Hybrid Delay Optimization and Workload Assignment in Mobile Edge Cloud Networks

Mahesar, Abdul Rasheed; Lakhan, Abdullah; Sajnani, Dileep Kumar; Jamali, Irfan Ali

doi:10.4236/oalib.1104854

Cited by 10 publications

(16 citation statements)

References 11 publications

(14 reference statements)

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“…A.Rasheed et al, Lixing et al and Dileep et al [10][11][12] proposed dynamic programmingbased iterative and full approximation methods based on Lyapunov optimization heuristics to solve the workload assignment problem in the distributed edge cloud network. The concave and convex optimization-based solution suggested solving the travelling salesmen's problem for workload in the network.…”

Section: Related Workmentioning

confidence: 99%

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%

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

et al. 2021

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“…The study [6] investigated scheduling problem for daily-check-system and submitted distributed fog cloud medical care network. The goal was to minimize cost and tardiness of the applications.…”

Section: Related Workmentioning

confidence: 99%

“…Many healthcare monitoring system suggested to offer different services to the patients. Many existing studies [6][7][8][9][10] suggested different pricing model for healthcare applications such as E-Blood-Pressure, E-Blood Analysis, EEG, and ECG and so on. Every provider rent services based on different pricing model such as on-demand, on-reserve, and spot-instants [11].…”

Section: Introductionmentioning

confidence: 99%

Serverless Based Functions Aware Framework for Healthcare Application

2021

IJETER

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These days, the usage of Internet of Medical Things (IoMT) to deal with the different diseases has been increasing with time in the practice. Cloud computing is a paradigm which offers services based on internet to run IoMT application in the efficient way. However, due to long latency, cloud computing introduces fog server which brings service at the edge of hospital network. All existing fog-cloud based system charged servicing rent based on hourly, weekly, monthly, and yearly. The result a lot of cost wastage for the patients whenever, they do not require service, but still, they are paying for rented services. To cope with the useless renting cost, the study devises the serverless function as a service system which offers services to run IoMT application with their usage instead of resource provisioning as existing studied exploited in the system. The virtual machine based healthcare charged based on hour, week, month, and year, and however, healthcare sensors need service at specific time. Therefore, there is a lot cost of services in the ideal time where services are free but users paid money due to rent from providers. This paper suggests serverless function as a service based system which charge only for usage instead of renting cost for execution inside system. Simulation results shows that, the proposed system not only minimize the IoMT application cost but improved the resource utilization of the system

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“…Initially, we arrange the offloaded tasks into the topological order by the known sorting algorithm [19]. Furthermore, each task has a set of vector attributes such as data size, required CPU instruction, and deadline.…”

Section: A Proposed Microservices Fog-cloud Architecturementioning

confidence: 99%

Microservice Based Computational Offloading Framework and Cost Efficient Task Scheduling Algorithm in Heterogeneous Fog Cloud Network

Zhao

Huang

2020

IEEE Access

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Nowadays, the usage of the fog cloud-based Internet of Things (IoT) applications among users has been growing progressively. These applications may be E-Transport, E-Healthcare, Augmented Reality, and 3D-Game. Generally, contemporary cloud frameworks offer services based on virtual machines. These frameworks incurred with following issues such as long boot-time, overhead and unnecessary cost to run the IoT applications. We propose a new Microservice container fog system (MSCFS) based framework to run the mobility and delay-sensitive applications with minimum cost. The study the cost-efficient task scheduling problem in the heterogeneous fog servers. Furthermore, the study introduces the Cost Aware Computational Offloading and task scheduling (CACOTS)framework, which solves the task scheduling into multiple steps. Such as task sequencing step, resource matching step and scheduling step. The experimental results prove that the proposed MSCFS and CACOTS schemes can enhance server utilization. As decrease the services latency and average services bootup time more effectively, and minimize costs. INDEX TERMS Task scheduling, fog servers, boot-up time, microservices, container.

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Hybrid Delay Optimization and Workload Assignment in Mobile Edge Cloud Networks

Cited by 10 publications

References 11 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

Serverless Based Functions Aware Framework for Healthcare Application

Microservice Based Computational Offloading Framework and Cost Efficient Task Scheduling Algorithm in Heterogeneous Fog Cloud Network

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