Load Balancing in Cloud Computing PhD Thesis

Load Balancing in Cloud Computing PhD Thesis

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Load Balancing in Cloud Computing PhD Thesis

   Load Balancing in Cloud Computing PhD Thesis offers utmost collection of knowledge materials for PhD scholars to effectively finish their scientific research. We are expert in Load Balancing in Cloud Computing PhD Thesis preparation since we prepared 1000+ theses and distributed around the globe. Today, load balancing has an important influence on cloud computing performance and it’s growing each day because large quantities of data are exchanged over the network. The main objective of load balancing is to get the maximum of throughput, improve resource consumption, avoid single resource overload and decrease response timeHere, the load balancing plays a vital role in cloud computing, let’s view on it,

…” Load balancing refers to the process of distributing computation resources and workloads across one or more servers.” The aims of using load balancing are:

  • To enhance the cloud system performance
  • To manage and maintain system firmness
  • To protect or prevent against system failures

Cloud Load Balancing Service Providers:

  • AWS (Amazon Web Services) offers Elastic Load Balancing Technology for EC2 instances distribution across the traffic
  • Microsoft Azure offers Traffic Manager users cloud servers for multiple datacenters allocation
  • Google offers Google Compute Engine between VM instances distribution across network traffic
  • Rackspace’s offers Cloud Load Balancers for workload distribution using multiple servers
  • Aruba Cloud offers Load Balancers to monitor the workload of each load balancer
  • Akamai offers Application Load Balancer for Modern Load Balancing

Advanced Technologies in Cloud Load Balancing:

  • Cloud enables Cost Control
  • Cloud containers for Development Simplification
  • Scalable Scheduling Algorithms
  • Middleware Solutions in Cloud
  • Load Balancer as a Service
  • Hyperconverged Shift Cloud Infrastructure
  • Tools for Migration to Boost Cloud Adoption
  • HPC as a Service in Cloud

Advanced Concepts in Cloud Load Balancing:

  • Virtual Machine Management and Containers across cloud infrastructures
  • Cross cloud systems for resource management across multiple clouds
  • Edge/fog computing offers fog based caching, performance, infrastructure and services
  • Cross Cloud Agility: flexibility, dynamic application deployment across multiple cloud providers
  • Hybrid Cloud Infrastructures Management (e.g. private and public clouds, cloud and cluster/grid systems, heterogeneous cloud systems)
  • Cloud Bursting for workload offloading and multi cloud resource scheduling
  • Scalable multi cloud monitoring for overhead analysis across different cloud environments

Platforms support for Cloud Load Balancing:

  • OS support: Windows, Ubuntu, CentOS, Debian, OpenSUSE and FreeBSD
  • Others: Microsoft SQL Server, Plesk, ORACLE, MySQL, Sense, WordPress

Parameters Considerations in Load Balancing:

  • CPU Load, Memory Capacity and Network Load for each server
  • Jobs Waiting Queue
  • CPU Processing Rate
  • Job Arrival Rate

Advantages of Cloud Load Balancing:

  • Increased Scalability
  • High Performance Applications
  • Sudden traffic spikes handling
  • Flexible with business service continuity
  • High Traffic Handling

Load Balancing Metrics for Performance Evaluation:

  • Scalability
  • Response Time
  • Migration Time
  • Throughput
  • Fault Tolerance
  • Execution Time

Load Balancing in Cloud Computing PhD Thesis Topics:

  • Dispatch Optimal Task on Integration of Multiple Heterogeneous Multi-server Systems and Dynamic Speed and Power Management
  • Resource Management and Scheduling Using Hybrid Bio-Inspired Algorithm in Cloud Paradigm
  • Unified Urban Mobile Cloud Computing Offloading Approach Used in Smart Cities
  • NSGA-II for Level Wise Load Balanced Scientific Workflow Execution Optimization
  • ICLoS Algorithm for Cloud Challenges of Security Issues and Load Balancing
  • Latency Aware Mobile Load Balancing and Task Assignment for Edge Cloudlets
  • Optimization Algorithms Designed Analysis in Cloud Computing to Fully Comply with SLA
  • Heterogeneous Cloud Storage Using Compact, Adaptive and Popularly Aware Hybrid Data Placement Strategies
  • Addressing Performance Heterogeneity in Integrated MapReduce Clusters and Elastic Tasks
  • OpenStack Fault Tolerance Enhancement for Provisioning Computing Paradigms
  • Distributed Subscribe/Publish Query Processing on the Stream of Spatio-Textual Data
  • Analyze Cloud Computing Performance using CloudSim for Distributed Data Center
  • Optical Virtual Network Configuration Method for Peripheral Computer Resources and Tightly Coupling Big Data
  • Node Load and File Heat Based Dynamic Replica Creation Strategy in Hybrid Cloud
  • Virtual Network functions Replication for Optimizing Resource Cost and Link Utilization