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. In the 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 data centers to proficiently utilize the server power and reduce the latency. Our cloud computing load balancing thesis is relatively a new field of research, which attracts large number of scholars and students.

We are also working on cloud computing for the past 7 years in order to dissipate the gap between the common man and technological modernization. Consequently We also 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 COMPUTING THESIS…………………

AN INNOVATIVE APPROACH TOWARDS THE RISING

TECHNOLOGY……

CLOUD COMPUTING IN LOAD BALANCING THESIS

     Load Balancing in Cloud Computing Thesis, a way to up heal the technology with the help your world-shattering research. Also We work on thesis for students undertaking their research (PhD, MS, and M.tech) on most recent and newfangled approaches. We also 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 also computing resources to achieve optimal resource utilization in Cloud.
Key features:
  • Factor for cloud Green computing (Reduce carbon emission and also 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, also SMTP,TCP Client-first)
  • Support for Full featured API(Use RESTFUL web service interface)
  • Database connectivity also 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 also in 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) also 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 also 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 also in client Aware policy)
Algorithms used:
  • Weighted Round Robin also in algorithm
  • Two phase scheduling load balancing also in algorithm
  • Honeybee Random sampling load balancing also in algorithm
  • Ant colony optimization also in algorithm
  • Biased Random sampling algorithm
  • Least connections and also weighted least mechanism
  • Token ring algorithm
  • Randomized algorithm
  • Central Queuing for dynamic distribution
  • Dynamic Round Robin scheduling also in algorithm
  • Min-Min and Max-Min also in algorithm
  • First come first serve algorithm
  • Throttled load balancing also in algorithm
  • Observed algorithm (Combination of least connections and also in fastest algorithms)
  • Equally Spread current execution load
  • Opportunistic Load Balancing also in Algorithm
  • Dynamically reconfigurable routing
  • Active Clustering load balancing also in 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 also 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 also 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 also 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, also Python)
  • Microsoft Azure(Supported languages: .net, Java, JS, Python and also ruby)
  • Google Cloud Platform Products and services(Used to access Google cloud storage, Google compute engine, also Google Big query)
  • Exploits Client libraries(Java, Python, Ruby, NodeJS, Php and also 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 also for resource management
  • Used by high traffic websites like Dropbox, NetFlix, also Zynga etc
  • Widely used in Cloud balancing technologies(Amazon web services, Microsoft azure, Google and also 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 also in Load balancing
  • Load balancing also 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 also agility
  • Use of Virtual local area network also to avoid noisy neighbors
  • Provide provision also for Green cloud computing
  • Provides flexibility, cost effectiveness and also availability of service users.
  • Boost Reliability(can also run an application on multiple cloud hubs)
  • Work globally enhancing scalability and also consistency.

       Referring this content will surely give an overall idea about Load balancing in Cloud computing. To take also a research in such an advanced technology, need some external guidance which will also give you a right path towards your success. We also 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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