Abstract
These days, with the emerging developments in wireless communication technologies, such as 6G and 5G and the Internet of Things (IoT) sensors, the usage of E-Transport applications has been increasing progressively. These applications are E-Bus, E-Taxi, self-autonomous car, E-Train and E-Ambulance, and latency-sensitive workloads executed in the distributed cloud network. Nonetheless, many delays present in cloudlet-based cloud networks, such as communication delay, round-trip delay and migration during the workload in the cloudlet-based cloud network. However, the distributed execution of workloads at different computing nodes during the assignment is a challenging task. This paper proposes a novel Multi-layer Latency (e.g., communication delay, round-trip delay and migration delay) Aware Workload Assignment Strategy (MLAWAS) to allocate the workload of E-Transport applications into optimal computing nodes. MLAWAS consists of different components, such as the Q-Learning aware assignment and the Iterative method, which distribute workload in a dynamic environment where runtime changes of overloading and overheating remain controlled. The migration of workload and VM migration are also part of MLAWAS. The goal is to minimize the average response time of applications. Simulation results demonstrate that MLAWAS earns the minimum average response time as compared with the two other existing strategies.
Highlights
These days, cloud computing-based smart applications are growing progressively to deal with different life activities
This study addresses the following research questions: (i) How do we design a resource-optimal, cloudlet-based cloud network that will not incur overloading and resource-constraint issues? (ii) How do we adopt dynamic arrival workloads based on the Poisson process in the network without waiting for the delay? (iii) How do we search for a local cloudlet that is optimal compared to other cloudlets for workload assignments? (iv) How do we adopt dynamic changes and obtain global search-based optimal solutions during mobility and the migration of workload in the network?
Mobile cloudlet based cloud computing is an emerging hybrid computing architecture in which the workload of applications is executed at different nodes to gain lower endto-end latency
Summary
These days, cloud computing-based smart applications are growing progressively to deal with different life activities. Conventional cloud computing, located multiple hops away from E-Transport, and the offloading of the workload of applications, will suffer from long end-to-end latency. Cloudlet is an extension of cloud computing that brings computing resources to the edge of the communication network. Cloudlet is a latency-optimal paradigm and is connected with the network’s base stations, controlled by the software-defined network. Offloading and workload assignment are two fundamental techniques in distributed cloudlet-based cloud computing networks where E-Transports send their workload for execution. It is called full offloading from E-Transport devices to the cloudlet-based cloud network [2]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
