Rerngvit Yanggratoke

Allocating Compute and Network Resources Under Management Objectives in Large-Scale Clouds

We consider the problem of jointly allocating compute and network resources in a large Infrastructure-as-a-service cloud. We formulate the problem of optimally allocating resources to virtual data centers (VDCs) for four well-known management objectives: balanced load, energy efficiency, fair allocation, and service differentiation. Then, we outline an architecture for resource allocation, which centers around a set of cooperating controllers, each solving a problem related to the chosen management objective. We illustrate how a global management objective is mapped onto objectives that govern the execution of these controllers. For a key controller, the Dynamic Placement Controller, we give a detailed distributed design, which is based on a gossip protocol that can switch between management objectives. The design is applicable to a broad class of management objectives, which we characterize through a property of the objective function. The property ensures the applicability of an iterative descent method that the gossip protocol implements. We evaluate, through simulation, the dynamic placement of VDCs for a large cloud under changing load and VDC churn. Simulation results show that this controller is effective and highly scalable, up to 100’000 nodes, for the management objectives considered.

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.

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.

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

We predict performance metrics of cloud services using statistical learning, whereby the behavior of a system is learned from observations. Specifically, we collect device and network statistics fr ...

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.