The digital transformation of all the conventional systems in the field, such as healthcare, transportation and education, has led to the massive outflow of data. The bulk of data generated by the digital devices is challenging to handle, resulting in latency issues while provisioning services to the end-users. To address this issue, a promising solution is provided by osmotic computing, which exploits as well as integrates cloud, edge, and Internet of things platforms. Osmotic computing is a growing research domain that has laid the foundation of a new state-of-the-art computing paradigm by objectifying the scenarios for handling hefty data and computations according to user’s requirement and resource availability. However, the eclectic range of computational resources and services available between datacenters of different platforms in the federated environment is a tedious task to manage. In this paper, we propose a task mapping algorithm by incorporating osmotic computing principles to complete the tasks in latency-sensitive systems. We present an osmotic task manager, which aims to optimize the mapping decisions for all the services based on two-fold categorization, namely task-level categorization and processing-level categorization. The objective is to categorize all the incoming requests on the basis of their resource requirement and efficient mapping of the tasks to resources. The proposed algorithm is simulated and evaluated using various synthetic test cases to show its efficacy.Please check and confirm if the author names and initials (Sanjaya Kumar Panda, Pradip Kumar Sahu) are correctConfirmed. The paper length is currently 7 pages. As it exceeds 6 pages, we have to pay the extra page fee. However, if we will pay the fee, this paper will not be considered as a part of my Ph. D. work. Considering this fact, I request you to kindly reduce it to 6 pages. 

该领域所有传统系统的数字化转型，如医疗、交通和教育，导致数据大量流出。数字设备生成的大量数据难以处理，导致在向最终用户提供服务时出现延迟问题。为了解决这个问题，渗透计算提供了一种很有前景的解决方案，它利用并集成了云、边缘和物联网平台。渗透计算是一个不断发展的研究领域，它通过根据用户的要求和资源可用性将处理大量数据和计算的场​​景对象化，为新的最先进的计算范式奠定了基础。然而，联合环境中不同平台的数据中心之间可用的各种计算资源和服务是一项繁琐的管理任务。在本文中，我们提出了一种任务映射算法，通过结合渗透计算原理来完成延迟敏感系统中的任务。我们提出了一个渗透任务管理器，旨在优化基于双重分类的所有服务的映射决策，即任务级分类和处理级分类。目标是根据所有传入请求的资源需求和任务到资源的有效映射对其进行分类。所提出的算法使用各种综合测试用例进行模拟和评估以显示其有效性。 请检查并确认作者姓名和首字母缩写（Sanjaya Kumar Panda，Pradip Kumar Sahu) 是正确的。纸张长度目前为 7 页。由于超过6页，我们必须支付额外的页费。但是，如果我们支付费用，这篇论文将不会被视为我博士工作的一部分。考虑到这一事实，我请求您将其减少到 6 页。