Abstract
Typically, the production data centers function with various risk factors, such as for instance the network dynamicity, topological asymmetry, and switch failures. Hence, the load-balancing schemes should consider the sensing accurate path circumstances as well as the reduction of failures. However, under dynamic traffic, current load-balancing schemes use the fixed parameter setting, resulting in suboptimal performances. Therefore, we propose a multi-level dynamic traffic load-balancing (MDTLB) protocol, which uses an adaptive approach of parameter setting. The simulation results show that the MDTLB outperforms the state-of-the-art schemes in terms of both the flow completion time and throughput in typical data center applications.
Highlights
The exponential growth of the information technology (IT) industry and its storage demand forces to move toward cloud computing, in which a huge amount of computing and storage resources are provided in large data centers [1,2]
This is interpreted by the vigorous rerouting; the congestion feedback (i.e., explicit congestion notification (ECN)) of the upper path constrains the congestion window, resulting in throughput loss in the bottom path
Our analysis leads us to conclude that the congestion mismatch problem occurs due to the continuous path rerouting, which is happening because of the sending rate and the rerouting state of the new path and the dynamic rerouting within a flow that allows the various congestion states of the paths to adjust the rate of newer paths
Summary
The exponential growth of the information technology (IT) industry and its storage demand forces to move toward cloud computing, in which a huge amount of computing and storage resources are provided in large data centers [1,2]. The load balancing for multiple paths with the utilization of whole network resources can improve throughput as well as reduce latency for data center applications [9]. The uncertainty factors are mainly the dynamicity of the traffics, unstable links, device heterogeneity, and switch failures [10] To overcome these situations due to uncertain circumstances, the load-balancing technique is seriously effective. The dynamic proactive allows the flows to become assigned to the available paths in a fixed assignment, where the initial assignment is mainly performed according to the available bandwidth This approach is better compared to static flow and dynamic reactive flow in terms of implementation overhead, flexibility, and performance. It rises the reordering for larger flows, which can be fragmented into several flow cells
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