Adaptive Cost-Based Task Scheduling in Cloud Environment

Table 1

Drawbacks of scheduling schemes in literature.

Author	Scheduling scheme	Drawbacks
Sahni and Vidyarthi [4]	Cost-effective deadline constraint dynamic scheduling algorithm	VM failures increase the workload of other VMs and affect the execution time
Tsai et al. [5]	Hyper-heuristic scheduling algorithm	High overhead of connection
Zhu et al. [6]	Agent-based scheduling algorithm in virtualized clouds (ANGEL)	Nonconsideration of communication and dispatching time reducing performance
Zhu et al. [7]	Evolutionary multiobjective (EMO) workflow scheduling	Nonconsideration of monetary costs and time overhead does not improve performance
Zhang et al. [8]	Phase and resource information-aware scheduler for MapReduce (PRISM)	Deadlines are not specified
Zhu et al. [9]	Energy aware rolling-horizon (EARH) optimization based scheduling	Lack of updation in number of VM cycles
Maguluri and Srikant [10]	Throughput-optimal scheduling & load-balancing algorithm	Utilizing queue lengths in weights is based on assumption
Zuo et al. [11]	Self-adaptive learning particle swarm optimization- (SLPSO-) based scheduling	Lack of priority to deadline constraint tasks results in task failures
Su et al. [12]	Cost efficient task scheduling	Does not consider the completion time and cost (computation cost and communication cost)