Latency and energy-aware provisioning of network slices in cloud networks

Boryło, Piotr; Tornatore, Massimo; Jaglarz, Piotr; Shahriar, Nashid; Chołda, Piotr; Boutaba, Raouf

doi:10.1016/j.comcom.2020.03.050

Cited by 20 publications

(14 citation statements)

References 43 publications

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“…During network slicing for a device, a service request is forwarded to the controller via the 5G AP. The proposed SliceNet is expressed as a mapping from the input layer to the output layer and can be expressed as , (5) where is the device i. The aforementioned parameters are fed into the proposed SliceNet as inputs.…”

Section: Network Slicing and Resource Allocationmentioning

confidence: 99%

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T-S3RA: Traffic-Aware Scheduling for Secure Slicing and Resource Allocation in SDN/NFVEnabled 5G Networks

Ramadhan¹

2021

IJETT

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Network slicing and resource allocation play pivotal roles in software-defined network (SDN)/network function virtualization (NFV)-assisted 5G networks. In 5G communications, the traffic rate is high, necessitating high data rates and low latency. Deep learning is a potential solution for overcoming these constraints. Secure slicing avoids resource wastage; however, DDoS attackers can exploit the sliced network. Therefore, we focused on secure slicing with resource allocation under massive network traffic. Traffic-aware scheduling is proposed for secure slicing and resource allocation over SDN/NFV-enabled 5G networks. In this approach (T-S3RA), user devices are authenticated using Boolean logic with a password-based key derivation function. The traffic is scheduled in 5G access points, and secure network slicing and resource allocation are implemented using deep learning models such as SliceNet and HopFieldNet, respectively. To predict DDoS attackers, we computed the Renyi entropy for packet classification. Experiments were conducted using a network simulator with 250 nodes in the network topology. Performance was evaluated using metrics such as throughput, latency, packet transmission ratio, packet loss ratio, slice capacity, bandwidth consumption, and slice acceptance ratio. T-S3RA was implemented in three 5G use cases with different requirements, including massive machine-type communication, ultra-reliable low-latency communication, and enhanced mobile broadband.

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Section: Network Slicing and Resource Allocationmentioning

confidence: 99%

“…Furthermore, the solution needs to offer a dynamic and quick response to meet the service level agreement (SLA) constraints. Most existing solutions for network slicing and resource allocation are computationally expensive and do not support mixed types of slice requests [5,6]. Fig.…”

Section: Introductionmentioning

confidence: 99%

T-S3RA: Traffic-Aware Scheduling for Secure Slicing and Resource Allocation in SDN/NFVEnabled 5G Networks

Ramadhan¹

2021

IJETT

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“…Cloud computing is one of emerging paradigms which is popular nowadays. Cloud computing companies provide almost all possible ways to process data: storing it, changing it, sharing it with anyone else, and eventually deleting it [1], [2]. But the most attractive feature, that distinguishes them from other traditional storage systems, is their convenience: data can be obtained anywhere and anytime instantly if you have a connection to the Internet [3].…”

Section: Introductionmentioning

confidence: 99%

Big Data Handling Approach for Unauthorized Access of Cloud Computing

Razaque¹,

Shaldanbayeva²,

Alotaibi³

et al. 2021

Preprint

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Nowadays, cloud computing is one of the important and rapidly growing paradigms that extend its capabilities and applications in various areas of life. The cloud computing system challenges many security issues, such as scalability, integrity, confidentiality, and unauthorized access, etc. An illegitimate intruder may gain access to the sensitive cloud computing system and use the data for inappropriate purposes that may lead to losses in business or system damage. This paper proposes a hybrid unauthorized data handling (HUDH) scheme for Big data in cloud computing. The HUDU aims to restrict illegitimate users from accessing the cloud and data security provision. The proposed HUDH consists of three steps: data encryption, data access, and intrusion detection. HUDH involves three algorithms; Advanced Encryption Standards (AES) for encryption, Attribute-Based Access Control (ABAC) for data access control, and Hybrid Intrusion Detection (HID) for unauthorized access detection. The proposed scheme is implemented using Python and Java language. Testing results demonstrate that the HUDH can delegate computation overhead to powerful cloud servers. User confidentiality, access privilege, and user secret key accountability can be attained with more than 97% high accuracy.

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“…Cloud Computing (CC) has recently emerged as a new framework to facilitate and implement online services [22], storing, analyzing, and displaying data requires significant modifications to the current cloud model, which in turn requires financial constraints and computational cost [23,24]. Many CC platforms provide these web services for ML and DL.…”

Section: Introductionmentioning

confidence: 99%

IDD-HPO: A Proposed Model for Improving Diabetic Detection using Hyperparameter Optimization and Cloud Mapping Storage

Zaky¹,

Soliman²,

Elkholy³

et al. 2021

IJACSA

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Readmission to the hospital is an important and critical procedure for the quality of health care as it is very costly and helps in determining the quality level of the point of care provided by the hospital to the patient. This paper proposes a group model to predict readmission by choosing between Machine Learning and Deep Learning algorithms based on performance improvement. The algorithms used for Machine Learning are Logistic Regression, K-Nearest Neighbors, and Support Vector Machine, while the algorithms used for Deep Learning are a Convolutional Neural Network and Recurrent Neural Network. The reasons for the appearance of the efficiency of the model depend on the are preparation of correct parameters and the values that control the learning. This paper aims to enhance the performance of both machine learning and deep learning based readmission models using hyperparameter optimization in both Personal Computer environments and Mobile Cloud Computing systems. The proposed model is called improving detection diabetic using hyperparameter optimization , the proposed model aims to achieve the best rate of between prediction rate accuracy for hospital readmission at the same time minimizing resources such as time delay and energy consumption. Results achieved by proposed model for Logistic Regression, K-Nearest Neighbors, and Support Vector Machine are (accuracy=0.671, 0.883, 0.901, time delay=5, 7, 20, and energy consumed=25, 32, 48) respectively, for Recurrent Neural Network and Convolutional Neural Network are (accuracy=0.854, 0.963, time delay=25, 660 energy consumed=89, 895) respectively. However, this proposed model takes a lot of time and energy consumed especially in Convolutional Neural Network. So, the experiments were conducted again, but in the cloud environment, based on the existence of two types of storage to preserve the accuracy but decreasing time and energy, the proposed model in cloud environment achieve

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Latency and energy-aware provisioning of network slices in cloud networks

Cited by 20 publications

References 43 publications

T-S3RA: Traffic-Aware Scheduling for Secure Slicing and Resource Allocation in SDN/NFVEnabled 5G Networks

T-S3RA: Traffic-Aware Scheduling for Secure Slicing and Resource Allocation in SDN/NFVEnabled 5G Networks

Big Data Handling Approach for Unauthorized Access of Cloud Computing

IDD-HPO: A Proposed Model for Improving Diabetic Detection using Hyperparameter Optimization and Cloud Mapping Storage

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