Jeff Dean (Google Senior Fellow) – The Rise of Cloud Computing Systems (Dec 2022)
Chapters
00:00:15 Cloud Computing Evolution: From Multics to Modern Data Centers
The History of Large-Scale Computational Systems: The concept of shared computational systems dates back to the Multics system in 1965, providing a vision for utility computing. Google’s early days involved intercepting machines destined for other research groups to utilize them for web page organization and search. Modern large-scale data centers like Google’s house numerous racks of machines spanning vast areas.
Distributed Systems and Adjacent Fields: Prior to the mid-1990s, distributed systems research focused on modest scale systems and widely distributed systems like DNS. High-performance computing emphasized performance but lacked fault tolerance, while transactional processing systems specialized in structured data management.
The Need for Giant Computing Systems: The late 1990s brought resource-intensive interactive services like search, requiring immense computational power. The growth of the web and the desire for sub-second search response times led to the demand for extensive computation.
The Berkeley Now Project and Inktomi: The Berkeley Now Project emphasized the use of multiple computers for search engine infrastructure. Inktomi, a spin-off from the project, provided search services as a white label product, utilizing Sun workstations.
Jeff Dean’s Perspective: After completing his PhD, Jeff Dean joined DEC World, a small research lab with diverse projects. AltaVista, a collaboration between DEC World and other labs, gained popularity for its comprehensive and fast search experience. AltaVista initially ran on high-end DEC Alpha systems scattered throughout the hallways of DEC World.
00:11:36 History and Evolution of Google's Computing Systems
The Early Days of Google: Google was founded in 1999 with a small team of 25 people. The company’s early office was located in a small space above a T-Mobile store in Palo Alto. Google’s focus was on creating a larger and more frequently updated index by leveraging commodity PCs for cost-effectiveness.
Building Custom Machines: Google initially assembled their own machines using components instead of buying pre-built PCs. The first design consisted of rows of cookie trays with bare motherboards and a shared power supply. Later iterations improved the design with better packaging and cooling systems.
Challenges of Scaling: As Google grew, managing thousands of machines became increasingly complex. Common issues included machine failures, hard drive failures, and network rewiring. Unforeseen events like dead horses and drunken hunters caused data center outages.
GFS: Google File System: To abstract away from individual disks, Google developed the Google File System (GFS). GFS featured a centralized master for metadata management and distributed chunk servers for data storage. The system provided high fault tolerance with three-way replication and automatic recovery. GFS enabled self-management of disks in the data center, improving operational efficiency.
00:22:19 Abstractions and Frameworks for Scaling Computation
Overview of Large-Scale Computing: Large-scale computing involves managing vast amounts of data, efficient processing, and effective resource utilization.
Centralized Master and Worker Architecture: A centralized master manages metadata and controls communication, while thousands of workers handle tasks. This enables efficient communication and simple metadata management.
Scheduling Jobs for Parallel Computation: Scheduling jobs with hundreds of thousands of tasks requires fast parallelization and efficient resource allocation. Virtual machines or containers are used to multitask and maximize resource utilization on a single machine.
Virtual Machines and Cluster Scheduling: Virtual machines allow consolidation of servers and more efficient use of compute resources. Cluster scheduling systems place containers or virtual machines on physical machines, optimizing resource allocation.
Performance Isolation and Tail Latency Control: Sharing machines across jobs improves utilization but can lead to unpredictable performance variations. Isolating performance while sharing resources is challenging, especially in distributed systems. Techniques like sending redundant requests can help control tail latency and reduce performance outliers.
Higher-Level Computation Frameworks: High-level abstractions simplify large-scale computations for programmers. MapReduce, Hadoop, and other frameworks enable expressing computations as simple map and reduce operations. These frameworks handle the complexities of scheduling, locality, and fault tolerance.
Data Flow Graphs and In-Memory Working Sets: Dryad, Sozol, and Flume use general data flow graphs for processing. Systems like Spark focus on optimizing in-memory working sets for improved performance.
Structured State Updates: Many applications require updating structured state with low latency and large scale.
00:29:27 Distributed Storage Systems for Large-Scale Data Processing
Introduction to Distributed Storage Systems: Distributed storage systems provide an abstraction of a table of state with various columns, resembling a giant spreadsheet distributed across thousands of machines. These systems can handle petabytes of data and millions of requests per second. Desirable features include the ability to scale automatically, handle machine failures, and prioritize performance over consistency.
Bigtable: Bigtable, a distributed storage system developed at Google, utilized GFS as its backing store and introduced a higher-level model of rows, columns, and timestamps. It offered versioned history of rows and columns but lacked cross-row consistency therapies and distributed transactions. Recovery was fast due to the management of state in small, sorted pieces called tablets, enabling rapid recovery by other machines in case of machine failures.
Dynamo: Dynamo, developed at Amazon, featured versioning and application-assisted conflict resolution.
Spanner: Spanner, an evolution of Bigtable, was designed to operate across multiple data centers, potentially serving all of Google. It supported both strong and weak consistency, including distributed transactions as part of the product.
Design Patterns for Scalability and Fault Tolerance: Successful design patterns employed by MapReduce, Bigtable, and Spanner involve assigning hundreds or thousands of units of work or state to each machine. This approach facilitates dynamic capacity sizing, load balancing, and faster failure recovery by distributing work among multiple machines in parallel.
Impact of Public Cloud: The public cloud has revolutionized access to these systems, making them available to developers beyond the cloud companies themselves. Cloud providers offer APIs that enable developers to build large-scale applications quickly and efficiently.
00:33:34 Machine Learning Transforming Computing with Specialized Hardware
Machine Learning’s Transformation of Computing: Increased use of machine learning for various tasks. Neural net-based computations have advantages for specialized hardware design. Reduced precision arithmetic and low-level linear algebra operations are suitable for machine learning.
TPU (Tensor Processing Unit) Development at Google: First TPU designed for neural net inference. AlphaGo competition used TPU machines for matches against world champions. Second-generation TPU designed for both training and inference. Simple design with a giant matrix multiply unit, scalar and vector units, HBM, and reduced precision arithmetic.
TPUv3 and TPU Pods: Third-generation TPU with higher clock rate, optimized ASIC design, and water-based cooling. TPU pods connect 1,000 chips together for high compute power. Available internally at Google and through cloud services.
MLPerf Results and PPUv4: MLPerf is an open benchmarking setup for measuring machine learning system performance. Google achieved good results using a four-pod system. PPUv4 is the latest generation of TPU, with promising performance.
00:38:31 ML Frameworks and Future Directions in Machine Learning
ML Frameworks: TensorFlow: Emphasis on research and production uses in large-scale data centers and edge devices. PyTorch: More focused on expressivity and research, with a rapidly evolving production stack.
Future Directions in Machine Learning: Moving away from training new models for each problem and towards using a single large model that can solve multiple tasks. Exploring the concept of component-based ML systems, where new tasks can leverage expertise from existing components. Investigating the use of large-scale machine learning hardware, like TPU pods, to support these new approaches.
Challenges and Opportunities: Combination of systems-level challenges, machine learning challenges, and software engineering challenges. Need for general approaches to address tail latency issues, rather than focusing on specific causes. Potential for using network resources and machine learning techniques to improve performance.
00:45:15 Emerging Trends in Hardware and Network Optimization for Machine Learning
Network vs. Machine Computation: Opportunities for moving computation to the edge devices for real-time processing. Specialization of hardware for tailored algorithms to enhance efficiency. Potential for more heterogeneous systems with diverse processing elements throughout networks.
Performance Guarantees: Distributional guarantees for latency instead of hard real-time guarantees for every request. Careful monitoring of latency percentiles (50th, 90th, 99th) for service performance understanding.
Scaling Large-Scale ML Models: High bandwidth between devices in TPU systems enables fast communication for large-scale ML training and inference. Data center bandwidth is crucial for building systems out of components like full-size pods. ML components can be mapped onto different parts of pods or communicate via high-speed networking.
Impact of Persistent Memory Technologies: Persistent memory has properties different from DRAM and slower devices like disks or Flash. Higher-level systems should be designed to take advantage of persistent memory’s characteristics. Understanding the performance and design properties of building blocks is essential for evolving system designs.
Abstraction for Non-CPU ML Acceleration Hardware: Frameworks like XLA and MLIR help abstract computations for various types of ML hardware. High-level abstractions can express computations and perform transformations across devices. Retargetable compilers can map ML compute onto diverse ML accelerators.
Reminiscing about the Past and Looking to the Future: Jeff Dean expressed his delight in seeing AltaVista mentioned during the event. He also recalled an anecdote shared by Peter Alvaro about Ask.com. These references to the past sparked fond memories among those present who had been involved in the tech industry for a long time.
Highlighting the Significance of Future Directions: Dean emphasized the importance of the future directions that the attendees were working on. He acknowledged the significant contributions of both hardware and software advancements to the overall progress in the field.
Appreciation for the Participants and Encouragement for Support: Dean thanked all the participants for joining the event and expressed his gratitude for their contributions. He also encouraged the audience to support the company hosting the event, recognizing their valuable work.
Abstract
The Evolution of Cloud Computing and Machine Learning: A Comprehensive Analysis
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Introduction
The digital age has witnessed transformative shifts in computing paradigms, notably through the rise of cloud computing systems and the integration of machine learning into various aspects of technology. This article delves into the historical evolution of these systems, their architectural innovations, and the future directions of machine learning, leveraging insights from key figures and events in the field. By employing an inverted pyramid style, the most critical developments and ideas are presented upfront, with subsequent sections offering detailed examinations of these concepts.
The Dawn of Cloud Computing
Cloud computing’s journey can be traced back to the advent of Multics in 1965, a system that pioneered large-scale shared computational systems. This concept evolved over the decades, with significant contributions from entities like Google. In Google’s early days, resource constraints led to innovative approaches in web page organization, foreshadowing the development of modern data centers. These centers, now vast facilities housing numerous computers, enable large-scale computations and services essential in today’s digital landscape.
Research and Challenges in Distributed Systems
The field of distributed systems research underwent significant changes from modest-scale systems in the pre-1990s to the complex, decentralized systems like DNS. Adjacent fields, such as high-performance computing and transactional processing systems, contributed differing priorities, from performance to fault tolerance and data management. These developments laid the foundation for the need for giant computing systems, highlighted by resource-intensive services like Google’s Search engine.
Perspectives from Pioneers
Jeff Dean, a notable figure in this evolution, joined DEC World’s research lab in 1996, contributing to the AltaVista search engine. AltaVista’s popularity underscored the necessity for scalable and efficient search systems, heavily reliant on powerful hardware. Similarly, Google’s journey in scaling distributed systems began in the late 1990s, with the company grappling with the challenges of managing and maintaining cost-effective commodity PCs.
The Berkeley Now Project and Inktomi:
The Berkeley Now Project emphasized the utilization of multiple computers for search engine infrastructure. Inktomi, a spin-off from the project, provided search services as a white label product, utilizing Sun workstations.
Innovations in Computing Systems
Google’s early machine designs were rudimentary yet innovative, featuring in-house assembled machines with shared power supplies. However, the scaling complexities soon became apparent as managing thousands of machines presented unprecedented challenges. Google responded by developing systems like the Google File System (GFS), a revolutionary approach to large-scale distributed storage. GFS’s architecture, featuring a centralized master for metadata and chunk servers for data storage, transformed data center operations by treating the entire center as a single file system.
GFS: Google File System:
To abstract away from individual disks, Google developed the Google File System (GFS). GFS featured a centralized master for metadata management and distributed chunk servers for data storage. The system provided high fault tolerance with three-way replication and automatic recovery. GFS enabled self-management of disks in the data center, improving operational efficiency.
Building Custom Machines:
In Google’s early days, the company assembled its machines using components rather than purchasing pre-built PCs. The first design consisted of rows of cookie trays with bare motherboards and a shared power supply. Later iterations improved the design with better packaging and cooling systems.
Advanced Computation and Storage Frameworks
The need for parallel computation led to the development of cluster scheduling systems, emphasizing efficient resource utilization and performance isolation. Higher-level computation frameworks like MapReduce and its various implementations, including Hadoop and Spark, emerged to address these needs. Additionally, systems like Bigtable and Spanner represented significant advancements in distributed storage, enabling large-scale operations with various consistency models.
Distributed Storage Systems and Spanner:
Distributed storage systems provide an abstraction of a table of state with various columns, resembling a giant spreadsheet distributed across thousands of machines. These systems can handle petabytes of data and millions of requests per second. Desirable features include the ability to scale automatically, handle machine failures, and prioritize performance over consistency. Bigtable, a distributed storage system developed at Google, utilized GFS as its backing store and introduced a higher-level model of rows, columns, and timestamps. It offered versioned history of rows and columns but lacked cross-row consistency therapies and distributed transactions. Recovery was fast due to the management of state in small, sorted pieces called tablets, enabling rapid recovery by other machines in case of machine failures. Dynamo, developed at Amazon, featured versioning and application-assisted conflict resolution. Spanner, an evolution of Bigtable, was designed to operate across multiple data centers, potentially serving all of Google. It supported both strong and weak consistency, including distributed transactions as part of the product. Successful design patterns employed by MapReduce, Bigtable, and Spanner involve assigning hundreds or thousands of units of work or state to each machine. This approach facilitates dynamic capacity sizing, load balancing, and faster failure recovery by distributing work among multiple machines in parallel. The public cloud has revolutionized access to these systems, making them available to developers beyond the cloud companies themselves. Cloud providers offer APIs that enable developers to build large-scale applications quickly and efficiently.
The Intersection with Machine Learning
The integration of machine learning transformed computing, with neural net-based computations becoming central to numerous tasks. The development of specialized computational devices like GPUs and TPUs marked a new era in machine learning. Google’s TPUs, in particular, have evolved through several iterations, each optimized for improved performance in training and inference.
Machine Learning’s Impact on Computing and Google’s TPU Development:
Increased use of machine learning for various tasks. Neural net-based computations have advantages for specialized hardware design. Reduced precision arithmetic and low-level linear algebra operations are suitable for machine learning. The first TPU designed for neural net inference. AlphaGo competition used TPU machines for matches against world champions. Second-generation TPU designed for both training and inference. Simple design with a giant matrix multiply unit, scalar and vector units, HBM, and reduced precision arithmetic. TPUv3 and TPU Pods: Third-generation TPU with higher clock rate, optimized ASIC design, and water-based cooling. TPU pods connect 1,000 chips together for high compute power. Available internally at Google and through cloud services. MLPerf Results and PPUv4: MLPerf is an open benchmarking setup for measuring machine learning system performance. Google achieved good results using a four-pod system. PPUv4 is the latest generation of TPU, with promising performance.
The Future of Machine Learning and Computing
As machine learning continues to evolve, the focus is shifting towards developing large, versatile models capable of multiple tasks, leveraging learnings from previous tasks. This evolution is accompanied by challenges in areas like tail latency and network programming, prompting innovations in hardware specialization and distributed processing.
State-of-the-Art Machine Learning Frameworks and Future Directions:
ML Frameworks: TensorFlow: Emphasis on research and production uses in large-scale data centers and edge devices. PyTorch: More focused on expressivity and research, with a rapidly evolving production stack. Future Directions in Machine Learning: Moving away from training new models for each problem and towards using a single large model that can solve multiple tasks. Exploring the concept of component-based ML systems, where new tasks can leverage expertise from existing components. Investigating the use of large-scale machine learning hardware, like TPU pods, to support these new approaches. Challenges and Opportunities: Combination of systems-level challenges, machine learning challenges, and software engineering challenges. Need for general approaches to address tail latency issues, rather than focusing on specific causes. Potential for using network resources and machine learning techniques to improve performance.
Conclusion
The trajectory of cloud computing and machine learning is marked by continuous innovation, adaptation, and gratitude for past contributions. The field stands at a crossroads, with persistent memory technologies, diverse ML acceleration hardware, and novel system designs shaping the future. As these technologies evolve, they promise to redefine the limits of what is computationally possible, offering exciting prospects for the world of technology.
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This article, adhering to a 1500-2000 word limit, encapsulates the essence of the historical and technical progression in cloud computing and machine learning, providing a comprehensive understanding of these pivotal technological advancements.
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