Task Scheduling in Cloud Computing Environment Using Advanced Phasmatodea Population Evolution Algorithms
Abstract
:1. Introduction
- It proposes an advanced Phasmatodea Population Evolution (APPE) algorithm. It combines the restart mechanism with a new evolutionary trend of stick insect populations to balance the algorithm’s exploration and development capabilities.
- It builds an evaluation function using the makespan, cost, and load balancing degree as indicators.
- Extensive simulation and comparison of the proposed approach with comparable algorithms utilizing CEC2014 benchmark suites for testing.
- To assess the performance of the algorithm, it is compared to five similar algorithms in two heterogeneous environments.
2. Related Work
3.1. System Design
3.2. Evaluation Model
- Makespan:Makespan is a critical metric for assessing the effectiveness of task scheduling in the cloud. The makespan is the completion time of the task, which reflects the total operating duration of all VMs, and is computed using the following formula:
- Cost:The cost calculated according to the specification of VM is as follows; , , , , , and per hour [55]. Calculate the cost of the virtual machine through the following formula.represents the hourly cost of the jth virtual machine, and in a heterogeneous environment, its resource cost is related to , , and , stands for virtual machine memory, represents the bandwidth of VM.
- Load:represents the degree of imbalance of the system. n represents the number of VM, represents the degree of impact of each MIPS, RAM and bandwidth on the virtual machine, as shown by the following formula:Here, stands for the running time of the VM i, represents the average running time of the VM, is related to MIPS, RAM and bandwidth, , , are three weight values, respectively. Through the above performance indicators, the objective function formula is obtained as follows:
3.3. Scheduling Model Based on the APPE
3.3.1. Advanced PPE Algorithm
Algorithm 1: Advanced PPE Algorithm |
|
Algorithm 2: Restart Strategy |
|
3.3.2. Task Scheduling Algorithm Based on the Advanced PPE
Algorithm 3: APPE based task scheduling |
|
4. Experiments
4.1. CEC 2014 Benchmark Function Test
4.2. Heterogeneous Cloud Environment Test
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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PSO | BOA | GSA | GA | PPE | APPE | |
---|---|---|---|---|---|---|
1.9144 (<) | 2.0540 (<) | 3.7244 (<) | 1.3810 (<) | 1.5112 (<) | 9.1968 | |
1.2384 (>) | 8.6593 (<) | 1.0383 (>) | 7.0194 (<) | 1.0129 (<) | 1.8361 | |
8.7023 (<) | 2.5869 (<) | 7.1666 (<) | 1.5172 (<) | 4.5206 (<) | 1.7352 | |
6.0384 (<) | 2.1446 (<) | 8.3904 (<) | 1.0381 (<) | 8.1387 (<) | 5.2926 | |
5.2092 (<) | 5.2136 (<) | 5.2000 (>) | 5.2080 (<) | 5.2102 (<) | 5.2004 | |
6.1649 (>) | 6.4896 (<) | 6.3146 (<) | 6.4134 (<) | 6.3256 (<) | 6.2694 | |
7.0099 (>) | 1.5987 (<) | 9.7041 (<) | 1.3437 (<) | 7.5837 (<) | 7.0101 | |
8.5619 (>) | 1.2821 (<) | 9.4964 (<) | 1.2125 (<) | 1.0379 (<) | 9.1413 | |
9.6858 (>) | 1.5070 (<) | 1.0612 (<) | 1.2635 (<) | 1.1685 (<) | 1.0477 | |
1.9757 (>) | 1.0350 (<) | 4.7221 (<) | 8.9101 (<) | 6.3289 (<) | 2.6770 | |
4.8385 (<) | 1.0541 (<) | 5.3969 (<) | 9.3226 (<) | 6.9845 (<) | 4.7614 | |
1.2023 (<) | 1.2059 (<) | 1.2000 (>) | 1.2031 (<) | 1.2025 (<) | 1.2004 | |
1.3005 (<) | 1.3099 (<) | 1.3004 (<) | 1.3073 (<) | 1.3006 (<) | 1.3004 | |
1.4003 (<) | 1.7676 (<) | 1.4003 (<) | 1.6209 (<) | 1.4009 (<) | 1.4003 | |
1.5138 (>) | 8.1727 (<) | 1.5131 (<) | 2.9158 (<) | 2.3954 (<) | 1.5207 | |
1.6127 (<) | 1.6143 (<) | 1.6137 (<) | 1.6136 (<) | 1.6128 (<) | 1.6124 | |
1.5357 (<) | 3.0978 (<) | 1.3658 (<) | 1.3668 (<) | 5.2233 (<) | 6.7243 | |
4.2728 (<) | 7.6174 (<) | 2.4175 (>) | 4.0280 (<) | 4.9473 (<) | 3.7621 | |
1.9119 (>) | 2.6985 (<) | 2.0044 (<) | 2.3272 (<) | 1.9734 (<) | 1.9118 | |
7.5211 (<) | 1.7481 (<) | 7.9748 (<) | 1.0676 (<) | 4.1029 (<) | 4.2456 | |
4.5395 (<) | 1.4404 (<) | 2.8147 (<) | 6.3294 (<) | 1.3997 (<) | 2.2188 | |
2.5628 (>) | 8.3690 (<) | 3.2168 (<) | 8.5066 (<) | 2.9189 (<) | 2.7239 | |
2.6165 (<) | 3.7753 (<) | 2.6164 (<) | 2.5350 (<) | 2.6561 (<) | 2.5019 | |
2.6388 (<) | 2.7454 (<) | 2.6081 (<) | 2.6032 (<) | 2.6563 (<) | 2.6019 | |
2.7117 (<) | 2.7847 (<) | 2.7050 (<) | 2.7006 (<) | 2.7248 (<) | 2.7000 | |
2.7138 (<) | 2.8593 (<) | 2.8001 (<) | 2.7975 (<) | 2.7881 (<) | 2.7004 | |
3.3299 (<) | 4.9037 (<) | 4.8355 (<) | 2.9759 (<) | 3.4583 (<) | 2.9220 | |
4.0983 (<) | 1.1954 (<) | 6.3250 (<) | 3.0835 (<) | 7.9688 (<) | 3.0019 | |
8.0862 (<) | 9.2385 (<) | 2.0767 (<) | 4.1516 (<) | 1.2057 (>) | 4.5678 | |
1.0400 (<) | 1.6330 (<) | 1.0514 (<) | 2.7623 (<) | 9.8454 (<) | 9.2265 | |
21 / 0 / 9 | 30 / 0 / 0 | 26 / 0 / 4 | 30 / 0 / 0 | 29 / 0 / 1 |
PSO | BOA | GSA | GA | PPE | |
---|---|---|---|---|---|
4.5153 (+) | 1.5099 (+) | 2.3080 (+) | 1.5099 (+) | 1.5099 (+) | |
1.0000 (−) | 1.5099 (+) | 1.0000 (−) | 1.5099 (+) | 1.5099 (+) | |
7.7904 (+) | 1.5099 (+) | 1.5099 (+) | 1.5099 (+) | 1.5099 (+) | |
1.0141 (+) | 1.5099 (+) | 1.5099 (+) | 1.5099 (+) | 1.5099 (+) | |
1.5099 (+) | 1.5099 (+) | 1.0000 (−) | 1.5099 (+) | 1.5099 (+) | |
1.0000 (−) | 1.5099 (+) | 5.7833 (+) | 1.5099 (+) | 4.4455 (+) | |
1.0000 (−) | 1.5099 (+) | 1.5099 (+) | 1.5099 (+) | 1.5099 (+) | |
1.0000 (−) | 1.5099 (+) | 3.2591 (+) | 1.5099 (+) | 1.5099 (+) | |
1.0000 (−) | 1.5099 (+) | 4.6670 (+) | 1.5099 (+) | 1.5099 (+) | |
1.0000 (−) | 1.5099 (+) | 1.5099 (+) | 1.5099 (+) | 1.5099 (+) | |
7.8118 (≈) | 1.5099 (+) | 2.0420 (+) | 1.5099 (+) | 1.5099 (+) | |
1.5099 (+) | 1.5099 (+) | 1.0000 (−) | 1.5099 (+) | 1.5099 (+) | |
3.8693 (+) | 1.5099 (+) | 1.1129 (≈) | 1.5099 (+) | 1.9101 (+) | |
9.2874 (+) | 1.5099 (+) | 2.0177 (≈) | 1.5099 (+) | 1.6692 (+) | |
9.9938 (−) | 1.5099 (+) | 1.5099 (+) | 1.5099 (+) | 1.5099 (+) | |
1.3770 (+) | 1.5080 (+) | 3.6901 (+) | 1.3034 (+) | 1.4194 (+) | |
4.0723 (+) | 1.5090 (+) | 3.1405 (+) | 1.5099 (+) | 2.4876 (+) | |
2.3195 (+) | 1.5099 (+) | 9.9218 (−) | 1.5099 (+) | 1.5099 (+) | |
9.9602 (−) | 1.5099 (+) | 2.7470 (+) | 1.5099 (+) | 8.4736 (+) | |
2.7806 (+) | 1.5099 (+) | 1.5099 (+) | 1.5099 (+) | 1.5099 (+) | |
1.6643 (≈) | 1.5099 (+) | 7.4724 (≈) | 1.5099 (+) | 1.9101 (+) | |
9.9902 (−) | 1.5099 (+) | 6.0116 (+) | 1.5099 (+) | 2.2296 (+) | |
1.5099 (+) | 1.5099 (+) | 3.3825 (+) | 1.5099 (+) | 1.5099 (+) | |
1.5099 (+) | 1.5099 (+) | 6.7929 (≈) | 1.5099 (+) | 1.5099 (+) | |
1.5099 (+) | 1.5099 (+) | 1.3543 (+) | 1.5099 (+) | 1.5099 (+) | |
2.4909 (+) | 1.5099 (+) | 1.5099 (+) | 1.5099 (+) | 2.4876 (+) | |
5.4683 (+) | 1.5099 (+) | 1.5099 (+) | 5.3328 (+) | 1.5099 (+) | |
1.5099 (+) | 1.5099 (+) | 1.5099 (+) | 1.5099 (+) | 1.5099 (+) | |
9.9999 (≈) | 1.5099 (+) | 9.0463 (≈) | 1.5099 (+) | 1.0000 (−) | |
5.3240 (≈) | 1.5099 (+) | 1.5099 (+) | 1.5099 (+) | 1.5099 (+) |
Function | Sum of Squares | Degree of Freedom | Mean Squares | p-Value |
---|---|---|---|---|
4.8847 | 5 | 9.7693 | 2.2171 | |
5.0833 | 5 | 1.0167 | 1.3767 | |
4.9640 | 5 | 9.9280 | 7.3103 | |
5.0313 | 5 | 1.0063 | 2.8500 | |
5.0047 | 5 | 1.0009 | 4.1388 | |
4.9533 | 5 | 9.9067 | 8.4866 | |
5.2127 | 5 | 1.0425 | 2.5197 | |
5.1380 | 5 | 1.0276 | 6.4053 | |
5.1107 | 5 | 1.0221 | 9.3906 | |
5.1613 | 5 | 1.0323 | 4.6205 | |
4.6787 | 5 | 9.3573 | 3.9463 | |
4.8173 | 5 | 9.6347 | 5.6836 | |
4.6647 | 5 | 9.3293 | 4.7988 | |
4.7707 | 5 | 9.5413 | 1.0912 | |
5.0693 | 5 | 1.0139 | 1.6746 | |
4.4293 | 5 | 8.8587 | 1.2823 | |
4.8733 | 5 | 9.7467 | 2.5978 | |
4.8947 | 5 | 9.7893 | 1.9278 | |
5.0260 | 5 | 1.0052 | 3.0708 | |
4.8827 | 5 | 9.7653 | 2.2800 | |
4.3720 | 5 | 8.7440 | 2.8532 | |
4.7253 | 5 | 9.4507 | 2.0564 | |
4.4827 | 5 | 8.9653 | 6.0920 | |
4.7887 | 5 | 9.5773 | 8.4849 | |
4.5980 | 5 | 9.1960 | 1.2176 | |
4.5840 | 5 | 9.1680 | 1.4805 | |
4.8987 | 5 | 9.7973 | 1.8229 | |
5.2127 | 5 | 1.0425 | 2.2520 | |
3.6307 | 5 | 7.2613 | 8.6256 | |
4.9567 | 5 | 9.8933 | 9.3160 |
Entity | Parameter | Values |
---|---|---|
Task | Nm of Task | 50–500 |
Length | 100–1000 | |
Virtual Machine | Nm of VM | 15 |
RAM | 512 MB | |
MIPS | 100–1000 | |
Bandwidth | 1000 MB | |
Size | 10,000 | |
VVM | XUN | |
Operating-System | Linux | |
Nm of CPUs | 1 | |
Host | Nm of Host | 2 |
RAM | 2048 MB | |
Storage | 1,000,000 | |
Bandwidth | 10,000 | |
Data Center | Amount | 2 |
Entity | Parameter | Values |
---|---|---|
Task | Nm of Task | 50–500 |
Length | 100–1000 | |
Virtual Machine | Nm of VM | 25 |
RAM | 128–15,360 MB | |
MIPS | 256–30,720 | |
Bandwidth | 128–15,360 MB | |
Size | 10 GB | |
VVM | XUN | |
Operating-System | Linux | |
Nm of CPUs | 1 | |
Host | Nm of Host | 2 |
RAM | 20 GB | |
Storage | 1 TB | |
Bandwidth | 10 GB | |
Data Center | Amount | 2 |
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Zhang, A.-N.; Chu, S.-C.; Song, P.-C.; Wang, H.; Pan, J.-S. Task Scheduling in Cloud Computing Environment Using Advanced Phasmatodea Population Evolution Algorithms. Electronics 2022, 11, 1451. https://doi.org/10.3390/electronics11091451
Zhang A-N, Chu S-C, Song P-C, Wang H, Pan J-S. Task Scheduling in Cloud Computing Environment Using Advanced Phasmatodea Population Evolution Algorithms. Electronics. 2022; 11(9):1451. https://doi.org/10.3390/electronics11091451
Chicago/Turabian StyleZhang, An-Ning, Shu-Chuan Chu, Pei-Cheng Song, Hui Wang, and Jeng-Shyang Pan. 2022. "Task Scheduling in Cloud Computing Environment Using Advanced Phasmatodea Population Evolution Algorithms" Electronics 11, no. 9: 1451. https://doi.org/10.3390/electronics11091451