Cost-efficient mobility offloading and task scheduling for microservices IoVT applications in container-based fog cloud network

Lakhan, Abdullah; Memon, Muhammad Suleman; Mastoi, Qurat-ul-ain; Elhoseny, Mohamed; Mohammed, Mazin Abed; Qabulio, Mumtaz; Abdel‐Basset, Mohamed

doi:10.1007/s10586-021-03333-0

Cited by 43 publications

(17 citation statements)

References 52 publications

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“…Table 1 describes the existing problem formulation and the proposed schemes for the workload assignment problem in the MCBC network. Nouha et al [1] formulated latency and flexible workload assignment distributed cloudlet networks. The goal is to minimize the response time of applications, which is determined by process delay.…”

Section: Related Workmentioning

confidence: 99%

“…These days, cloud computing-based smart applications are growing progressively to deal with different life activities. The applications are E-Healthcare, E-Banking, Augmented Reality and E-Commerce [1]. Cloud computing offers the abandonment of being ubiquitous and omnipresent in order to entertain the requests of applications.…”

Section: Introductionmentioning

confidence: 99%

“…Conventional Method: This method is implemented widely in different studies [1][2][3][4][5][6][7][8] to solve the offloading and scheduling problem for the coarse-grained E-Bus workload.…”

mentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…In [6][7][8] explored the fog-cloud interplay to resolve such challenges in the related domains. Similarly, in [9], a multiagent-based fog computing model was proposed for task management jobs similar to the agent-based architecture proposed for UAV navigation in [10].…”

Section: Introductionmentioning

confidence: 99%

Derived blockchain architecture for security-conscious data dissemination in edge-envisioned Internet of Drones ecosystem

2022

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Internet of Drones (IoD) facilitates the autonomous operations of drones into every application (warfare, surveillance, photography, etc) across the world. The transmission of data (to and fro) related to these applications occur between the drones and the other infrastructure over wireless channels that must abide to the stringent latency restrictions. However, relaying this data to the core cloud infrastructure may lead to a higher round trip delay. Thus, we utilize the cloud close to the ground, i.e., edge computing to realize an edge-envisioned IoD ecosystem. However, as this data is relayed over an open communication channel, it is often prone to different types of attacks due to it wider attack surface. Thus, we need to find a robust solution that can maintain the confidentiality, integrity, and authenticity of the data while providing desired services. Blockchain technology is capable to handle these challenges owing to the distributed ledger that store the data immutably. However, the conventional block architecture pose several challenges because of limited computational capabilities of drones. As the size of blockchain increases, the data flow also increases and so does the associated challenges. Hence, to overcome these challenges, in this work, we have proposed a derived blockchain architecture that decouples the data part (or block ledger) from the block header and shifts it to off-chain storage. In our approach, the registration of a new drone is performed to enable legitimate access control thus ensuring identity management and traceability. Further, the interactions happen in the form of transactions of the blockchain. We propose a lightweight consensus mechanism based on the stochastic selection followed by a transaction signing process to ensure that each drone is in control of its block. The proposed scheme also handles the expanding storage requirements with the help of data compression using a shrinking block mechanism. Lastly, the problem of additional delay anticipated due to drone mobility is handled using a multi-level caching mechanism. The proposed work has been validated in a simulated Gazebo environment and the results are promising in terms of different metrics. We have also provided numerical validations in context of complexity, communication overheads and computation costs.

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“…These static heuristics, such as heterogeneous earliest finish time, genetic algorithm, simulated annealing, and particle swarm optimization, are widely implemented to run healthcare applications in polynomial time [ 7 ]. Local and global searches (e.g., simulated annealing and tabu search) are fundamental guided candidate solutions to the aforementioned algorithms to achieve optimal results of healthcare applications in heterogeneous fog cloud networks [ 8 ]. There are two types of edge cloud networks widely implemented in the literature, namely, homogeneous and heterogeneous nodes.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Secure Adaptive Approach for Internet-of-Medical-Things Healthcare Applications in Edge-Cloud-Based Networks

Lakhan

Sodhro

Majumdar

et al. 2022

Sensors

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Mobile-cloud-based healthcare applications are increasingly growing in practice. For instance, healthcare, transport, and shopping applications are designed on the basis of the mobile cloud. For executing mobile-cloud applications, offloading and scheduling are fundamental mechanisms. However, mobile healthcare workflow applications with these methods are widely ignored, demanding applications in various aspects for healthcare monitoring, live healthcare service, and biomedical firms. However, these offloading and scheduling schemes do not consider the workflow applications’ execution in their models. This paper develops a lightweight secure efficient offloading scheduling (LSEOS) metaheuristic model. LSEOS consists of light weight, and secure offloading and scheduling methods whose execution offloading delay is less than that of existing methods. The objective of LSEOS is to run workflow applications on other nodes and minimize the delay and security risk in the system. The metaheuristic LSEOS consists of the following components: adaptive deadlines, sorting, and scheduling with neighborhood search schemes. Compared to current strategies for delay and security validation in a model, computational results revealed that the LSEOS outperformed all available offloading and scheduling methods for process applications by 10% security ratio and by 29% regarding delays.

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Cost-efficient mobility offloading and task scheduling for microservices IoVT applications in container-based fog cloud network

Cited by 43 publications

References 52 publications

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

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

Derived blockchain architecture for security-conscious data dissemination in edge-envisioned Internet of Drones ecosystem

A Lightweight Secure Adaptive Approach for Internet-of-Medical-Things Healthcare Applications in Edge-Cloud-Based Networks

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