Machine Learning Models for Live Migration Metrics Prediction

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2019. 2. Egger, Bernhard.오늘날 데이터 센터에서 가상머신의 라이브 마이그레이션 기술은 매우 중요하게 사용된다. 현존하는 데이터 센터 관리 프레임워크에서는 복잡한 알고리즘을 이용하여 언제, 어디서, 어디로 가상머신의 마이그레션을 실행할지를 결정한다. 하지만 어떤 마이그레이션 방법을 사용하는지에 따라서 성능이 크게 차이가 날 수 있음에도 불구하고 이에 대한 논의는 주요하게 다뤄지지 않았다. 이러한 성능의 차이는 라이브 마이그레이션 알고리즘의 차이나 가상머신에 할당된 워크로드의 양의 차이 그리고 마이그레이션을 하는 곳과 목적 host의 상태 차이에 의하여 일어난다. 빠르고 정확하게 올바른 마이그레이션 방법을 정하는 것은 필수적인 과제이다. 이러한 과제를 performance model을 이용하여 해결할 것이다. 본 논문에서는, 가상머신의 라이브 마이그레이션 성능을 예측하는 여러 머신 러닝 모델을 제시한다. 여기서 12개의 서로 다른 마이그레이션 알고리즘에 대해 7가지의 다른 metric들을 예측한다. 이 모델은 기존 연구에 비해 훨씬 정확한 예측을 성공하였다. 각각의 target metric과 여러 알고리즘들에 대하여 input feature evaluation을 수행하였고 각각의 특성에 맞는 모델을 만들어 84개의 서로다른 머신 러닝 모델들을 훈련시켰다. 이러한 모델들은 실제 라이브 마이그레이션 프레임워크에 쉽게 적용 가능하다. 각각의 마이그레이션 알고리즘에 대하여 target metric 예측을 사용함으로써 올바른 마이그레이션 알고리즘을 쉽게 결정할 수 있고 이는 결과적으로 다운타임과 마이그레이션에 소요되는 총 시간의 감소 효과를 볼 수 있다.Live migration of Virtual Machines (VMs) is an important technique in today's data centers. In existing data center management frameworks, complex algorithms are used to determine when, where, and to which host a migration of a VM is to be performed. However, very little attention is paid to the selection of the right migration technique depending on which the migration performance can vary greatly. This performance fluctuation is caused by the different live migration algorithms, the different workloads that each VM is executing, and the state of the destination and the source host. Choosing the right migration technique is a crucial task that has to be made quickly and precisely. Therefore, a performance model is the best and the right candidate for such a task. In this thesis, we propose various machine learning models for predicting live migration metrics of virtual machines. We predict seven different metrics for twelve distinct migration algorithms. Our models achieve a much higher accuracy compared to existing work. For each target metric and algorithm, an input feature evaluation is conducted and a strictly specific model is generated, leading to 84 different trained machine learning models. These models can easily be integrated into a live migration framework. Using the target metric predictions for each migration algorithm, a framework can easily choose the right migration algorithm, which can lead to downtime and total migration time reduction and less service-level agreement violations.Abstract Contents List of Figures List of Tables Chapter 1 Introduction and Motivation Chapter 2 Background 2.1 Virtualization 2.2 Live Migration 2.3 SLA and SLO 2.4 Live Migration Techniques 2.4.1 Pre-copy (PRE) 2.4.2 Post-copy (POST) 2.4.3 Hybrid Migration Techniques 2.5 Live Migration Performance Metrics 2.6 Artificial Neural Networks 2.6.1 Feedforward Neural Network (FNN) 2.6.2 Deep Neural Network (DNN) 2.6.3 Convolution Neural Network (CNN) Chapter 3 Related Work Chapter 4 Overview and Design Chapter 5 Implementation 5.1 Deep Neural Network design 5.2 Convolutional Neural Network design Chapter 6 Evaluation metrics 6.1 Geometric Mean Absolute Error (GMAE) 6.2 Geometric Mean Relative Error (GMRE) 6.3 Mean Absolute Error (MAE) 6.4 Weighted Absolute Percentage Error (WAPE) Chapter 7 Results 7.1 Deep Neural Network 7.2 SVR with bagging 7.3 DNN vs. SVR comparison 7.4 Overhead Chapter 8 Conclusion and Future Work 8.1 Conclusion 8.2 Future Work AppendicesMaste

Scalable Network Design and Management with Decentralized Software-defined Networking

Network softwarization is among the most significant innovations of computer networks in the last few decades. The lack of uniform and programmable interfaces for network management led to the design of OpenFlow protocol for the university campuses and enterprise networks. This breakthrough coupled with other similar efforts led to an emergence of two complementary but independent paradigms called software-defined networking (SDN) and network function virtualization (NFV). As of this writing, these paradigms are becoming the de-facto norms of wired and wireless networks alike. This dissertation mainly addresses the scalability aspect of SDN for multiple network types. Although centralized control and separation of control and data planes play a pivotal role for ease of network management, these concepts bring in many challenges as well. Scalability is among the most crucial challenges due to the unprecedented growth of computer networks in the past few years. Therefore, we strive to grapple with this problem in diverse networking scenarios and propose novel solutions by harnessing capabilities provided by SDN and other related technologies. Specifically, we present the techniques to deploy SDN at the Internet scale and to extend the concepts of softwarization for mobile access networks and vehicular networks. Multiple optimizations are employed to mitigate latency and other overheads that contribute to achieve performance gains. Additionally, by taking care of sparse connectivity and high mobility, the intrinsic constraints of centralization for wireless ad-hoc networks are addressed in a systematic manner. The state-of-the-art virtualization techniques are coupled with cloud computing methods to exploit the potential of softwarization in general and SDN in particular. Finally, by tapping into the capabilities of machine learning techniques, an SDN-based solution is proposed that inches closer towards the longstanding goal of self-driving networks. Extensive experiments performed on a large-scale testbed corroborates effectiveness of our approaches

Colocation-aware Resource Management for Distributed Parallel Applications in Consolidated Clusters

Department of Computer Science and EngineeringConsolidated clusters, which run various distributed parallel applications such as big data frameworks, machine learning applications, and scienti???c applications to solve complex problems in wide range of fields, are already used commonly. Resource providers allow various applications with different characteristics to execute together to efficiently utilize their resources. There are some important issues about scheduling applications to resources. When applications share the same resources, interference between them affects their performance. The performance of applications can be improved or degraded depending on which resources are used to execute them based on various characteristics of applications and resources. Characteristics and resource requirements of applications can constrain their placement, and these constraints can be extended to constraints between applications. These issues should be considered to manage resource e???ciently and improve the performance of applications. In this thesis, we study how to manage resources e???ciently while scheduling distributed parallel applications in consolidated clusters. First, we present a holistic VM placement technique for distributed parallel applications in heterogeneous virtual cluster, aiming to maximize the e???ciency of the cluster and consequently reduce cost for service providers and users. We analyze the e???ect of heterogeneity of resource, di???erent VM con???gurations, and interference between VMs on the performance of distributed parallel applications and propose a placement technique that uses a machine learning algorithm to estimate the runtime of a distributed parallel application. Second, we present a two-level scheduling algorithms, which distribute applications to platforms then map tasks to each node. we analyze the platform and co-runner a???nities of looselycoupled applications and use them for scheduling decision. Third, we study constraint-aware VM placement in heterogeneous clusters. We present a modelofVMplacementconstraintsandconstraint-awareVMplacementalgorithms. Weanalyze the e???ect of VM placement constraint, and evaluate the performance of algorithms over various settings with simulation and experiments in a small cluster. Finally, we propose interference-awareresource management system for CNN models in GPU cluster. We analyze the e???ect of interference between CNN models. We then propose techniques to mitigate slowdown from interference for target model, and to predict performance of CNN models when they are co-located. We propose heuristic algorithm to schedule CNN models, and evaluate the techniques and algorithm from experiments in GPU cluster.clos