Load Balancing Algorithms in Cloud Computing - IT Assignment Help

Assignment Task

Introduction and justification 
Unpredictable loads are major threat to the process of e-commerce and online transactions. This study focuses on load balancing and fault tolerance in cloud environment. Unpredictability of traffic is a major threat and this work focuses on algorithm, and the changes in the technologies of create balance in loads. This work compares the various LB techniques on basis of efficiency, fault tolerance, throughput, energy efficiency and several other performance metrics. Based on the faster response times and minimal failure by process of replication faults are avoided. This work analyses various overheads and assesses the need for the modern elastic load balancers. 
Advanced use of clustering techniques, and higher replications are preventive maintenance process and plays an important role in the process of optimization. Virtualization has limitations in application and threshold limits are assessed with dynamic factors and need for replication with preventive factors for nodal failure are prime objective of the study. 
Research questions, aims, objectives and deliverables
The use of advanced machine learning algorithms play a vital role in prevention of failure of nodes. By assessing the nodes with the minimal failure and other consistency factors, the process of clustering is enhanced. The optimal nodes are picked in live manner to form active clusters and high priority jobs are allocated to these nodes to prevent the failure. This kind of clustering is also termed as the BEOWULF clustering and this is mainly used in highly critical applications like air craft, or aerospace, bombers, nuclear reactors, etc. the use of the live monitoring programs helps in detection of loads at specific nodes and this leads to resolving the errors in the system. The process of automation in predicting the consistent system and allocation of loads to the system with the identification of backup systems in case of failures leads to efficient automation. 
Research objectives
The key research objectives are to identify the major issues in the real time scheduling of jobs. The main issue faced in real time scenarios is the higher time needed for the process of sorting of jobs and this takes more time and resources. Hence only after the process of sorting the jobs they are allocated to the corresponding nodes. But, the use of the parallel sorting algorithm helps in allocation of the jobs in parallel to the process of sorting. Both sorting and allocations are in  parallel and this helps in minimization of time and even in case of failures the process of identification of the alternative active nodes are easier as not all the nodes are occupied at the same time period. 

1.To identify the major factors leading to nodal failure
2.To assess the health of the node using the LIVE factors
3.To mitigate the risk of nodal failure and load imbalance in cloud computing environment 
4.To identify the most dynamic methods, parallel sorting algorithms and BEOWULF clustering approaches to increase the system performances 
Deliverables  
The main deliverables in this case are the creation of a unique framework and a working system to demonstrate the process of system improvement in terms of fault tolerance, parallel scheduling and active clustering. The performance factors are measured by live demo with real time implementation of the system. The other deliverables are the report and proof of outcomes of the implementation of the software system. 
Literature review 
Paper 1
A Comprehensive Study of Load Balancing Approaches in the Cloud Computing Environment and a Novel Fault Tolerance Approach
Aim:
Load balancing plays an important role in the process of cloud computing and acts as a source to split the loads across the clouds with the minimal loads. The researchers clearly focus on the metrics those increase the load balancing efficiency, compare various LB techniques. This study focuses on various algorithms and the fault tolerance metrics deployed by them. Several factors have been analyzed in this research like the time for migration, overall throughput, performance management, and efficiency in terms of energy efficiency have been discussed. Analysis of competence of the algorithm in terms of resource utilization, higher response times, and other overheads have been critically analyzed in the cloud computing environments. 
 

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