Predicting service metrics for cluster-based services using real-time analytics

Yanggratoke, Rerngvit; Ahmed, Jawwad; Ardelius, John; Flinta, Christofer; Johnsson, Andreas; Gillblad, Daniel; Stadler, Rolf

doi:10.1109/cnsm.2015.7367349

Cited by 25 publications

(16 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Table 3 shows the evaluation results for the VoD application, Table 4 shows the evaluation results for the KV application, and Table 5 shows the evaluation results for the VoD application under cross traffic. The results displayed in these tables are consistent with measurement results from our earlier work that is based exclusively on X cluster statistics [1,2,18].…”

Section: Estimating Service-level Metrics Using the Full Feature Set Xsupporting

confidence: 89%

“…Additional details about the VoD and KV application setup and configuration can be found in [2,17,18].…”

Section: The Testbedmentioning

confidence: 99%

“…First, they include mappings from low-level device statistics, such as packet rates, onto higher-level application statistics like frame rates or response times, which is hard to do with traditional network engineering techniques. Second, while application statistics highly depend on the configuration and the state of the backend system that runs the service [1,2], we compute mappings without considering backend statistics. We thus conclude that end-to-end application statistics must be ''encoded'' in network device statistics in the scenarios we investigated.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Learning from Network Device Statistics

2017

Self Cite

View full text Add to dashboard Cite

We estimate end-to-end service metrics from network device statistics. Our approach is based upon statistical, supervised learning, whereby the mapping from device-level to service-level metrics is learned from observations, i.e., through monitoring the system. The approach enables end-to-end performance prediction without requiring an explicit model of the system, which is different from traditional engineering techniques that use stochastic modeling and simulation. The fact that end-to-end service metrics can be estimated from local network statistics with good accuracy in the scenarios we consider suggests that service-level properties are ''encoded'' in network-level statistics. We show that the set of network statistics needed for estimation can be reduced to a set of measurements along the network path between client and service backend, with little loss in estimation accuracy. The reported work is largely experimental and its results have been obtained through testbed measurements from a video streaming service and a KV store over an OpenFlow network .

show abstract

Section: Estimating Service-level Metrics Using the Full Feature Set Xsupporting

confidence: 89%

“…Additional details about the VoD and KV application setup and configuration can be found in [2,17,18].…”

Section: The Testbedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Learning from Network Device Statistics

2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…This work is a significant extension of the conference publication presented at CNSM 2015. First, we validate our approach for predicting real‐time service metrics on two different services, video streaming and KV, instead of a single one.…”

Section: Introductionmentioning

confidence: 92%

“…For instance, the prediction errors for the audio buffer rate are consistently higher than those for the video frame rate, etc. Some of the figures can be found in Yanggratoke et al…”

Section: Model Computation and Evaluationmentioning

confidence: 99%

A service‐agnostic method for predicting service metrics in real time

et al. 2017

Self Cite

View full text Add to dashboard Cite

Summary We predict performance metrics of cloud services using statistical learning, whereby the behaviour of a system is learned from observations. Specifically, we collect device and network statistics from a cloud testbed and apply regression methods to predict, in real‐time, client‐side service metrics for video streaming and key‐value store services. Results from intensive evaluation on our testbed indicate that our method accurately predicts service metrics in real time (mean absolute error below 16% for video frame rate and read latency, for instance). Further, our method is service agnostic in the sense that it takes as input operating systems and network statistics instead of service‐specific metrics. We show that feature set reduction significantly improves the prediction accuracy in our case, while simultaneously reducing model computation time. We find that the prediction accuracy decreases when, instead of a single service, both services run on the same testbed simultaneously or when the network quality on the path between the server cluster and the client deteriorates. Finally, we discuss the design and implementation of a real‐time analytics engine, which processes streams of device statistics and service metrics from testbed sensors and produces model predictions through online learning.

show abstract