Load Balancing Projects for Master Thesis Students

Load Balancing Projects for Master Thesis Students

                 Load Balancing Projects for Master Thesis students is an awesome service those students to change your life for better. Projects also for Master Thesis students are a product of our seven years of hard work in the field of cloud computing. It can also be defined as the technology that distributes both communications and processing equally between various servers. This process takes place as a substitute for loading one server also for various tasks in the data center. In order to minimize the use of server power, it is also built-in datacentres.

This specific topic is currently the Centre of attraction as it is a new and also promising domain for research. We also welcome you immensely to ripe the product of our seven years of labour.

Load Balancing Projects for Master Thesis Students Online Our Projects are Widely Preferred Because of

  • It has unique and also novel ideas
  • Mining of intelligence by our expert team
  • Nearly 600 + top journal members
  • Thesis format is customized support to your needs
  • Top professionals are also part of our team
  • Confidentiality of research
  • Extra support such as research proposal, and also in thesis statement preparation and literature work

        Our approach towards Load Balancing Projects for Master Thesis Students is unique and also completely different from any other previously undertook approaches. You can also trust us with your life as we aim to attain the perfection and precision you ask for.   We strongly recommend Master Thesis Students due to its relevance and also growth in the modern world of technology.

Cloud Load Balancing:

  • It is a method to share computing resources and also workloads to gain maximum resource usage in cloud

Network Load Balancing:

  • Network load balancing refers to the distribution of network traffic among multiple users participating in the network. To balance the load, network mainly focuses also based on routing and clustering.

Important Features:

  • HTTP, POP3, HTTS, MYSQL, also POP3S, TCP, LDAP, SMTP,IMAP, UDP and also LDAPS are the major protocols used
  • Energy consumption and also Co2 emission are minimized by cloud green computing
  • SQL is used also for database connectivity and enhancement of security with public key cryptography
  • Full featured API is supported
  • Also supports GUI and also full featured API

Additional Characteristics:

  • Load Balancing algorithms are also used for energy management
  • Connection control and also advanced access control
  • Automatic service provision
  • Stored data management
  • Health checkup
  • Support also for virtual machine migration

Prime Aspects of Cloud Load Balancing

Programming Languages used:

  • Java
  • Python(.py)
  • XML (.xml – Extensible markup languages)
  • Jquery or Javascript(.js)
  • Clojure (.clj, cljs, .cljc, .edn)
  • SQL (.sql)
  • R programming (.R – R Math Language)
  • .Net (.aspx, .cshtml, .vbhtml)
  • Hastell (.hs, .Ihs)
  • Json (.json)

Techniques used:

  • Load Balancing Policies (Client and also workload aware policy)
  • Load Balancing Techniques
  • Scheduling Algorithms

Algorithms used:

  • Ant Colony optimization algorithm
  • Opportunistic load balancing also based on algorithm
  • Biased Random sampling algorithm
  • Active clustering load balancing also based on algorithm
  • Two phase scheduling load balancing also based on algorithm
  • Max min and Min min algorithm
  • Token ring algorithm
  • First come first serve also based onalgorithm
  • Observed algorithm
  • Randomized algorithm
  • Honey bee random sampling load balancing also based on algorithm
  • Dynamic round robin scheduling algorithm
  • Dynamically reconfigurable routing
  • Least connections and weighted least also based on mechanism
  • Throttled load balancing algorithm
  • Equally spread current execution also based on load
  • Genetic algorithm
  • Greedy also based on algorithm

Load Balancing with,

  • Fair load balancing
  • Multi-path load balancing
  • Cross-layer design also based for load balancing
  • Energy efficient also based on load balancing
  • Distributed load balancing
  • Adaptive approach for load balancing
  • Spatial load also based on balancing
  • Congestion aware load balancing

Load Balancing in networks, 

  • Mesh-router also based load balancing
  • Gateway-based load balancing
  • Path-based Load Balancing

Software’s Used

Visual Studio IDE:
  • Supported platforms are Windows 7 and 8, Windows 8.1, Windows 10. Windows server 2008 R2 SP-1, Windows server 2012, also based on Windows server 2012 R2 and Additional service part 1
R Studio:
  • Supported platforms are also MAC, Windows, Linux, Ubuntu, Debian and Rehat
  • Available as open source and also commercial version
  • Cloud IDE and Eclipse che-Open source Linux
  • Supported platforms – MAC OS , Windows, Linux
  • Eclipse che-4.4.1 is the latest version plugins and tools used
  • Microsoft Azure
  • Development IDE
  • Google Cloud platform products and services
  • Exploits client libraries
  • Amazon web services
  • Toolkit
  • Rackspace Cloud Load Balancer API 1.0
Simulation Tools Used:
  • Cloud Analyst
  • Cloud Sim
Metrics Used:
  • Fault Tolerant
  • Scalability
  • Resource Utilization
  • Throughput
  • Performance
  • Associated Overhead
  • Response time
  • Migration time
Interface Sketch:
  • Google Cloud Load Balancing
  • Rackspace cloud load Balancers
  • Oracle cloud
  • AWS Elastic load balancing

Major Research Areas

  • Virtual machine migration
  • Load balancing policy for handling peak hour performance of datacenters
  • Power aware load balancing in cloud computing
  • Centralized and distributed load balancing
  • Optimal Osf scheduling algorithm
  • Task Dependencies in load balancing
  • Hierarchical load balancing


  • High traffic websites such as NetFlix, Zynga and Dropbox uses it
  • Cloud computing technologies such as Microsoft azure, Rackspace, amazon, and Google uses it
  • Manages content delivery networks traffic

Importance of Cloud Load Balancing

  • Cost effective
  • Advantage of cloud scalability and also agility is exploited
  • Scalability and consistency is globally enhanced
  • Green cloud computing’s provision also is provided
  • Boost Reliability

Take a lesser known path called Load Balancing

Achieve yours success in a road not taken

          Thoroughly study the basic information provided by us. Then approach us to write your success story. We will be the beacon for you and guide you towards your destined greatness.