Assured cloud computing roy campbell 3 pdf

Assured Cloud Computing IEEE Press Editorial Board Ekram Hossain, Editor in Chief Giancarlo Fortino David Alan Grier Donald Heirman Xiaoou Li Andreas Molisch Saeid Nahavandi Ray Perez Jeffrey Reed Linda Shafer Mohammad Shahidehpour Sarah Spurgeon Ahmet Murat Tekalp About IEEE Computer Society IEEE Computer Society is the world’s leading computing membership organization and the trusted information and career-development source for a global workforce of technology leaders including: professors, researchers, software engineers, IT pro­ fessionals, employers, and students The unmatched source for technology infor­ mation, inspiration, and collaboration, the IEEE Computer Society is the source that computing professionals trust to provide high-quality, state-of-the-art information on an on-demand basis The Computer Society provides a wide range of forums for top minds to come together, including technical conferences, publications, and a comprehensive digital library, unique training webinars, professional training, and the TechLeader Training Partner Program to help organizations increase their staff’s technical knowledge and expertise, as well as the personalized information tool myComputer To find out more about the community for technology leaders, visit http://www.computer.org IEEE/Wiley Partnership The IEEE Computer Society and Wiley partnership allows the CS Press authored book program to produce a number of exciting new titles in areas of computer science, computing, and networking with a special focus on software engineering IEEE Computer Society members continue to receive a 15% discount on these titles when purchased through Wiley or at wiley.com/ieeecs To submit questions about the program or send proposals, please contact Mary Hatcher, Editor, Wiley-IEEE Press: Email: mhatcher@wiley.com, Telephone: 201­ 748-6903, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030-5774 Assured Cloud Computing Edited by Roy H Campbell, Charles A Kamhoua, and Kevin A Kwiat This edition first published 2018  2018 the IEEE Computer Society, Inc All rights reserved No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions The rights of Roy H Campbell, Charles A Kamhoua, and Kevin A Kwiat to be identified as the authors of the editorial material in this work have been asserted in accordance with law Registered Office John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA Editorial Office 111 River Street, Hoboken, NJ 07030, USA For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com Wiley also publishes its books in a variety of electronic formats and by print-on-demand Some content that appears in standard print versions of this book may not be available in other formats Limit of Liability/Disclaimer of Warranty While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make This work is sold with the understanding that the publisher is not engaged in rendering professional services The advice and strategies contained herein may not be suitable for your situation You should consult with a specialist where appropriate Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages Library of Congress Cataloging-in-Publication Data Names: Campbell, Roy Harold, editor | Kamhoua, Charles A., editor | Kwiat, Kevin A., editor Title: Assured cloud computing / edited by Roy H Campbell, Charles A Kamhoua, Kevin A Kwiat Description: First edition | Hoboken, NJ : IEEE Computer Society, Inc./Wiley, 2018 | Includes bibliographical references and index | Identifiers: LCCN 2018025067 (print) | LCCN 2018026247 (ebook) | ISBN 9781119428503 (Adobe PDF) | ISBN 9781119428480 (ePub) | ISBN 9781119428633 (hardcover) Subjects: LCSH: Cloud computing Classification: LCC QA76.585 (ebook) | LCC QA76.585 A87 2018 (print) | DDC 004.67/82–dc23 LC record available at https://lccn.loc.gov/2018025067 Cover image: Abstract gray polka dots pattern background - shuoshu/Getty Images; Abstract modern background - tmeks/iStockphoto; Abstract wave - Keo/Shutterstock Cover design by Wiley Set in 10/12 pt WarnockPro-Regular by Thomson Digital, Noida, India Printed in the United States of America 10 v Table of Contents Preface xiii Editors’ Biographies xvii List of Contributors xix Introduction Roy H Campbell 1.1 1.1.1 1.2 Introduction Mission-Critical Cloud Solutions for the Military Overview of the Book References Survivability: Design, Formal Modeling, and Validation of Cloud Storage Systems Using Maude 10 Rakesh Bobba, Jon Grov, Indranil Gupta, Si Liu, José Meseguer, Peter Csaba Ölveczky, and Stephen Skeirik 2.1 2.1.1 2.1.2 2.1.3 2.1.4 2.2 2.3 2.3.1 2.3.2 2.3.2.1 2.4 2.5 2.5.1 2.5.2 Introduction 10 State of the Art 11 Vision: Formal Methods for Cloud Storage Systems 12 The Rewriting Logic Framework 13 Summary: Using Formal Methods on Cloud Storage Systems 15 Apache Cassandra 17 Formalizing, Analyzing, and Extending Google’s Megastore 23 Specifying Megastore 23 Analyzing Megastore 25 Megastore-CGC 29 RAMP Transaction Systems 30 Group Key Management via ZooKeeper 31 ZooKeeper Background 32 System Design 33 vi Table of Contents 2.5.3 2.5.4 2.6 2.6.1 2.6.2 2.6.3 2.7 2.8 2.8.1 Maude Model 34 Analysis and Discussion 35 How Amazon Web Services Uses Formal Methods Use of Formal Methods 37 Outcomes and Experiences 38 Limitations 39 Related Work 40 Concluding Remarks 42 The Future 43 Acknowledgments 44 References 44 Risks and Benefits: Game-Theoretical Analysis and Algorithm for Virtual Machine Security Management in the Cloud 49 Luke Kwiat, Charles A Kamhoua, Kevin A Kwiat, and Jian Tang 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.8.1 3.8.2 3.8.3 3.8.4 3.8.5 3.9 Introduction 49 Vision: Using Cloud Technology in Missions State of the Art 54 System Model 57 Game Model 59 Game Analysis 61 Model Extension and Discussion 67 Numerical Results and Analysis 71 Changes in User 2’s Payoff with Respect to L2 Changes in User 2’s Payoff with Respect to e Changes in User 2’s Payoff with Respect to π Changes in User 2’s Payoff with Respect to qI Model Extension to n = 10 Users 75 The Future 78 References 79 Detection and Security: Achieving Resiliency by Dynamic and Passive System Monitoring and Smart Access Control 81 Zbigniew Kalbarczyk 4.1 4.2 4.3 4.4 4.4.1 4.4.2 4.4.3 4.4.3.1 4.4.3.2 Introduction 82 Vision: Using Cloud Technology in Missions 83 State of the Art 84 Dynamic VM Monitoring Using Hypervisor Probes 85 Design 86 Prototype Implementation 88 Example Detectors 90 Emergency Exploit Detector 90 Application Heartbeat Detector 91 37 51 71 72 73 74 Table of Contents 4.4.4 4.4.4.1 4.4.4.2 4.4.5 4.5 4.5.1 4.5.1.1 4.5.1.2 4.5.1.3 4.5.2 4.5.2.1 4.5.2.2 4.5.3 4.5.3.1 4.5.3.2 4.5.3.3 4.5.4 4.6 4.6.1 4.6.1.1 4.6.1.2 4.6.2 4.6.3 4.6.3.1 4.6.3.2 4.6.4 4.6.4.1 4.6.4.2 4.6.5 4.6.5.1 4.6.5.2 4.6.6 4.7 4.7.1 4.7.2 4.7.2.1 4.7.2.2 4.7.2.3 4.7.3 4.7.3.1 Performance 93 Microbenchmarks 93 Detector Performance 94 Summary 95 Hypervisor Introspection: A Technique for Evading Passive Virtual Machine Monitoring 96 Hypervisor Introspection 97 VMI Monitor 97 VM Suspend Side-Channel 97 Limitations of Hypervisor Introspection 98 Evading VMI with Hypervisor Introspection 98 Insider Attack Model and Assumptions 98 Large File Transfer 99 Defenses against Hypervisor Introspection 101 Introducing Noise to VM Clocks 101 Scheduler-Based Defenses 101 Randomized Monitoring Interval 102 Summary 103 Identifying Compromised Users in Shared Computing Infrastructures 103 Target System and Security Data 104 Data and Alerts 105 Automating the Analysis of Alerts 106 Overview of the Data 107 Approach 109 The Model: Bayesian Network 109 Training of the Bayesian Network 110 Analysis of the Incidents 112 Sample Incident 112 Discussion 113 Supporting Decisions with the Bayesian Network Approach 114 Analysis of the Incidents 114 Analysis of the Borderline Cases 116 Conclusion 118 Integrating Attribute-Based Policies into Role-Based Access Control 118 Framework Description 119 Aboveground Level: Tables 119 Environment 120 User-Role Assignments 120 Role-Permission Assignments 121 Underground Level: Policies 121 Role-Permission Assignment Policy 122 vii viii Table of Contents 4.7.3.2 4.7.4 4.7.4.1 4.7.4.2 4.7.5 4.8 User-Role Assignment Policy 123 Case Study: Large-Scale ICS 123 RBAC Model-Building Process 124 Discussion of Case Study 127 Concluding Remarks 128 The Future 128 References 129 Scalability, Workloads, and Performance: Replication, Popularity, Modeling, and Geo-Distributed File Stores 133 Roy H Campbell, Shadi A Noghabi, and Cristina L Abad 5.1 5.2 5.3 5.4 5.4.1 5.4.1.1 5.4.1.2 5.4.2 5.4.3 Introduction 133 Vision: Using Cloud Technology in Missions 134 State of the Art 136 Data Replication in a Cloud File System 137 MapReduce Clusters 138 File Popularity, Temporal Locality, and Arrival Patterns 142 Synthetic Workloads for Big Data 144 Related Work 147 Contribution from Our Approach to Generating Big Data Request Streams Using Clustered Renewal Processes 149 Scalable Geo-Distributed Storage 149 Related Work 151 Summary of Ambry 152 Summary 153 The Future 153 References 154 5.4.3.1 5.4.4 5.4.5 5.5 5.6 Resource Management: Performance Assuredness in Distributed Cloud Computing via Online Reconfigurations 160 Mainak Ghosh, Le Xu, and Indranil Gupta 6.1 6.2 6.3 6.3.1 Introduction 161 Vision: Using Cloud Technology in Missions 163 State of the Art 164 State of the Art: Reconfigurations in Sharded Databases/ Storage 164 Database Reconfigurations 164 Live Migration 164 Network Flow Scheduling 164 State of the Art: Scale-Out/Scale-In in Distributed Stream Processing Systems 165 Real-Time Reconfigurations 165 Live Migration 165 6.3.1.1 6.3.1.2 6.3.1.3 6.3.2 6.3.2.1 6.3.2.2 Table of Contents 6.3.2.3 6.3.3 Real-Time Elasticity 165 State of the Art: Scale-Out/Scale-In in Distributed Graph Processing Systems 166 6.3.3.1 Data Centers 166 6.3.3.2 Cloud and Storage Systems 166 6.3.3.3 Data Processing Frameworks 166 6.3.3.4 Partitioning in Graph Processing 166 6.3.3.5 Dynamic Repartitioning in Graph Processing 167 6.3.4 State of the Art: Priorities and Deadlines in Batch Processing Systems 167 6.3.4.1 OS Mechanisms 167 6.3.4.2 Preemption 167 6.3.4.3 Real-Time Scheduling 168 6.3.4.4 Fairness 168 6.3.4.5 Cluster Management with SLOs 168 6.4 Reconfigurations in NoSQL and Key-Value Storage/Databases 169 6.4.1 Motivation 169 6.4.2 Morphus: Reconfigurations in Sharded Databases/Storage 170 6.4.2.1 Assumptions 170 6.4.2.2 MongoDB System Model 170 6.4.2.3 Reconfiguration Phases in Morphus 171 6.4.2.4 Algorithms for Efficient Shard Key Reconfigurations 172 6.4.2.5 Network Awareness 175 6.4.2.6 Evaluation 175 6.4.3 Parqua: Reconfigurations in Distributed Key-Value Stores 179 6.4.3.1 System Model 180 6.4.3.2 System Design and Implementation 181 6.4.3.3 Experimental Evaluation 183 6.5 Scale-Out and Scale-In Operations 185 6.5.1 Stela: Scale-Out/Scale-In in Distributed Stream Processing Systems 186 6.5.1.1 Motivation 186 6.5.1.2 Data Stream Processing Model and Assumptions 187 6.5.1.3 Stela: Scale-Out Overview 187 6.5.1.4 Effective Throughput Percentage (ETP) 188 6.5.1.5 Iterative Assignment and Intuition 190 6.5.1.6 Stela: Scale-In 191 6.5.1.7 Core Architecture 191 6.5.1.8 Evaluation 193 6.5.1.9 Experimental Setup 193 6.5.1.10 Yahoo! Storm Topologies and Network Monitoring Topology 193 6.5.1.11 Convergence Time 195 6.5.1.12 Scale-In Experiments 196 ix x Table of Contents 6.5.2 6.5.2.1 6.5.2.2 6.5.2.3 6.5.2.4 6.6 6.6.1 6.6.1.1 6.6.1.2 6.6.1.3 6.6.1.4 6.6.1.5 6.6.1.6 6.7 6.8 Scale-Out/Scale-In in Distributed Graph Processing Systems 197 Motivation 197 What to Migrate, and How? 199 When to Migrate? 201 Evaluation 203 Priorities and Deadlines in Batch Processing Systems 204 Natjam: Supporting Priorities and Deadlines in Hadoop 204 Motivation 204 Eviction Policies for a Dual-Priority Setting 206 Natjam Architecture 209 Natjam-R: Deadline-Based Eviction 215 Microbenchmarks 216 Natjam-R Evaluation 221 Summary 223 The Future 224 References 225 Theoretical Considerations: Inferring and Enforcing Use Patterns for Mobile Cloud Assurance 237 Gul Agha, Minas Charalambides, Kirill Mechitov, Karl Palmskog, Atul Sandur, and Reza Shiftehfar 7.1 7.2 7.3 7.3.1 7.3.2 7.3.3 7.4 7.4.1 7.4.2 7.4.3 7.4.4 7.4.5 7.4.6 7.4.7 7.4.7.1 7.4.7.2 7.4.7.3 7.4.8 7.4.8.1 7.4.8.2 7.4.8.3 7.4.8.4 Introduction 237 Vision 239 State of the Art 240 Code Offloading 241 Coordination Constraints 241 Session Types 242 Code Offloading and the IMCM Framework 243 IMCM Framework: Overview 244 Cloud Application and Infrastructure Models 244 Cloud Application Model 245 Defining Privacy for Mobile Hybrid Cloud Applications 247 A Face Recognition Application 247 The Design of an Authorization System 249 Mobile Hybrid Cloud Authorization Language 250 Grouping, Selection, and Binding 252 Policy Description 252 Policy Evaluation 253 Performance- and Energy-Usage-Based Code Offloading 254 Offloading for Sequential Execution on a Single Server 254 Offloading for Parallel Execution on Hybrid Clouds 255 Maximizing Performance 255 Minimizing Energy Consumption 256 C09 06/28/2018 16:17:49 Page 323 References Likewise, SOC and ISO/IEC 27001 are more flexible than FedRAMP in specifying how to implement the criteria and controls that they require Naturally, the value of a security standard lies in how well the approaches that it enforces actually protect systems against threats Therefore, the existence of multiple standards with notable similarities raises the question of why multiple standards are needed, if everyone is trying to achieve the same goal of having the best possible security? Upon close examination, we found that the three standards are not in fact redundant; rather, they show high complementarity and compensate for each other’s weaknesses and omissions There is good reason for a cloud service provider to invest in complying with multiple standards instead of only one Even so, given that obtaining certifications is costly, it would still be desirable for the cloud computing community to develop a single standard that offers all the protections that are currently articulated piecemeal across multiple standards However, another challenge to standardization is the reality that new vulner­ abilities and threats are continually appearing, and new defensive counter­ measures are being continually developed in response The impact of the “Treacherous Twelve” on the effectiveness of IT security standards in cloud environments points toward a possible path to the improvement of IT security standards Observations of threats, issues, and vulnerabilities can help cloud providers and users understand the need for new or different control measures, and their connection to security standards can lead to better effectiveness, completeness, and efficiency The goal of locking down standardized protection methods will for the foreseeable future be in tension with the constant evolution of the threat landscape that those methods must handle Worse, to date, the academic and industrial stakeholders have tended to study new threats more or less in isolation, and coordination with standards developers has been limited To optimize the responsiveness of standards, the study of vulnerabilities should be more actively and closely connected to the maintenance of security standards References U.S Department of Defense (2014) The DoD Cloud Way Forward, version 1.0, Jul 23 Available at http://iase.disa.mil/Documents/dodciomemo_w­ attachment_cloudwayforwardreport-20141106.pdf Defense Information Systems Agency (DISA) (2015) Best Practices Guide for Department of Defense Cloud Mission Owners, version 1.0, Aug Available at http://iasecontent.disa.mil/stigs/pdf/unclass-best_practices_guide_for_dod_ cloud_mission_owners_FINAL.pdf 323 C09 06/28/2018 324 16:17:49 Page 324 Summary and Future Work Owens, K (2017) MilCloud 2.0 upgraded with commercial cloud 10 11 12 13 14 15 infrastructure, Defense Systems, Jun 12 Available at https://defensesystems com/articles/2017/06/12/milcloud.aspx International Organization for Standardization (ISO) (2009) ISO/IEC 11889­ 1:2009: Information Technology – Trusted Platform Module – Part 1: Overview Available at https://www.iso.org/standard/50970.html (accessed Nov 29, 2013) Holgate, R and Cannon, N (2017) Get ready for the inflection point in U.S federal government cloud adoption, Gartner, Inc Jun Available at https:// www.gartner.com/doc/3187120/ready-inflection-point-federal-government Top Threats Working Group, Cloud Security Alliance (CSA) (2016) The treacherous 12: cloud computing top threats in 2016 Available at https:// downloads.cloudsecurityalliance.org/assets/research/top-threats/Treacherous­ 12_Cloud-Computing_Top-Threats.pdf FedRAMP Program overview Available at https://www.fedramp.gov/about­ us/about/ (accessed May 26, 2016) Bernstein, D (2014) Containers and cloud: from LXC to Docker to Kubernetes IEEE Cloud Computing, (3), 81–84 Murray, D.G., McSherry, F., Isaacs, R., Isard, M., Barham, P., and Abadi, M (2013) Naiad: a timely dataflow system, in Proceedings of the 24th ACM Symposium on Operating Systems Principles (SOSP), pp 439–455 Ousterhout, K., Wendell, P., Zaharia, M., and Stoica, I (2013) Sparrow: distributed, low latency scheduling, in Proceedings of the 24th ACM Symposium on Operating Systems Principles (SOSP), pp 69–84 Noghabi, S.A., Paramasivam, K., Pan, Y., Ramesh, N., Bringhurst, J., Gupta, I., and Campbell, R.H (2017) Samza: stateful scalable stream processing at LinkedIn Proceedings of the VLDB Endowment, 10 (12), 1634–1645 Venkataraman, S., Panda, A., Ousterhout, K., Ghodsi, A., Armbrust, M., Recht, B., Franklin, M.J., and Stoica, I (2017) Drizzle: fast and adaptable stream processing at scale, in Proceedings of the 26th Symposium on Operating Systems Principles (SOSP), pp 374–389 Wikipedia (2017) Comparison of deep learning software Available at https:// en.wikipedia.org/wiki/Comparison_of_deep_learning_software (accessed Sep 2, 2017) Halvorsen, T.A., (2014) Updated Guidance on the Acquisition and Use of Commercial Cloud Computing Services, Chief Information Officer, U.S Department of Defense, Dec 15 Available at http://iase.disa.mil/Documents/ commercial_cloud_computing_services.pdf Information Assurance Support Environment (IASE) (2017) DoD Cloud Computing Security, May 17, Defense Information Systems Agency (DISA) Available at http://iase.disa.mil/cloud_security/Pages/index.aspx C09 06/28/2018 16:17:49 Page 325 References 16 Intel (2015) Improving Real-Time Performance by Utilizing Cache Allocation Technology: Enhancing Performance via Allocation of the Processor’s Cache: White Paper, Document No 331843-001US Available at http://www intel.com/content/dam/www/public/us/en/documents/white-papers/cache­ allocation-technology-white-paper.pdf (accessed Aug 23, 2017) 325 BINDEX 07/18/2018 13:13:5 Page 327 327 Index a Access control See Attribute-based access control (ABAC) and Role-based access control (RBAC) Access patterns See File access patterns ACID databases, 170 Actor-Role-Coordinator (ARC) model, 242 Actors (fine-grained units of computation), 8, 237, 239–243, 246–247, 250–254, 258–265, 268–269, 271, 321 coordinating, 259–264 synchronizers for coordinating, 260–264 Adaptive replication scheme, 140–141 Adobe, 295 AFS, 151 AGILE, 166 AICPA See American Institute of Certified Public Accountants (AICPA) Air Force Office of Scientific Research (AFOSR), 44, 160, 272 Air Force Research Laboratory (AFRL), 44, 160, 272 Air Force, U.S., 1, 3, 51–52 Albatross, 164, 166, 168 Alerts, 104–117 automating the analysis of, 106–107 Amazon, 11, 16, 153, 175, 178, 318 Amazon EC2 cloud, 56, 138 Amazon Web Services (AWS), 4, 10, 12, 37–40, 42, 186, 286 Ambry, 134, 149, 152, 153, 317, 318 American Association for Laboratory Accreditation, 287 American Institute of Certified Public Accountants (AICPA), 9, 285, 288–290, 296, 298 Amoeba, 167, 206 Apache Cassandra See Cassandra Apache Hadoop, 7, 8, 32, 137, 138– 139, 141–142, 144, 160, 161, 162, 163, 166, 167–168, 197, 204–223, 224, 317, 319, 320 Apache Hadoop MapReduce See MapReduce Apache HBase, 32 Apache Storm, 8, 160, 161, 162, 165, 168, 185, 186, 187, 191–197, 224, 319, 320 Apache Storm Nimbus daemon, 191 Apache ZooKeeper See ZooKeeper ARIA, 168 Arrival patterns, 134, 142–144, 147, 317 Assured Cloud Computing, First Edition Edited by Roy H Campbell, Charles A Kamhoua, and Kevin A Kwiat  2018 the IEEE Computer Society, Inc Published 2018 by John Wiley & Sons, Inc BINDEX 328 07/18/2018 13:13:5 Page 328 Index Assured Cloud Computing Center of Excellence (ACC-UCoE), 7, 15–16 Asynchronous session types, 243 Asynchronous writes, 150, 152 Attribute-based access control (ABAC), 6, 82, 118–128, 316 environment, 119–121 framework, 119 policies, 121–123 role-permission assignments, 121 tables, 119–121 user-role assignments, 120–121 Aurora, 165 Authorization language (for mobile hybrid clouds), 250–254 grouping, selection, and binding, 252 policy description, 252–253 policy evaluation, 253–254 Authorization systems, 245–247, 249–250, 253–254, 258 hard policies, 249 soft policies, 249 Autonomous vehicles, 154, 319 AutoScale, 166 b Bad neighbor effect, 53, 58 Batch processing systems, 7, 8, 160–162, 167–168, 204–223, 224, 319–320 Bayesian networks, 6, 106, 109–118, 316 decision support with, 114–117 BerkeleyDB database, 41 Big data, 135, 161, 204 analytics, 142–144, 317 request stream generation (See Workload generation, synthetic) storage, 146 workloads for, 143 synthetic workloads for (See Synthetic workloads for big data) Bing (Microsoft service), 139 Bipartite matching, 162, 172–174, 319–320 BlinkDB, 168 Blobs (large sets of immutable data), 150–152, 317 Blue and gray networks, 3, 314 Borealis, 165 British Standard 7799, 291 Bundesamt für Sicherheit in der Informationstechnik (BSI), 294 Business considerations, 2, 52, 56, 123–125, 168, 169, 278, 295 c C5 See Cloud Computing Compliance Control Catalogue (C5) Cache partitioning, 316 Caching, 57, 95, 101, 141, 143, 145, 146, 147, 150, 151, 152, 167, 171, 177, 315 Caffe, 321 Cake, 168 Canadian Institute of Chartered Accountants (CICA), 290 CAP theorem, 5, 10 Cassandra, 4, 8, 10, 15–23, 26, 40–42, 44, 160–162, 164, 169, 170, 180, 183, 185, 200, 224, 313, 319, 320 CCT Hadoop production cluster, 139 CDRM, 141 Centrifuge, 168 Ceph, 151 Certifications See Standards Checkpointing, 94–95, 162, 167–168, 205, 208, 210–214, 216, 217, 218, 320 Chief Information Officers Council, U.S (CIO Council), 286 China, 293 Chord, 200 BINDEX 07/18/2018 13:13:5 Page 329 Index Chunk-based strategy, 99–103, 162, 170–179, 185, 319 Churning, 142, 143, 145–147, 317 C-I-A (confidentiality, integrity, and availability), 279, 281–282, 288, 289, 290, 292, 295, 297, 302, 304 Civil War, U.S., 52 CloneCloud, 241, 255 Cloud application models, 244–247 Cloud computing community (See Community cloud) definition, 1–2 future of, 43–44, 78–79, 128–129, 153–154, 224–225, 271–272, 302–305, 312–323 general principles, 51, 277–279 growth of, 49, 278 history of, 1–3 hybrid (See Hybrid cloud) military, 1–3, 50–54, 314 nationalization of (hypothetical), 52 NIST definition of, private (See Private cloud) public (See Public cloud) resilience, 6, 55, 81–129 Cloud Computing Compliance Control Catalogue (C5), 294 Cloud Control Matrix (CCM), 294, 296, 299, 300, 301–302 Cloud file systems, 137–152, 212, 317 Cloudlet solutions, 154 CloudScale, 166 Cloud Security Alliance (CSA), 294, 296, 299, 302 Cloud storage systems, 4–5, 7–8, 10–44, 133–154, 160–166, 169–185, 223, 312–313, 317–320 Code offloading, 238–241, 243–259 to maximize performance, 255–256 to minimize energy consumption, 256–257 for parallel execution on hybrid clouds, 255 performance- and energy-usage­ based, 254–259 for sequential execution on a single server, 254–255 Community cloud, 2, Competitive aging algorithm, 134, 138 Compromised users, 6, 50, 55, 57–79, 82 identification of in shared computing infrastructures, 103–118 Bayesian network approach, 109–112, 316 Conductor, 168 Congestion, 5, 10, 162, 165, 178–179, 187–196, 320 Consistency guarantees, 5, 11, 15–17, 21, 30, 33, 41, 152, 183 Consistent hashing, 17, 165, 180, 199, 200 Containers, 207, 208, 209–218, 221, 250–252, 259, 316 Contiguous Vertex Repartitioning (CVR), 198–199, 201, 203–204 Coordination hierarchical model of, 241 Coordination constraints, 238, 240, 241–242, 265, 271, 321–322 Coq (higher-order theorem prover), 41 Coresident attacks, 56–57 COS, 241 CouchDB, 170 Credential-stealing incidents See Compromised users CRUD operations, 170, 171, 183 CSA See Cloud Security Alliance (CSA) CVE-2008-0600 vulnerability, 90–91, 94–95 d DARE algorithm, 137, 140–141, 142, 317 329 BINDEX 330 07/18/2018 13:13:5 Page 330 Index Data access patterns, 139–144, 151 Database reconfigurations See Reconfiguration Data locality, 137, 138, 140–141, 150, 167 Data replication, 23, 31, 134, 137–152, 317 Deadline-based eviction, 206, 215–216 Deadlines See Priorities Debian, 179 Delay scheduling, 141–142, 167 Department of Defense, U.S (DoD), 284, 286, 312 Department of Homeland Security, U.S (DHS), 286 Design exploration, 12, 17, 37, 43 Directors coordination model, 241 Distributed file systems, 6–7, 139, 151–152, 212 Distributed graph processing See Graph processing Distributed storage systems, 7, 40, 133–135, 146, 149–152, 160–162, 179–185, 318, 319 Distributed stream processing systems, 7, 8, 160, 162, 165, 185–197, 224, 319, 320 DOT, 204 DryadLINQ, 168, 204 Dual-priority settings, 205–209, 216 Dynamic process creation, 272, 322 Dynamic proportional share scheduling, 168 Dynamo, 180 DynamoDB database, 37–38, 42 e EC2 See Amazon EC2 cloud Edge computing, 153, 154, 312, 314 Effective Throughput Percentage (ETP), 186–191, 196–197 Elasticity, 2, 5, 6, 10, 51, 52, 53, 135–136, 162, 165–167, 186–187, 192, 197–198, 203, 243, 245–247, 250, 256, 320, 321 evaluation of, 203–204 Elastisizer, 166 ElephantTrap, 138, 141 Emulab, 176, 178, 193, 206, 221 Encryption, 32, 33, 244–245, 247 keys, 55, 314–315 Energy management, 271, 321 Energy monitoring, 246, 257–259 and security policies, 258–259 Enforcing use patterns, 8, 237–272, 321–322 ETP See Effective Throughput Percentage (ETP) Eventual consistency, 5, 11, 15, 17, 19–21, 41–42, 183 Eviction policies, 163, 167–168, 205–210, 214–223, 320 f Facebook, 17, 138, 139, 149, 152, 162, 197, 208, 318, 320 Facial recognition application, 239, 247–249, 251, 253 Fair Scheduler See Hadoop Fair Scheduler Fault/attack injectors, 12, 27, 38, 91, 93, 129, 315 Federal Cloud Computing Initiative, 286 Federal Information Security Management Act (FISMA), 285, 286, 287 FedRAMP Authorization To Operate (ATO) See FedRAMP certification FedRAMP certification, 9, 277, 283–288, 294, 296, 298–303, 312, 322, 323 BINDEX 07/18/2018 13:13:5 Page 331 Index compared to other standards, 292–293, 296–302 popularity of, 293 FedRAMP Joint Authorization Board (JAB), 287 FedRAMP Program Management Office (PMO), 287 FedRAMP Third Party Assessment Organizations (3PAO), 287 File access patterns, 137–142, 144, 146, 148, 151, 317 File sharing, 149, 318 First-in, first-out (FIFO) scheduler, 137, 138, 141 Formal methods, 4, 10, 12–16, 23, 37–40, 42, 44, 260, 312–313 Formal modeling, 4–5, 10–44, 86, 312–313 Formal pattern, 44 Formal specification See Formal modeling Future of cloud computing See Cloud computing, Future of g Game analysis, 61–67 Game model, 59–61 extension, 67–71 Game theory, 5, 49–79, 313–314 Generally Accepted Privacy Principles (GAPP), 290 General Services Administration (GSA), 286 Geo-distributed storage, 5, 6–7, 10, 133–135, 149–153, 318 scalable, 7, 149–151 German Information Security Office See Bundesamt für Sicherheit in der Informationstechnik (BSI) GFS, 139, 151 GISMO, 147 Global preemption, 167 Global types, 242–243, 264–272, 322 Google, 4, 10, 15, 23, 30, 136, 138, 139, 153, 164, 197, 318 Google Cloud, 179 Google Megastore See Megastore (from Google) GPS, 167, 197, 258 GraphLab, 166, 197 Graph processing, 7, 8, 153, 160, 162, 163, 166–167, 185, 197–204, 224, 319–320 Greedy assignment, 172–173, 174, 176, 200 Greedy reactive schemes, 140 Group key management, 16, 31–37 h Hadoop See Apache Hadoop Hadoop Capacity Scheduler, 168, 209–210, 214, 216, 217–218 Hadoop Fair Scheduler, 137, 138, 141, 142, 168, 206, 217 Hadoop Online, 168 Hadoop YARN, 162, 163, 205, 206, 207, 209–212, 320 Hard cap (in Hadoop), 218 Haystack, 152 HDFS, 139, 140, 144, 146, 147, 151, 211, 212, 214, 217, 317 Health applications, 5, 11, 38, 154, 169, 284, 295, 319 Hedera, 164 Heron (Twitter’s stream processing system), 161, 165 Heuristics, 54, 55, 139, 167, 199 Hive, 204 Hotelling’s law, 69 HP Labs, 146 331 BINDEX 07/18/2018 332 13:13:5 Page 332 Index Hprobes, 85–95, 315 application heartbeat detector, 91–93, 95 emergency exploit detector, 90–91, 94–95 event forwarder, 88, 89 hprobe-based detector, 88–89 hprobe kernel agent, 88, 89 performance evaluation, 93–95 Hungarian strategy, 173, 174, 176, 200 Hybrid cloud, 1, 2, 3, 56, 238–239, 244, 245, 247, 250, 253–256, 258, 321 Hypervisor Introspection (HI), 6, 96–103, 315 defenses against, 101–103 introducing noise to VM clocks, 101 randomized monitoring interval, 102–103 scheduler-based defenses, 101–102 evading VMI with, 98–100 limitations, 98 Hypervisor probes See Hprobes Hypervisors, 6, 49–50, 57–79, 82, 83, 84–103, 128, 278, 315, 316 i IBM, 1, 3, 17, 165, 187, 195 IBM Infosphere See Infosphere IBM System S, 165, 186 Illinois Mobile Cloud Manager (IMCM), 238–241, 243–259 Image (image-processing application), 239–240, 247–249, 251–253 IMCM See Illinois Mobile Cloud Manager (IMCM) Inactive storage, 143 Indexed names, overlapping nested, 272, 322 India, 293 Inferring use patterns, 8, 237–272, 321–322 Infiltration See Side-channels Information flow, 8, 237, 240, 321 Information Security and Identity Management Committee (ISIMC), 286 Infosphere, 168 Infrastructure as a service (IaaS) model, 2, 98, 166, 278 Intel, 84, 93–94, 97, 316 Interaction types, 242–243, 265 Interdependency, 50, 57, 58, 78, 82 International Electrotechnical Commission (IEC), 9, 285, 291 International Organization for Standardization (ISO), 9, 280, 285, 291 IronFleet framework, 41 ISO 17799, 291 ISO/IEC 17020, 287 ISO/IEC 17021, 292 ISO/IEC 27001 certification, 9, 277, 285, 286, 291–292, 294, 302–303, 322–323 compared to other standards, 292–293, 296–302 popularity of, 291, 293 ISO/IEC 27002, 291 ISO/IEC 27006, 292 j Japan, 293 Java, 191 Job eviction policies, 163, 167–168, 205–210, 215–217, 220–221 Jockey, 168 k Key encrypting key (KEK), 32–33 BINDEX 07/18/2018 13:13:5 Page 333 Index Key-value storage/databases, 8, 15, 17–18, 21, 41, 44, 146, 152, 161, 162, 168–185, 224, 319, 320 KVM hypervisor, 58, 88–89, 93, 94, 97 l Lamport, Leslie, 37, 40 Lang-A (programming language), 265, 268–270 Latency, 4–5, 7, 10, 17, 19–22, 27, 29, 32, 34, 35, 36, 92–94, 133, 149, 150, 153, 161, 163, 165, 175–177, 180, 184–185, 193, 224, 238–240, 243, 254, 318, 320 Least frequently used (LFU) strategy, 141 Least recently used (LRU) strategy, 138, 141 Least Resources (LR) (job eviction policy), 207, 220–221 LFGraph, 8, 160, 162, 185, 197, 198, 201, 202, 224, 319, 320 Lincoln, President Abraham, 52 LinkedIn, 134, 149–151, 153, 161, 318 Live migration, 164, 165 Load balance, 54–55, 78, 137, 139, 152, 164–165, 172, 174, 188, 198–200, 242, 243 Log-structured file systems (LFS), 151 Longest Remaining Time (LRT) (task eviction policy), 208, 209, 214, 215, 219–220 m Machine learning, 153, 166, 204, 224, 312, 318, 321 MapReduce, 32, 134, 136, 137, 153, 161, 167–168, 204–207, 208, 209, 210 MapReduce clusters, 138–147 Maude, 4, 10, 13–17, 26, 29, 30, 32, 34–35, 37, 39, 40, 42–44, 313 MAUI (mobile cloud system), 241 Maximizing performance In code offloading, 255–256 Maximum Deadline First (MDF) (eviction policy), 215–216, 221–222 Maximum Laxity First (MLF) (eviction policy), 215–216, 221–222 MediSyn streaming media service workload generator, 146, 147, 148 Megastore (from Google), 4, 10, 15–16, 23–30, 42, 313 Megastore-CGC, 29–30, 42, 313 Mesos, 168 MeT, 168 Microbenchmarks, 93–94, 187, 193, 216–221 Microsoft, 136, 139, 153, 295, 318 Microsoft Azure, 152, 168 Microsoft Research, 41 Middleware, 166, 240 Migration, 8, 9, 54, 127, 161–167, 169, 170, 172, 175, 176, 178–179, 180, 187, 191, 195, 196, 198–203, 237–238, 241, 243, 246, 250, 253–254, 257–258, 277, 285, 301, 320–321 timing of, 201–204 Military specifications (mil-spec), 53 Mimesis synthetic workload generator, 148 Minimizing energy consumption In code offloading, 256–257 Mobile clouds, 8, 237–272, 314, 321–322 Model checking, 4, 10, 12–16, 19–20, 22–30, 32, 37–43, 129, 315 Model Predictive Control framework, 166 333 BINDEX 334 07/18/2018 13:13:5 Page 334 Index MongoDB, 8, 160, 161, 162, 164, 169–172, 176, 180, 224, 319 Monitoring techniques, 6, 81–105, 116–118, 128–129, 193–196, 257–259, 271, 314–316, 321 Morphus, 8, 162, 163, 164–165, 169, 170–179, 180, 223, 224, 225, 319–320 Morphus-G, 176 Morphus-H, 176, 179 Moseley, Gen T Michael, 51 Most Resources (MR) (job eviction policy), 207, 208, 217, 220–221 n Nash equilibria, 49, 55, 61–79 mixed, 65–67, 68–72, 76–78 National Center for Supercomputing Applications (NCSA), 6, 104–118, 316 data from security incidents, 107–108 National information infrastructure, 52–53 National Institute of Standards and Technology (NIST), 2, 280, 281, 286, 298, 301 See also NIST Special Publication SP 800-53 National Science Foundation, U.S., 44, 160, 272 Natjam, 8, 162, 163, 167–168, 204–223, 224, 319, 320 Natjam-R, 206, 215–216, 221–223 evaluation of, 221–223 Netty, 167 Network awareness, 175, 178 Network flow scheduling, 164–165 NFS, 146, 151 NIST See National Institute of Standards and Technology (NIST) NIST Special Publication 800-37, 286 NIST Special Publication SP 500-293, 301 NIST Special Publication SP 800-53, 287–288, 298, 300 NoSQL, 8, 18, 161, 162, 166, 168–185, 223, 319, 320 Numerical analysis, 70, 71–78 o Obama Administration, Object request streams, 145, 149, 317–318 Office of Management and Budget (OMB), 285, 286 Offloading of code See Code offloading Offloading of computation See Code offloading Omega, 168 Online reconfigurations, 7–8, 160–225, 319–321 Operating system design patterns, 128, 315 Optimization, 15, 19–20, 30, 37, 39, 54–55, 78, 151–154, 161, 164, 165, 168, 186, 197, 198, 202–203, 205, 215, 254–257, 304, 319, 320 Oracle’s Database, 152 Orchestra, 178 p PACMan, 167 Parameterized protocols, 265 Parqua, 8, 162, 163, 164, 169, 170, 179–185, 224, 225, 319, 320 Path Integral Quantum Monte Carlo (pi-qmc), 92, 94–95 Payment Card Industry (PCI) Security Standard Council, 293 BINDEX 07/18/2018 13:13:5 Page 335 Index PCI Data Security Standard (PCI­ DSS), 293–294 P-D-C-A (Plan, Do, Check, Act) approach, 291–292 Performance, 4–8, 10–17, 22–23, 25–27, 29–30, 34, 37, 39–40, 42–43, 81, 82, 84–87, 90, 92–97, 101–102, 104, 110, 133, 136–137, 141, 143–146, 153–154, 160–225, 243–244, 246, 254–259, 271, 280–281, 312–313, 315–321 Performance assuredness, 7–8, 160–225, 319–321 Performance estimation, 14, 15–16, 22–23, 26–27, 40, 42, 93, 133, 168, 175, 188, 190, 214, 222, 243–244, 313 Petal file system, 152 Petri nets See Queueing Petri nets Piccolo, 168 Pig Latin, 204 Pisces, 168 Platform as a service (PaaS) model, 2, 278 PNUTS, 152 Policy Decision Point (PDP), 250 Policy Enforcement Point (PEP), 250 Policy Manager Machine (PMM), 249–250 Popularity, 6, 134, 137–138, 139–149, 151, 317–318 PowerGraph, 166, 197 Preemption mechanism, 210–211 Pregel (from Google), 197 Priorities, 7–8, 77, 141, 160, 162, 163, 165, 167–168, 190, 204–223, 224, 253–254, 304, 319–320 Privacy for mobile hybrid cloud applications, 247 Private cloud, 1, 2, 3, 238, 239, 240, 244, 245, 246, 248, 249, 252, 254, 256 Probabilistically Weighted on Resources (PR) (job eviction policy), 207–208, 220–221 ProWGen, 147 Public cloud, 1, 2, 3, 49, 52–54, 57, 58, 138, 238, 239, 240, 244, 245, 246, 247, 248, 249, 253, 254, 256, 278 PVeStA tool, 14, 16, 20–22, 32 PyTorch, 321 q Queueing Petri nets, 40 Quincy, 168 r Raft (consensus algorithm), 41, 44 Railways, 52 RAMP, 4, 10, 16, 30–31, 42, 313 Read-Atomic Multi-Partition transactions See RAMP Real-time elasticity See Elasticity Real-Time Maude, 14, 16, 23–24, 26–28, 30, 40, 42 Reconfiguration, 7–8, 160–225, 241–242, 261, 319–321 Relational Cloud, 164 Resource management, 7–8, 55, 160–225, 246, 312, 319–321 RethinkDB, 170 Rewriting logic, 4, 10, 13–15, 32, 37, 42–44 Riak, 180, 200 Ring-based key value stores, 8, 162, 180, 224, 319, 320 Ring-based Vertex Repartitioning (RVR), 198, 200–201, 203–204 Role-based access control (RBAC), 6, 82, 118–128, 316 See also Attribute-based access control (ABAC) attributes needed, 122 case study, 123–128 335 BINDEX 336 07/18/2018 13:13:5 Page 336 Index s Sailfish, 167 SALSA (actor-model-based programming language), 251, 258 Sampling, 92, 95, 133, 138, 188, 271, 322 Samza stream processing solution, 153, 161, 317, 318 SAS 70 See Statement on Auditing Standards (SAS) No 70 Scalability, 1, 2, 5, 6–7, 10, 49, 55, 133–137, 149, 151, 153, 176, 185, 312, 316–319 Scale-out/scale-in, 7, 8, 134, 151, 160, 161, 162, 163, 165–167, 174, 185–204, 218, 224, 319, 320 Scaling, 2, 7, 96, 133–135, 145, 147, 317 See also Scale-out/ scale-in horizontal, 135 vertical, 135 Scarlett, 141 Scribble, 243 Security certifications See Standards Security policy, 4, 237–272 hard (See Authorization systems, Hard policies) soft (See Authorization systems, Soft policies) Security, Trust & Assurance Registry (STAR), 294, 296 SEEP, 165 Service-level agreements/objectives, 7, 54, 136, 160, 161, 162, 168, 197, 206, 224, 319, 320–321 Service Organization Control audits See SOC reports Session delegation, 272, 322 Session types, 238–239, 240, 264–272, 321–322 Sharded cloud databases, 7, 8, 41, 160, 161, 162, 164–165, 169, 170–179, 180, 223, 319 Shard keys, 7, 160, 161, 162, 163, 169–176, 319 Shared computing infrastructures Identification of compromised users in (See Compromised users, Identification of in shared computing infrastructures) Shortest Remaining Time (SRT) (task eviction policy), 208–209, 214, 215, 217, 219–221 ShuttleDB, 164 Side-channels, 6, 55, 90, 97–98, 101, 103, 278, 295, 315 Simple Event Correlator (SEC), 105 Simple Storage System, 37, 42 Situational awareness, 6, 81, 82, 85, 314 SLAs/SLOs See Service-level agreements/objectives Sliding window protocol, 260–261, 265–266, 268 example, 265–266 SOC audits, 9, 285–286, 289–290, 322–323 Social networks, 5, 11, 104, 149, 153, 162, 166, 197, 318, 320 SOC reports, 9, 277, 285–286, 288–290, 303 compared to other standards, 292–293, 295–296, 298–299, 303 Soft cap (in Hadoop), 218, 223 Software as a service (SaaS) model, 2, 278 SPADE, 165 Spanner, 152 Spark, 168 Spark Streaming, 186 SSTable (Sorted String Table), 180, 181–183 BINDEX 07/18/2018 13:13:5 Page 337 Index Standards, 8–9, 124, 277–305, 312, 322–323 definition, 279–281 nongovernmental standards, 281 performance standards, 280–281 purpose of, 281–283 technical standards, 280 voluntary standards, 281 STAR See Security, Trust & Assurance Registry (STAR) Starfish, 166 Statement on Auditing Standards (SAS) No 70, 288–289, 299 Statistical sampling, 271, 322 Stela, 8, 162, 163, 165, 185–188, 190–197, 224, 319, 320 StopWatch, 101 Storage See Cloud storage systems Storm See Apache Storm Stormy, 165 StreamCloud, 165 STream processing ELAsticity See Stela Stream processing systems See Distributed stream processing systems Strong consistency, 5, 10, 15–16, 19–21, 152 Survivability, 3–5, 10–44, 56, 312–313 SWIM, 144, 217 Synchronization between actors See Actors ( ), coordinating Synchronizers, 260–265, 272, 322 capabilities, 262 constraints, 262 scoped, 262–264 security issues in, 260–263 Synthetic workload generation See Workload generation, synthetic Synthetic workloads for big data, 144–147 System-A, 265, 266, 269, 272, 322 System model, 57–59 System S See IBM System S t TAPIR transaction protocol, 40 Task eviction policies, 167, 206–210, 214–221 Temporal locality, 134, 142–144, 145, 147, 317 TensorFlow, 153, 318, 321 ThinkAir, 241, 255 Thrashing, 138, 141, 215 Timestream, 168 TIRAMOLA, 166 TLA+ (specification formalism), 37–42 TLC (model checker), 37, 40, 42 Transactional Auto Scaler, 166 Treacherous Twelve, 296, 299, 301, 305, 323 Trusted Platform Module hardware, 315 Trust Services Principles and Criteria (TSPC), 285, 286, 288–290, 292, 293, 296, 298–303, 322 TSPC See Trust Services Principles and Criteria (TSPC) Tuba, 164 Twitter, 17, 136, 138, 152, 161, 165, 197, 198, 203 u Ubiquitous sensors, 154, 237, 319 Underprivileged users, expanding access to, 53 United Kingdom, 293 University of Illinois at UrbanaChampaign, 15, 136, 272 Use patterns, 8, 237–272, 321–322 v Validation, 4–5, 10–11, 24, 29, 92, 113, 114, 117, 129, 312–313, 315 Verdi framework, 41 Verizon, 279 Virtualization, 6, 84, 96, 243, 302, 315 337 BINDEX 338 07/18/2018 13:13:5 Page 338 Index Virtual machine introspection (VMI) See also Virtual machines (VMs), monitoring, evasion of passive monitoring evading with Hypervisor Introspection, 98–100 transfer of large files without detection by VMI, 99–100 VMI monitor, 96–100, 102, 103, 315 Virtual machines (VMs), 5, 6, 7, 49–79, 82–103, 128, 160–161, 163–164, 166, 179, 185, 186, 238, 241, 254, 278–279, 314–316 monitoring, 84–103, 314–316 (See Hprobes; Hypervisor Introspection (HI)) evasion of passive monitoring, 96–103, 315 hook-based systems, 6, 84–87 Ksplice, 85 Lares, 84, 86 passive vs active monitoring systems, 86 Secure In-VM Monitoring (SIM), 84 VM suspend side-channel, 97–98, 100–102 VMware hypervisor, 58 Voldemort, 180 w Web Services Choreography Description Language, 242 Weighted fair sharing (WFS), 164, 175, 178, 179 Windows Azure Storage See Microsoft Azure Workload generation, synthetic, 144–151, 317–318 Workloads, 6–7, 15, 22, 40, 42, 49, 55, 101–102, 133–154, 161, 163, 166, 168, 169, 176–177, 183, 184–185, 205–206, 208, 216, 221, 223, 224, 246, 312–313, 316–319 World War I, 51 x XACML usage model, 250 Xen, 58, 84, 101 y Yahoo!, 32, 136, 137, 138, 139, 140, 142, 146, 187, 193–195, 196, 205, 206, 223, 317, 318 Yahoo! Cloud Service Benchmark (YCSB), 176, 183, 193 YAML, 191 YCSB See Yahoo! Cloud Service Benchmark (YCSB) YouTube, 149, 318 z Zephyr, 164 ZooKeeper, 4, 10, 16, 31–36, 41, 42, 313 Zynga, 32 ... Management in the Cloud 49 Luke Kwiat, Charles A Kamhoua, Kevin A Kwiat, and Jian Tang 3. 1 3. 2 3. 3 3. 4 3. 5 3. 6 3. 7 3. 8 3. 8.1 3. 8.2 3. 8 .3 3.8.4 3. 8.5 3. 9 Introduction 49 Vision: Using Cloud Technology... 8.2 8 .3 8 .3. 1 8 .3. 2 8 .3. 3 8 .3. 4 8 .3. 5 8 .3. 5.1 8 .3. 5.2 8 .3. 5 .3 8 .3. 6 Introduction 277 What Is a Standard? 279 Standards and Cloud Computing 281 Vision: Using Cloud Technology in Missions 2 83 State... 6 .3. 1.1 6 .3. 1.2 6 .3. 1 .3 6 .3. 2 6 .3. 2.1 6 .3. 2.2 Table of Contents 6 .3. 2 .3 6 .3. 3 Real-Time Elasticity 165 State of the Art: Scale-Out/Scale-In in Distributed Graph Processing Systems 166 6 .3. 3.1