Lecture 1

Roadmap

• Introduction

• Parallel and Distributed Computing Landscape • Cloud vs. Grid • Cloud Computing

– Possibilities – Some Characteristics of Cloud Computing – SaaS and Cloud Computing – Supercomputing & Cloud Computing

• Clouds Examples • Conclusions • References

Introduction

• The landscape of parallel and distributed computing has significantly evolved over the last sixty years.

• It is forecast that between 20-50 billion devices will be added to the internet by 2020

• 43 trillion gigabytes of data will be generated and will need to be processed in cloud data centers.

• How is the journey so far?

1: http://www.gartner.com/newsroom/id/3165317 2: http://spectrum.ieee.org/tech-talk/telecom/internet/ popular-internet-of-things-forecast-of-50-billion-devices-by-2020-is-outdated

Data Canters • In an increasingly data-driven world, solutions that

provide real-time information benefit us all, from the data center, its customers, and ultimately the consumers at the end of the chain.

(1) Drinking Water Distribution Systems

Seamless Electricity

delivery as a Utility to Users

kero

sene

lam

p o

r ca

nd

les

(2) Electricity Distribution Systems

(3) Computing Resource Distribution Systems

Water Distribution Network

Seamlessly delivering Water as a

Utility to Users

UNIX OS Web Tier

Windows OS Web Tier

IDS

Seamless computing delivery as a Utility 24/7 from any where

Computing Trends

Internet

Conventional Computing

19

60

s

Year

Parallel Computing & Distributed Computing -- help solve a single large problem by breaking it down into several tasks where each task is computed in the individual processor of the distributed system.

Mainframe

• Used primarily by large organizations where you could submit your jobs to and have it return the result,

• Common use includes bulk data processing (e.g, census, enterprise resource planning; and transaction processing).

Distributed Computing

Network

CPU CPU CPU CPU

Mem Mem Mem Mem

Shared Memory

CPU CPU CPU CPU

Parallel Computing

Computing Trends 1

97

0s

Distributed memory

Year

19

80

s

• Clients are diskless and all file, print, http and even cycle computation requests are sent to servers.

• Server are minicomputers dedicated to one or more different types of services.

• Communication through RPC (Remote Procedure Call)/ RMI (Remote Method Invocation)

• No process migration invoked

LAN

(R

PC

/RM

I )

Server Diskless clients

Client-Server

Cluster Computing

Master Node 1Gbps

SAN

100Gbps LAN

Clu

ster

Client

• Tightly coupled distributed system

• A cluster consists of a master node and several slave nodes connected to a high-speed network.

• Provide high performance/high throughput (Harnessing many idle resources) where requests are served in parallel.

• Condor was notable example

20

00

Computing Trends

Bahman Javadi, Jemal H. Abawajy, Mohammad K. Akbari: Analytical modeling of interconnection networks in heterogeneous multi-cluster systems. The Journal of Supercomputing 40(1): 29-47 (2007)

Year

20

00

s

Grid Computing

Cluster Computing (Bengaluru)

Master Node 1Gbps

SAN

100Gbps LAN

Clu

ster

Client

Slaves Clients Master

Cluster Computing (Kolkata)

Supercomputer (New Delhi)

Grid Computing

• Grid computing consists of loosely coupled supercomputers and clusters sparsely located over different administrative domains.

• Suitable for very large problems needing lots of CPU, memory, etc.

• Requires distributed resource management & scheduling

Computing Trends

Z. Pooranian, M. Shojafar, J. H. Abawajy, A. Abraham, "An Efficient Meta-heuristic Algorithm for Grid Computing", Springer, Journal of Combinatorial Optimization (JOCO), ISSN: 1382-6905, Impact Factor: 0.939, Vol. 30, Iss. 3, pp. 413-434, October 2015

Data Grids

Compute Site

Instrument

Storage Facility

Storage Facility

Compute Site

Compute Site Scientist

Instrument

Scientist

Storage Facility

Storage Facility

Storage Facility

Information & Discovery

Compute Site

Jemal H. Abawajy, Mustafa Mat Deris: Data Replication Approach with Consistency Guarantee for Data Grid. IEEE Trans. Computers 63(12): 2975-2987 (2014)

Cloud Computing Motivation

TIME

IT C

APA

CIT

Y

Actual Load

Allocated IT-capacities

“Waste“ of capacities

“Under-supply“ of capacities

Fixed cost of IT-capacities

Load Forecast

Barrier for innovations

In a non-cloud view, there are inefficiencies

• Cloud computing is the on-demand delivery of computing as a service where you pay only for the service you used.

Virtualization Technology

Hypervisor/VMM

abstracts HW

from an OS

VM pool

App 1 App 2 App 3

UNIX O SWeb Tier

Windows OSWeb Tier

Remote Physical

Machine Pool

Consumer applications

VM is SW that

executes apps as

if it was running

on a PM

Computing Trends

M. Shojafar, C. Canali, R. Lancellotti, J. H. Abawajy, ”Adaptive Computing-plus-Communication Optimization Framework for Multimedia Processing in Cloud Systems", IEEE Transactions on Cloud Computing, TCC, ISSN: 2168-7161, Impact Factor: 1.59, Vol. PP, Iss. 99, pp. 1-14, October 2017

Elasticity Automated

Management Availability Multi-Tenancy

Cloud Improves

Actual Load

Allocated IT capacities

Reduction of initial

investments

Reduction of “over-supply“

No “under-supply“

Possible reduction of IT-

capacities in case of reduced

load

Time

IT C

APA

CIT

Y

Load Forecast

Cloud Service Models Infrastructure Platform

Storage

Networking

Servers

Databases

Virtualization Runtimes Applications

Security & Integration • Private Cloud (On-Premise) - Owner manages every thing

• Public Cloud (IaaS) – Vendor manages the infrastructure owner manages the rest Databases

Storage

Networking

Servers

Virtualization

Runtimes Applications

Security & Integration

Managed by owner Managed by vendor

• Public Cloud (PaaS) – Vendor manages the infrastructure and platform while owner manages the rest

Databases

Storage

Networking

Servers

Virtualization

Runtimes Applications

Security & Integration

Managed by owner Managed by vendor

• Public Cloud (SaaS) – Vendor manages everything

Databases

Storage

Networking

Servers

Virtualization

Runtimes Applications

Security & Integration

Managed by owner Managed by vendor

Cloud Types

HYBRID CLOUD PUBLIC CLOUD

PRIVATE CLOUD

3rd party, multi-tenant Cloud infrastructure & services: * available on subscription basis

Cloud model run within a company’s own Data Center / infrastructure for internal and/or partners use.

Mixed usage of private and public Clouds: Leasing public cloud services when private cloud capacity is insufficient

Bahman Javadi, Jemal H. Abawajy, Richard O. Sinnott: Hybrid Cloud resource provisioning policy in the presence of resource failures. CloudCom 2012: 10-17

• A plethora of energy limited devices (smartphones, tablets and wearables) are increasingly becoming a mainstream element of our lives.

Wearables

TV

Meter

Ph

one

Sm

art

Th

ings

UNIX O S

Web Tier

Windows OS

Web Tier

Ap

pli

cati

ons

Smart House Smart HealthcareSmart Cities Smart GridSmart Cars

Clo

ud

Lay

er

Sara Ghanavati, Jemal Abawajy and Davood Izadi (2017), Opportunities & Challenges of Integration of IoT and Cloud Computing with WBANs, The Internet of Things: Foundation for Smart City, E-health and Ubiquitous Computing, edited by Armentano, Bhadoria, Chatterjee, and Deka, CRC Press, Taylor and Francis

Computing Trends

• These devices are expected to exceed 50 billion by 2020.

• Cloud as a centralised server will soon become an untenable computing model.

Edge nodes (eg., routers, mobile base stations and switches that route network traffic

Computing Trends

Paola G. V. Naranjo, Zahra Pooranian, Shahaboddin Shamshirband, Jemal H. Abawajy and Mauro Conti, Fog over Virtualized IoT: New Opportunity for Context-Aware Networked Applications and a Case Study, Appl. Sci. 2017, 7(12), 1325; doi:10.3390/app7121325

• Harnessing computational capabilities of resources at the edge of the network

Enzo Baccarelli; Paola G. Vinueza Naranjo; Michele Scarpiniti; Mohammad Shojafar; Jemal H. Abawajy, Fog of Everything: Energy-Efficient Networked Computing Architectures, Research Challenges, and a Case Study, IEEE Access Year: 2017, Volume: 5, Pages: 9882 - 9910

• Integrated Fog computing (FC) and Internet of Everything (IoE)

Computing Trends

• Virtualization technology is key – allows a single physical machine to run many independent virtual

machine thus increases utilization of physical servers

– Enables multiple types of OSs to run in isolation of other OSs

– Separating applications from the underlying infrastructure

– Enables portability of virtual machines between physical machines

Virtualization Technology

Hypervisor/VMM

abstracts HW

from an OS

VM pool

App 1 App 2 App 3

UNIX O SWeb Tier

Windows OSWeb Tier

Remote Physical

Machine Pool

Consumer applications

VM is SW that

executes apps as

if it was running

on a PM

Computing Trends

Virtual Data Center

Baker Alrubaiey, Jemal H. Abawajy: Virtual networks dependability assessment framework. IJHPCN 10(1/2): 3-12 (2017)

Workload Types

Task 1

Task 2 Task 3 Task n

Structure of matrix multiplication application

• Divisible jobs (an application can be arbitrarily partitioned into smaller tasks)

• Example: matrix multiplication application

• Indivisible processes (entire process must be assign to a single processor).

Workflow Applications

• Workflow applications are represented by a directed acyclic task graph.

T1

T2

T3

T5

T4

T7

T8

T6

Structure of divide-and-conquer programs

MapReduce Computation Workflow

• A programming framework good for processing large data sets (e.g, signal processing, image processing) in a distributed environment

• The computation of MapReduce applications is organized in a workflow of map and reduce tasks. – reducer tasks execute after map tasks completed. – The output of a map tasks is input to the reducer tasks.

key-

valu

e p

air

Aggregates intermediate data tuples into a smaller set of tuples or key-value pairs

Cloud Workload Types Workload Description and Examples Key Quality-of-Service Metrics

Server Centric

Web sites Freely available web sites for social networking, informational web sites large number of users

Large amounts of storage, high network bandwidth,

Scientific computing Bioinformatics, atmospheric modeling, other numerical computations

Computing capacity

Enterprise software Email servers, SAP, enterprise content management

Security, high availability, customer support

Performance testing Simulation of large workloads to test the performance characteristics of software under development

Computing capacity

Online financial services Online banking, insurance Security, high availability, Internet accessibility

E-commerce Retail shopping Variable computing load, especially at holiday times

Core financial services Banking and insurance systems Security, high availability

Storage and backup services General data storage and backup Large amounts of reliable storage

Client Centric

Productivity applications Users logging on interactively for email, word processing, and so on

Network bandwidth and latency, data backup, security

Development and testing Software development of web applications with Rational Software Architect, Microsoft® Visual Studio, and so on

User self-service, flexibility, rich set of infrastructure services

Graphics intensive Animation and visualization software applications Network bandwidth and latency, data backup

Rich Internet applications Web applications with a large amount of JavaScript

Mobile Centric

Mobile services Servers to support rich mobile applications High availability

Workload Patterns Optimal For Cloud

Usage

Co

mp

ute

Time

Average

Inactivity

Period

On & off workloads (e.g. batch job) Over provisioned capacity is wasted Time to market can be cumbersome

Co

mp

ute

Time

Average Usage

Unexpected/unplanned peak in demand Sudden spike impacts performance Can’t over provision for extreme cases

Average Usage Co

mp

ute

Time

Successful services needs to grow/scale Keeping up w/ growth is big IT challenge Complex lead time for deployment

Co

mp

ute

Time

Average Usage

Services with micro seasonality trends Peaks due to periodic increased demand IT complexity and wasted capacity

Google MapReduce Infrastructure Overview

25

Worker

Worker

Worker

Worker

Worker

Master

Split 0

Split 1

Split 2

Split 3

Split 4

Output File 0

Output File 1

User Program

(1) fork (1) fork

(1) fork

(2) assign map (2) assign reduce

(3) read (4) local

write (5) remote read

(6) write

input files map phase reduce phase output files intermediate

files

OCloud Architecture

Dispatcher

VM

Monitor

Service Request

Monitor

Pricing Accounting

Service Request Examiner and

Admission Control

- Customer-driven Service Management

- Computational Risk Management

- Autonomic Resource Management

Users/

Brokers

SLA

Resource

Allocator

Virtual

Machines

(VMs)

Physical

Machines

Market-Oriented Cloud Architecture: QoS negotiation

and SLA-based Resource Allocation

Dispatcher

VM

Monitor

Service Request

Monitor

Pricing Accounting

Service Request Examiner and

Admission Control

- Customer-driven Service Management

- Computational Risk Management

- Autonomic Resource Management

Users/

Brokers

SLA

Resource

Allocator

Virtual

Machines

(VMs)

Physical

Machines

Market-Oriented Cloud Architecture: QoS negotiation

and SLA-based Resource Allocation

Dispatcher

VM

Monitor

Service Request

Monitor

Pricing Accounting

Service Request Examiner and

Admission Control

- Customer-driven Service Management

- Computational Risk Management

- Autonomic Resource Management

Users/

Brokers

SLA

Resource

Allocator

Virtual

Machines

(VMs)

Physical

Machines

29

Thank you.

Questions, Comments, …?