Requirements of Inter Data Center Networks to Meet the Explosive Growth of Cloud Services

The data center networks encompass various cloud services. Network congestion and network load imbalance may occur in data center networks due to elephant flows. In order to improve the throughput and overall utilization of the network, a dynamic load balancing mechanism has to be in place. Software Defined Networking (SDN) is used to perform the balancing of the network load. SDN can obtain the global view of the network and hence contain the status and topology of the entire data center network. The elephant flows can be split and send to multiple paths based on the current state of the network. The described idea is implemented in the OpenFlow environment and tested for improvement. The result shows the enhancement in throughput and network utilization.

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A novel framework for content connectivity through optical data centers

Journal of Optical Communications ◽

10.1515/joc-2020-0180 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Rajat Gupta ◽

Mona Aggarwal ◽

Swaran Ahuja

Keyword(s):

Linear Programming ◽

Integer Linear Programming ◽

Data Center ◽

Web Search ◽

Data Centers ◽

Cloud Services ◽

Optical Data ◽

Mixed Integer ◽

Data Center Networks ◽

Internet Applications

Abstract Nowadays, the data centers have become a significant physical infrastructure for the purpose of supporting various Internet applications like cloud services, entertainment, web search and social networking. The traffic among the data centers is growing rapidly accompanied by the services that are obtained. The data centers are connected via fiber channels to support long-haul networks and high data rate transmissions by utilizing various modulation techniques. However, it has some drawbacks such as increased delay, computational complexities, high wavelength consumption, link failures, etc. Recently, researchers are focusing on improving the survivability and wavelength usage efficiency in optical data center networks. In this work, a novel framework depending on the concept of content connectivity is proposed for optical data center networks. Here, a mixed integer linear programming (MILP) is utilized for transmitting the data through optical data centers. The main intention of this research is to improve the performance and wavelength efficiency in optical data center networks. The performance of the MILP approach is evaluated and compared with the existing integer linear programming (ILP) technique and found that this new approach provides better performance with higher wavelength efficiency and reduced wavelength consumption.

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Comparative Study of Optical Interconnection Architectures in Data Center Networks

Journal of Optical Communications ◽

10.1515/joc-2017-0074 ◽

2019 ◽

Vol 40 (3) ◽

pp. 225-238 ◽

Cited By ~ 1

Author(s):

Abhilasha Sharma ◽

Sangeetha R G

Keyword(s):

Comparative Study ◽

Data Center ◽

Interconnection Network ◽

Cloud Services ◽

Low Latency ◽

Optical Interconnection ◽

Data Center Networks ◽

Efficient Data ◽

Average Latency ◽

High Scalability

Abstract The internet traffic is increasing exponentially with cloud services. This demands high and efficient data center networks (DCNs). Current DCNs are equipped with electronic counter parts which consumes high power to provide the cloud services. Optical interconnection network (OIN) architectures provide high scalability, low latency, high throughput and low power consumption. This paper presents a study of the OIN architectures as the future requirements of the DCNs are the need for high scalability and low latency. This paper also presents a comparative study of their average latency and scalability.

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Analysis on Buffer Occupancy of Quantized Congestion Notification in Data Center Networks

IEICE Transactions on Communications ◽

10.1587/transcom.2016ebp3052 ◽

2016 ◽

Vol E99.B (11) ◽

pp. 2361-2372 ◽

Cited By ~ 1

Author(s):

Chang RUAN ◽

Jianxin WANG ◽

Jiawei HUANG ◽

Wanchun JIANG

Keyword(s):

Data Center ◽

Data Center Networks ◽

Buffer Occupancy

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HTPC: heterogeneous traffic-aware partition coding for random packet spraying in data center networks

Journal of Cloud Computing Advances Systems and Applications ◽

10.1186/s13677-021-00248-4 ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Jiawei Huang ◽

Shiqi Wang ◽

Shuping Li ◽

Shaojun Zou ◽

Jinbin Hu ◽

...

Keyword(s):

Data Center ◽

Large Scale ◽

Network Performance ◽

Rooted Tree ◽

Heterogeneous Traffic ◽

Data Center Networks ◽

Packet Reordering ◽

Traffic Characteristics ◽

Network Utilization ◽

The Impact

AbstractModern data center networks typically adopt multi-rooted tree topologies such leaf-spine and fat-tree to provide high bisection bandwidth. Load balancing is critical to achieve low latency and high throughput. Although the per-packet schemes such as Random Packet Spraying (RPS) can achieve high network utilization and near-optimal tail latency in symmetric topologies, they are prone to cause significant packet reordering and degrade the network performance. Moreover, some coding-based schemes are proposed to alleviate the problem of packet reordering and loss. Unfortunately, these schemes ignore the traffic characteristics of data center network and cannot achieve good network performance. In this paper, we propose a Heterogeneous Traffic-aware Partition Coding named HTPC to eliminate the impact of packet reordering and improve the performance of short and long flows. HTPC smoothly adjusts the number of redundant packets based on the multi-path congestion information and the traffic characteristics so that the tailing probability of short flows and the timeout probability of long flows can be reduced. Through a series of large-scale NS2 simulations, we demonstrate that HTPC reduces average flow completion time by up to 60% compared with the state-of-the-art mechanisms.

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