가상화 환경을 위한 원격 메모리

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·컴퓨터공학부, 2021.8. Bernhard Egger.클라우드 환경은 거대한 연산 자원을 상시 가동할 필요 없고 원하는 순간 원하는 양의 대한 연산 비용만을 지불하면 되기 때문에, 최근 인공지능 및 빅데이터 연산의 유행으로 인해 그 수요가 크게 증가하고 있다. 이러한 클라우드 컴퓨팅의 도입으로인해 고객은 서버 유지에 대한 비용을 크게 절감할 수 있고 서비스 제공자는 연산 자원의 이용 효율을 극대화 할 수 있다. 이러한 시나리오에서 데이터센터 입장에서는 연산 자원 활용 효율을 개선하는 것이 중요한 목표가 된다. 특히 최근 폭증하고 있는 데이터 센터의 규모를 고려하면 작은 효율 개선으로도 막대한 경제적 가치를 창출 할 수 있다. 데이터 센터의 효율은 위치 선정, 구조 설계, 냉각 시스템, 하드웨어 구성 등등 다양한 요소들에 영향을 받지만, 이 논문에서는 특히 연산 및 메모리 자원을 관리하는 소프트웨어 설계 및 구현을 다룬다. 본 논문에서는 데이터 센터 효율 개선을 획기적으로 개선하는 두가지 소프트웨어 기반 기술을 제안한다. 첫 째로 가상화 환경을 위한 소프트웨어 기반 메모리 분리 시스템을 제안한다. 최근 고속 네트워크의 발전으로 인해 원격 메모리 접근 비용이 획기적으로 줄어 들었고, 이 논문에서는 고성능 네트워킹 하드웨어를 이용하여 원격 메모리 위에서 실행되는 가상 머신의 큰 성능 저하 없이 실행할 수 있음을 보인다. 제안된 기술을 QEMU/KVM 가상머신 하이퍼바이저를 통해 평가한 결과, 본 논문에서 제안한 기법은 기존 시스템 대비 원격 페이징에 대한 꼬리 지연시간을 98.2% 개선함을 보인다. 또한 랙 규모의 작업처리 시뮬레이션을 통한 실험에서, 제안된 시스템은 전체 작업 처리 시간을 기존 시스템 대비 40.9% 줄일 수 있음을 보인다. 두 번째로 원격 메모리를 이용하는 즉각적인 가상머신 이주 기법을 제안하다. 가상화 환경의 원격 메모리 활용에 대한 확장은 그것만으로 자원 이용률 향상에 대해 큰 기여를 하지만, 여전히 한 서버에서 여러 어플리케이션이 경쟁적으로 자원을 이용하는 경우 성능이 크게 저하 될 수 있다. 이 논문에서 제안하는 즉각적인 가상머신 이주 기법은 원격 메모리 상에서 아주 작은 메타데이터의 전송만으로 가상머신의 이주를 가능하게 하며, 메모리 상에 키와 값을 저장하는 데이터베이스 벤치마크를 실행하는 가상머신을 기반으로 한 평가에서 기존 기법대비 실질적인 서비스 중단시간을 최대 92.6% 개선함을 보인다.The raising importance of big data and artificial intelligence (AI) has led to an unprecedented shift in moving local computation into the cloud. One of the key drivers behind this transformation was the exploding cost of owning and maintaining large computing systems powerful enough to process these new workloads. Customers experience a reduced cost by renting only the required resources and only when needed, while data center operators benefit from efficiency at scale. A key factor in operating a profitable data center is a high overall utilization of its resources. Due to the scale of modern data centers, small improvements in efficiency translate to significant savings in the total cost of ownership (TCO). There are many important elements that constitute an efficient data center such as its location, architecture, cooling system, or the employed hardware. In this thesis, we focus on software-related aspects, namely the utilization of computational and memory resources. Reports from data centers operated by Alibaba and Google show that the overall resource utilization has stagnated at a level of around 50 to 60 percent over the past decade. This low average utilization is mostly attributable to peak demand-driven resource allocation despite the high variability of modern workloads in their resource usage. In other words, data centers today lack an efficient way to put idle resources that are reserved but not used to work. In this dissertation we present RackMem, a software-based solution to address the problem of low resource utilization through two main contributions. First, we introduce a disaggregated memory system tailored for virtual environments. We observe that virtual machines can use remote memory without noticeable performance degradation under moderate memory pressure on modern networking infrastructure. We implement a specialized remote paging system for QEMU/KVM that reduces the remote paging tail-latency by 98.2% in comparison to the state of the art. A job processing simulation at rack-scale shows that the total makespan can be reduced by 40.9% under our memory system. While seamless disaggregated memory helps to balance memory usage across nodes, individual nodes can still suffer overloaded resources if co-located workloads exhibit high resource usage at the same time. In a second contribution, we present a novel live migration technique for machines running on top of our remote paging system. Under this instant live migration technique, entire virtual machines can be migrated in as little as 100 milliseconds. An evaluation with in-memory key-value database workloads shows that the presented migration technique improves the state of the art by a wide margin in all key performance metrics. The presented software-based solutions lay the technical foundations that allow data center operators to significantly improve the utilization of their computational and memory resources. As future work, we propose new job schedulers and load balancers to make full use of these new technical foundations.Chapter 1. Introduction 1 1.1 Contributions of the Dissertation 3 Chapter 2. Background 5 2.1 Resource Disaggregation 5 2.2 Transparent Remote Paging 7 2.3 Remote Direct Memory Access (RDMA) 9 2.4 Live Migration of Virtual Machines 10 Chapter 3. RackMem Overview 13 3.1 RackMem Virtual Memory 13 3.2 RackMem Distributed Virtual Storage 14 3.3 RackMem Networking 15 3.4 Instant VM Live Migration 16 Chapter 4. Virtual Memory 17 4.1 Design Considerations for Achieving Low-latency 19 4.2 Pagefault handling 20 4.2.1 Fast-path and slow-path in the pagefault handler 21 4.2.2 State transition of RackVM page 23 4.3 Latency Hiding Techniques 25 4.4 Implementation 26 4.4.1 RackMem Virtual Memory Module 27 4.4.2 Dynamic Rebalancing of Local Memory 29 4.4.3 RackVM for Virtual Machines 29 4.4.4 Running Unmodified Applications 30 Chapter 5. RackMem Distributed Virtual Storage 31 5.1 The distributed Storage Abstraction 32 5.2 Memory Management 33 5.2.1 Remote memory allocation 33 5.2.2 Remote memory reclamation 33 5.3 Fault Tolerance 34 5.3.1 Fault-tolerance and Write-duplication 34 5.4 Multiple Storage Support in RackMem 36 5.5 Implementation 38 5.5.1 The Remote Memory Backend 38 5.5.2 Linux Demand Paging on RackDVS 39 Chapter 6. Networking 40 6.1 Design of RackNet 40 6.2 Implementation 41 6.2.1 RPC message layout 41 6.2.2 RackNet RPC Implementation 42 Chapter 7. Instant VM Live Migration 44 7.1 Motivation 45 7.1.1 The need for a tailored live migration technique 45 7.1.2 Software Bottlenecks 46 7.1.3 Utilizing workload variability 46 7.2 Design of Instant 47 7.2.1 Instant Region Migration 47 7.3 Implementation 48 7.3.1 Extension of RackVM for Instant 49 7.3.2 Instant region migration 49 7.3.3 Pre-fetch optimizations 51 7.3.4 Downtime optimizations 51 7.3.5 QEMU modification for Instant 52 Chapter 8. Evaluation - RackMem 53 8.1 Execution Environment 54 8.2 Pagefault Handler Latency 56 8.3 Single Application Performance 57 8.3.1 Batch-oriented Applications 58 8.3.2 Internal Pagesize and Performance 59 8.3.3 Write-duplication overhead 60 8.3.4 RackDVS slab size and performance 62 8.3.5 Latency-oriented Applications 63 8.3.6 Network Bandwidth Analysis 64 8.3.7 Dynamic Local Memory Partitioning 66 8.3.8 Rack-scale Job Processing Simulation 67 Chapter 9. Evaluation - Instant VM Live Migration 69 9.1 Experimental setup 69 9.2 Target Applications 70 9.3 Comparison targets 70 9.4 Database and client setups 71 9.5 Memory disaggregation scenarios 71 9.6.1 Time-to-responsiveness 71 9.6.2 Effective Downtime 73 9.6.3 Effect of Instant optimizations 75 Chapter 10. Conclusion 77 10.1 Future Directions 78 요약 89박

QuePaxa: Escaping the tyranny of timeouts in consensus

Leader-based consensus algorithms are fast and efficient under normal conditions, but lack robustness to adverse conditions due to their reliance on timeouts for liveness. We present QuePaxa, the first protocol offering state-of-the-art normal-case efficiency without depending on timeouts. QuePaxa uses a novel randomized asynchronous consensus core to tolerate adverse conditions such as denial-of-service (DoS) attacks, while a one-round-trip fast path preserves the normal-case efficiency of Multi-Paxos or Raft. By allowing simultaneous proposers without destructive interference, and using short hedging delays instead of conservative timeouts to limit redundant effort, QuePaxa permits rapid recovery after leader failure without risking costly view changes due to false timeouts. By treating leader choice and hedging delay as a multi-armed-bandit optimization, QuePaxa achieves responsiveness to prevalent conditions, and can choose the best leader even if the current one has not failed. Experiments with a prototype confirm that QuePaxa achieves normal-case LAN and WAN performance of 584k and 250k cmd/sec in throughput, respectively, comparable to Multi-Paxos. Under conditions such as DoS attacks, misconfigurations, or slow leaders that severely impact existing protocols, we find that QuePaxa remains live with median latency under 380ms in WAN experiments

RAID Organizations for Improved Reliability and Performance: A Not Entirely Unbiased Tutorial (1st revision)

RAID proposal advocated replacing large disks with arrays of PC disks, but as the capacity of small disks increased 100-fold in 1990s the production of large disks was discontinued. Storage dependability is increased via replication or erasure coding. Cloud storage providers store multiple copies of data obviating for need for further redundancy. Varitaions of RAID based on local recovery codes, partial MDS reduce recovery cost. NAND flash Solid State Disks - SSDs have low latency and high bandwidth, are more reliable, consume less power and have a lower TCO than Hard Disk Drives, which are more viable for hyperscalers.Comment: Submitted to ACM Computing Surveys. arXiv admin note: substantial text overlap with arXiv:2306.0876

On the many faces of atomic multicast

Many current online services need to serve clients distributed across geographic areas. Coordinating highly available and scalable geographically distributed replicas, however, is challenging. While State Machine Replication is the most direct way of achieving availability, no scalability comes from the traditional approach. Typically, scalability is obtained by partitioning the original application state among groups of servers, which leads to further challenges. Atomic multicast is a group communication abstraction that groups processes, providing reliability and ordering guarantees, and can be explored to provide partially replicated applications a scalable and consistent alternative. This work confronts the challenges of providing practical group communication abstractions for crash fault-tolerant and Byzantine fault-tolerant (BFT) models. Although there are plenty of atomic multicast algorithms that tolerate crash failures, they suffer from two major issues: (a) high latency for messages addressed to multiple groups, and (b) low performance when proportion of messages to multiple groups is high. To solve the first problem and reduce the latency of multi-group messages, this work presents FastCast, an algorithm with unprecedented four communication delays. The second problem can be addressed by maximizing the proportion of single- group messages and eliminating additional communication among groups to execute operations. In this direction, this document introduces GeoPaxos, a protocol that partitions the ordering of operations like atomic multicast while still keeping the state fully replicated. In the BFT model, the task is more challenging, since servers can behave arbitrarily. This thesis presents ByzCast, the first algorithm that tolerates Byzantine failures. ByzCast is hierarchical and introduces a new class of atomic multicast defined as partially genuine. Lastly, since at the very core of most strong consistent replicated system resides a consensus protocol, the thesis concludes with Kernel Paxos, a Paxos implementation provided as a loadable kernel module, providing at the same time high performance, and abstracting ordering from the application execution

Automatic Rescaling and Tuning of Big Data Applications on Container-Based Virtual Environments

Programa Oficial de Doutoramento en Investigación en Tecnoloxías da Información. 524V01[Resumo] As aplicacións Big Data actuais evolucionaron dun xeito significativo, dende fluxos de traballo baseados en procesamento por lotes ata outros máis complexos que poden requirir múltiples etapas de procesamento usando diferentes tecnoloxías, e mesmo executándose en tempo real. Doutra banda, para despregar estas aplicacións, os clusters ‘commodity’ foron substituídos nalgúns casos por paradigmas máis flexibles como o Cloud, ou mesmo por outros emerxentes como a computación ‘serverless’, precisando ambos paradigmas de tecnoloxías de virtualización. Esta Tese propón dúas contornas que proporcionan modos alternativos de realizar unha análise en profundidade e unha mellor xestión dos recursos de aplicacións Big Data despregadas en contornas virtuais baseadas en contedores software. Por unha banda, a contorna BDWatchdog permite realizar unha análise de gran fino e en tempo real en termos do uso dos recursos do sistema e do perfilado do código. Doutra banda, descríbese unha contorna para o reescalado dinámico e en tempo real dos recursos segundo un conxunto de políticas configurables. A primeira política proposta céntrase no reescalado automático dos recursos dos contedores segundo o uso real que as aplicacións fan dos mesmos, proporcionando así unha contorna ‘serverless’. Ademais, preséntase unha política alternativa centrada na xestión enerxética que permite implementar os conceptos de limitación e presuposto de potencia, que poden aplicarse a contedores, aplicacións ou mesmo usuarios. En xeral, as contornas propostas nesta Tese tratan de poñer de relevo o potencial de aplicar novos xeitos de analizar e axustar os recursos das aplicacións Big Data despregadas en clusters de contedores, mesmo en tempo real. Os casos de uso presentados son exemplos diso, demostrando que as aplicacións Big Data poden adaptarse a novas tecnoloxías ou paradigmas sen teren que cambiar as súas características máis intrínsecas.[Resumen] Las aplicaciones Big Data actuales han evolucionado de forma significativa, desde flujos de trabajo basados en procesamiento por lotes hasta otros más complejos que pueden requerir múltiples etapas de procesamiento usando distintas tecnologías, e incluso ejecutándose en tiempo real. Por otra parte, para desplegar estas aplicaciones, los clusters ‘commodity’ se han reemplazado en algunos casos por paradigmas más flexibles como el Cloud, o incluso por otros emergentes como la computación ‘serverless’, requiriendo ambos paradigmas de tecnologías de virtualización. Esta Tesis propone dos entornos que proporcionan formas alternativas de realizar un análisis en profundidad y una mejor gestión de los recursos de aplicaciones Big Data desplegadas en entornos virtuales basados en contenedores software. Por un lado, el entorno BDWatchdog permite realizar un análisis de grano fino y en tiempo real en lo que respecta a la monitorización de los recursos del sistema y al perfilado del código. Por otro lado, se describe un entorno para el reescalado dinámico y en tiempo real de los recursos de acuerdo a un conjunto de políticas configurables. La primera política propuesta se centra en el reescalado automático de los recursos de los contenedores de acuerdo al uso real que las aplicaciones hacen de los mismos, proporcionando así un entorno ‘serverless’. Además, se presenta una política alternativa centrada en la gestión energética que permite implementar los conceptos de limitación y presupuesto de potencia, pudiendo aplicarse a contenedores, aplicaciones o incluso usuarios. En general, los entornos propuestos en esta Tesis tratan de resaltar el potencial de aplicar nuevas formas de analizar y ajustar los recursos de las aplicaciones Big Data desplegadas en clusters de contenedores, incluso en tiempo real. Los casos de uso que se han presentado son ejemplos de esto, demostrando que las aplicaciones Big Data pueden adaptarse a nuevas tecnologías o paradigmas sin tener que cambiar su características más intrínsecas.[Abstract] Current Big Data applications have significantly evolved from its origins, moving from mostly batch workloads to more complex ones that may involve many processing stages using different technologies or even working in real time. Moreover, to deploy these applications, commodity clusters have been in some cases replaced in favor of newer and more flexible paradigms such as the Cloud or even emerging ones such as serverless computing, usually involving virtualization techniques. This Thesis proposes two frameworks that provide alternative ways to perform indepth analysis and improved resource management for Big Data applications deployed on virtual environments based on software containers. On the one hand, the BDWatchdog framework is capable of performing real-time, fine-grain analysis in terms of system resource monitoring and code profiling. On the other hand, a framework for the dynamic and real-time scaling of resources according to several tuning policies is described. The first proposed policy revolves around the automatic scaling of the containers’ resources according to the real usage of the applications, thus providing a serverless environment. Furthermore, an alternative policy focused on energy management is presented in a scenario where power capping and budgeting functionalities are implemented for containers, applications or even users. Overall, the frameworks proposed in this Thesis aim to showcase how novel ways of analyzing and tuning the resources given to Big Data applications in container clusters are possible, even in real time. The supported use cases that were presented are examples of this, and show how Big Data applications can be adapted to newer technologies or paradigms without having to lose their distinctive characteristics

Core Technologies for Native XML Database Management Systems

This work investigates the core technologies required to build Database Management Systems (DBMSs) for large collections of XML documents. We call such systems XML Base Management Systems (XBMSs). We identify requirements, and analyze how they can be met using a conventional DBMS. Our conclusion is that an XML support layer on top of an existing conventional DBMS does not address the requirements for XBMSs. Hence, we built a Native XBMS, called Natix. Natix has been developed completely from scratch, incorporating optimizations for high-performance XML processing in those places where they are most effective

Investigation of parallel programming on heterogeneous multiprocessors

Multi-core processors have become ordinary in modern commodity computers. Computationally intensive applications, like video processing, that previously only ran on specialized hardware, are now common on home computers. However, the demand for more computing power is ever-increasing, and with the introduction of high definition video, more performance is desired. As an alternative to having multiple identical processor cores, heterogeneous multiprocessors have cores with different capabilities. This allows tasks to be processed on simple cores with specialized functionality. The simplicity furthers low power consumption, small die usage, and low price. Dealing with heterogeneous cores increases the complexity of writing programs for the architecture. The reasons for this includes different capabilities of the cores, and some heterogeneous architectures do not have shared memory. Without shared memory, accessing main memory requires explicit transfers to local memory. In this thesis, we consider two architectures, the STI Cell/B.E. and Intel IXP2400, and evaluate parallelization strategies and performance for real-world problems. Our tests show promising throughput for some applications, and we propose a scheme for offloading computationally intensive parts of an existing application

Communication Architectures for Parallel-Programming Systems

Bal, H.E. [Promotor]Tanenbaum, A.S. [Promotor

SoK: Understanding BFT Consensus in the Age of Blockchains

Blockchain as an enabler to current Internet infrastructure has provided many unique features and revolutionized current distributed systems into a new era. Its decentralization, immutability, and transparency have attracted many applications to adopt the design philosophy of blockchain and customize various replicated solutions. Under the hood of blockchain, consensus protocols play the most important role to achieve distributed replication systems. The distributed system community has extensively studied the technical components of consensus to reach agreement among a group of nodes. Due to trust issues, it is hard to design a resilient system in practical situations because of the existence of various faults. Byzantine fault-tolerant (BFT) state machine replication (SMR) is regarded as an ideal candidate that can tolerate arbitrary faulty behaviors. However, the inherent complexity of BFT consensus protocols and their rapid evolution makes it hard to practically adapt themselves into application domains. There are many excellent Byzantine-based replicated solutions and ideas that have been contributed to improving performance, availability, or resource efficiency. This paper conducts a systematic and comprehensive study on BFT consensus protocols with a specific focus on the blockchain era. We explore both general principles and practical schemes to achieve consensus under Byzantine settings. We then survey, compare, and categorize the state-of-the-art solutions to understand BFT consensus in detail. For each representative protocol, we conduct an in-depth discussion of its most important architectural building blocks as well as the key techniques they used. We aim that this paper can provide system researchers and developers a concrete view of the current design landscape and help them find solutions to concrete problems. Finally, we present several critical challenges and some potential research directions to advance the research on exploring BFT consensus protocols in the age of blockchains