Workflow scheduling in scientific computing systems is one of the most challenging
problems that focuses on satisfying user-defined quality of service requirements while …

Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML
techniques unlock the potential of IoT with intelligence, and IoT applications increasingly …

Cloud computing provides effective mechanisms for distributing the computing tasks to the
virtual resources. To provide cost-effective executions and achieve objectives such as load …

Dynamic on‐demand resource provisioning is one of the primary goals of the cloud
computing task scheduling process. Task scheduling is a nondeterministic polynomial time …

The effectiveness of the machine learning methods for real-world tasks depends on the
proper structure of the modeling pipeline. The proposed approach is aimed to automate the …

The cloud infrastructures provide a suitable environment for the execution of large‐scale
scientific workflow application. However, it raises new challenges to efficiently allocate …

Scientific workflows, like other applications, benefit from the cloud computing, which offers
access to virtually unlimited resources provisioned elastically on demand. In order to …

The scientific workflows are high-level complex applications that demand more computing
power. The cloud data center (CDC) remains one of the essential models of economic …

The problem of scheduling of tasks in distributed, heterogeneous, and multiprocessing
computing environment like grid and cloud computing is considered as one of the most …

The speedup is usually limited by two main laws in high-performance computing, that is, the
Amdahl's and Gustafson's laws. However, the speedup sometimes can reach far beyond the …