Cnmaestro On-Premises User Guide 3.0.3

Total Page:16

File Type:pdf, Size:1020Kb

Cnmaestro On-Premises User Guide 3.0.3 USER GUIDE cnMaestro On-Premises Accuracy While reasonable efforts have been made to assure the accuracy of this document, Cambium Networks assumes no liability resulting from any inaccuracies or omissions in this document, or from use of the information obtained herein. Cambium reserves the right to make changes to any products described herein to improve reliability, function, or design, and reserves the right to revise this document and to make changes from time to time in content hereof with no obligation to notify any person of revisions or changes. Cambium does not assume any liability arising out of the application or use of any product, software, or circuit described herein; neither does it convey license under its patent rights or the rights of others. It is possible that this publication may contain references to, or information about Cambium products (machines and programs), programming, or services that are not announced in your country. Such references or information must not be construed to mean that Cambium intends to announce such Cambium products, programming, or services in your country. Copyrights This document, Cambium products, and 3rd Party software products described in this document may include or describe copyrighted Cambium and other 3rd Party supplied computer programs stored in semiconductor memories or other media. Laws in the United States and other countries preserve for Cambium, its licensors, and other 3rd Party supplied software certain exclusive rights for copyrighted material, including the exclusive right to copy, reproduce in any form, distribute and make derivative works of the copyrighted material. Accordingly, any copyrighted material of Cambium, its licensors, or the 3rd Party software supplied material contained in the Cambium products described in this document may not be copied, reproduced, reverse engineered, distributed, merged or modified in any manner without the express written permission of Cambium. Furthermore, the purchase of Cambium products shall not be deemed to grant either directly or by implication, estoppel, or otherwise, any license under the copyrights, patents or patent applications of Cambium or other 3rd Party supplied software, except for the normal non-exclusive, royalty free license to use that arises by operation of law in the sale of a product. Restrictions Software and documentation are copyrighted materials. Making unauthorized copies is prohibited by law. No part of the software or documentation may be reproduced, transmitted, transcribed, stored in a retrieval system, or translated into any language or computer language, in any form or by any means, without prior written permission of Cambium. License Agreements The software described in this document is the property of Cambium and its licensors. It is furnished by express license agreement only and may be used only in accordance with the terms of such an agreement. High Risk Materials Cambium and its supplier(s) specifically disclaim any express or implied warranty of fitness for any high-risk activities or uses of its products including, but not limited to, the operation of nuclear facilities, aircraft navigation or aircraft communication systems, air traffic control, life support, or weapons systems (“High Risk Use”). This product is not restricted in the EU. Any High Risk is unauthorized, is made at your own risk and you shall be responsible for any and all losses, damage or claims arising out of any High-Risk Use. © 2021 Cambium Networks Limited. All Rights Reserved Contents Contents 3 Introduction 13 Overview 13 Supported Browsers 15 Supported Virtualization Infrastructures 16 Device Software 17 Software Download 20 Differences with cnMaestro Cloud 20 Quick Start 22 Installation 22 Virtualization 22 Desktop Virtualization 22 Bare Metal Hypervisor 22 cnMaestro Deployment 22 Device Software 32 PMP Configuration Prerequisites 39 DHCP Options (Linux) 40 UI Navigation 44 Basic 44 Account View 44 Access and Backhaul Account 45 Enterprise Account 45 Industrial Internet Account 46 Home Page 46 Page Structure 46 Page Navigation 47 Menu 47 Header 47 Access and Backhaul Account 47 Overview 47 Device Tree Navigation 47 Enterprise Account 53 Overview 53 System 53 Devices 53 AP Groups and WLANs 54 Sites 54 Side Menu 55 Section Tabs 55 System Status 56 Logout 56 Architecture 58 Overview 58 Networking 58 Device Onboarding 60 Overview 60 60 GHz cnWave Onboarding 60 Pre-Configuration and Approval of Devices (Optional) 64 Device/Agent Authentication (Optional) 64 Claiming the Wi-Fi Devices from AP Group 65 Claiming the Wi-Fi Devices from Site Dashboard 66 High Availability (HA) 68 Overview 68 Primary vs Secondary 68 Shared (Floating) IP Address 68 Network Ports 68 Recommendations 69 Dual Interfaces 69 Add eth1 Network Adapter 69 HA Cluster Setup 71 Bootstrap (Primary) 71 Accept (Primary) 71 Join (Secondary) 71 Basic HA Cluster Creation Flow 72 Secondary Server 74 HA Menus 75 High Availability Cluster Menu (pre-Bootstrap) 75 High Availability Menu (post-Bootstrap) 75 New Cluster 76 Accept Join Requests 76 Join Existing Cluster 77 Validate SSH Fingerprints 77 HA Cluster Status 77 Delete Node 79 Leave Cluster 80 Information 80 Behaviour of cnMaestro features When HA is Enabled 80 Monitoring 82 Network Monitoring 82 Dashboard 82 KPI (Key Performance Indicators) 82 Device Health 83 Connection Health 83 Charts and Graphs 84 Notifications 84 Overview 84 Events 85 Alarms 87 Statistics and Details 88 Performance 96 Maps 108 Map Navigation 109 Mode 110 Tools 112 60 GHz cnWave Tools 112 cnMatrix Tools 112 cnPilot Home Tools 114 cnRanger Tools 115 cnReach Tools 116 cnVision Tools 117 Enterprise Wi-Fi Tools 119 ePMP Tools 122 Machfu 125 PMP Tools 126 Tower-to-Edge View 128 WIDS 129 Detecting Rogue APs 129 cnPilot Dashboards 133 Device Dashboard 133 Overview 133 Clients 133 Network Info 136 Mesh Peers 138 Neighbors 138 Site Dashboard 139 Wi-Fi Devices Availability (Total and Offline) 139 Wireless 140 Throughput 140 RF Quality 140 AP Types 140 Top Wi-Fi APs 140 Channel Distribution by Band 141 Radio/WLAN Distribution by Band 141 Clients by SNR 141 Clients by Performance 142 Clients Graph 142 Throughput Graph 142 Statistics 143 Wireless Clients 143 Floor Plan 144 Inventory 145 Inventory Export 145 Bulk Delete 145 Bulk Reboot 146 Schedule Reboot 147 CSV Configuration Import 147 Sample Configuration File 148 Sample Configuration File (60 GHz cnWave) 148 Uploading a Configuration File 149 Reports 152 Generating Reports 152 Device Report 152 Performance Report 157 Active Alarms Report 162 Alarms History Report 162 Events Report 163 Clients Report 163 Mesh Peers Report 164 Remote Upload 165 Report Jobs 166 Provisioning 167 Software Update 167 Software Update Overview 167 Create Software Update Job 168 Software Update 170 Viewing Running Jobs in Header 174 cnReach Bulk Software Upgrade 174 Fixed Wireless Configuration 177 Overview 177 Configuration Templates 177 Configuration Variables 178 Macros 178 Variable Caching 179 Device Type-Specific Configurations 179 Variable Validation 179 Sample Templates 179 Template File Creation 179 Template 179 Configuration Update 181 Device Selection 181 Device Type 181 Device Table 181 Configuration Update Steps 183 Configuration Backup 183 Jobs 187 Configuration Update 187 Wireless LAN Configuration 188 cnPilot Home and Enterprise Wi-Fi 188 Configure cnPilot using cnMaestro 188 Create an AP Group 194 Pre-Defined Overrides 199 User-Defined Overrides (Advanced) 199 User-Defined Variables (Advanced) 199 Synchronize (Sync) Configuration 200 Configuration Job Status 201 Factory Reset 201 Association ACL 203 Overview 203 Configuring Association ACL 203 cnMatrix Switches 204 Switch Groups Configuration 204 Synchronize (Sync) Configuration 210 Policy Based Automation(PBA) 211 Switches 214 Switch Ports 219 Device Details 226 60 GHz cnWave Network Configuration 230 Managing E2E Network 230 Site Configuration 260 Node Configuration 263 PoP Node 271 DN/CN Node 287 Auto-Provisioning 295 Creating Auto-Provisioning Rule 295 Services 297 Managed Service Provider (MSP) 297 Overview 297 Managed Accounts 297 Managed Service 298 Managed Service Provider (MSP) 299 Managed Service Users (Administrators) 300 Configuring Managed Services 301 Enable Managed Service Provider (MSP) 302 Create Managed Services 303 Create Managed Account 305 Validate Managed Account Administrators 306 Managed Services Administration 308 Overview 308 System Dashboard 309 Managed Account Administration 310 Device Management 311 Disabling Managed Service Provider Feature 313 API Client 314 Overview 314 API Clients 314 RESTful API Specification 315 Authentication 315 Swagger API 316 Introduction 316 Sample Swagger UI Screenshot 317 Client ID and Client Secret Generation 317 cnMaestro User Interface 317 API Session 318 Introduction 318 Retrieve Access Token 318 Access Resources 320 API Details 320 HTTP Protocol 320 REST Protocol 321 Parameters 323 Access API 328 Token (basic request) 328 Token (alternate request) 329 Validate Token 330 Selected APIs 331 Overview 331 cnMaestro v2 API 331 Devices API Response (v2 Format) 332 Statistics API Response (v2 Format) 334 Performance API Response (v2 Format) 346 cnPilot Guest Access 354 Configuration 354 Create the Guest Access Portal in cnMaestro 354 Mapping the Device to Guest Access Portal in cnMaestro 364 Access Types 366 Guest Access using Social Login 366 SMS Authentication 377 Generic SMS Gateway Configuration 377 cnPilot GRE Tunnels 384 Overview 384 Typical Deployment Model (Two Port Solution) 384
Recommended publications
  • Full Functional Verification of Linked Data Structures
    Full Functional Verification of Linked Data Structures Karen Zee Viktor Kuncak Martin C. Rinard MIT CSAIL, Cambridge, MA, USA EPFL, I&C, Lausanne, Switzerland MIT CSAIL, Cambridge, MA, USA ∗ [email protected] viktor.kuncak@epfl.ch [email protected] Abstract 1. Introduction We present the first verification of full functional correctness for Linked data structures such as lists, trees, graphs, and hash tables a range of linked data structure implementations, including muta- are pervasive in modern software systems. But because of phenom- ble lists, trees, graphs, and hash tables. Specifically, we present the ena such as aliasing and indirection, it has been a challenge to de- use of the Jahob verification system to verify formal specifications, velop automated reasoning systems that are capable of proving im- written in classical higher-order logic, that completely capture the portant correctness properties of such data structures. desired behavior of the Java data structure implementations (with the exception of properties involving execution time and/or mem- 1.1 Background ory consumption). Given that the desired correctness properties in- In principle, standard specification and verification approaches clude intractable constructs such as quantifiers, transitive closure, should work for linked data structure implementations. But in prac- and lambda abstraction, it is a challenge to successfully prove the tice, many of the desired correctness properties involve logical generated verification conditions. constructs such transitive closure and
    [Show full text]
  • CSI33 Data Structures
    Outline CSI33 Data Structures Department of Mathematics and Computer Science Bronx Community College December 2, 2015 CSI33 Data Structures Outline Outline 1 Chapter 13: Heaps, Balances Trees and Hash Tables Hash Tables CSI33 Data Structures Chapter 13: Heaps, Balances Trees and Hash TablesHash Tables Outline 1 Chapter 13: Heaps, Balances Trees and Hash Tables Hash Tables CSI33 Data Structures Chapter 13: Heaps, Balances Trees and Hash TablesHash Tables Python Dictionaries Various names are given to the abstract data type we know as a dictionary in Python: Hash (The languages Perl and Ruby use this terminology; implementation is a hash table). Map (Microsoft Foundation Classes C/C++ Library; because it maps keys to values). Dictionary (Python, Smalltalk; lets you "look up" a value for a key). Association List (LISP|everything in LISP is a list, but this type is implemented by hash table). Associative Array (This is the technical name for such a structure because it looks like an array whose 'indexes' in square brackets are key values). CSI33 Data Structures Chapter 13: Heaps, Balances Trees and Hash TablesHash Tables Python Dictionaries In Python, a dictionary associates a value (item of data) with a unique key (to identify and access the data). The implementation strategy is the same in any language that uses associative arrays. Representing the relation between key and value is a hash table. CSI33 Data Structures Chapter 13: Heaps, Balances Trees and Hash TablesHash Tables Python Dictionaries The Python implementation uses a hash table since it has the most efficient performance for insertion, deletion, and lookup operations. The running times of all these operations are better than any we have seen so far.
    [Show full text]
  • CIS 120 Midterm I October 13, 2017 Name (Printed): Pennkey (Penn
    CIS 120 Midterm I October 13, 2017 Name (printed): PennKey (penn login id): My signature below certifies that I have complied with the University of Pennsylvania’s Code of Academic Integrity in completing this examination. Signature: Date: • Do not begin the exam until you are told it is time to do so. • Make sure that your username (a.k.a. PennKey, e.g. stevez) is written clearly at the bottom of every page. • There are 100 total points. The exam lasts 50 minutes. Do not spend too much time on any one question. • Be sure to recheck all of your answers. • The last page of the exam can be used as scratch space. By default, we will ignore anything you write on this page. If you write something that you want us to grade, make sure you mark it clearly as an answer to a problem and write a clear note on the page with that problem telling us to look at the scratch page. • Good luck! 1 1. Binary Search Trees (16 points total) This problem concerns buggy implementations of the insert and delete functions for bi- nary search trees, the correct versions of which are shown in Appendix A. First: At most one of the lines of code contains a compile-time (i.e. typechecking) error. If there is a compile-time error, explain what the error is and one way to fix it. If there is no compile-time error, say “No Error”. Second: even after the compile-time error (if any) is fixed, the code is still buggy—for some inputs the function works correctly and produces the correct BST, and for other inputs, the function produces an incorrect tree.
    [Show full text]
  • Verifying Linked Data Structure Implementations
    Verifying Linked Data Structure Implementations Karen Zee Viktor Kuncak Martin Rinard MIT CSAIL EPFL I&C MIT CSAIL Cambridge, MA Lausanne, Switzerland Cambridge, MA [email protected] viktor.kuncak@epfl.ch [email protected] Abstract equivalent conjunction of properties. By processing each conjunct separately, Jahob can use different provers to es- The Jahob program verification system leverages state of tablish different parts of the proof obligations. This is the art automated theorem provers, shape analysis, and de- possible thanks to formula approximation techniques[10] cision procedures to check that programs conform to their that create equivalent or semantically stronger formulas ac- specifications. By combining a rich specification language cepted by the specialized decision procedures. with a diverse collection of verification technologies, Jahob This paper presents our experience using the Jahob sys- makes it possible to verify complex properties of programs tem to obtain full correctness proofs for a collection of that manipulate linked data structures. We present our re- linked data structure implementations. Unlike systems that sults using Jahob to achieve full functional verification of a are designed to verify partial correctness properties, Jahob collection of linked data structures. verifies the full functional correctness of the data structure implementation. Not only do our results show that such full functional verification is feasible, the verified data struc- 1 Introduction tures and algorithms provide concrete examples that can help developers better understand how to achieve similar results in future efforts. Linked data structures such as lists, trees, graphs, and hash tables are pervasive in modern software systems. But because of phenomena such as aliasing and indirection, it 2 Example has been a challenge to develop automated reasoning sys- tems that are capable of proving important correctness prop- In this section we use a verified association list to demon- erties of such data structures.
    [Show full text]
  • CSCI 2041: Data Types in Ocaml
    CSCI 2041: Data Types in OCaml Chris Kauffman Last Updated: Thu Oct 18 22:43:05 CDT 2018 1 Logistics Assignment 3 multimanager Reading I Manage multiple lists I OCaml System Manual: I Records to track lists/undo Ch 1.4, 1.5, 1.7 I option to deal with editing I Practical OCaml: Ch 5 I Higher-order funcs for easy bulk operations Goals I Post tomorrow I Tuples I Due in 2 weeks I Records I Algebraic / Variant Types Next week First-class / Higher Order Functions 2 Overview of Aggregate Data Structures / Types in OCaml I Despite being an older functional language, OCaml has a wealth of aggregate data types I The table below describe some of these with some characteristics I We have discussed Lists and Arrays at some length I We will now discuss the others Elements Typical Access Mutable Example Lists Homoegenous Index/PatMatch No [1;2;3] Array Homoegenous Index Yes [|1;2;3|] Tuples Heterogeneous PatMatch No (1,"two",3.0) Records Heterogeneous Field/PatMatch No/Yes {name="Sam"; age=21} Variant Not Applicable PatMatch No type letter = A | B | C; Note: data types can be nested and combined in any way I Array of Lists, List of Tuples I Record with list and tuple fields I Tuple of list and Record I Variant with List and Record or Array and Tuple 3 Tuples # let int_pair = (1,2);; val int_pair : int * int = (1, 2) I Potentially mixed data # let mixed_triple = (1,"two",3.0);; I Commas separate elements val mixed_triple : int * string * float = (1, "two", 3.) I Tuples: pairs, triples, quadruples, quintuples, etc.
    [Show full text]
  • Alexandria Manual
    alexandria Manual draft version Alexandria software and associated documentation are in the public domain: Authors dedicate this work to public domain, for the benefit of the public at large and to the detriment of the authors' heirs and successors. Authors intends this dedication to be an overt act of relinquishment in perpetuity of all present and future rights under copyright law, whether vested or contingent, in the work. Authors understands that such relinquishment of all rights includes the relinquishment of all rights to enforce (by lawsuit or otherwise) those copyrights in the work. Authors recognize that, once placed in the public domain, the work may be freely reproduced, distributed, transmitted, used, modified, built upon, or oth- erwise exploited by anyone for any purpose, commercial or non-commercial, and in any way, including by methods that have not yet been invented or conceived. In those legislations where public domain dedications are not recognized or possible, Alexan- dria is distributed under the following terms and conditions: Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTIC- ULAR PURPOSE AND NONINFRINGEMENT.
    [Show full text]
  • TAL Recognition in O(M(N2)) Time
    TAL Recognition in O(M(n2)) Time Sanguthevar Rajasekaran Dept. of CISE, Univ. of Florida raj~cis.ufl.edu Shibu Yooseph Dept. of CIS, Univ. of Pennsylvania [email protected] Abstract time needed to multiply two k x k boolean matri- ces. The best known value for M(k) is O(n 2"3vs) We propose an O(M(n2)) time algorithm (Coppersmith, Winograd, 1990). Though our algo- for the recognition of Tree Adjoining Lan- rithm is similar in flavor to those of Graham, Har- guages (TALs), where n is the size of the rison, & Ruzzo (1976), and Valiant (1975) (which input string and M(k) is the time needed were Mgorithms proposed for recognition of Con- to multiply two k x k boolean matrices. text Pree Languages (CFLs)), there are crucial dif- Tree Adjoining Grammars (TAGs) are for- ferences. As such, the techniques of (Graham, et al., malisms suitable for natural language pro- 1976) and (Valiant, 1975) do not seem to extend to cessing and have received enormous atten- TALs (Satta, 1993). tion in the past among not only natural language processing researchers but also al- gorithms designers. The first polynomial 2 Tree Adjoining Grammars time algorithm for TAL parsing was pro- A Tree Adjoining Grammar (TAG) consists of a posed in 1986 and had a run time of O(n6). quintuple (N, ~ U {~}, I, A, S), where Quite recently, an O(n 3 M(n)) algorithm N is a finite set of has been proposed. The algorithm pre- nonterminal symbols, sented in this paper improves the run time is a finite set of terminal symbols disjoint from of the recent result using an entirely differ- N, ent approach.
    [Show full text]
  • Appendix a Answers to Some of the Exercises
    Appendix A Answers to Some of the Exercises Answers for Chapter 2 2. Let f (x) be the number of a 's minus the number of b 's in string x. A string is in the language defined by the grammar just when • f(x) = 1 and • for every proper prefix u of x, f( u) < 1. 3. Let the string be given as array X [O .. N -1] oflength N. We use the notation X ! i, (pronounced: X drop i) for 0:::; i :::; N, to denote the tail of length N - i of array X, that is, the sequence from index ion. (Or: X from which the first i elements have been dropped.) We write L for the language denoted by the grammar. Lk is the language consisting of the catenation of k strings from L. We are asked to write a program for computing the boolean value X E L. This may be written as X ! 0 E L1. The invariant that we choose is obtained by generalizing constants 0 and 1 to variables i and k: P : (X E L = X ! i ELk) /\ 0:::; i :::; N /\ 0:::; k We use the following properties: (0) if Xli] = a, i < N, k > 0 then X ! i E Lk = X ! i + 1 E Lk-1 (1) if Xli] = b, i < N, k ~ 0 then X ! i E Lk = X ! i + 1 E Lk+1 (2) X ! N E Lk = (k = 0) because X ! N = 10 and 10 E Lk for k = 0 only (3) X ! i E LO = (i = N) because (X ! i = 10) = (i = N) and LO = {€} The program is {N ~ O} k := Ij i:= OJ {P} *[ k =1= 0/\ i =1= N -+ [ Xli] = a -+ k := k - 1 404 Appendix A.
    [Show full text]
  • Specification Patterns and Proofs for Recursion Through the Store
    Specification patterns and proofs for recursion through the store Nathaniel Charlton and Bernhard Reus University of Sussex, Brighton Abstract. Higher-order store means that code can be stored on the mutable heap that programs manipulate, and is the basis of flexible soft- ware that can be changed or re-configured at runtime. Specifying such programs is challenging because of recursion through the store, where new (mutual) recursions between code are set up on the fly. This paper presents a series of formal specification patterns that capture increas- ingly complex uses of recursion through the store. To express the neces- sary specifications we extend the separation logic for higher-order store given by Schwinghammer et al. (CSL, 2009), adding parameter passing, and certain recursively defined families of assertions. Finally, we apply our specification patterns and rules to an example program that exploits many of the possibilities offered by higher-order store; this is the first larger case study conducted with logical techniques based on work by Schwinghammer et al. (CSL, 2009), and shows that they are practical. 1 Introduction and motivation Popular \classic" languages like ML, Java and C provide facilities for manipulat- ing code stored on the heap at runtime. With ML one can store newly generated function values in heap cells; with Java one can load new classes at runtime and create objects of those classes on the heap. Even for C, where the code of the program is usually assumed to be immutable, programs can dynamically load and unload libraries at runtime, and use function pointers to invoke their code.
    [Show full text]
  • Modification and Objects Like Myself
    JOURNAL OF OBJECT TECHNOLOGY Online at http://www.jot.fm. Published by ETH Zurich, Chair of Software Engineering ©JOT, 2004 Vol. 3, No. 8, September-October 2004 The Theory of Classification Part 14: Modification and Objects like Myself Anthony J H Simons, Department of Computer Science, University of Sheffield, U.K. 1 INTRODUCTION This is the fourteenth article in a regular series on object-oriented theory for non- specialists. Previous articles have built up models of objects [1], types [2] and classes [3] in the λ-calculus. Inheritance has been shown to extend both types [4] and implementations [5, 6], in the contrasting styles found in the two main families of object- oriented languages. One group is based on (first-order) types and subtyping, and includes Java and C++, whereas the other is based on (second-order) classes and subclassing, and includes Smalltalk and Eiffel. The most recent article demonstrated how generic types (templates) and generic classes can be added to the Theory of Classification [7], using various Stack and List container-types as examples. The last article concentrated just on the typeful aspects of generic classes, avoiding the tricky issue of their implementations, even though previous articles have demonstrated how to model both types and implementations separately [4, 5] and in combination [6]. This is because many of the operations of a Stack or a List return modified objects “like themselves”; and it turns out that this is one of the more difficult things to model in the Theory of Classification. The attentive reader will have noticed that we have so far avoided the whole issue of object modification.
    [Show full text]
  • Automatically Verified Implementation of Data Structures Based on AVL Trees Martin Clochard
    Automatically verified implementation of data structures based on AVL trees Martin Clochard To cite this version: Martin Clochard. Automatically verified implementation of data structures based on AVL trees. 6th Working Conference on Verified Software: Theories, Tools and Experiments (VSTTE), Jul 2014, Vienna, Austria. hal-01067217 HAL Id: hal-01067217 https://hal.inria.fr/hal-01067217 Submitted on 23 Sep 2014 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Automatically verified implementation of data structures based on AVL trees Martin Clochard1,2 1 Ecole Normale Sup´erieure, Paris, F-75005 2 Univ. Paris-Sud & INRIA Saclay & LRI-CNRS, Orsay, F-91405 Abstract. We propose verified implementations of several data struc- tures, including random-access lists and ordered maps. They are derived from a common parametric implementation of self-balancing binary trees in the style of Adelson-Velskii and Landis trees. The development of the specifications, implementations and proofs is carried out using the Why3 environment. The originality of this approach is the genericity of the specifications and code combined with a high level of proof automation. 1 Introduction Formal specification and verification of the functional behavior of complex data structures like collections of elements is known to be challenging [1,2].
    [Show full text]
  • An English-Persian Dictionary of Algorithms and Data Structures
    An EnglishPersian Dictionary of Algorithms and Data Structures a a zarrabibasuacir ghodsisharifedu algorithm B A all pairs shortest path absolute p erformance guarantee b alphab et abstract data typ e alternating path accepting state alternating Turing machine d Ackermans function alternation d Ackermanns function amortized cost acyclic digraph amortized worst case acyclic directed graph ancestor d acyclic graph and adaptive heap sort ANSI b adaptivekdtree antichain adaptive sort antisymmetric addresscalculation sort approximation algorithm adjacencylist representation arc adjacencymatrix representation array array index adjacency list array merging adjacency matrix articulation p oint d adjacent articulation vertex d b admissible vertex assignmentproblem adversary asso ciation list algorithm asso ciative binary function asso ciative array binary GCD algorithm asymptotic b ound binary insertion sort asymptotic lower b ound binary priority queue asymptotic space complexity binary relation binary search asymptotic time complexity binary trie asymptotic upp er b ound c bingo sort asymptotically equal to bipartite graph asymptotically tightbound bipartite matching
    [Show full text]