Alts: An Adaptive Load Balanced Task Scheduling Approach for Cloud Computing
Abstract
:1. Introduction
2. Related Work
3. Adaptive Load Balancing Task Scheduling (ALTS) Approach and Architecture
3.1. ALTS Algorithm Workflow
3.2. Execution of ALTS Approach
3.2.1. GA Initial Population and Formation of Chromosomes
3.2.2. Calculation of Fitness Function
3.2.3. Selection Operation
3.2.4. Execution of ACO Algorithm
3.2.5. Crossover and Mutation Operation of GA
3.3. Steps for Proposed ALTS Algorithm
- Initialize the parameter values for GA and ACO that are required for population size, mutation, crossover, iteration number and pheromone evaluation, etc.
- Set the values of the initial iteration number and initial population to 0.
- Set iteration number to 1.
- Fitness function of the GA is calculated using Equation (1).
- When the iteration number approaches the highest value then check the value of fitness function if it reach then go back to step 6 otherwise go to step 5.
- After the evaluation of fitness value, then we select two best chromosomes as (nth and nth −1) for next level.
- Provide the nth − 1 solution to the ACO as initial pheromone, where ants are represented by m and points are represented by n.
- Equation (6) builds a mapping of cloudlets to VMs after selecting next state for each cloudlet.
- Then the algorithm updates the pheromone value for each path and make a survey by checking that all the cloudlets are completed or not, if yes go to step 11, otherwise go to step 8.
- To obtain a new offspring, perform the crossover and mutation operations.
- Now analyze if the optimal solution is reached or not; if yes then move to step 12, otherwise move backward to Equation (9).
- Returns the best optimal cloudlet to VM allocation.
3.4. ALTS Algorithm Pseudo Code
Algorithm 1: Adaptive Load Balancing Task Scheduling (ALTS) Algorithm |
Input: Number of tasks (, , …, ) arranged in queues Output: Optimal allocation strategy for each task. Step 1: Initialize tasks population size and VMs capacity. Step 2: Evaluate the fitness function. Step 3: While (until all tasks are allocated to their suitable resources) do Step 4: Selection operation generates two foremost solutions (nth and nth − 1 chromosome). Step 5: Modify the best solution (nth − 1 chromosome) from the GA fitness function to ACO Step 6: Initialize values of pheromone, m ← 0, n ← 0. Step 7: For all VMs m in tasks n Step 8: Calculate heuristic information by Equation (6). Step 9: Calculate current pheromone trail. Step 10: Calculate the probability of pheromone trail value by Equation (7). Step 11: Choose the task with the highest probability value. Step 12: Update pheromone trail values on each path. Step 13: Modified solution of ACO (nth − 1) chromosome and GA solution (nth chromosome) combined for crossover operation to form a new optimal allocation strategy. Step 14: End While Step 15: Return optimal solution Step 16: End. |
4. Simulation Modeling, Design, and Analysis
Simulation Results
5. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Task Scheduling Algorithms | Cost | ARUR | Makespan | Energy Consumption |
---|---|---|---|---|
IPSO | ||||
[22] | ✓ | ✓ | ✓ | |
ICGA | ||||
(Farnoosh et al., 2019) [32] | ✓ | ✓ | ||
PSO | ||||
(Near et al., 2017) [23] | ✓ | ✓ | ||
LBACO | ||||
(Selvakumar et al., 2019) [7] | ✓ | ✓ | ✓ | |
ICDFS | ||||
(Shadi et al., 2018) [47] | ✓ | ✓ | ||
HC | ||||
(Keshanchiet al., 2017) [41] | ✓ | ✓ | ✓ | |
ACO | ||||
(Sayantani et al., 2018) [31] | ✓ | ✓ | ✓ | |
GAACO | ||||
(Sayantani et al., 2018) [9] | ✓ | ✓ | ✓ | ✓ |
Parameter | Value |
---|---|
Simulation Tool | CloudSim Software Version 3.0.3 |
Computing Power of Host Machine | Intel (R) Core (TM) i5-5300U CPU @ 2.30 GHz |
Host Machine Memory | 20 GB |
Total number of VMs | 32 |
Total number of Cloudlets | 1024 |
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Mubeen, A.; Ibrahim, M.; Bibi, N.; Baz, M.; Hamam, H.; Cheikhrouhou, O. Alts: An Adaptive Load Balanced Task Scheduling Approach for Cloud Computing. Processes 2021, 9, 1514. https://doi.org/10.3390/pr9091514
Mubeen A, Ibrahim M, Bibi N, Baz M, Hamam H, Cheikhrouhou O. Alts: An Adaptive Load Balanced Task Scheduling Approach for Cloud Computing. Processes. 2021; 9(9):1514. https://doi.org/10.3390/pr9091514
Chicago/Turabian StyleMubeen, Aroosa, Muhammad Ibrahim, Nargis Bibi, Mohammad Baz, Habib Hamam, and Omar Cheikhrouhou. 2021. "Alts: An Adaptive Load Balanced Task Scheduling Approach for Cloud Computing" Processes 9, no. 9: 1514. https://doi.org/10.3390/pr9091514
APA StyleMubeen, A., Ibrahim, M., Bibi, N., Baz, M., Hamam, H., & Cheikhrouhou, O. (2021). Alts: An Adaptive Load Balanced Task Scheduling Approach for Cloud Computing. Processes, 9(9), 1514. https://doi.org/10.3390/pr9091514