Using Markov Learning Utilization Model for Resource Allocation in Cloud of Thing Network

Mirmohseni, Seyedeh Maedeh; Tang, Chunming; Javadpour, Amir

doi:10.1007/s11277-020-07591-w

Cited by 40 publications

(14 citation statements)

References 36 publications

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“…Many end-users tend to use these devices instead of desktops or clients. In this case, CloudIoT and fog computing have appeared as a practical explanation, and their tasks can be offloaded on the cloud substructure [1]. As another example, network 2…”

Section: Literature Reviewmentioning

confidence: 99%

“…Users need strong processors and fast networks to set up cloud data centers in different parts of the world. One of the most important concerns in this area is related to energy [1,2]. For this purpose, in recent years, attention has been paid to enhanced techniques for providing power management for cloud servers.…”

Section: Introductionmentioning

confidence: 99%

“…is technique could attract attention due to its simplicity of implementation. e cloud services providers, because of the benefits they gain from serving customers as well as cloud consumers, such as organizations and economic organizations, tend to cloud computing because of the benefits they receive from reducing or eliminating infrastructure maintenance costs for these services [1]. However, since applications for business operations of users running in the cloud may be highly sensitive, consumers need to have a guarantee by the cloud providers when the service is delivering.…”

Section: Introductionmentioning

confidence: 99%

“…Users also do not need hardware systems with high processing capacity and storage capacity, as all cloud computing operations and storage are handled in the cloud service providers section by equipped and advanced servers [11]. Currently, cloud computing services are offered in three different forms: SaaS_Software as a Service, PaaS_-Platform as a Service, and IaaS_Infrastructure as a Service [1]. Organizations and companies can select and use one or more service strategies depending on their needs.…”

Section: Introductionmentioning

confidence: 99%

“…e amount of unlimited resource capacity (such as CPUs, memory, input-output network, and disk) is provided at a competitive rate [12]. is subject provides opportunities for startup companies to host their applications in the cloud, so it eliminates the overflow of prepared old ultrastructural resources that lasted several months [1,13,14].…”

Section: Introductionmentioning

confidence: 99%

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LBPSGORA: Create Load Balancing with Particle Swarm Genetic Optimization Algorithm to Improve Resource Allocation and Energy Consumption in Clouds Networks

Mirmohseni

Javadpour

Tang

2021

Mathematical Problems in Engineering

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Due to the purpose of this study that reducing power consumption in the cloud network is based on load balancing, the fitness function measures the load balance between cloud network and servers (the hosts). This technique is appropriate for handling the resource optimization challenges, due to the ability to convert the load balancing problem into an optimization problem (reducing imbalance cost). In this research, combining the results of the particle swarm genetic optimization (PSGO) algorithm and using a combination of advantages of these two algorithms lead to the improvement of the results and introducing a suitable solution for load balancing operation, because in the proposed approach (LBPSGORA), instead of randomly assigning the initial population in the genetic algorithm, the best result is procured by putting the initial population. The LBPSGORA method is compared with PSO, GA, and hybrid GA-PSO. The execution cost, load balancing, and makespan have been evaluated and our method has performed better than similar methods.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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LBPSGORA: Create Load Balancing with Particle Swarm Genetic Optimization Algorithm to Improve Resource Allocation and Energy Consumption in Clouds Networks

Mirmohseni

Javadpour

Tang

2021

Mathematical Problems in Engineering

Self Cite

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An intelligent resource allocation strategy for machine type communication environment

Sharmila,

Satyanarayana

2023

Int J Communication

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SummaryMobile communication has multiplied in many digital intelligent applications. The machine‐type communication (MTC) services have afforded flexible communication facilities considering those facilities. However, high energy consumption and poor data rate have made the MTC a complex system. So, the resource allocation strategy has been introduced by different procedures such as neural models, optimization, and mathematical models. But, in some cases, these algorithms have recorded high complexity and high resource requirement. Hence, the present research article has planned to develop a novel Chimp‐based Extreme Neural Model (CbENM) for allocating the desired optimal resource for each machine user. Moreover, the resources were assigned based on the task completion deadline. Before the resource allocation process, the active state users were predicted by analyzing their data rate and deadlines. Finally, the desired resources were allocated to each user, and the performance was validated. The presented model has a lower delay bond rate and a high data aggregate rate.

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A solution for resource allocation through complex systems in fog computing for the internet of things

Faraji,

Javadpour,

Sangaiah

et al. 2023

Computing

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To manage the IoT resources to process the massive amount of collected data in complex systems, Allocation of tasks to nodes to increase user satisfaction has been noticed by researchers. In allocating tasks, the necessity of a powerful auxiliary platform is felt. Cloud and fog networks are two suitable options for performing and completing tasks on the Internet of Things. In the cloud network, due to offloading tasks to remote computing centers, problems such as increasing complete time, increasing traffic in the network, and high energy consumption for the Internet of Things network occur. The fog network solves the problem of completing tasks due to reducing the geographical distance between Internet of Things devices and processor nodes. The devices in the fog network cooperate with the Internet of Things network’s diverse and variable nodes with time computing, storage, and processing resources. In this heterogeneous environment, the non-cooperation and dishonesty of malicious fog nodes through misrepresentation of shared resources and non-completion of tasks can disrupt the performance of the Internet of Things network. As a result, two fundamental challenges in the heterogeneous fog network include providing resources to allocate tasks and recognizing valid fog nodes. This paper focuses on creating a resource provider network to allocate tasks to authentic fog nodes with appropriate resources. When an Internet of Things node faces a shortage of resources and link prediction, it can form a resource supply network with neighboring nodes to solve the two stated challenges. For this purpose, in the first stage, efforts are made to select the most appropriate options among the neighboring nodes based on specific criteria. In the second step, reliable nodes are identified based on a trust mechanism, including resource trust and entity trust. In the last step, tasks are divided among reliable devices with sufficient resources. The evaluation of the proposed method in the OMNET++ simulator shows that the trust mechanism can lead to the identification of reliable sources and, as a result, reduce the time to complete tasks.

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Using Markov Learning Utilization Model for Resource Allocation in Cloud of Thing Network

Cited by 40 publications

References 36 publications

LBPSGORA: Create Load Balancing with Particle Swarm Genetic Optimization Algorithm to Improve Resource Allocation and Energy Consumption in Clouds Networks

LBPSGORA: Create Load Balancing with Particle Swarm Genetic Optimization Algorithm to Improve Resource Allocation and Energy Consumption in Clouds Networks

An intelligent resource allocation strategy for machine type communication environment

A solution for resource allocation through complex systems in fog computing for the internet of things

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