Efficient Resource Allocation in Fog Computing Using QTCS Model

Iyapparaja, M.; Alshammari, Naif; Kumar, Mohit; Krishnan, S. S.; Chowdhary, Chiranji Lal

doi:10.32604/cmc.2022.015707

Cited by 21 publications

(7 citation statements)

References 31 publications

(30 reference statements)

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“…Some researchers have also used this queuing theory in fog computing. Iyapparaja et al [26] designed a model based on queueing theory-based cuckoo search (QTCS) to improve QoS of resource allocation. Li et al [4] considered heterogeneous tasks to be placed in parallel virtual queues.…”

Section: Related Workmentioning

confidence: 99%

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A Whale Optimization Algorithm Based Resource Allocation Scheme for Cloud-Fog Based IoT Applications

et al. 2022

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Fog computing has been prioritized over cloud computing in terms of latency-sensitive Internet of Things (IoT) based services. We consider a limited resource-based fog system where real-time tasks with heterogeneous resource configurations are required to allocate within the execution deadline. Two modules are designed to handle the real-time continuous streaming tasks. The first module is task classification and buffering (TCB), which classifies the task heterogeneity using dynamic fuzzy c-means clustering and buffers into parallel virtual queues according to enhanced least laxity time. The second module is task offloading and optimal resource allocation (TOORA), which decides to offload the task either to cloud or fog and also optimally assigns the resources of fog nodes using the whale optimization algorithm, which provides high throughput. The simulation results of our proposed algorithm, called whale optimized resource allocation (WORA), is compared with results of other models, such as shortest job first (SJF), multi-objective monotone increasing sorting-based (MOMIS) algorithm, and Fuzzy Logic based Real-time Task Scheduling (FLRTS) algorithm. When 100 to 700 tasks are executed in 15 fog nodes, the results show that the WORA algorithm saves 10.3% of the average cost of MOMIS and 21.9% of the average cost of FLRTS. When comparing the energy consumption, WORA consumes 18.5% less than MOMIS and 30.8% less than FLRTS. The WORA also performed 6.4% better than MOMIS and 12.9% better than FLRTS in terms of makespan and 2.6% better than MOMIS and 4.3% better than FLRTS in terms of successful completion of tasks.

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Section: Related Workmentioning

confidence: 99%

“…If the absolute value of A is less than 1, then update D and W i using Equations ( 18), (19), and (29) in step 8. Otherwise, select a random whale W rand and update D and W i using Equations ( 25), (26), and ( 29) in steps 10 and 11.…”

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confidence: 99%

A Whale Optimization Algorithm Based Resource Allocation Scheme for Cloud-Fog Based IoT Applications

et al. 2022

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“…Makespan was discovered to be reduced to a greater extent with the help of reinforcement learning. To ensure service quality, another method called Queuing theorybased cuckoo search was developed in [11]. e fog computing environment's challenges and outstanding issues based on reinforcement learning were discussed in [12].…”

Section: Work That Are Relatedmentioning

confidence: 99%

“…"Rnd 2 ��→ ," and "Rnd 3 ��→ " forming random coefficient vector of computing nodes and "A → " denoting the position vector of computing node, respectively. Equations (9)(10)(11) measure the distance between the position of current computing nodes in the fog and that of individual nodes (i.e., "alpha," " beta," and "delta"). So the final position vectors of the current individual are mathematically obtained as given below.…”

Section: Differential Evolution-based Grey Wolf Optimized Loadmentioning

confidence: 99%

Differential Grey Wolf Load-Balanced Stochastic Bellman Deep Reinforced Resource Allocation in Fog Environment

Nethaji

Chidambaram

2022

Applied Computational Intelligence and Soft Computing

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Fog computing is becoming a dynamic and sought-after computing prototype for Internet of Things (IoT) application deployments. It works in conjunction with the cloud computing environment. Load balancing, which is employed by IoT applications when deciding, which fog or cloud computing nodes to use, is one of the most critical components for enhancing resource efficiency and avoiding problems like overloading or underloading. However, for IoT applications, ensuring that all CPU nodes are evenly distributed in terms of latency and energy utilization remains a challenge. To solve these issues, this work introduces Differential Grey Wolf (DGW) load balancing with stochastic Bellman deep reinforced resource optimization (DGW-SBDR) in fog situations. A Differential Evolution-based Grey Wolf Optimization algorithm based on load balancing has been developed for optimal resource management. The Grey Wolf Optimization algorithm, which employs differential evolution, assigns jobs to virtual machines based on user demands (VMs). In the event of an overutilized VM pool, a grey wolf optimization strategy based on differential evolution can detect both under and overutilized VMs, allowing for smooth transit between fogs. This step disables a number of virtual machines in order to reduce latency. In a Stochastic Gradient and Deep Reinforcement Learning-based Resource Allocation Model, a stochastic gradient bellman optimality function and Deep Reinforcement Learning are integrated for optimal resource allocation. According to the proposed method, QoS may be supplied to end-users by reducing energy consumption and better managing cache resources utilizing stochastic gradient bellman optimality.

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“…[2][3][4] The resources are allocated and offers in the simple way, but it requires slight variations from the service provider. With the view of customers, the cloud structure provides large amount of resources 5,6 and it is used to satisfy customer needs. In the cloud paradigm, customers do not contain any computing servers, whereas they have convenience to diverse services on cloud management as well as the data with less overhead.…”

Section: Introductionmentioning

confidence: 99%

A network intrusion detection system in cloud computing environment using dragonfly improved invasive weed optimization integrated Shepard convolutional neural network

Sathiyadhas¹,

Vigila

2022

Adaptive Control & Signal

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SummaryIn cloud computing, the resources and memory are dynamically allocated to the user based on their needs. Security is considered as a major issue in cloud as the use of cloud is increased. Intrusion detection is considered as a significant tool to develop a reliable and secure cloud environment. Performing intrusion detection in cloud is a difficult task because of its distributed nature and extensive usage. Intrusion detection system (IDS) is widely considered to find the malicious actions in network. In cloud, most conventional IDS are vulnerable to attacks and have no capability for maintaining the balance between sensitivity and accuracy. Thus, we proposed an effective dragonfly improved invasive weed optimization‐based Shepard convolutional neural network (DIIWO‐based ShCNN) to detect the intruders and to mitigate the attacks in cloud paradigm and are more feasible to detect the intruders with ShCNN. The proposed method outperforms the existing method with maximum accuracy of 0.960%, sensitivity of 0.967%, and specificity 0.961%, respectively.

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Efficient Resource Allocation in Fog Computing Using QTCS Model

Cited by 21 publications

References 31 publications

A Whale Optimization Algorithm Based Resource Allocation Scheme for Cloud-Fog Based IoT Applications

A Whale Optimization Algorithm Based Resource Allocation Scheme for Cloud-Fog Based IoT Applications

Differential Grey Wolf Load-Balanced Stochastic Bellman Deep Reinforced Resource Allocation in Fog Environment

A network intrusion detection system in cloud computing environment using dragonfly improved invasive weed optimization integrated Shepard convolutional neural network

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