Express YourSelf !: 2017

Elliptic-curve cryptography (ECC)

Elliptic-curve cryptography (ECC)

finding the discrete logarithm of a random elliptic curve element with respect to a publicly known base point is infeasible: this is the "elliptic curve discrete logarithm problem" (ECDLP).

Applicable for

key agreement : elliptic curve Diffie–Hellman (ECDH)
digital signatures : Elliptic Curve Digital Signature Algorithm (ECDSA) FIPS 186-3
pseudo-random generators
Integer Factorization algo. E.g. Lenstra elliptic-curve factorization.
Encryption = key agreement + symmetric key encryption

ECC Key length comparison with RSA Key length.

RSA	ECC
512	112
1024	160
2048	224
3072	256
7680	384
15360	512

Elliptic Curve

Symmetric to X axis
Straight line : intersect with 3 points
y ^ 2 = x ^3 + a * x + b Weierstrass equation.
No self-intersections,
No isolated points
R = P + Q : First, draw the line that intersects P and Q to curve then third point will be -R
If line is tangent then also it interacts with one more curve.
All vertical lines intersects the curve at infinity.
Other algorithms

Elliptic curve cryptography
Elliptic-curve Diffie–Hellman (ECDH) Key agreement
Elliptic Curve Digital Signature Algorithm (ECDCA)
EdDSA : The Edwards-curve Digital Signature Algorithm (EdDSA) is based on Schnorr signatureand uses twisted Edwards curves,
Dual_EC_DRBG
Lenstra elliptic-curve factorization
Elliptic curve primality proving
Supersingular isogeny key exchange

ECC implementation

Module form https://en.wikipedia.org/wiki/Modular_form and Field arithmetic https://en.wikipedia.org/wiki/Field_arithmetic

* Software

ecclib : https://github.com/bluemurder/ecclib
Microsoft MSR ECCLib : https://www.microsoft.com/en-us/download/details.aspx?id=52511
Node.js https://www.npmjs.com/package/eccrypto

* Hardware

Advance Vector Extension : https://en.wikipedia.org/wiki/Advanced_Vector_Extensions

Public Key Cryptography

PKI (Public Key Infrastructure)

CA (Certificate Authority) binds public key with identity. = TTP Trusted Third
Party. E.g. Symantec, Comodo, GoDaddy
OSCP Responder
RA (Registration Authority) = subordinate CA in Microsoft PKI.
VA (Validation Authority)
Central Directory to store index keys
Certificate Management System
Certificate Policy

Method of certification

1. CA
2. Web of Trust. E.g. PGP (Pretty Good Privacy) and GnuPG
3. Simple Public Key Infrastructure (SPKI). Authorization loop : verifier = issuers

Open Source implementation of CA

OpenSSL is the simplest CA and tool to build PKI enabled apps. C. Part of all major Linux distributions,
EJBCA is a full featured, Enterprise grade, CA implementation. Java.
OpenCA is a full featured CA implementation
XCA is a graphical interface, and database.
(Discontinued) TinyCA was a graphical interface for OpenSSL.
XiPKI CA and OCSP responder. With SHA3 support, OSGi-based Java.
IoT_pki is a simple PKI. Python cryptography library

DogTag
gnoMint
EasyRSA, OpenVPN's command line CA utilities using OpenSSL.
r509
Boulder is an automated server that uses the Automated Certificate Management Environment (ACME) protocol.
Windows Server : Active Directory Certificate Services.

Free digital certificate for public by CA

CAcert https://en.wikipedia.org/wiki/CAcert
Let's Encrypt. https://en.wikipedia.org/wiki/Let%27s_Encrypt

Tools

OpenSSL : https://www.feistyduck.com/books/openssl-cookbook/
genrsa
ssh-keygen

Standards

Public Key Cryptography Standards : https://en.wikipedia.org/wiki/PKCS

Cryptographic Message Syntax : https://en.wikipedia.org/wiki/Cryptographic_Message_Syntax and RFC 2315, RFC 2360, RFC 3369

Books

1. Introduction to cryptography and network security

https://www.amazon.in/Introduction-Cryptography-Network-Security-Sunil/dp/819069197X

https://www.snapdeal.com/product/introduction-to-cryptography-network-security/673653962559?supc=SDL628564666

2. Cryptography theory and practice

https://www.amazon.in/Cryptography-Practice-Discrete-Mathematics-Applications-ebook/dp/B006PKWCRI

3. Field Arithmetic

http://www.springer.com/gp/book/9783540772699

https://books.google.co.in/books/about/Field_Arithmetic.html?id=jOr7CAAAQBAJ

https://books.google.co.in/books/about/Field_Arithmetic.html?id=FNkMaZrvpvsC

4. Problems in the Theory of Modular Forms

https://www.amazon.in/Problems-Theory-Modular-Hindustan-Agency/dp/938025072X

http://www.springer.com/in/book/9789811026515

Kubernates - practicals

To get more practical insight about internals of Kubernetes

https://github.com/kelseyhightower/kubernetes-the-hard-way

Learn Kubernetes using Interactive Browser-Based Scenarios

https://www.katacoda.com/courses/kubernetes

Handon with Minikube: single node kubernates cluster

To install Minikube :

https://gist.github.com/osowski/adce22b01fadd6e2bc3331c066d7d612

Then execute command:
minikube start

Now play around with Minicube with kubectl

Overview of kubectl
https://kubernetes.io/docs/reference/kubectl/overview/

ubectl Cheat Sheet
https://kubernetes.io/docs/reference/kubectl/cheatsheet/

istio

istio

Micro-service mesh management framework

It provides a uniform way to connect, manage, and secure microservices. It supports managing traffic flows between microservices, enforcing access policies, and aggregating telemetry data, all without requiring changes to the microservice code.

Benifit
=======

* A/B testing,
* canary releases,
* failure recovery,
* metrics,

Key Capablity

* Traffic Management
* load balancing,
* rate limiting,
* Observability
* monitoring
* Policy Enforcement
* access control,
* load balancing,
* Servie identity and security
* service-to-service authentication,
* discovery of services,
* end-to-end authentication.
* Platform Support
* Cloud,
* on-premise,
* Kubernetes,
* Mesos
* Integration and Customization : integrate with existing solutions for
* ACLs,
* logging,
* monitoring,
* quotas,
* auditing
* etc.

Istio pre-configured addons
==========================

* Grafana : dashboard to visulize service mesh traffic data
* Prometheus : to query istio metrics
* ServiceGraph : generating and visualizing a graph of services within a mesh
* Zipkin : distributed tracing system

Architecture
============

1. Data plane :
set of intelligent proxy (Envoy)
2. Control plane :
manage and configure proxy
to route traffic
to enforce policy runtime.

1. Envoy : sidecar proxy in same pod with features :
dynamic service discovery,
load balancing,
TLS termination,
HTTP & gRPC proxying,
circuit breakers,
health checks,
staged rollouts with %-based traffic split,
fault injection,
rich metrics.

2. Mixer:
platform independant
flexible plugin model
with a variety of host environments and infrastructure backend
Tasks:
enforce access control
enforce usage policies
collect telemetry data from envoy
Mixer configuration for
attribute extractation
policy evaluation

3. Pilot
Tasks:
converts high level routing rules that control traffic behavior into Envoy-specific configurations
propagates Envoy-specific configurations to the sidecars at runtime
abstracts platform-specifc service discovery mechanisms
transalate service discovery to Envoy data plane API
Benefits
service discovery
traffic management
intelligent routing
A/B tests,
canary deployments
resiliency
timeouts,
retries,
circuit breakers,
etc.
multiple environments
Kubernetes,
Consul/Nomad

4. istio-Auth
Authentication using mutua TLS
Built-in identity + credentials management
enforce policy based on service identity

Kubernetes

1. Design
=========

API -> Primitives (Building Blocks) for
1. deploy
2. maintain
3. scale
apps.

1.1 Pod
=======

* Scheduling unit
* Pod = 1+ co-located containers.
* Pod has unique IP within cluster.
* Can be managed by Kubernetes API or controller.

1.2 Labels & Selectors
======================

* Key-Value pair
* attached to pod and node
* grouping mechanism

1.3 Controllers
===============

* Manage a set of podes as per "Labels and Selector"
* reconciliation loop drive cluster state from actual to desirable
E.g
1. Replication controller: to scale up and down
2. Daemonset controller to run 1 pod on 1 machine,
3. Job controller

1.4 Services
============

* set of pods works together, E.g. tier in multi-tier
* set defined by labels & selector.
* service discovery by Kubernetes

2. Architecture
===============

* Master-slave

2.1 C-plane
===========

2.1.1 etcd
==========

* key value data store
* configuration data of cluster
* represent overall state of cluster
* other componenets monitors changed at etcd

2.1.2 API server
================

* JSON over HTTP
* Validate REST request and update API objects's state at etcd
* so client can configure workloads, containers across the worker nodes

2.1.3 Schedular
===============

* plugable
* match resource "supploy" to workload "demands"
* select nod to run pod
* inputs
- resource availability
- resource untilization
- resource requirement
- QoS
- afinity requirements
- anti-afinity requirements
- data locality

2.1.4 contoller manager
=======================
* process to run (1) Daemonset controller (2) Replication controller
* communicate with API server to create, update, delete (1) pod, (2) service end points (3) etc.

2.2 Kubernetes Node
===================

= Worker = Minion
* run container runtime. e.g Docker and below componenets

2.2.1 Kubelet
=============

* hearbeat for health of node.

2.2.2 Kube-proxy
================

* n/w proxy + load balancer
* route to container based on IP + port

2.2.3 cAdvisor
==============

Agent to collect resource usage.

Other alternatives

1. Docker Swarm
2. Apache Meos

Artificial Intelligence / Machine Learning : Videos

Machine Learning online courses

1. Machine Learning

https://www.coursera.org/learn/machine-learning
https://www.youtube.com/playlist?list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN

2. Deep Learning

https://www.coursera.org/specializations/deep-learning

3. Convolutional Neural Network : CS231N

http://cs231n.stanford.edu/

https://www.youtube.com/playlist?list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC

https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv

4. Neural Networks for Machine Learning by Geoffrey Hinton
https://www.coursera.org/learn/neural-networks
https://www.youtube.com/playlist?list=PLoRl3Ht4JOcdU872GhiYWf6jwrk_SNhz9
https://www.youtube.com/playlist?list=PLLssT5z_DsK_gyrQ_biidwvPYCRNGI3iv
http://www.cs.toronto.edu/~hinton/nntut.html
http://www.cs.toronto.edu/~hinton/

5. Deep Leaning for NLP : CS224d
http://cs224d.stanford.edu/
https://www.quora.com/Are-the-video-lectures-of-the-CS224d-Deep-Learning-for-Natural-Language-Processing-Stanford-course-available-online

6. Intro to Machine Learning

https://in.udacity.com/course/intro-to-machine-learning--ud120

7. Distributed Machine Learning with Apache Spark

https://www.edx.org/course/distributed-machine-learning-apache-uc-berkeleyx-cs120x

8. http://www.fast.ai/
https://suriyadeepan.github.io/4ccon/#/10

9. Deep Learning for Self-Driving Cars
https://selfdrivingcars.mit.edu/

Machine Learning

Today, Let me share a list of website related to Machine Learning.This list is created by referring to dhilipsiva@gmail.com 's e-mails to BangML meetup group. Acknowledged.

CycleGAN: Software that generates photos from paintings, turns horses into zebras, performs style transfer, and more (from UC Berkeley): https://github.com/junyanz/CycleGAN
weld: a runtime and language for accelerating data analytics frameworks: https://github.com/weld-project/weld
deform-conv: D eformable Convolution in TensorFlow / Keras: https://github.com/felixlaumon/deform-conv
sact: Spatially Adaptive Computation Time for Residual Networks: https://github.com/mfigurnov/sact
CLR: Cyclical Learning Rate: https://github.com/bckenstler/CLR
seq2seq: A general-purpose encoder-decoder framework for Tensorflow: https://github.com/google/seq2seq
deep-photo-styletransfer: Code and data for paper "Deep Photo Style Transfer": https://github.com/luanfujun/deep-photo-styletransfer
DiscoGAN-pytorch: PyTorch implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks": https://github.com/carpedm20/DiscoGAN-pytorch
evolution-strategies-starter: Starter code for Evolution Strategies: https://github.com/openai/evolution-strategies-starter
AdaIN-style: Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization: https://github.com/xunhuang1995/AdaIN-style
TC-Bot: User Simulation for Task-Completion Dialogues: https://github.com/MiuLab/TC-Bot
e2e-model-learning: Task-based end-to-end model learning: https://github.com/locuslab/e2e-model-learning
csgm: Code to reproduce results from the paper: "Compressed Sensing using Generative Models": https://github.com/AshishBora/csgm
rnnprop: RNNprop: https://github.com/vfleaking/rnnprop
DeepSketchHashing: Deep Sketch Hashing: https://github.com/ymcidence/DeepSketchHashing
tensorflow/models: Learning to Remember Rare Events: https://github.com/tensorflow/models/tree/master/learning_to_remember_rare_events
ORB_SLAM2: Real-Time SLAM for Monocular, Stereo and RGB-D Cameras, with Loop Detection and Relocalization Capabilities: https://github.com/raulmur/ORB_SLAM2
rllab: is a framework for developing and evaluating reinforcement learning algorithms, fully compatible with OpenAI Gym: https://github.com/openai/rllab
voxnet: 3D/Volumetric Convolutional Neural Networks with Theano+Lasagne: https://github.com/dimatura/voxnet
dynamics: A Compositional Object-Based Approach to Learning Physical Dynamics: https://github.com/mbchang/dynamics
CommAI-env - A platform for developing AI systems as described in A Roadmap towards Machine Intelligence: https://github.com/facebookresearch/CommAI-env
rwa - Machine Learning on Sequential Data Using a Recurrent Weighted Average: https://github.com/jostmey/rwa
MazeBase - Simple environment for creating very simple 2D games and training neural network models to perform tasks within them: https://github.com/facebook/MazeBase
ontology - The Audio Set Ontology aims to provide a comprehensive set of categories to describe sound events: https://github.com/audioset/ontology
mv3d - Multi-view 3D Models from Single Images with a Convolutional Network: https://github.com/mtatarchenko/mv3d
universal - Universal adversarial perturbations: https://github.com/LTS4/universal
prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth: https://github.com/facebookincubator/prophet
faiss - A library for efficient similarity search and clustering of dense vectors: https://github.com/facebookresearch/faiss
CausalImpact - An R package for causal inference in time series: https://github.com/google/CausalImpact
GPflow - Gaussian processes in TensorFlow: https://github.com/GPflow/GPflow/
optnet - Differentiable Optimization as a Layer in Neural Networks: https://github.com/locuslab/optnet
RNN-based generative models for speech: https://github.com/sotelo/parrot
An SSBM player based on Deep Reinforcement Learning: https://github.com/vladfi1/phillip
Shake-Shake regularization of 3-branch residual networks: https://github.com/xgastaldi/shake-shake
Benchmarking State-of-the-Art Deep Learning Software Tools: https://github.com/hclhkbu/dlbench
Public repo for helpful scripts when using the YouTube Bounding Boxes dataset: https://github.com/mbuckler/youtube-bb
AirSim - Open source simulator based on Unreal Engine for autonomous vehicles from Microsoft AI & Research: https://github.com/Microsoft/AirSim
dlib - A toolkit for making real world machine learning and data analysis applications in C++: https://github.com/davisking/dlib/
Cyclades - This repository contains code for Cyclades, a general framework for parallelizing stochastic optimization algorithms in a shared memory setting: https://github.com/amplab/cyclades
Deep learning with dynamic computation graphs in TensorFlow: https://github.com/tensorflow/fold
Starter code for working with the YouTube-8M dataset: https://github.com/google/youtube-8m
Vid2speech: Speech Reconstruction from Silent Video: https://github.com/arielephrat/vid2speech
PatchBatch - a Batch Augmented Loss for Optical Flow: https://github.com/DediGadot/PatchBatch
Wasserstein GAN: https://github.com/martinarjovsky/WassersteinGAN
Keras Implementation of Google's Inception-V4 Architecture: https://github.com/kentsommer/keras-inceptionV4
Implementation of Meta-RL A3C algorithm: https://github.com/awjuliani/Meta-RL
DeepPose implementation on TensorFlow: https://github.com/asanakoy/deeppose_tf
Exact Soft Confidence-Weighted Learning: https://github.com/IshitaTakeshi/SoftConfidenceWeighted.jl
DyNet: The Dynamic Neural Network Toolkit: https://github.com/clab/dynet
ENet Training: https://github.com/e-lab/ENet-training
Generic Foreground Segmentation in Images: https://github.com/suyogduttjain/pixelobjectness
Plexus - Interactive Emotion Visualization based on Social Media: https://github.com/xavierwu2016/plexus
Code for Residual and Plain Convolutional Neural Networks for 3D Brain MRI Classification paper: https://github.com/neuro-ml/resnet_cnn_mri_adni
Image Compression with Neural Networks: https://github.com/tensorflow/models/tree/master/compression
A library for probabilistic modeling, inference, and criticism. Deep generative models, variational inference: https://github.com/blei-lab/edward
KOC University deep learning framework: https://github.com/denizyuret/Knet.jl
Domain Transfer Network. Tensorflow implementation of unsupervised cross-domain image generation: https://github.com/yunjey/dtn-tensorflow
Improving Convolutional Networks via Attention Transfer: https://github.com/szagoruyko/attention-transfer
Open-Source Neural Machine Translation in Torch: https://github.com/OpenNMT/OpenNMT
Texture Networks: Feed-forward Synthesis of Textures and Stylized Images: https://github.com/DmitryUlyanov/texture_nets
The Predictron: End-To-End Learning and Planning: https://github.com/zhongwen/predictron
Deluge Networks: https://github.com/xternalz/DelugeNets
Learning from Simulated and Unsupervised Images through Adversarial Training (TensorFlow): https://github.com/carpedm20/simulated-unsupervised-tensorflow
FastMask: Segment Multi-scale Object Candidates in One Shot: https://github.com/voidrank/FastMask
Visual7W visual question answering models: https://github.com/yukezhu/visual7w-qa-models
Language Modeling with Gated Convolutional Networks: https://github.com/DingKe/nn_playground/tree/master/gcnn
Semantic JPEG image compression using deep convolutional neural network (CNN): https://github.com/iamaaditya/image-compression-cnn
TextBoxes: A Fast Text Detector with a Single Deep Neural Network: https://github.com/MhLiao/TextBoxes
Implementation of video captioning from the paper "Temporal Tessellation for Video Annotation and Summarization": https://github.com/dot27/temporal-tessellation
TFLearn - Deep learning library featuring a higher-level API for TensorFlow: http://tflearn.org

C++

Today Let's revise / refresh few aspects of C++ object oriented programming language

A class has implicit members

Default constructor
Copy constructor. It cannot take object as argument. It always takes object reference as argument.
Assignment operator
Default destructor

Now some specific points about constructor

A class can have default constructor and explicit constructor. Explicit constructor force compiler for not to do implicit conversion
However a constructor cannot be virtual

The copy constructor is called in following cases

If function return a object by value
Function argument is object by value
When we construct a object using another object
Many times compiler generates temporary object

Storage class

auto
register. Here '&' operator cannot be used
static. The variable is initialize with value 0
extern The variable is initialize with value 0

Storage qualifier OR type qualifier

volatile
mutable
restrict C99 standard. compiler optimization that avoid multiple loading of same variable.
_Atomic . C99 standard
const . In the context of pointer, const can be used 3 different ways

int const * const x
int const * x
int * const x

As we know for member function of class "this" pointer is passed by default. It is always passed as constant pointer.

The following operator cannot be overloaded

sizeof
"."
".*"
typeid
"::"
"?:"
alignof

Now about type casting of variables

dynamic_cast . Runtime type check. It is used to case

base class pointer to derived class pointer
derive class pointer to another derive class pointer of same base class as parent.

reinterpret_cast : cast to unrelated object
static cast. compile time type check. for related object
const_cast
duration cast from chrono library

C++ has specific features like

Class
Inline function
Default argument
Exception
Namespace
Boolean type

Object Oriented Programming features

Abstraction
Encapsulation (information hiding)
Inheritance
Polymorphism

Something about Inheritance

Virtual inheritance is used to avoid diamond problem
Inheritance applications are (1) code reuse and (2) overriding base class implementaiton
5 types of inheritance

Single
Multiple
Multilevel
Hierarchical
Hybrid

Something about polymorphism

Static polymorphism

Function overloading is also called ad-hoc polymorphism
Templates is also called parametric polymorphism

Dynamic polymorphism

Subtyping is also called inclusion polymorphism
C++ implements virtual table with single dispatch only. No multiple dispatch

We will some more topics about C++ and C programming language. Stay tunes

Service-Oriented Architecture

Service-oriented architecture is a buzzword. It is all about 1. Loose coupling 2. Modular programme 3. connected components using various protocols over IP. SOA is the philosophy of encapsulating application logic in services with a uniformly defined interface and making these publicly available via discovery mechanism. Let's look at some more aspects about SOA

Core Values

More focus on 'business value' compare to 'technical strategy'
So more focus on 'strategic goal' compare to 'project benifits'
The components should have 'intrinsic inter-operability'
More importance to 'shared services' compare to 'implementation for specific purpose'
Flexibility is more important compare to optimisation
Here, 'Evolution refinement' is more important compare to 'initial perfection'

SOA is similar to 1. Modular programming 2. Distributed computing 3. mashups 4. SaaS and 5. Cloud computing. Still it is different. SOA has 11 principals.

1. The service contract should be standardized. That includes functional expressions may be using WSDL. Data model may be using XML schema and 3. Policy document.

2. Reference autonomy standardization : The other components are merely aware about presence of other service component. It fosters the loose coupling

3. Service Location Transparency : Service can be executed, deployed anywhere.

4. Service Longevity : Service should run for ever. It also means backward compatible implementation.

5. Service Abstraction means the service is jut a black box. (1) The functional aspects of service (2) Technical information (3) Logic / Algorithm and (2) Quality of service, all these are hidden.

6. Service autonomy : It means, service should be more predictable, reliable and less depends on external environment. Run time service autonomy achieved by dedicated CPU and memory so external environment has lesser impact. Design time service autonomy means, service can be evolve and re-design without impacting consumers. More applicable to entity service and utility service.

7. Service statelessness: The service is implemented as stateless. However it can take support for other system to maintain state. Service may need to maintain state about (1) Context data (2) Business data and (3) Session data.

8. Service granularity define the scope of service. It impacts performance and message size for the service.

9. Service normalization is trade off between redundancy and performance

10. Service composability A service can play two role here (1) Service controller and (2) Service member

11. Service discovery : Using service discovery protocol

12. Service re-usability : The service, which is part of multiple business processes is more reusable. The service with extra functionality will be reusable in future.

13. Service encapsulation: The service not part of SOA, initially.

Each SOA building block plays any of the three roles

1. Service provider

2. Service broker = service registry = service repository

3. Service consumer = service requester

Implementation

SOA can be implemented in multiple ways

1. SOA can be implemented using any service based technology. It can be web services using SOAP+WSDL or RESTful protocol. It can be using (1) Jini and JBI (2) CORBA (3) REST (4) WCF

2. Messaging technology can be used (1) ActiveMQ (2) JMS (3) RabbitMQ

3. OPC-UA is a M2M protocol. It can be web service oriented. It can be binary protocol over TCP/IP. It can by hybrid approach of binary protocol over SOAP.

4. Apache Thrift is a RPC based framework by FaceBook. It provides IDL and binary protocol.

5. SORCER is Java based distributed computing platform, for service oriented computing environment.

6. The programming language like C# .Net, Java, BPEL also has support for SOA

7. WS-CDL and WS-Coordination Specifications extend SOA further for workflows and business process.

Service Oriented Modelling

In SOA framework, Service Oriented Modelling identifies various disciplines to conceptualize, analyze, design, and architect service-oriented assets. Here three approaches are identified

1. SDDM: Service-oriented design and development methodology

2. SOMA: Service-oriented modeling and architecture

3. SOMF: Service-oriented modeling framework. It is about what to do.

Extension and variants

SOA can be further extended with various variants

1. SOA 2.0 is combination of SOA and EDA. Event Driven Architecture. It has three components. (1) Event emitters (agents) (2) Event consumers (sinks OR event processing engine) (3) Event channel. It can be (3.1) message oriented middleware or (2) TCP/IP link (3) File: XML, flat file, or even e-mail.

2. Web 2.0 has same philosophy as SOA, but it serves different user needs.

3. Microservices : (1) Fine grained services may be a process of OSGi Bundle (2) Light weight protocol may be HTTP or shared memory. Results in continuous software development process. Jolie programming language allows to think and program in terms of microservices. The complexity moves to network as (1) network latency (2) message format (3) load balancing and (4) fault tolerance

I hope, the readers like this overview of SOA.

Life is an exercise to express the InExpressible.

Express YourSelf !

Elliptic-curve cryptography (ECC)

Public Key Cryptography

Kubernates - practicals

istio

Kubernetes

Artificial Intelligence / Machine Learning : Videos

Machine Learning online courses

Machine Learning

C++

Service-Oriented Architecture

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