualization and abstraction, they can effectively offer resources and reliable services to cloud users. Generally, every large-scale service organization is shifting by building its own cloud platforms, such as Microsoft Cloud, Google Cloud, IBM Cloud, and Amazon EC2, and they are dedicated to implementing web search, highly reliable storage applications, and social networking. Cloud computing also provides services with silent features such as multi-tenancy, shared resource pooling, service-oriented features, efficient elasticity, linear scalability, dynamic resource provisioning, low pricing, and geo-distribution. However, new issues keep emerging from industrial applications, as follows: automated service provisioning, VM migration, energy management, traffic management, data security, homogenous and heterogeneous workload, handling long-running jobs, software frameworks, storage technologies, novel cloud architectures, and so on [1; 2; 3]. In the cloud environment, a workflow is an organized series of actions or processes to achieve a particular objective, frequently including various participants, tools, or systems. The workflow commences with a triggering event, such as a data receipt or a specific request. It advances through job execution, decision-making junctures, and integrations with tools or platforms to optimize operations. Upon the fulfillment of all tasks, the workflow yields the desired result. Workflows are essential for automating procedures, optimizing resource allocation, and improving productivity [4]. Workflow is modeled by Directed Acyclic Graph (DAG) [4], Petri net (PN) [5], and Time Stream Petri Net [6] to denote the set of precedence-constrained tasks. There are many workflow applications to model precedence-constrained tasks, such as multi-tier web, scientific computing, project management, distributed systems, medical analytics, genome analysis, and big data applications [7; 8]. Such applications have the precedence constraint, in which predecessor tasks finish execution before successor ones begin. This constraint assures that tasks are processed in the proper sequence to satisfy the dependencies within the workflow. Various methods are used to manage the precedence constraints, such as the ranking method [9] and the level attribute-based methods [10; 11]. The level attribute in a workflow indicates a task's hierarchical depth levels in the process. It shows how distant a task is from the start of the workflow. Additionally, tasks at the same level can be performed concurrently, but higher-level tasks depend on lower-level tasks. This helps visualize the workflow's structure, simplify scheduling, and optimize resource allocation by recognizing parallel and sequential tasks. In cloud computing, workflow allocation (WFA) distributes workflow tasks across virtual machines (VMs) to optimize the QoS parameters from service level agreement (SLA). The workflow allocation (WFA) problem in cloud computing involves optimizing workflow task mapping to cloud resources while fulfilling constraints and goals. These tasks often depend on varying execution time and resource requirements, while the resources differ in costs, availability, and capabilities. The goal of workflow allocation is to allocate and manage the processing of workflows on virtualized resources of cloud platforms controlled by a Workflow Management System (WMS).