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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

ReferenceAlgorithm UsedTrait UsedType of Load BalancingTechnique involvedAlgorithm ComplexityAdvantagesDisadvantages
[40]Conventional Non ClassicalTask SchedulingTask LBHeuristic (Classical, Deterministic)Not SpecifiedCapable 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 ratioThe experimental are run on Cloudsim using space shared policy only and not time shared policy.
Perform automatic scaling of resources
[41]Full Set algorithm and Column generation algorithmVM schedulingVM LBOptimization (Classical, Deterministic, LP)[O(2)N – n O(2k)/2]Load balancing is performed among minimum number of VMsAlgorithm evaluates only single objective function.
Improved resource utilizationThe experiments are run on C++ programs
Resource over provisioning is avoided
The algorithm runs in real-time scale with simple complexity.
[37]Dragonfly optimization and constraint measure-based load balancingTask SchedulingTask LBOptimization (Swarm Based)Not SpecifiedLoad balancing is performed with less power consumptionCannot handle tasks beyond threshold limit.
Task rejection ratio is high
[38]Fairness Aware AlgorithmResource SchedulingCPU LBOptimization (non cooperative game theory based)Not SpecifiedOptimal Lb is achieved at Nash equilibrium point.High task execution time
Minimize expected response time
[42]Honey Bee BehaviourTask SchedulingTask LBOptimization (Swarm Based)Not SpecifiedLow response time.Low scalability
Low makespan
[43]ACOTask SchedulingTask LBOptimization (Swarm Based)Not SpecifiedLess makespanTasks are mutually independent
Measures degree of imbalance among VMsMemory intensive tasks are not taken
[44]Agent based Nature Inspired AlgorithmResource SchedulingResource LBMetaheuristicNot SpecifiedHigh scalabilityExecution cost not considered
Less response timeService level violations not considered
Improved resource utilizationTask rejection rate not considered
[45]Non- ClassicalResource SchedulingResource LBHeuristicNot SpecifiedHigh fault toleranceHigh response time
Less overheadHigh execution time
High makespan
[46]Weighted Round RobinResource SchedulingServer LBHeuristicO(1)Good resource utilizationresponse time not chosen
Enhanced throughputdegree of balance not chosen
Less overheadenergy efficiency not chosen
High fault tolerance
[47]Nature Inspired GATask and Resource SchedulingTask LBOptimizationO(1)Efficient resource utilizationPriority based
Less resource wastageLess scalability
Small energy consumptionLess fault tolerance
Less SLV
Improved degree of balance