Software-Defined Network Resource Optimization of the Data Center Based on P4 Programming Language

This paper makes use of the new architecture software-defined network (SDN) in the cloud data center based on P4 language to realize the flexible management and configuration of the network equipment to achieve (a) data center virtualization management and (b) data center resource optimization based on the P4 programming language. Furthermore, error tolerance of dynamic network optimization depends on the virtual machine (VM) online migration technology, and the load balancing mechanism has a very good flexibility. At the same time, the paper proposed a multipath VM migration strategy based on a quality of service (QoS) mechanism, which divides the VM migration resources into different QoS flows by network dynamic transmission and then selects valid forwarding for each flow path to migrate VMs. This ensures to improve the overall migration performance of VMs and ultimately the dynamic optimization of the network resources and their management. Our experimental evaluations show that the proposed model is approximately 13% and 17% better than the traditional state-of-the-art methods in terms of minimum migration time and the least downtime, respectively.

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A Switch Based Resource Management Method for Energy Optimization in Cloud Data Center

International Journal of Computing ◽

10.47839/ijc.20.1.2103 ◽

2021 ◽

pp. 85-91

Author(s):

Shally Vats ◽

Sanjay Kumar Sharma ◽

Sunil Kumar

Keyword(s):

Data Center ◽

Data Centers ◽

Energy Requirement ◽

Energy Optimization ◽

Cloud Service ◽

Optimization Method ◽

Level Energy ◽

Cloud Data Center ◽

Vm Migration ◽

Cloud Data

Proliferation of large number of cloud users steered the exponential increase in number and size of the data centers. These data centers are energy hungry and put burden for cloud service provider in terms of electricity bills. There is environmental concern too, due to large carbon foot print. A lot of work has been done on reducing the energy requirement of data centers using optimal use of CPUs. Virtualization has been used as the core technology for optimal use of computing resources using VM migration. However, networking devices also contribute significantly to the responsible for the energy dissipation. We have proposed a two level energy optimization method for the data center to reduce energy consumption by keeping SLA. VM migration has been performed for optimal use of physical machines as well as switches used to connect physical machines in data center. Results of experiments conducted in CloudSim on PlanetLab data confirm superiority of the proposed method over existing methods using only single level optimization.

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Enhanced Load Balancing Approach for Cloud Data Center Load Optimization

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2021.9399 ◽

2021 ◽

Vol 18 (4) ◽

pp. 1270-1274

Author(s):

J. Prassanna ◽

V. Neelanarayanan

Keyword(s):

Cloud Computing ◽

Load Balancing ◽

Data Center ◽

Resource Control ◽

Resource Optimization ◽

Computing Technology ◽

Cloud Data Center ◽

Cloud Data ◽

Load Optimization ◽

Related Data

Cloud computing is a most popular technology that has huge response in markets. Cloud computing has the potential to access applications and their related data via the Internet anywhere. Most companies already pay for the use of cloud resources for storage purposes and ultimately reduce the costs of infrastructure spending. They can make use of this technology for accessing to company applications like pay-as-you-go approach. One of the major obstacles associated with cloud computing technology is to better optimization of resource allocation. Assigning of workloads to the servers using load balancing techniques is used to achieve less response time and better resource optimization across the server. Resource control and balance of load are the major conflicts in the cloud environment, which is why there are different load balancing algorithms, each with its own advantages and disadvantage. In order to achieve a better economy and mutual benefit, efficient algorithms can be derived simultaneously by optimizing servers, green computing and better utilization of resources. The objective of this paper is to analyze and enhance existing load balancing algorithms.

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