Optimizing data center network throughput by solving TCP Incast problem using k‐means algorithm

• Published in: International journal of communication systems Vol. 38; no. 4
• Main Authors: Thiruvenkatam, Banudoss, Mukeshkrishnan, Munusamy Babu
• Format: Journal Article
• Language: English
• Published: Chichester Wiley Subscription Services, Inc 10.03.2025
• Subjects: Algorithms; centroid; clustering; Complexity; Computer centers; Data centers; Internet of Things; k‐means; machine learning; open closed congestion; Packets (communication); Pro‐Ack; Queues; Robust control; Traffic volume
• ISSN: 1074-5351; 1099-1131
• DOI: 10.1002/dac.4535

Summary TCP in casting referred to as one of the complex problems in the data center, which receives a huge amount of traffic volume from n number of senders. There are many methods proposed to solve the TCP Incast problem. In this paper, there are two steps proposed: (i) grouping sender nodes using the k‐means algorithm and (ii) handling incoming packets from the senders after grouping. Grouping senders make it easy to handle the incoming packets effectively as the complexity reduces reasonably. This is achieved through the k‐means algorithm that groups the senders and makes a priority queue for each group. The second step can be achieved through Pro‐Ack (Pro‐Acknowledgement) control in one of the methods for handling a huge volume of data at the receiver side. We demonstrated how the incoming packets can be processed and handled effectively through Pro‐Ack control with the help of the k‐means algorithm. Along with the above concepts, the robust algorithm is proposed to make priority queues. The input throughput also out performs with the existing algorithms. The first grouping of the senders is achieved through the k‐means algorithm which learns the current scenario through attribute designed and assigns priority to all groups effectively. The attributes are very powerful to group the senders so that the priority is given an incorrect manner. Second, this paper describes the way of handling a large volume of data by the Pro‐Ack control algorithm, which outperforms 60% higher than the existing algorithms.