Energy Aware Task Assignment with Cost Optimization in Mobile Cloud Computing

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

doi:10.4236/ijcns.2018.118010

Cited by 3 publications

(2 citation statements)

References 14 publications

(10 reference statements)

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“…The solutions are fully approximated, and exponentially gained optimal objectives in an exploration and exploitation environment. The studies [20][21][22][23] formulated dynamic content and failure-aware workload assignment-based scheduling and resource allocation techniques. Different heuristics, such as Hill Climbing, Earliest Deadline First, and Earliest Finish Time methods, suggested minimizing the tardiness latency of applications.…”

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 goal was to decrease the renting costs of resources and execute all applications under their deadlines. These studies [18]- [20] considered the real-time and cost-efficient task scheduling problem to minimize application costs. The main goal is to enhance the performance for cloud service platforms by reducing uncertainty propagation in scheduling workflow applications that have both uncertain task execution time and data transference time.…”

Section: Related Workmentioning

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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Pervasive Futuristic Healthcare and Blockchain enabled Digital Identities-Challenges and Future Intensions

Sathio¹,

Dootio²,

Lakhan³

et al. 2021

2021 International Conference on Computing, Electronics &Amp; Communications Engineering (iCCECE)

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Energy Aware Task Assignment with Cost Optimization in Mobile Cloud Computing

Cited by 3 publications

References 14 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

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

Pervasive Futuristic Healthcare and Blockchain enabled Digital Identities-Challenges and Future Intensions

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