LOAD BALANCING IN CLOUD COMPUTING THESIS

 

 

 

LOAD BALANCING IN CLOUD COMPUTING THESIS

     Load Balancing in Cloud Computing Thesis plays as a game changer in the cloud computing technology due to its rising need and significance. Load balancing- a technological augmentation is a way to distribute the processing and communications uniformly among multiple server, instead of loading a single server with multiple tasks within the data centre. It is constructive in large datacenters to proficiently utilize the server power and reduce the latency. Load balancing in cloud computing is relatively a new field of research, which attracts large number of scholars and students. We are working on cloud computing for the past 7 years in order to dissipate the gap between the common man and technological modernization. We act as a juggernaut for the budding scholars to create technology revolution in the arising technology of cloud, which will definitely change the face of earth.

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LOAD BALANCING IN CLOUD COMPUTING THESIS…………………

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LOAD BALANCING IN CLOUD COMPUTING THESIS

     Load Balancing in Cloud Computing Thesis, a way to up heal the technology with the help your world-shattering research. We work on thesis for students undertaking their research (PhD, MS, and M.tech) on most recent and newfangled approaches. We have emphasized on load balancing in cloud computing thesis for students due to its immense significance in the field of Cloud computing. To make our point clear let’s see few highlights about Cloud load balancing.

Cloud Load Balancing:

  • A way to distribute workloads and computing resources to achieve optimal resource utilization in Cloud.

Key features:

  • Factor for cloud Green computing (Reduce carbon emission and energy consumption)
  • Support for GUI(Google cloud load balancing UI,Cloud load balancer GUI(for scalable traffic management))
  • Major libraries and frameworks(Cloudsim framework, Spring cloud, Map reduce(Used in Google’s bigtable, hadoop etc ))
  • Major protocol used(LDAP,LDAPS,HTTP, IMAP,POP3,FTP, HTTS, UDP, SFTP, MySQL, SMTP,POP3S, SMTP,TCP Client-first)
  • Support for Full featured API(Use RESTFUL web service interface)
  • Database connectivity using SQL and security is enhanced using public key cryptography.

Added features

  • Automated service provisioning
  • Virtual Machine Migration support
  • Energy management using load balancing algorithms
  • Stored data Management
  • Health checkup(Periodic checking of workload distribution)
  • Advanced access control and connection control

Know about cloud load balancing:

These are the major aspects, which you need to know, before you take a research work (along with thesis) in cloud load balancing.

Programming languages and file formats used:

Programming Languages:

  • Python(.py)
  • Java(for Web services/ hadoop Map reduce)

                                    Format(.java) – to configure load balancer uses Oracle Traffic Director administration console

                                   Access it from Web logic server console, Load balancer console and fusion Middleware console

  • Jquery/JavaScript(.js)
  • XML(.xml-Extensible Markup Language)

                                Additional need- RESTful web services, HTTP/1.1, JSON/XML Serialization formats

  • R programming(R Math Language- .R)
  • Json(.json)-JavaScript Object Notation
  • Haskell(Haskell functional language-.hs, .lhs)
  • Clojure(.clj,.cljs,.cljc,.edn)
  • net(Windows visual studio-.aspx,.cshtml,.vbhtml)
  • SQL(.sql) for database connectivity

Important Techniques and Algorithms:

Techniques used:

  • Load Balancing Techniques
  • Scheduling Algorithms
  • Load Balancing Policies(Workload and client Aware policy)

Algorithms used:

  • Weighted Round Robin algorithm
  • Two phase scheduling load balancing algorithm
  • Honeybee Random sampling load balancing algorithm
  • Ant colony optimization algorithm
  • Biased Random sampling algorithm
  • Least connections and weighted least mechanism
  • Token ring algorithm
  • Randomized algorithm
  • Central Queuing for dynamic distribution
  • Dynamic Round Robin scheduling algorithm
  • Min-Min and Max-Min algorithm
  • First come first serve algorithm
  • Throttled load balancing algorithm
  • Observed algorithm (Combination of least connections and fastest algorithms)
  • Equally Spread current execution load
  • Opportunistic Load Balancing Algorithm
  • Dynamically reconfigurable routing
  • Active Clustering load balancing algorithm

Software’s Used:

Eclipse Che- Open source workspace server and cloud IDE:

  • Platform supported- Linux, Mac OS, Windows
  • Dependencies(Docker 1.8+, Maven 3.3.1+ and Java 1.8)
  • Latest version- Eclipse Che- 4.4.1

Visual Studio IDE:

  • Version(Microsoft Visual studio 2015)
  • Platform support(Windows 10, Windows 8.1, Windows 8 and 7, Additional software- Service pack 1, Windows server 2012 R2, Windows server 2008 R2 SP1, Windows server 2012)

RStudio:

  • Available as Open source and commercial version
  • Platform Supported(Desktop– Windows, Mac, Linux and Server– Debian/Ubuntu, RedHat/ CentOS, SUSE Linux)

Tools and Plugins used

  • Amazon Web Services(AWS) Toolkit[Elastic Load Balancing]

                           Supported Software’s (Eclipse(Java), Visual Studio(.Net))

  • Rackspace Cloud Load Balancers API 1.0

                         Supported Languages(Java, Javascript, .NET, Ruby, PHP, Python)

  • Microsoft Azure(Supported languages: .net, Java, JS, Python and ruby)
  • Google Cloud Platform Products and services(Used to access Google cloud storage, Google compute engine, Google Big query)
  • Exploits Client libraries(Java, Python, Ruby, NodeJS, Php and GO)
  • Development IDE(Eclipse and IntelliJ)

Can be simulated using (Simulation tools):

  • CloudSim
  • Cloud Analyst

Qualitative Metrics and Interface Used:

Interface sketch:

  • Oracle Cloud
  • Google Cloud Load Balancing
  • AWS Elastic Load balancing
  • Rackspace Cloud Load Balancers API 1.0

Metrics used:

  • Throughput
  • Associated Overhead
  • Fault Tolerant
  • Migration time
  • Response time
  • Scalability
  • Performance
  • Resource Utilization

Major Research areas and applications of Cloud load balancing:

Applications:

  • Content Delivery Networks traffic management
  • In Amazon EC2 for resource management
  • Used by high traffic websites like Dropbox, NetFlix, Zynga etc
  • Widely used in Cloud balancing technologies(Amazon web services, Microsoft azure, Google and Rackspace)

Major Research Areas:

  • Power aware load balancing approach in cloud computing
  • Optimal Cost scheduling Algorithm for Load balancing
  • Distributed and Centralized load balancing
  • Virtual Machine Migration and hierarchical load balancing
  • Task Dependencies in Load balancing
  • Load balancing for Cloud task scheduling
  • Load balancing policy to handle optimal peak hour performance in Datacenters.

Significance of Cloud load balancing:

  • Exploits the advantage of Cloud Scalability and agility
  • Use of Virtual local area network to avoid noisy neighbors
  • Provide provision for Green cloud computing
  • Provides flexibility, cost effectiveness and availability of service users.
  • Boost Reliability(can run an application on multiple cloud hubs)
  • Work globally enhancing scalability and consistency.

       Referring this content will surely give an overall idea about Load balancing in Cloud computing. To take a research in such an advanced technology, need some external guidance which will give you a right path towards your success. We will guide you in the best way to create your own identity in the field of research, along with your contented thesis work.

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