Estimating and Forecasting Network Traffic Performance Based on Statistical Patterns Observed in SNMP Data

Hu, Kejia; Sim, Alex; Antoniades, D.; Dovrolis, Constantine

doi:10.1007/978-3-642-39712-7_46

Cited by 15 publications

(10 citation statements)

References 9 publications

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“…In the figure, x-axis shows a pair of adjacent windows compared to one another, and y-axis shows the calculated ∆ for the associated pair of windows (normalized). As shown in the figure, pairs of windows of (1,2), (4,5), (5,6), (9,10), (12,13) and (15,16) show relatively high ∆'s, whereas windows of (3,4), (6,7), (8,9), (10,11) and (13,14) reported minor changes, agreeing in overall with the results of clustered patterns. Although not adjacent, two windows of 1 and 4 look alike in Figure 4, and its calculated degree of changes is fairly low with ∆ (1,4) = 0.34 (not shown in Figure 5).…”

Section: The Proposed Approachsupporting

confidence: 80%

“…We can see that several time windows such as (3,4), (6,7), (8,9) and (10,11) look pretty similar. On the other hand, some others such as (1,2), (2,3), (12,13) and (15,16) show highly different patterns. Figure 5 shows the corresponding degree of changes.…”

Section: The Proposed Approachmentioning

confidence: 99%

“…Network traffic monitoring has long been an essential task in network operations and management for various purposes, such as network performance measurement and forecasting [27,18,13], traffic classification [16,10,19,6], analysis and visualization [14, . 19], security [26], and so forth.…”

Section: Introductionmentioning

confidence: 99%

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Peeking Network States with Clustered Patterns

Kim

Sim

2015

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Network traffic monitoring has long been a core element for effective network management and security. However, it is still a challenging task with a high degree of complexity for comprehensive analysis when considering multiple variables and ever-increasing traffic volumes to monitor. For example, one of the widely considered approaches is to scrutinize probabilistic distributions, but it poses a scalability concern and multivariate analysis is not generally supported due to the exponential increase of the complexity. In this work, we propose a novel method for network traffic monitoring based on clustering, one of the powerful deep-learning techniques. We show that the new approach enables us to recognize clustered results as patterns representing the network states, which can then be utilized to evaluate "similarity" of network states over time. In addition, we define a new quantitative measure for the similarity between two compared network states observed in different time windows, as a supportive means for intuitive analysis. Finally, we demonstrate the clustering-based network monitoring with public traffic traces, and show that the proposed approach using the clustering method has a great opportunity for feasible, costeffective network monitoring.

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Section: The Proposed Approachsupporting

confidence: 80%

Section: The Proposed Approachmentioning

confidence: 99%

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Peeking Network States with Clustered Patterns

Kim

Sim

2015

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“…Predictive algorithms have also been used to improve cloud capacity planning and execution times by predicting server workloads . Research has also explored the application of predictive techniques to estimate future network load for workflow scheduling on the cloud . The results showed that, by adopting more proactive solutions, it can enable better planning to improve the efficiency of scheduling decisions while also reducing costs significantly.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

“…Another key metric to monitor when investigating live migration is the network bandwidth availability, as it affects how quickly a VM will be transferred from a source to a destination host. Akoush et al presented models that predict migration performance for specific workloads on a Xen virtual machine monitor and research by Hu et al demonstrated that estimating future network traffic can enable better planning, greater resource provisioning, and faster transferring of data . At the moment, resource prediction approaches only examine single time step ahead prediction.…”

Section: Introductionmentioning

confidence: 99%

A multitime‐steps‐ahead prediction approach for scheduling live migration in cloud data centers

et al. 2018

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One of the major challenges facing cloud computing is to accurately predict future resource usage to provision data centers for future demands. Cloud resources are constantly in a state of flux, making it difficult for forecasting algorithms to produce accurate predictions for short times scales (ie, 5 minutes to 1 hour). This motivates the research presented in this paper, which compares nonlinear and linear forecasting methods with a sequence prediction algorithm known as a recurrent neural network to predict CPU utilization and network bandwidth usage for live migration. Experimental results demonstrate that a multitime-ahead prediction algorithm reduces bandwidth consumption during critical times and improves overall efficiency of a data center.

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