Multi-Layer Latency Aware Workload Assignment of E-Transport IoT Applications in Mobile Sensors Cloudlet Cloud Networks

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.

Hybrid Workload Enabled and Secure Healthcare Monitoring Sensing Framework in Distributed Fog-Cloud Network

“…The studies [6][7][8] devised dynamic and secure IoMT systems based on different primitives such as workflow applications, deadlines, Genetic Algorithm (GA) on virtual machines (VMs), which enable cloud data centers, and RSA-based networks. The purpose of these studies is to gain dynamic results for healthcare applications in distributed cloud data centers.…”

“…The execution time and energy consumption are anticipated in advance before scheduling tasks to any node by exploiting the energy profiler and workload execution profiler at the design time of applications. These mechanisms of application partitioning and the time estimation are already published in our previous work [7]. Therefore, this work only focuses on scheduling, not application partitioning and offloading in the current model.…”

“…These healthcare applications are distributed and implemented in different places (e.g., fog cloud nodes). However, they execute onto geographically distributed network [7]. One of the main concerns is resource-constraint issues in devices (e.g., limited storage, small CPU, and short bandwidth utilization) and cannot execute applications locally [8].…”

“…On the other hand, developers of serverless applications are not concerned with the container, VM, or physical server capacity planning, configuration, management, maintenance, fault tolerance, or scaling. In the literature, many studies [2,3,5,7,8,11,12] proposed different systems, architectures, and heuristicsbased IoMT network to solve the offloading and scheduling problem of workflow and fine-grained applications. These studies improved devices' battery lives, offloading cost and the obtained Service Level Agreement (SLA) and Quality of Service (QoS) applications during their problem formulation.…”

“…This resource-provisioning model is very costly for fine-grained tasks where they need only usage-based services instead of monthly and yearly contract-based services. (iii) Existing resource allocation policies of theses studies [2,3,7,9,10,[12][13][14] in the IoMT network only make offloading decisions at the user level and based on the threshold. However, optimal scheduling and failure aware mechanisms at the distributed computing node level are widely ignored.…”