SCEHO-IPSO: A Nature-Inspired Meta Heuristic Optimization for Task-Scheduling Policy in Cloud Computing
Abstract
:1. Introduction
- Proposed a SCEHO-IPSO algorithm to resolve task-scheduling problems in the cloud computing platforms. In this scenario, the scheduler effectively ranks user tasks based on execution time and memory details.
- Based on the capacity criteria, the SCEHO-IPSO algorithm determines the efficient VMs to execute tasks in the queue. The SCEHO-IPSO algorithm simultaneously enhances resource utilization and decreases the makespan value.
- The SCEHO-IPSO algorithm optimizes task scheduling by identifying the optimal solutions with better convergence rates. The effectiveness of the SCEHO-IPSO algorithm is analyzed by conducting different experiments. The performance measures cost, execution time, makespan, and latency, and memory storage demonstrates the efficacy of the SCEHO-IPSO algorithm over other optimization algorithms.
2. Literature Survey
3. Methodology
- Minimization of total cost: According to the user’s QoS parameters, the limited total monetary cost states that the SCEHO-IPSO algorithm is efficient.
- Maximization of the QoS parameters: The QoS parameters play a crucial role in cloud computing environments and are utilized to analyze the effectiveness of the SCEHO-IPSO algorithm. The higher QoS is superior, while other parameters remain unchanged.
- Workload balancing: Workload balancing is closely related to the resource utilization rate. If it is an excellent task-scheduling algorithm, the majority of resources should be fully used in cloud environments.
- Minimization of makespan: It represents that the proposed optimization algorithm completes the scheduling of tasks with limited execution time.
- Minimization of latency: Latency is an important measure for evaluating the proposed task-scheduling algorithm. The latency and response time should be limited if it is an excellent task-scheduling algorithm. The flow diagram of the proposed work is mentioned in Figure 1.
3.1. Resource Allocation to the VMs
3.2. Load Balancing in the VMs
3.3. Task Scheduling
3.4. SCEHO Algorithm
3.4.1. Process of Clan Updating
3.4.2. Process of Separation
3.5. IPSO Algorithm
Algorithm 1. SCEHO-IPSO algorithm. |
Step 1: Initialize the objective functions. Step 2: Create initial population. Step 3: Evaluate fitness value. Step 4: For every task, find the best local optimal solutions using SCEHO algorithm. Step 5: For every task, find the best global optimal solutions using IPSO algorithm. Step 6: Find the hybrid solutions. Step 7: If the hybrid new solution value is higher than the current value, then Step 8: replace the current value with the hybrid new solution. Step 9: Select any resources among the population. Step 10: If the execution time is higher for the selected resource, then eliminate the respective resource and select another resource. Step 11: Update personal best and global best solutions. Step 12: Retain it and rank the best solutions. Step 13: End. |
4. Simulation Results
4.1. Performance Measures
4.2. Quantitative Analysis
4.3. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
ACO | Ant Colony Optimization |
ALO | Ant Lion Optimization |
ABC | Artificial Bee Colony |
BW | Bandwidth |
CPU | Computer Processing Unit |
GA | Genetic Algorithm |
GWO | Grey Wolf Optimization |
HGO | Human Group Optimizer |
IPSO | Improved Particle Swarm Optimization |
MVO | Multi-Verse Optimization |
QoS | Quality of Service |
SCEHO | Sine Cosine based Elephant Herding Optimization |
UBS | Utility Based Scheduler |
VMs | Virtual Machines |
Parameters | Definition |
Computer processing unit of the physical machines | |
Memory of the physical machines | |
Bandwidth of the physical machines | |
Computer processing unit of the VMs | |
Memory of the VMs | |
Bandwidth of the VMs | |
Binary variable | |
Task count | |
Number of tasks | |
Degree of load | |
Maximum load in the host | |
Minimum load in the host | |
Finishing time of the task , | |
Arrival time of the task , | |
Dead-line of the task | |
Execution time of the task . | |
Completion time of the task | |
Waiting time of the task | |
Scaling factor | |
Old positions of elephant in a clan | |
New positions of elephant in a clan , | |
Global or best fitted positions of a matriarch elephant in a clan | |
Random numbers performs uniform distribution | |
and | Lower and upper bounds of the elephant position |
Worst elephants in a clan | |
Acceleration coefficients | |
Inertia weight used to balance local and global search | |
and | Particles’ global best position and the personal best position |
Nonlinear function | |
Execution cost of the task on a resource | |
Processing power of the VMs | |
Task size | |
Instances generated from the source code | |
Number of instances per unit | |
Total count of the workload |
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Datacenter | |
---|---|
Number of hosts | 2 |
Number of datacenters | 10 |
VMs | |
Number of processing elements | 2 |
Bandwidth | 500 |
Million instructions per seconds | 500 |
Number of VMs | 1000 |
Number of service providers | 5 |
Task (cloud-let) | |
Number of tasks | 1000 |
Task length | 1000 |
Execution Time (ms) | ||||||||
---|---|---|---|---|---|---|---|---|
Platform | ALO | GA | ACO | PSO | GWO | MVO | EHO | SCEHO-IPSO |
Storm | 894 | 1203 | 910 | 887 | 978 | 963 | 834 | 733 |
Flink | 873 | 1129 | 905 | 876 | 956 | 904 | 820 | 720 |
Spark | 802 | 1082 | 890 | 864 | 944 | 896 | 802 | 652 |
Kafka | 772 | 910 | 787 | 793 | 892 | 834 | 772 | 612 |
Cost | ||||||||
Platform | ALO | GA | ACO | PSO | GWO | MVO | EHO | SCEHO-IPSO |
Storm | 202 | 190 | 208 | 152 | 188 | 192 | 116 | 102 |
Flink | 193 | 182 | 201 | 144 | 172 | 188 | 102 | 88 |
Spark | 170 | 177 | 188 | 138 | 166 | 177 | 92 | 82 |
Kafka | 154 | 173 | 177 | 122 | 152 | 152 | 72 | 62 |
Latency (ms) | ||||||||
---|---|---|---|---|---|---|---|---|
Platform | ALO | GA | ACO | PSO | GWO | MVO | EHO | SCEHO-IPSO |
Storm | 2800 | 2914 | 3018 | 3920 | 2990 | 2560 | 1982 | 1630 |
Flink | 2773 | 2888 | 2822 | 3892 | 2967 | 2521 | 1928 | 1626 |
Spark | 2822 | 2880 | 2902 | 3620 | 2940 | 2490 | 1802 | 1550 |
Kafka | 2754 | 2635 | 2772 | 3450 | 2829 | 2339 | 1820 | 1510 |
Makespan | ||||||||
---|---|---|---|---|---|---|---|---|
Platform | ALO | GA | ACO | PSO | GWO | MVO | EHO | SCEHO-IPSO |
Storm | 288 | 283 | 193 | 188 | 190 | 144 | 94 | 88 |
Flink | 276 | 279 | 187 | 176 | 158 | 142 | 90 | 73 |
Spark | 244 | 232 | 143 | 165 | 148 | 122 | 88 | 45 |
Kafka | 240 | 212 | 123 | 142 | 122 | 110 | 78 | 44 |
Memory storage (kb) | ||||||||
Platform | ALO | GA | ACO | PSO | GWO | MVO | EHO | SCEHO-IPSO |
Storm | 512 | 538 | 727 | 721 | 573 | 698 | 413 | 338 |
Flink | 532 | 521 | 632 | 658 | 549 | 690 | 479 | 336 |
Spark | 454 | 477 | 553 | 630 | 490 | 532 | 493 | 322 |
Kafka | 380 | 392 | 442 | 532 | 422 | 504 | 379 | 309 |
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Rajashekar, K.J.; Channakrishnaraju; Gowda, P.C.; Jayachandra, A.B. SCEHO-IPSO: A Nature-Inspired Meta Heuristic Optimization for Task-Scheduling Policy in Cloud Computing. Appl. Sci. 2023, 13, 10850. https://doi.org/10.3390/app131910850
Rajashekar KJ, Channakrishnaraju, Gowda PC, Jayachandra AB. SCEHO-IPSO: A Nature-Inspired Meta Heuristic Optimization for Task-Scheduling Policy in Cloud Computing. Applied Sciences. 2023; 13(19):10850. https://doi.org/10.3390/app131910850
Chicago/Turabian StyleRajashekar, Kaidala Jayaram, Channakrishnaraju, Puttamadappa Chaluve Gowda, and Ananda Babu Jayachandra. 2023. "SCEHO-IPSO: A Nature-Inspired Meta Heuristic Optimization for Task-Scheduling Policy in Cloud Computing" Applied Sciences 13, no. 19: 10850. https://doi.org/10.3390/app131910850
APA StyleRajashekar, K. J., Channakrishnaraju, Gowda, P. C., & Jayachandra, A. B. (2023). SCEHO-IPSO: A Nature-Inspired Meta Heuristic Optimization for Task-Scheduling Policy in Cloud Computing. Applied Sciences, 13(19), 10850. https://doi.org/10.3390/app131910850