Regressive Whale Optimization for Workflow Scheduling in Cloud Computing

Abstract

Cloud computing is the advancing technology that aims at providing services to the customers with the available resources in the cloud environment. When the multiple users request service from the cloud server, there is a need of the proper scheduling of the resources to attain good customer satisfaction. Therefore, this paper proposes the Regressive Whale Optimization (RWO) algorithm for workflow scheduling in the cloud computing environment. RWO is the meta-heuristic algorithm, which schedules the task depending on a fitness function. Here, the fitness function is defined based on three major constraints, such as resource utilization, energy, and the Quality of Service (QoS). Therefore, the proposed task scheduling requires minimum time and cost for executing the task in the virtual machines. The performance of the proposed method is analyzed using the four experimental setups, and the results of the analysis prove that the proposed multi-objective task scheduling method performs well than the existing methods. The evaluation metrics considered for analyzing the performance of the proposed workflow scheduling method are resource utilization, energy, cost, and time. Resource utilization is the process of making the most of the resources available for performing tasks. Energy is the quantitative property of the resource to perform tasks. The proposed method attains the maximum resource utilization at a rate of 0.0334, minimal rate of energy, scheduling cost, and time as 0.2291, 0.0181, and 0.0007, respectively.

An Improved Ant Colony Algorithm for Virtual Resource Scheduling in Cloud Computing

“…Step 2: ants select the energy characteristics required for scheduling with transfer probability. At the same time, taboo table k tabu and temporary pool   temppool k are set, k tabu is used to record the sample scheduling feature association pair selected by ant k , and application   temppool k is used to save the probability value selected by ant [14]. Among them, under the limitation of energy required for information resource scheduling of communication network in time slot, the calculation formula of transfer probability is…”

A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing

“…This mechanism improved the makespan of the proposed approach over existing state-of-the-art algorithms. In [24], the authors developed a task assignment mechanism in fog computing nodes while achieving reliability in executing tasks, i.e., execution of tasks under deadline constraints. The main aim of this approach is to identify suitable fog nodes to compute the tasks and assign tasks to them by using an RL-based approach.…”

“…Methodology Parameters [17] RL to make a scheduling model for cloud computing Makespan, total power cost in datacenters, energy consumption, migration time [18] RL task scheduling with Q-learning Makespan, total power cost, energy consumption [19] Task scheduling optimization algorithm called MPSO Makespan, energy consumption, packet delivery ratio, trust value [20] FELARE Ontime task completion rate, energy saving [21] RLFTWS Makespan, resource usage [22] WBCNF Computation time of tasks [23] BRCH-GWO Makespan [24] RELIEF Communication delay, reliability [25] PSO-based multipurpose algorithm Turnaround time, makespan [26] Regressive WO algorithm Processing cost, load balancing tasks [27] GO Makespan, resource utilization [28] Dynamic task scheduling algorithm based on an improved GA Total execution time and resource utilization ratio [29] PSO based on an AC algorithm Task completion time, makespan [30] DRL-based task scheduling Makespan, computation time [31] MRLCC is an approach for organizing tasks that are based on Meta RL Energy consumption, total cost, makespan [32] A novel DRL-based framework Cost and throughput, makespan [33] RLFTWS Execution time, degree of imbalance [15] DRL model Response time, makespan, CPU utilization [34] Deep reinforcement learning with PPSO SLA violation, makespan [35] DRL Cloud is an NDR-learning-based RP and TS system Estimated completion time, resource utilization [36] Deep Q-network model Degree of imbalance, cost, makespan [37] SDM reinforcement learning Energy consumption, resource utilization [38] DRLHCE Response time, degree of imbalance [39] DQN Makespan, total cost [40] Reinforcement learning Makespan [41] DDDQN-TS Task response time [31] Q-learning Makespan…”