Christofer Flinta

Predicting real-time service-level metrics from device statistics

While real-time service assurance is critical for emerging telecom cloud services, understanding and predicting performance metrics for such services is hard. In this paper, we pursue an approach based upon statistical learning whereby the behavior of the target system is learned from observations. We use methods that learn from device statistics and predict metrics for services running on these devices. Specifically, we collect statistics from a Linux kernel of a server machine and predict client-side metrics for a video-streaming service (VLC). The fact that we collect thousands of kernel variables, while omitting service instrumentation, makes our approach service-independent and unique. While our current lab configuration is simple, our results, gained through extensive experimentation, prove the feasibility of accurately predicting client-side metrics, such as video frame rates and RTP packet rates, often within 10–15% error (NMAE), also under high computational load and across traces from different scenarios.

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

Predicting the performance of cloud services is intrinsically hard. In this work, we pursue an approach based upon statistical learning, whereby the behaviour of a system is learned from observations. Specifically, our testbed implementation collects device statistics from a server cluster and uses a regression method that accurately predicts, in real-time, client-side service metrics for a video streaming service running on the cluster. The method is service-agnostic in the sense that it takes as input operating-systems statistics instead of service-level metrics. We show that feature set reduction significantly improves prediction accuracy in our case, while simultaneously reducing model computation time. We also discuss 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.

KVM virtualization impact on active round-trip time measurements

Active measurements tools transmit probe packets between a sender and a receiver to estimate performance metrics such as round-trip time, jitter and loss. In this paper we evaluate how KVM virtualization affects measurements of performance metrics, specifically the round-trip time (RTT). To understand the impact we investigate the interplay of various environment and measurement parameters with virtualization. A number of experiments are performed in order to investigate which parameters had major impact on the RTT. The paper shows that the measurements are affected by CPU load in the host as well as network load while I/O load seemed to have limited impact.

Online network performance degradation localization using probabilistic inference and change detection

Detecting and localizing performance degradations is a difficult problem that increases in importance as telecom network transition to all-packet equipment. Operators require solutions that are accurate in localization and do not impose large additional costs in terms of hardware deployment or manual labor for operations. Existing commercial solutions are generally difficult to operate, while many academic proposals typically require significant computational resources and are difficult to adapt to production networks. This paper describes a novel network fault localization algorithm based on active network measurements, probabilistic inference and change detection. The algorithm is computationally efficient for networks with thousands of nodes and requires few configuration parameters. Results obtained in a simulated environment on tree topologies show that the solution provides fast and accurate localization of performance degradations.

A scheme for measuring subpath available bandwidth

This paper presents a novel probing scheme which can be used for estimating the available bandwidth of subpaths, without the requirement of control over both endpoints of a network path. Instead of a probe-packet receiver, this scheme uses the ICMP capability of routers. An estimate of the available bandwidth from the endpoint to a router is obtained in much the same way as for state-of-the-art end-to-end probing methods. Taking into account ICMP packet generation limitations and delay, the estimate should be interpreted as a lower bound of the actual available bandwidth.

A platform for predicting real-time service-level metrics from device statistics

While real-time service assurance is critical for emerging telecom cloud services, understanding and predicting performance metrics for such services is hard. In this paper, we pursue an approach based upon statistical learning whereby the behavior of the target system is learned from observations. We use methods that learn from device statistics and predict metrics for services running on these devices. Specifically, we collect statistics from a Linux kernel of a server machine and predict client-side metrics for a video-streaming service (VLC). The fact that we collect thousands of kernel variables, while omitting service instrumentation, makes our approach service-independent and unique. While our current lab configuration is simple, our results, gained through extensive experimentation, prove the feasibility of accurately predicting client-side metrics, such as video frame rates and RTP packet rates, often within 10-15% error (NMAE), also under high computational load and across traces from different scenarios.

ConMon: An automated container based network performance monitoring system

The popularity of container technologies and their widespread usage for building microservices demands solutions dedicated for efficient monitoring of containers and their interactions. In this paper we present ConMon, an automated system for monitoring the network performance of container-based applications. It automatically identifies newly instantiated application containers and observes passively their traffic. Based on these observations, it configures and executes monitoring functions inside adjacent monitoring containers. The system adapts the monitoring containers to changes driven by either the application or the execution platform. The evaluation results validate the feasibility of the ConMon approach and illustrate its scalability in terms of low overhead on compute resources, moderate impact on applications, and negligible impact on the background network traffic.

Predicting SLA conformance for cluster-based services using distributed analytics

Service assurance for the telecom cloud is a challenging task and is continuously being addressed by academics and industry. One promising approach is to utilize machine learning to predict service quality in order to take early mitigation actions. In previous work we have shown how to predict service-level metrics, such as frame rate for a video application on the client side, from operational data gathered at the server side. This gives the service provider early indications on whether the platform can support the current load demand. This paper extends previous work by addressing scalability issues for cluster-based services. Operational data being generated in large volumes, from several sources, and at high velocity puts strain on computational and communication resources. We propose and evaluate a distributed machine learning system based on the Winnow algorithm to tackle scalability issues, and then compare the new distributed solution with the previously proposed centralized solution. We show that network overhead and computational execution time is substantially reduced while maintaining high prediction accuracy making it possible to achieve real-time service quality predictions in large systems.

On time-stamp accuracy of passive monitoring in a container execution environment

Passive monitoring of network performance parameters is experiencing a revival due to widespread adoption of virtualization and software-based implementation of network functions. Timestamping is one of the most challenging operations needed for passively monitoring network traffic performance parameters such as latency and jitter. We develop a setup whereby functions that monitor the network traffic are deployed in monitoring containers adjacently to, and interconnected through a virtual switch with the monitored Virtual Network Function instance. In this scenario, we evaluate the effects of container virtualization and virtual switch mirroring of traffic on the measurement of latency. The evaluation results indicate very low measurement errors (a few microseconds in our testbed) which are consistent over different measurement scenarios, thus validating the feasibility of this technique for passively monitoring latency.

Real-time resource prediction engine for cloud management

Predicting resource requirements for cloud services is critical for dimensioning, anomaly detection and service assurance. We demonstrate a system for real-time estimation of the needed amount of infrastructure resources, such as CPU and memory, for a given service. Statistical learning methods on server statistics and load parameters of the service are used for learning a resource prediction model. The model can be used as a guideline for service deployment and for real-time identification of resource bottlenecks.