scholarly journals Analysis on route information failure in IP core networks by NFV-based test environment

2021 ◽  
Vol 2 (4) ◽  
pp. 101-112
Author(s):  
Xia Fei ◽  
Aerman Tuerxun ◽  
Jiaxing Lu ◽  
Ping Du ◽  
Akihiro Nakao
Keyword(s):  
Mobile Networks ◽  
Large Scale ◽  
Practical Method ◽  
Recall Rate ◽  
Test Environment ◽  
Cloud Data ◽  
Core Networks ◽  
Refinement Method ◽  
Route Information ◽  
Cloud Data Centers

Stable and high-quality Internet connectivity is mandatory for 5G mobile networks. However, the pandemic of COVID-19 has forced global and large-scale staying at home and telecommuting in many countries. The increasing traffic has induced more pressure on networks, devices and cloud data centers. It becomes an essential task for network operators to enable their ability to automatically and rapidly detect network and device failures. We propose a highly practical method based on highly practical technology. Our method has a high generalization ability that can efficiently extract features from large-scale unstructured data and ensure high accuracy prediction. First, 997 useful features are extracted from 28GB-per-day network logs. Then, a differential approach is employed to preprocess the extracted features so as to highlight the differences between normal and abnormal states. Third, those features are refined based on the feature importance we calculated. According to our experiment, the proposed feature extraction and refinement method can reduce computation without degrading the performance. Among the five types of failures, we achieve a 100% recall rate in four types and the rest can also reach 71%. Overall, the total average prediction accuracy of the proposed method is 94%.

scholarly journals ENERGY EFFICIENT LOAD BALANCING FOR CLOUD DATA CENTER

2017 ◽  
Vol 10 (13) ◽  
pp. 162
Author(s):  
Amey Rivankar ◽  
Anusooya G
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Energy Efficient ◽  
Large Scale ◽  
Virtual Machines ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Efficient Resource ◽  
Cloud Data Centers ◽  
Near Future

Cloud computing is the latest trend in large-scale distributed computing. It provides diverse services on demand to distributive resources such asservers, software, and databases. One of the challenging problems in cloud data centers is to manage the load of different reconfigurable virtual machines over one another. Thus, in the near future of cloud computing field, providing a mechanism for efficient resource management will be very significant. Many load balancing algorithms have been already implemented and executed to manage the resources efficiently and adequately. The objective of this paper is to analyze shortcomings of existing algorithms and implement a new algorithm which will give optimized load balancingresult.

scholarly journals Automated Clustering of Virtual Machines based on Correlation of Resource Usage

2012 ◽  
Vol 8 (4) ◽  
pp. 102 ◽  
Author(s):  
Claudia Canali ◽  
Riccardo Lancellotti
Keyword(s):  
Cloud Computing ◽  
High Performance ◽  
Large Scale ◽  
Virtual Machines ◽  
Resource Usage ◽  
Cloud Data ◽  
Multiple Resources ◽  
Computing Paradigm ◽  
Innovative Methodology ◽  
Cloud Data Centers

The recent growth in demand for modern applicationscombined with the shift to the Cloud computing paradigm have led to the establishment of large-scale cloud data centers. The increasing size of these infrastructures represents a major challenge in terms of monitoring and management of the system resources. Available solutions typically consider every Virtual Machine (VM) as a black box each with independent characteristics, and face scalability issues by reducing the number of monitored resource samples, considering in most cases only average CPU usage sampled at a coarse time granularity. We claim that scalability issues can be addressed by leveraging thesimilarity between VMs in terms of resource usage patterns.In this paper we propose an automated methodology to cluster VMs depending on the usage of multiple resources, both systemand network-related, assuming no knowledge of the services executed on them. This is an innovative methodology that exploits the correlation between the resource usage to cluster together similar VMs. We evaluate the methodology through a case study with data coming from an enterprise datacenter, and we show that high performance may be achieved in automatic VMs clustering. Furthermore, we estimate the reduction in the amount of data collected, thus showing that our proposal may simplify the monitoring requirements and help administrators totake decisions on the resource management of cloud computing datacenters.

scholarly journals MatrixDCN: a high performance network architecture for large-scale cloud data centers

2015 ◽  
Vol 16 (8) ◽  
pp. 942-959
Author(s):  
Yantao Sun ◽  
Min Chen ◽  
Limei Peng ◽  
Mohammad Mehedi Hassan ◽  
Abdulhameed Alelaiwi
Keyword(s):  
Network Architecture ◽  
High Performance ◽  
Large Scale ◽  
Data Centers ◽  
Cloud Data ◽  
Cloud Data Centers

scholarly journals Performance Evaluation and Implementation of Tasks in Virtual Machines

2019 ◽  
Vol 9 (1) ◽  
pp. 1524-1527
Keyword(s):  
Large Scale ◽  
Data Centers ◽  
Virtual Machines ◽  
Optimization Problems ◽  
Experimental Result ◽  
Cloud Data ◽  
Simulation Techniques ◽  
Cloud Data Centers ◽  
Application Codes ◽  
Cache Miss

In the present situation, it may be essential to build a simple data sharing environment to monitor and protect the unauthorized modification of data. In such case, mechanisms may be required to develop to focus on significant weakened networking with proper solutions. In some situations, block chain data management may be used considering the cloud environment. It is well understood that in virtual environment, allocating resources may have significant role towards evaluating the performance including utilization of resources linked to the data center. Accuracy towards allocation of virtual machines in cloud data centers may be more essential considering the optimization problems in cloud computing. In such cases, it may also be desirable to prioritize on virtual machines linked to cloud data centers. Consolidating the dynamic virtual machines may also permit the virtual server providers to optimize utilization of resources and to focus on energy consumption. In fact, tremendous rise in acquiring computational power driven by modern service applications may be linked towards establishment of large-scale virtualized data centers. Accordingly, the joint collaboration of smart connected devices with data analytics may also enable enormous applications towards different predictive maintenance systems. To obtain the near optimal as well as feasible results in this case, it may be desirable to simulate implementing the algorithms and focusing on application codes. Also, different approaches may also be needed to minimize development time and cost. In many cases, the experimental result proves that the simulation techniques may minimize the cache miss and improve the execution time. In this paper, it has been intended towards distribution of tasks along with implementation mechanisms linked to virtual machines.

scholarly journals Clustered Multicast Source Routing for Large-Scale Cloud Data Centers

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 12693-12705
Author(s):  
Jarallah Alqahtani ◽  
Hassan H. Sinky ◽  
Bechir Hamdaoui
Keyword(s):  
Large Scale ◽  
Data Centers ◽  
Cloud Data ◽  
Source Routing ◽  
Cloud Data Centers

scholarly journals A Time-Efficient Solution to the General Resource Placement Problem in Cloud

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Wei Wei ◽  
Yuhong Zhang ◽  
Yang Liu
Keyword(s):  
Large Scale ◽  
Efficient Solution ◽  
Optimal Algorithm ◽  
Optimal Solution ◽  
Placement Problem ◽  
Discrete Function ◽  
Cloud Data ◽  
Stochastic Demands ◽  
Cloud Data Centers ◽  
Problem Setting

Cloud based large-scale online services are faced with regionally distributed stochastic demands for various resources. With multiple regional cloud data centers, a crucial problem that needs to be settled is how to properly place resources to satisfy massive stochastic demands from many different regions. For the general stochastic demands oriented cross region resource placement problem, the time complexity of existing optimal algorithm is linear to total amount of resources and thus may be inefficient when dealing with a large number of resources. To end this, we propose an efficient algorithm, named discrete function based unbound resource placement (D-URP). Experiments show that in scenarios with general settings, D-URP can averagely achieve at least 97% revenue of optimal solution, with reducing time by three orders of magnitude. Moreover, due to the generality of problem setting, it can be extended to get efficient solution for a broad range of similar problems under various scenarios with different constraints. Therefore, D-URP can be used as an effective supplement to existing algorithm under time-tense scheduling scenarios with large number of resources.

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