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Advances, Systems and Applications

Table 1 Proactive approaches of Cloud load balancing in existing literature

From: Load balancing in cloud computing – A hierarchical taxonomical classification


Algorithm Used

Trait Used

Type of Load Balancing

Technique involved

Algorithm Complexity




Conventional Non Classical

Task Scheduling

Task LB

Heuristic (Classical, Deterministic)

Not Specified

Capable of handling heavy workloads within predefined deadline.

Tasks whose execution time is more than defined deadline are rejected.

Provides enhanced elasticity.

Thresholds for defining overloaded and under loaded VMs are set arbitrarily without formulating equation for them.

Minimize makespan with improved task acceptance ratio.

Minimize task rejection ratio

The experimental are run on Cloudsim using space shared policy only and not time shared policy.

Perform automatic scaling of resources


Full Set algorithm and Column generation algorithm

VM scheduling


Optimization (Classical, Deterministic, LP)

[O(2)N – n O(2k)/2]

Load balancing is performed among minimum number of VMs

Algorithm evaluates only single objective function.

Improved resource utilization

The experiments are run on C++ programs

Resource over provisioning is avoided

The algorithm runs in real-time scale with simple complexity.


Dragonfly optimization and constraint measure-based load balancing

Task Scheduling

Task LB

Optimization (Swarm Based)

Not Specified

Load balancing is performed with less power consumption

Cannot handle tasks beyond threshold limit.

Task rejection ratio is high


Fairness Aware Algorithm

Resource Scheduling


Optimization (non cooperative game theory based)

Not Specified

Optimal Lb is achieved at Nash equilibrium point.

High task execution time

Minimize expected response time


Honey Bee Behaviour

Task Scheduling

Task LB

Optimization (Swarm Based)

Not Specified

Low response time.

Low scalability

Low makespan



Task Scheduling

Task LB

Optimization (Swarm Based)

Not Specified

Less makespan

Tasks are mutually independent

Measures degree of imbalance among VMs

Memory intensive tasks are not taken


Agent based Nature Inspired Algorithm

Resource Scheduling

Resource LB


Not Specified

High scalability

Execution cost not considered

Less response time

Service level violations not considered

Improved resource utilization

Task rejection rate not considered


Non- Classical

Resource Scheduling

Resource LB


Not Specified

High fault tolerance

High response time

Less overhead

High execution time

High makespan


Weighted Round Robin

Resource Scheduling

Server LB



Good resource utilization

response time not chosen

Enhanced throughput

degree of balance not chosen

Less overhead

energy efficiency not chosen

High fault tolerance


Nature Inspired GA

Task and Resource Scheduling

Task LB



Efficient resource utilization

Priority based

Less resource wastage

Less scalability

Small energy consumption

Less fault tolerance

Less SLV

Improved degree of balance