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Cloud Computing Theory and Practice THIRD EDITION

Dan C. Marinescu Department of Computer Science, University of Central Florida, Orlando, FL, United States

Table of Contents Cover image Title page Copyright Dedication Preface to third edition Abbreviations Chapter 1: Introduction 1.1. Cloud computing, an old idea whose time has come 1.2. Energy use and ecological impact of cloud computing 1.3. Ethical issues in cloud computing 1.4. Factors affecting cloud service availability 1.5. Network-centric computing and network-centric content

Literature Chapter 2: The cloud ecosystem 2.1. Cloud computing delivery models and services 2.2. Amazon Web Services 2.3. Google Clouds 2.4. Microsoft Windows Azure and online services 2.5. IBM clouds 2.6. Cloud storage diversity and vendor lock-in 2.7. Cloud interoperability 2.8. Service-level Agreements and Compliance-level Agreements 2.9. Responsibility sharing between user and service provider 2.10. User challenges and experience 2.11. Software licensing 2.12. Challenges faced by cloud computing 2.13. Cloud computing as a disruptive technology 2.14. Exercises and problems Literature Chapter 3: Parallel processing and distributed computing

3.1. Computer architecture concepts 3.2. Grand architectural complications 3.3. ARM architecture 3.4. SIMD architectures 3.5. Graphics processing units 3.6. Tensor processing units 3.7. Systems on a chip 3.8. Data, thread-level, and task-level parallelism 3.9. Speedup, Amdhal's law, and scaled speedup 3.10. Multicore processor speedup 3.11. From supercomputers to distributed systems 3.12. Modularity. Soft modularity versus enforced modularity 3.13. Layering and hierarchy 3.14. Peer-to-peer systems 3.15. Large-scale systems 3.16. Composability bounds and scalability (R) 3.17. Distributed computing fallacies and the CAP theorem 3.18. Blockchain technology and applications 3.19. History notes and further readings 3.20. Exercises and problems

Literature Chapter 4: Cloud hardware and software 4.1. Cloud infrastructure challenges 4.2. Cloud hardware; warehouse-scale computer (WSC) 4.3. WSC performance 4.4. Hypervisors 4.5. Execution of coarse-grained data-parallel applications 4.6. Fine-grained cluster resource sharing in Mesos 4.7. Cluster management with Borg 4.8. Evolution of a cluster management system 4.9. Shared state cluster management 4.10. QoS-aware cluster management 4.11. Resource isolation 4.12. In-memory cluster computing for Big Data 4.13. Containers; Docker containers 4.14. Kubernetes 4.15. Further readings 4.16. Exercises and problems Literature

Chapter 5: Cloud resource virtualization 5.1. Resource virtualization 5.2. Performance and security isolation in computer clouds 5.3. Virtual machines 5.4. Full virtualization and paravirtualization 5.5. Hardware support for virtualization 5.6. QEMU 5.7. Kernel-based Virtual Machine 5.8. Xen—a hypervisor based on paravirtualization 5.9. Optimization of network virtualization in Xen 2.0 5.10. Nested virtualization 5.11. A trusted kernel-based virtual machine for ARMv8 5.12. Paravirtualization of Itanium architecture 5.13. A performance comparison of virtual machines 5.14. Open-source software platforms for private clouds 5.15. The darker side of virtualization 5.16. Virtualization software 5.17. History notes and further readings 5.18. Exercises and problems Literature

Chapter 6: Cloud access and cloud interconnection networks 6.1. Packet-switched networks and the Internet 6.2. Internet evolution 6.3. TCP congestion control 6.4. Content-centric networks; named data networks (R) 6.5. Software-defined networks; SD-WAN 6.6. Interconnection networks for computer clouds 6.7. Multistage interconnection networks 6.8. InfiniBand and Myrinet 6.9. Storage area networks and the Fibre Channel 6.10. Scalable data center communication architectures 6.11. Network resource management algorithms (R) 6.12. Content delivery networks 6.13. Vehicular ad hoc networks 6.14. Further readings 6.15. Exercises and problems Literature Chapter 7: Cloud data storage 7.1. Dynamic random access memories and hard disk drives

7.2. Solid-state disks 7.3. Storage models, file systems, and databases 7.4. Distributed file systems; the precursors 7.5. General parallel file system 7.6. Google file system 7.7. Locks; Chubby—a locking service 7.8. RDBMS—cloud mismatch 7.9. NoSQL databases 7.10. Data storage for online transaction processing systems 7.11. BigTable 7.12. Megastore 7.13. Storage reliability at scale 7.14. Disk locality versus data locality in computer clouds 7.15. Database provenance 7.16. History notes and further readings 7.17. Exercises and problems Literature Chapter 8: Cloud security 8.1. Security—the top concern for cloud users

8.2. Cloud security risks 8.3. Security as a service (SecaaS) 8.4. Privacy and privacy impact assessment 8.5. Trust 8.6. Cloud data encryption 8.7. Security of database services 8.8. Operating system security 8.9. Virtual machine security 8.10. Security of virtualization 8.11. Security risks posed by shared images 8.12. Security risks posed by a management OS 8.13. Xoar—breaking the monolithic design of the TCB 8.14. Mobile devices and cloud security 8.15. Mitigating cloud vulnerabilities in the age of ransomware 8.16. AWS security 8.17. Further readings 8.18. Exercises and problems Literature Chapter 9: Cloud resource management and scheduling

9.1. Policies and mechanisms for resource management 9.2. Scheduling algorithms for computer clouds 9.3. Delay scheduling (R) 9.4. Data-aware scheduling (R) 9.5. Apache capacity scheduler 9.6. Start-time fair queuing (R) 9.7. Borrowed virtual time (R) 9.8. Cloud scheduling subject to deadlines (R) 9.9. MapReduce application scheduling subject to deadlines (R) 9.10. Resource bundling; combinatorial auctions for cloud resources 9.11. Cloud resource utilization and energy efficiency 9.12. Resource management and dynamic application scaling 9.13. Control theory and optimal resource management (R) 9.14. Stability of two-level resource allocation strategy (R) 9.15. Feedback control based on dynamic thresholds (R) 9.16. Coordination of autonomic performance managers (R) 9.17. A utility model for cloud-based web services (R) 9.18. Cloud self-organization 9.19. Cloud interoperability

9.20. Further readings 9.21. Exercises and problems Literature Chapter 10: Concurrency and cloud computing 10.1. Enduring challenges 10.2. Communication and concurrency 10.3. Computational models; communicating sequential processes 10.4. The bulk synchronous parallel model 10.5. A model for multicore computing 10.6. Modeling concurrency with Petri nets 10.7. Process state; global state of a process or thread group 10.8. Communication protocols and process coordination 10.9. Communication, logical clocks, and message delivery rules 10.10. Runs and cuts; causal history 10.11. Threads and activity coordination 10.12. Critical sections, locks, deadlocks, and atomic actions 10.13. Consensus protocols 10.14. Load balancing

10.15. Multithreading in Java; FlumeJava; Apache Crunch 10.16. History notes and further readings 10.17. Exercises and problems Literature Chapter 11: Cloud applications 11.1. Cloud application development and architectural styles 11.2. Coordination of multiple activities 11.3. Workflow patterns 11.4. Coordination based on a state machine model—zookeeper 11.5. MapReduce programming model 11.6. Case study: the GrepTheWeb application 11.7. Hadoop, Yarn, and Tez 11.8. SQL on Hadoop: Pig, Hive, and Impala 11.9. Current cloud applications and new applications opportunities 11.10. Clouds for science and engineering 11.11. Cloud computing and biology research 11.12. Social computing, digital content, and cloud computing 11.13. Software fault isolation 11.14. Further readings

11.15. Exercises and problems Literature Chapter 12: Big Data, data streaming, and the mobile cloud 12.1. Big Data 12.2. Data warehouses and Google databases for Big Data 12.3. Dynamic data-driven applications 12.4. Data streaming 12.5. A dataflow model for data streaming 12.6. Joining multiple data streams 12.7. Mobile computing and applications 12.8. Energy efficiency of mobile computing 12.9. Alternative mobile cloud computing models 12.10. System availability at scale (R) 12.11. Scale and latency (R) 12.12. Edge computing and Markov decision processes (R) 12.13. Bootstrapping techniques for data analytics (R) 12.14. Approximate query processing (R) 12.15. Further readings 12.16. Exercises and problems

Literature Chapter 13: Emerging clouds 13.1. A short-term forecast 13.2. Machine learning on clouds 13.3. Quantum computing on clouds 13.4. Vehicular clouds 13.5. Final thoughts Literature Appendix A: Cloud projects A.1. Cloud simulation of a distributed trust algorithm A.2. A trust management service A.3. Simulation of traffic management in a smart city A.4. A cloud service for adaptive data streaming A.5. Optimal FPGA synthesis A.6. Tensor network contraction on AWS A.7. A simulation study of machine-learning scalability A.8. Cloud-based task alert application A.9. Cloud-based health-monitoring application Literature

Appendix B: Cloud application development B.1. AWS EC2 instances B.2. Connecting clients to cloud instances through firewalls B.3. Security rules for application- and transport-layer protocols in EC2 B.4. How to launch an EC2 Linux instance and connect to it B.5. How to use S3 in Java B.6. How to manage AWS SQS services in C# B.7. How to install SNS on Ubuntu 10.04 B.8. How to create an EC2 placement group and use MPI B.9. StarCluster—a cluster computing toolkit for EC2 B.10. An alternative setting of an MPI virtual cluster B.11. How to install hadoop on eclipse on a windows system B.12. Exercises and problems Literature Literature Literature Glossary Index

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Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility.

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Dedication To Vera Rae and Luke Bell

Preface to third edition This text targets a broad audience, including the large number of individuals without a formal training in computer science, yet addicted to cloud computing, who want to optimize their applications and wish to better understand the inner workings of intricate systems such as computer clouds. The text could also be useful to software engineers tempted to exploit concurrency and take advantage of the computing power of multicore processors, graphics processing units, and tensor processing units. Graduate students who conduct research on different aspects of organization and management of computer clouds and complex systems could benefit from research sections marked (R) in several chapters.

Cloud computing has had a transformative impact not only on how we compute today but also on the algorithms we use and on computer systems' hardware and software. The text tracks the evolution of ideas in the field striving to maintain a delicate balance between what a gardener would call perennials, i.e., theoretical concepts mostly invariant in time and critical for understanding and exploiting concurrency, and annuals, i.e., practical recipes with a limited lifespan, helpful for using computer clouds now.

Any text dedicated to cloud computing must be periodically updated to keep up with the breathtaking evolution of cloud computing engines, the cloud software stack, cloud interconnection networks, and applications. The third edition of this book includes a reorganization of chapters, major revisions of most chapters, cloud computing applications to machine learning, Big Data, and other important areas, and a new chapter on emerging clouds.

Chapters 1 and 2 give an informal introduction to computer clouds and to the cloud ecosystem. Cloud computing is intimately

tied to parallel and distributed processing, and Chapter 3 introduces important theoretical and practical concepts related to parallel and distributed computing. Chapters 4 and 5 discuss the cloud infrastructure and virtualization critical for facilitating access to cloud resources. Chapter 6 is dedicated to communication and cloud access and presents the cloud networking infrastructure, interconnection networks, and a scalable data-center communication architectures. Chapter 7 covers storage models, storage reliability at scale, and database services. Chapter 8 is dedicated to cloud security, and Chapter 9 covers resource management and scheduling.

The next three chapters are focused on the other important component of the cloud ecosystem, cloud applications. Chapter 10 covers subjects critical for understanding and using concurrency, including computational models, atomic actions, load balancing, and consensus protocols. Chapter 11 dissects the components of the cloud software stack and the frameworks supporting processing of large data sets. Chapter 12 analyzes challenges posed by Big Data, data streaming, mobile applications, and edge computing. Chapter 13 covers emerging clouds including cloud services supporting machine learning, quantum computing on clouds and vehicular clouds; the vehicular clouds section is written by one of the pioneers of this field, Prof. Stefan Olariu from the Old Dominion University.

Appendix A presents several cloud projects in large-scale simulations and cloud services, including applications when multiple design alternatives are evaluated concurrently, as well as Big Data applications in computational sciences and machine learning applications. Appendix B covers cloud application development. Some 540 references are cited in the text. A glossary and a list of abbreviations are also provided. The author is grateful for the advice and support of Stephen Merken and the Morgan Kaufmann team.

Teaching ancillaries for this book, including solutions manual, PowerPoint lecture slides, and image bank, are available online to qualified instructors.

Visit https://inspectioncopy.elsevier.com/book/details/9780323852777 for more information and to register for access.

Abbreviations ABI Application Binary Interface ACID Atomicity, Consistency, Isolation, Durability ACL Access Control List AMI Amazon Machine Image API Application Program Interface ARM Advanced RISC Machine ASIC Application Specific Integrated Circuit AVX Advanced Vector Extensions AWS Amazon Web Services AWSLA Amazon Web Services Licensing Agreement BASE Basically Available, Soft state, Eventually consistent BCE Basic Core Equivalent BIOS Basic Input Output System BPD Bootstrap Performance Diagnostic BSP Bulk Synchronous Parallel CCN Content Centric Network CDN Content Delivery Network CISC Complex Instruction Set Computer

COMA Cache Only Memory Access CORBA Common Object Request Broker Architecture CPI Cycles per Instruction CPU Central Processing Unit CRM Custom Relation Management CSP Cloud Service Provider CUDA Compute Unified Device Architecture DAT Dynamic Address Translation DBaaS Database as a Service DDoS Distributed Denial of Service DHCP Dynamic Host Configuration Protocol DMA Direct Memory Access DRAM Dynamic Random Access Memory DVFS Dynamic Voltage and Frequency Scaling EBS Elastic Block Store ECS EC2 Container Service EC2 Elastic Cloud Computing EMR Elastic Map Reduce EPIC Explicitly Parallel Instruction Computing FC Fiber Channel FCFS First Come First Serve FIFO First In First Out FPGA Field Programmable Gate Array

FSM Finite State Machine GFS Google File System GiB Gibi Byte GIMP GNU Manipulation Program GPFS General Parallel File System GPU Graphics Processing Unit HDD Hard Disk Drive HDFS Hadoop File System HTML Hypertext Markup Language HTTP Hypertext Transfer Protocol IaaS Infrastructure as a Service IDE Integrated Drive Electronics IoT Internet of Things IPC Instructions per Clock Cycle IRTF Internet Research Task Force ISA Instruction Set Architecture JDBC Java Database Connectivity JMS Java Message Service JSON Javascript Object Notation KVM Kernel-based Virtual Machine MAC Medium Access Control MFLOPS Million Floating Point Operations per Second MIMD Multiple Instruction Multiple Data

MIPS Million Instructions per Second MLMM Multi-level Memory Manager MMX Multi Media Extension MPI Message Passing Interface MSCR Map Shuffle Combine Reduce NAT Network Address Translation NDN Named Data Networks NFS Network File System NTFS New Technology File System NUMA Non-Uniform Memory Access NV-RAM Non-Volatile Random Access Memory OCCI Open Cloud Computing Interface OGF Open Grid Forum OLTP On Line Transaction Processing OLAP On Line Analytical Processing PaaS Platform as a Service PHP recursive acronym for PHP: Hypertext Preprocessor PN Petri Net QOS Quality of Service RAID Redundant Array of Independent Disks RAM Random Access Memory RAR Read After Read RAW Read After Write