A Comparative Study on the Performance Isolation of Virtualization Technologies

abstract: Virtualization technologies are widely used in modern computing systems to deliver shared resources to heterogeneous applications. Virtual Machines (VMs) are the basic building blocks for Infrastructure as a Service (IaaS), and containers are widely used to provide Platform as a Service (PaaS). Although it is generally believed that containers have less overhead than VMs, an important tradeoff which has not been thoroughly studied is the effectiveness of performance isolation, i.e., to what extent the virtualization technology prevents the applications from affecting each other’s performance when they share the resources using separate VMs or containers. Such isolation is critical to provide performance guarantees for applications consolidated using VMs or containers. This paper provides a comprehensive study on the performance isolation for three widely used virtualization technologies, full virtualization, para-virtualization, and operating system level virtualization, using Kernel-based Virtual Machine (KVM), Xen, and Docker containers as the representative implementations of these technologies. The results show that containers generally have less performance loss (up to 69% and 41% compared to KVM and Xen in network latency experiments, respectively) and better scalability (up to 83.3% and 64.6% faster compared to KVM and Xen when increasing number of VMs/containers to 64, respectively), but they also suffer from much worse isolation (up to 111.8% and 104.92% slowdown compared to KVM and Xen when adding disk stress test in TeraSort experiments under full usage (FU) scenario, respectively). The resource reservation tools help virtualization technologies achieve better performance (up to 85.9% better disk performance in TeraSort under FU scenario), but cannot help them avoid all impacts.Dissertation/ThesisMasters Thesis Computer Science 201

ANALISIS PERBANDINGAN PERFORMA VIRTUALISASI BERBASIS CONTAINER DENGAN VIRTUALISASI BERBASIS HYPERVISOR

Pengunaan virtualisasi bagi perusahaan merupakan sesuatu yang tidak asing lagi. Virtualisasi membantu perusahaan untuk menghemat sumber daya yang diperlukan dalam membangun server-server yang berbeda. Diantara virtualisasi yang ada, terdapat dua jenis virtualisasi yang mendominasi, yaitu hypervisor dan container. Hypervisor adalah virtualisasi memungkinkan suatu mesin untuk menjalankan beberapa mesin virtual dengan sistem operasi yang berbeda, dan container adalah virtualisasi yang mengisolasi pada level sistem operasi. Terdiri dari arsitektur yang berbeda, performa dari kedua virtualisasi juga dianggap berbeda. Untuk menemukan perbandingan performa dari kedua virtualisasi maka dibangun dua mesin berbasis cloud yang memiliki spesifikasi yang sama kemudian dijalankan performance test pada kedua mesin tersebut dengan menggunakan apache jmeter. Variabel yang menjadi acuan pada performance test ini adalah response time, deviation, throughput, error dan CPU Utilization, dengan dua jenis pengujian yang pertama dengan 200 user dan yang kedua dengan 500 user. Mesin yang menggunakan virtualisasi berbasis hypervisor menghasilkan performa yang sama dengan mesin yang menggunakan virtualisasi berbasis container pada 200 user, namun memiliki penurunan performa yang signifikan ketika dihadapi dengan 500 user dibandingkan dengan mesin yang menggunakan virtualisasi berbasis container

Adaptive Process Distribution at the Edge of IoT using the Integration of BPMS and Containerization

Täna levivad pilvepõhised värkvõrgu (asjade interneti) süsteemid tuginevad protsesside halduseks kaugel asuvatel andmekeskustel, mis toob endaga kaasa latentsusprobleeme. Vastusena sellele probleemile on varem välja pakutud servaarvutuse lähenemine, kus arvutused viiakse läbi asjade interneti süsteemi võrgule füüsiliselt lähemal. Mitmete servaarvutuse metoodikate seas on uduarvutus lähenemine, kus rõhk on arvutuste liigutamisel värkvõrgu seadmetele endile. Ehkki uduarvutusel põhinev arhitektuur on paljutõotav, tõstatab see küsimuse – kuidas värkvõrgu protsessihaldussüsteemid (BPMS4IoT-süsteemid) äriprotsesse heterogeensetele värkvõrgu seadmetele jaotama peaksid? Levinud on lähenemine, kus protsesside töövooülesannete käituseks tuginetakse ühisele platvormile. Näiteks, kui haldusserver defineerib teatud töövoo ülesandena Pythoni skripti ja määrab selle seadmele, siis peab seadme töövookäitusmootor toetama vastavat mehhanismi skriptide jooksutamiseks. Selline nõue ei ole paindlik, arvestades värkvõrgu seadmete heterogeensust. Käesolevas magistritöös pakub autor välja raamistiku, mis eraldab töövoo ülesannete käitusmeetodi käitusmootorist kasutades selleks konteinertehnoloogiat. Töö käigus arendati välja raamistiku prototüüp ning viidi läbi katseid mikroarvutitel põhinevail seadmetel. Lisaks võrreldi väljapakutud uduarvutuse raamistiku jõudlust pilvearvutusel põhineva süsteemiga.Emerging cloud-centric Internet of Things (IoT) system relies on distant data centers to manage the entire processes, which raises the issue of latency. To address the issue, researchers have introduced the Edge computing methodologies that carry out computation closer to the edge network of IoT system. Among the numerous Edge computing approaches, Mist computing paradigm emphasises the mechanism that moves the computation further to the front-end IoT devices. Although the architecture of Mist computing is promising, it raises a new challenge in how the Business Process Management System for IoT (BPMS4IoT) distributes the business process workflow to the heterogeneous IoT devices? In general, executing business process workflows relies on the common platform for executing customized tasks. For example, if the management server defines a Python script task in a workflow, which has been allocated to an IoT device, the workflow engine of the IoT device must have the compatible execution method. Such a requirement is less flexible when one considers the heterogeneity of the IoT devices. Therefore, in this thesis, the author proposes a framework to decouple the workflow task execution method from the workflow engines using the containerization technology. A proof-of-concept prototype has been developed and has been tested on several single-board computers-based IoT devices. Further, a case study has been performed to demonstrate the performance of the proposed framework comparing to the cloud-centric system

Container-Based Virtualization for Bluetooth Low Energy Sensor Devices in Internet of Things Applications

Internet of Things (IoT) has become a continuously growing concept with the developments of ubiquitous computing, wireless sensor networks (WSN). With the industry 4.0 revolution, all production activities such as logistics, finance, agriculture, energy and almost all the service and infrastructure applications used by people in the cities we live in will undergo a major change within the IoT paradigm. In this study, a prototype model has been developed and its performance is investigated. Our prototype model can reach the advertisement data of Bluetooth Low Energy sensor devices by using container-based virtualization technology and directly working at layer 2 (L2) of Transmission Control Protocol/Internet Protocol (TCP/IP). Virtualization mechanism for the sensor devices could help to exchange context-aware information with Internet Protocol Version 6 (IPv6) structure. Also with virtualization may emerge interoperable sensor node platforms of heterogeneous environments from different vendors

A Strategy for Performance Evaluation and Modeling of Cloud Computing Services

On-demand services and reduced costs made cloud computing a popular mechanism to provide scalable resources according to the user’s expectations. This paradigm is an important role in business and academic organizations, supporting applications and services deployed based on virtual machines and containers, two different technologies for virtualization. Cloud environments can support workloads generated by several numbers of users, that request the cloud environment to execute transactions and its performance should be evaluated and estimated in order to achieve clients satisfactions when cloud services are offered. This work proposes a performance evaluation strategy composed of a performance model and a methodology for evaluating the performance of services configured in virtual machines and containers in cloud infrastructures. The performance model for the evaluation of virtual machines and containers in cloud infrastructures is based on stochastic Petri nets. A case study in a real public cloud is presented to illustrate the feasibility of the performance evaluation strategy. The case study experiments were performed with virtual machines and containers supporting workloads related to social networks transactions

Interoperability in the Heterogeneous Cloud Environment: A Survey of Recent User-centric Approaches

© 2016 Copyright held by the owner/author(s). Cloud computing provides users the ability to access shared, online computing resources. However, providers often offer their own proprietary applications, interfaces, APIs and infrastructures, resulting in a heterogeneous cloud environment. This heterogeneous environment makes it difficult for users to change cloud service providers; exploring capabilities to support the automated migration from one provider to another is an active, open research area. Many standards bodies (IEEE, NIST, DMTF and SNIA), industry (middleware) and academia have been pursuing approaches to reduce the impact of vendor lock-in by investigating the cloud migration problem at the level of the VM. However, the migration downtime, decoupling VM from underlying systems and security of live channels remain open issues. This paper focuses on analysing recently proposed live, cloud migration approaches for VMs at the infrastructure level in the cloud architecture. The analysis reveals issues with flexibility, performance, and security of the approaches, including additional loads to the CPU and disk I/O drivers of the physical machine where the VM initially resides. The next steps of this research are to develop and evaluate a new approach LibZam (Libya Zamzem) that will work towards addressing the identified limitations

클라우드 컴퓨팅 환경기반에서 수치 모델링과 머신러닝을 통한 지구과학 자료생성에 관한 연구

학위논문(박사) -- 서울대학교대학원 : 자연과학대학 지구환경과학부, 2022. 8. 조양기.To investigate changes and phenomena on Earth, many scientists use high-resolution-model results based on numerical models or develop and utilize machine learning-based prediction models with observed data. As information technology advances, there is a need for a practical methodology for generating local and global high-resolution numerical modeling and machine learning-based earth science data. This study recommends data generation and processing using high-resolution numerical models of earth science and machine learning-based prediction models in a cloud environment. To verify the reproducibility and portability of high-resolution numerical ocean model implementation on cloud computing, I simulated and analyzed the performance of a numerical ocean model at various resolutions in the model domain, including the Northwest Pacific Ocean, the East Sea, and the Yellow Sea. With the containerization method, it was possible to respond to changes in various infrastructure environments and achieve computational reproducibility effectively. The data augmentation of subsurface temperature data was performed using generative models to prepare large datasets for model training to predict the vertical temperature distribution in the ocean. To train the prediction model, data augmentation was performed using a generative model for observed data that is relatively insufficient compared to satellite dataset. In addition to observation data, HYCOM datasets were used for performance comparison, and the data distribution of augmented data was similar to the input data distribution. The ensemble method, which combines stand-alone predictive models, improved the performance of the predictive model compared to that of the model based on the existing observed data. Large amounts of computational resources were required for data synthesis, and the synthesis was performed in a cloud-based graphics processing unit environment. High-resolution numerical ocean model simulation, predictive model development, and the data generation method can improve predictive capabilities in the field of ocean science. The numerical modeling and generative models based on cloud computing used in this study can be broadly applied to various fields of earth science.지구의 변화와 현상을 연구하기 위해 많은 과학자들은 수치 모델을 기반으로 한 고해상도 모델 결과를 사용하거나 관측된 데이터로 머신러닝 기반 예측 모델을 개발하고 활용한다. 정보기술이 발전함에 따라 지역 및 전 지구적인 고해상도 수치 모델링과 머신러닝 기반 지구과학 데이터 생성을 위한 실용적인 방법론이 필요하다. 본 연구는 지구과학의 고해상도 수치 모델과 머신러닝 기반 예측 모델을 기반으로 한 데이터 생성 및 처리가 클라우드 환경에서 효과적으로 구현될 수 있음을 제안한다. 클라우드 컴퓨팅에서 고해상도 수치 해양 모델 구현의 재현성과 이식성을 검증하기 위해 북서태평양, 동해, 황해 등 모델 영역의 다양한 해상도에서 수치 해양 모델의 성능을 시뮬레이션하고 분석하였다. 컨테이너화 방식을 통해 다양한 인프라 환경 변화에 대응하고 계산 재현성을 효과적으로 확보할 수 있었다. 머신러닝 기반 데이터 생성의 적용을 검증하기 위해 생성 모델을 이용한 표층 이하 온도 데이터의 데이터 증강을 실행하여 해양의 수직 온도 분포를 예측하는 모델 훈련을 위한 대용량 데이터 세트를 준비했다. 예측모델 훈련을 위해 위성 데이터에 비해 상대적으로 부족한 관측 데이터에 대해서 생성 모델을 사용하여 데이터 증강을 수행하였다. 모델의 예측성능 비교에는 관측 데이터 외에도 HYCOM 데이터 세트를 사용하였으며, 증강 데이터의 데이터 분포는 입력 데이터 분포와 유사함을 확인하였다. 독립형 예측 모델을 결합한 앙상블 방식은 기존 관측 데이터를 기반으로 하는 예측 모델의 성능에 비해 향상되었다. 데이터합성을 위해 많은 양의 계산 자원이 필요했으며, 데이터 합성은 클라우드 기반 GPU 환경에서 수행되었다. 고해상도 수치 해양 모델 시뮬레이션, 예측 모델 개발, 데이터 생성 방법은 해양 과학 분야에서 예측 능력을 향상시킬 수 있다. 본 연구에서 사용된 클라우드 컴퓨팅 기반의 수치 모델링 및 생성 모델은 지구 과학의 다양한 분야에 광범위하게 적용될 수 있다.1. General Introduction 1 2. Performance of numerical ocean modeling on cloud computing 6 2.1. Introduction 6 2.2. Cloud Computing 9 2.2.1. Cloud computing overview 9 2.2.2. Commercial cloud computing services 12 2.3. Numerical model for performance analysis of commercial clouds 15 2.3.1. High Performance Linpack Benchmark 15 2.3.2. Benchmark Sustainable Memory Bandwidth and Memory Latency 16 2.3.3. Numerical Ocean Model 16 2.3.4. Deployment of Numerical Ocean Model and Benchmark Packages on Cloud Clusters 19 2.4. Simulation results 21 2.4.1. Benchmark simulation 21 2.4.2. Ocean model simulation 24 2.5. Analysis of ROMS performance on commercial clouds 26 2.5.1. Performance of ROMS according to H/W resources 26 2.5.2. Performance of ROMS according to grid size 34 2.6. Summary 41 3. Reproducibility of numerical ocean model on the cloud computing 44 3.1. Introduction 44 3.2. Containerization of numerical ocean model 47 3.2.1. Container virtualization 47 3.2.2. Container-based architecture for HPC 49 3.2.3. Container-based architecture for hybrid cloud 53 3.3. Materials and Methods 55 3.3.1. Comparison of traditional and container based HPC cluster workflows 55 3.3.2. Model domain and datasets for numerical simulation 57 3.3.3. Building the container image and registration in the repository 59 3.3.4. Configuring a numeric model execution cluster 64 3.4. Results and Discussion 74 3.4.1. Reproducibility 74 3.4.2. Portability and Performance 76 3.5. Conclusions 81 4. Generative models for the prediction of ocean temperature profile 84 4.1. Introduction 84 4.2. Materials and Methods 87 4.2.1. Model domain and datasets for predicting the subsurface temperature 87 4.2.2. Model architecture for predicting the subsurface temperature 90 4.2.3. Neural network generative models 91 4.2.4. Prediction Models 97 4.2.5. Accuracy 103 4.3. Results and Discussion 104 4.3.1. Data Generation 104 4.3.2. Ensemble Prediction 109 4.3.3. Limitations of this study and future works 111 4.4. Conclusion 111 5. Summary and conclusion 114 6. References 118 7. Abstract (in Korean) 140박