Region aware dynamic task scheduling and resource virtualization for load balancing in IoT–fog multi-cloud environment

Kanbar, Asan Baker; Faraj, Kamaran

doi:10.1016/j.future.2022.06.005

Cited by 19 publications

(6 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In fact, fog-cloud hierarchical task scheduling has been foreseen to be dominant for future heterogeneous network (HetNet) deployment, requiring systemic optimization regarding service-centric and user-centric performance, e.g., energy efficiency, QoS, overall latency, etc. [16]. However, complicated features with multi-layer modeling cannot be tackled with the abovementioned homogeneousfog-cloud-only task scheduling.…”

Section: Related Workmentioning

confidence: 99%

Service-Aware Hierarchical Fog–Cloud Resource Mappingfor e-Health with Enhanced-Kernel SVM

AlZailaa,

Chi,

Radwan

et al. 2024

JSAN

View full text Add to dashboard Cite

Fog–cloud-based hierarchical task-scheduling methods are embracing significant challenges to support e-Health applications due to the large number of users, high task diversity, and harsher service-level requirements. Addressing the challenges of fog–cloud integration, this paper proposes a new service/network-aware fog–cloud hierarchical resource-mapping scheme, which achieves optimized resource utilization efficiency and minimized latency for service-level critical tasks in e-Health applications. Concretely, we develop a service/network-aware task classification algorithm. We adopt support vector machine as a backbone with fast computational speed to support real-time task scheduling, and we develop a new kernel, fusing convolution, cross-correlation, and auto-correlation, to gain enhanced specificity and sensitivity. Based on task classification, we propose task priority assignment and resource-mapping algorithms, which aim to achieve minimized overall latency for critical tasks and improve resource utilization efficiency. Simulation results showcase that the proposed algorithm is able to achieve average execution times for critical/non-critical tasks of 0.23/0.50 ms in diverse networking setups, which surpass the benchmark scheme by 73.88%/52.01%, respectively.

show abstract

Section: Related Workmentioning

confidence: 99%

Service-Aware Hierarchical Fog–Cloud Resource Mappingfor e-Health with Enhanced-Kernel SVM

AlZailaa,

Chi,

Radwan

et al. 2024

JSAN

View full text Add to dashboard Cite

show abstract

“…Відомі адаптивні методи для роботи у розподіленій системі [6], а у [7] використано алгоритми перенаправлення трафіку веб-сервера для балансування навантаження. Широко розповсюджені методи статичного [8] та динамічного балансування навантаження з використанням оперативної інформації про стан системи прийняття рішень на рівні завантаження CPU [9] та доступної пам'яті [10].…”

Section: Research Of Work Of a Single Server In A Clusterunclassified

Дослідження Роботи Одиночного Сервера У Кластері

Токар¹

2023

НТС

View full text Add to dashboard Cite

“…The MFO algorithm is described as being made up of moths and flames in the quick summary. Moths are referred to as search agents in Equation ( 1), where N is total number of moths and M(t) is their organisational matrix that they use to search across the D-dimensional search space by eqn (21).…”

Section: Meta-heuristic Moth Flame Optimization With Chaotic Maps In ...mentioning

confidence: 99%

“…In [20], fuzzy dominance is used to enhance MOHEFT's performance, which maximises both makespan and cost index. Constrained optimization challenges in cloud fog platforms were related task scheduling problems that the author [21] formulated. It is suggested to handle these issues using an LBP-ACS strategy.…”

Section: Introductionmentioning

confidence: 99%

Mobile Cloud IoT for Resource Allocation with Scheduling in Device- Device Communication and Optimization based on 5G Networks

Pise¹

2022

ijfrcsce

View full text Add to dashboard Cite

Internet of Things (IoT) is revolutionising technical environment of traditional methods as well as has applications in smart cities, smart industries, etc. Additionally, IoT enabled models' application areas are resource-constrained as well as demand quick answers, low latencies, and high bandwidth, all of which are outside of their capabilities. The above-mentioned issues are addressed by cloud computing (CC), which is viewed as a resource-rich solution. However, excessive latency of CC prevents it from being practical. The performance of IoT-based smart systems suffers from longer delay. CC is an affordable, emergent dispersed computing pattern that features extensive assembly of diverse autonomous methods. This research propose novel technique resource allocation and task scheduling for device-device communication in mobile Cloud IoT environment based on 5G networks. Here the resource allocation has been carried out using virtual machine based markov model infused wavelength division multiplexing. Task scheduling is carried out using meta-heuristic moath flame optimization with chaotic maps. So, by scheduling tasks in a smaller search space, system resources are conserved. We run simulation tests on benchmark issues and real-world situations to confirm the effectiveness of our suggested approach. The parameters measured here are resource utilization of 95%, response time of 89%, computational cost of 35%, power consumption of 38%, QoS of 85%.

show abstract

Region aware dynamic task scheduling and resource virtualization for load balancing in IoT–fog multi-cloud environment

Cited by 19 publications

References 30 publications

Service-Aware Hierarchical Fog–Cloud Resource Mappingfor e-Health with Enhanced-Kernel SVM

Service-Aware Hierarchical Fog–Cloud Resource Mappingfor e-Health with Enhanced-Kernel SVM

Дослідження Роботи Одиночного Сервера У Кластері

Mobile Cloud IoT for Resource Allocation with Scheduling in Device- Device Communication and Optimization based on 5G Networks

Contact Info

Product

Resources

About