Fault-Tolerant Trust-Based Task Scheduling Algorithm Using Harris Hawks Optimization in Cloud Computing
Abstract
:1. Introduction
Motivation and Contributions
- The aim is to design a fault-tolerant trust-based task scheduling algorithm by mapping all incoming tasks to VMs using Harris hawks optimization.
- For an effective scheduling process, the carefully calculated priorities of all incoming tasks should be accurately mapped to appropriate and low-electricity-unit-cost VMs by calculating VM priorities. Task-level and VM-level priorities are calculated to schedule tasks precisely onto respective VMs.
- We introduce a deadline constraint into our scheduler to carefully assign a single task to a VM at a time, and after completion of that task within the stipulated time, the next task can be assigned to that respective VM.
- We conduct workload generation in two phases. In the first phase, we use random generated workload employing different statistical distributions. In the second phase, we use real-time computing cluster worklogs.
- In this research work, we address parameters such as failure rate, makespan, success rate, availability and turnaround efficiency.
2. Related Works
3. Fault-Tolerant Trust-Based Task Scheduling
3.1. FTTATS Problem Definition and System Architecture
3.2. FTTATS Mathematical Modelling
3.3. Fitness Function for FTTATS
3.4. Fault-Tolerant Trust-Aware Task Scheduler (FTTATS) Using Harris Hawks Optimization
3.5. Proposed FTTA Task-Scheduling Algorithm
Algorithm 1. Fault-tolerant trust-aware task scheduling algorithm using Harris hawks optimization. |
Input:, .
Output: Efficient generation of schedules for tasks by mapping them to precise VMs while minimizing , , and improving , , , . |
Start
Initialization of Hawk birds population in a random manner. Initialization of fitness function. Calculation of task priorities by Equation (6). Calculation of VM priorities by Equation (7). Calculate fitness function by Equation (18). then Update position vectors using Equation (20). It is in exploitation. then Soft besiege begins, position vectors updation by Equation (23). then Hard besiege begins, position vectors updation by Equation (25). then Soft besiege by constructive steps begin, position vectors updation by Equation (30). then Hard besiege by constructive steps begin, position vectors updation by Equation (31). Identify best mapped tasks to VMs using above Hawks and calculate . current trust is increased then add current trust value to existing trust value of cloud provider. Trust value of cloud provider exponentially decreases Repeat process until all iterations completed. End |
4. Simulation and Results
4.1. Simulation Setup and Configuration Settings
4.2. Evaluation of Makespan
4.3. Evaluation of Rate of Failures
4.4. Evaluation of Availability of VMs
4.5. Evaluation of Success Rate of VMs
4.6. Evaluation of Turnaround Efficiency
4.7. Analysis and Result Discussion
5. Conclusions and Future Works
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Authors | Technique Used | Addressed Parameters |
---|---|---|
[7] | APSO | Makespan, throughput |
[8] | LJ-PSO, M-PSO | makespan, total execution time, degree of imbalance |
[9] | GAGELS | makespan, resource utilization |
[10] | MPSO | Makespan, resource utilization |
[11] | IT2FCM | Data movements, data placement, makespan |
[12] | PSO-RDAL | Response time, task deadline, penalty cost |
[13] | EPSOCHO | Makespan, processing cost, resource utilization |
[14] | GSOS | Makespan, cost |
[15] | AINN-BPSO | makespan, cost, degree of imbalance |
[16] | QPSO | Scheduling efficiency |
[17] | MVO-GA | Task transfer time |
[18] | NSGAIII | runtime, cost, power consumption |
[19] | Hybrid Lion-GA | Load balancing |
[20] | GSAGA | Makespan |
[21] | GBO | Makespan, accuracy of scheduling |
[22] | HWOA-MBA | Makespan, cost |
[23] | IWHOLF-TSC | Makespan, cost |
[24] | HWACOA | Makespan, cost |
[25] | LBACO | Datacenter processing time, response time, cost |
[26] | QOGSHO | Makespan, resource utilization, consistency, SLA violations |
[27] | ELHHO | Schedule length, execution cost, resource utilization |
[28] | RATSA | Failure rate |
[29] | SOATS | Cost, energy consumption |
[30] | HunterPlus | Energy consumption, job completion rate |
[31] | IQSSA | QOS parameters |
[32] | RAO | Makespan |
[33] | HFSGA | Makespan, cost |
[34] | DRL | Makespan, throughput |
[35] | IMOMVO | Execution time, throughput |
[36] | HBSFD | Task processing time, turnaround time |
[37] | Wale | Disk space |
[38] | Docker Containers | Disk space |
Notation | Meaning |
---|---|
Workload on all considered VMs | |
Workload on each VM | |
Workload on all considered physical hosts | |
Capacity of a VM | |
Total capacity of all VMs | |
Length of all considered tasks | |
Priorities of all considered tasks | |
Priorities of all considered VMs based on electricity cost | |
Deadline constraint | |
Execution time of all considered tasks | |
Finish time for a task | |
Makespan of a task | |
Rate of failures | |
Availability of considered VMs | |
Success rate of considered VMs | |
Turnaround time of considered VMs | |
Trust in cloud provider |
Name | Quantity |
---|---|
No. tasks | 1000 |
Length of tasks | 900,000 |
Memory of virtual host | 2048 MB |
Bandwidth of virtual resources | 15 Mbps |
Processing elements | 1200 MIPS |
Physical host memory | 32 GB |
Physical host hard disk capacity | 2 TB |
Bandwidth capacity of physical host | 100 Mbps |
Hypervisor type | Monolithic |
Name of the hypervisor | Xen |
OS of physical host | MAC |
Operating system of virtual host | Linux |
No. of datacenters | 10 |
No. of Tasks | ACO | GA | PSO | FTTATS |
---|---|---|---|---|
100 (D01) | 708.12 | 715.43 | 692.34 | 612.43 |
500 (D01) | 925.17 | 1338.26 | 1114.8 | 812.43 |
1000 (D01) | 1412.45 | 1697.31 | 1824.6 | 921.37 |
No. of Tasks | ACO | GA | PSO | FTTATS |
100 (D02) | 952.18 | 921.39 | 830.17 | 727.5 |
500 (D02) | 1323.71 | 1308.34 | 1419.18 | 931.26 |
1000 (D02) | 1628.92 | 1698.13 | 1822.57 | 1308.21 |
No. of Tasks | ACO | GA | PSO | FTTATS |
100 (D03) | 872.43 | 742.56 | 837.28 | 621.53 |
500 (D03) | 924.53 | 1298.21 | 1098.22 | 702.78 |
1000 (D03) | 1413.7 | 1502.56 | 1267.87 | 953.12 |
No. of Tasks | ACO | GA | PSO | FTTATS |
100 (D04) | 624.98 | 711.28 | 624.78 | 509.32 |
500 (D04) | 761.67 | 823.78 | 724.37 | 646.31 |
1000 (D04) | 1412.76 | 1498.32 | 1331.27 | 1202.62 |
No. of Tasks | ACO | GA | PSO | FTTATS |
100 (D05) | 1472.1 | 1408.72 | 1873.16 | 821.37 |
500 (D05) | 1784.6 | 2621.35 | 2187.23 | 1409.11 |
1000 (D05) | 2653.98 | 3432.78 | 2821.11 | 1812.46 |
No. of Tasks | ACO | GA | PSO | FTTATS |
100 (D06) | 821.77 | 722.99 | 687.67 | 603.45 |
500 (D06) | 931.45 | 1098.21 | 902.32 | 802.19 |
1000 (D06) | 1421.76 | 1921.46 | 1902.32 | 1056.34 |
No. of Tasks | ACO | GA | PSO | FTTATS |
---|---|---|---|---|
100 (D01) | 54.32 | 51.13 | 50.11 | 18.11 |
500 (D01) | 63.52 | 60.29 | 59.37 | 22.13 |
1000 (D01) | 45.14 | 47.14 | 48.22 | 17.11 |
No. of Tasks | ACO | GA | PSO | FTTATS |
100 (D02) | 51.67 | 48.43 | 46.78 | 21.15 |
500 (D02) | 60.32 | 57.17 | 35.33 | 19.28 |
1000 (D02) | 53.24 | 42.43 | 49.15 | 17.31 |
No. of Tasks | ACO | GA | PSO | FTTATS |
100 (D03) | 62.76 | 51.88 | 32.17 | 16.22 |
500 (D03) | 45.17 | 48.37 | 27.32 | 24.37 |
1000 (D03) | 56.18 | 38.21 | 20.17 | 14.29 |
No. of Tasks | ACO | GA | PSO | FTTATS |
100 (D04) | 45.31 | 63.88 | 71.56 | 18.13 |
500 (D04) | 32.12 | 54.16 | 69.88 | 21.35 |
1000 (D04) | 36.76 | 37.44 | 41.26 | 16.38 |
No. of Tasks | ACO | GA | PSO | FTTATS |
100 (D05) | 77.37 | 68.56 | 66.09 | 21.29 |
500 (D05) | 69.87 | 71.22 | 71.43 | 18.98 |
1000 (D05) | 71.23 | 75.32 | 61.33 | 24.17 |
No. of Tasks | ACO | GA | PSO | FTTATS |
100 (D06) | 56.34 | 68.32 | 56.87 | 19.72 |
500 (D06) | 64.87 | 58.32 | 65.47 | 18.35 |
1000 (D06) | 71.82 | 64.32 | 78.34 | 12.34 |
No. of Tasks | ACO | GA | PSO | FTTATS |
---|---|---|---|---|
100 (D01) | 65.21 | 67.88 | 71.24 | 86.87 |
500 (D01) | 71.36 | 62.75 | 69.37 | 87.36 |
1000 (D01) | 78.47 | 71.37 | 75.87 | 89.99 |
No. of Tasks | ACO | GA | PSO | FTTATS |
100 (D02) | 69.89 | 65.12 | 72.37 | 88.91 |
500 (D02) | 72.77 | 59.88 | 67.17 | 82.99 |
1000 (D02) | 65.44 | 61.17 | 74.88 | 90.37 |
No. of Tasks | ACO | GA | PSO | FTTATS |
100 (D03) | 77.32 | 58.47 | 68.09 | 85.44 |
500 (D03) | 66.19 | 63.67 | 78.67 | 89.23 |
1000 (D03) | 79.35 | 71.98 | 69.11 | 91.56 |
No. of Tasks | ACO | GA | PSO | FTTATS |
100 (D04) | 67.99 | 79.34 | 78.61 | 89.93 |
500 (D04) | 72.43 | 82.11 | 62.19 | 91.65 |
1000 (D04) | 76.97 | 69.14 | 79.98 | 94.35 |
No. of Tasks | ACO | GA | PSO | FTTATS |
100 (D05) | 52.47 | 59.45 | 54.12 | 82.56 |
500 (D05) | 67.18 | 62.08 | 62.18 | 89.17 |
1000 (D05) | 74.86 | 67.44 | 69.23 | 90.12 |
No. of Tasks | ACO | GA | PSO | FTTATS |
100 (D06) | 42.37 | 54.32 | 62.21 | 87.42 |
500 (D06) | 54.65 | 61.12 | 71.88 | 91.26 |
1000 (D06) | 63.18 | 69.98 | 67.10 | 94.31 |
No. of Tasks | ACO | GA | PSO | FTTATS |
---|---|---|---|---|
100 (D01) | 72.17 | 69.21 | 71.24 | 87.19 |
500 (D01) | 62.34 | 75.32 | 62.17 | 91.35 |
1000 (D01) | 49.36 | 79.21 | 59.61 | 96.36 |
No. of Tasks | ACO | GA | PSO | FTTATS |
100 (D02) | 54.61 | 59.57 | 74.88 | 87.38 |
500 (D02) | 61.37 | 64.32 | 80.19 | 90.14 |
1000 (D02) | 69.57 | 71.37 | 82.17 | 94.66 |
No. of Tasks | ACO | GA | PSO | FTTATS |
100 (D03) | 64.89 | 55.31 | 73.16 | 89.09 |
500 (D03) | 72.61 | 61.16 | 80.11 | 93.46 |
1000 (D03) | 65.15 | 69.16 | 84.57 | 96.17 |
No. of Tasks | ACO | GA | PSO | FTTATS |
100 (D04) | 56.39 | 58.81 | 75.20 | 84.16 |
500 (D04) | 60.47 | 68.16 | 62.11 | 91.98 |
1000 (D04) | 70.12 | 72.39 | 59.26 | 97.15 |
No. of Tasks | ACO | GA | PSO | FTTATS |
100 (D05) | 48.15 | 62.26 | 57.87 | 90.29 |
500 (D05) | 55.06 | 68.67 | 67.31 | 94.37 |
1000 (D05) | 61.02 | 73.22 | 74.36 | 98.51 |
No. of Tasks | ACO | GA | PSO | FTTATS |
100 (D06) | 46.44 | 52.21 | 66.43 | 88.21 |
500 (D06) | 59.11 | 63.76 | 76.06 | 95.18 |
1000 (D06) | 66.29 | 74.07 | 84.01 | 98.29 |
No. of Tasks | ACO | GA | PSO | FTTATS |
---|---|---|---|---|
100 (D01) | 64.28 | 53.12 | 53.19 | 88.36 |
500 (D01) | 69.96 | 61.27 | 65.28 | 90.94 |
1000 (D01) | 51.02 | 67.16 | 60.37 | 97.54 |
No. of Tasks | ACO | GA | PSO | FTTATS |
100 (D02) | 57.37 | 58.66 | 62.18 | 85.87 |
500 (D02) | 62.49 | 60.97 | 71.19 | 91.27 |
1000 (D02) | 70.17 | 73.59 | 75.37 | 96.29 |
No. of Tasks | ACO | GA | PSO | FTTATS |
100 (D03) | 61.33 | 58.13 | 61.18 | 87.11 |
500 (D03) | 64.19 | 65.10 | 77.26 | 94.48 |
1000 (D03) | 69.26 | 74.16 | 69.19 | 97.28 |
No. of Tasks | ACO | GA | PSO | FTTATS |
100 (D04) | 56.57 | 53.27 | 71.51 | 86.16 |
500 (D04) | 65.36 | 62.03 | 82.27 | 95.12 |
1000 (D04) | 72.43 | 69.18 | 83.68 | 97.38 |
No. of Tasks | ACO | GA | PSO | FTTATS |
100 (D05) | 45.15 | 60.19 | 59.36 | 91.17 |
500 (D05) | 52.46 | 63.46 | 63.39 | 95.29 |
1000 (D05) | 59.15 | 71.58 | 76.12 | 98.09 |
No. of Tasks | ACO | GA | PSO | FTTATS |
100 (D06) | 56.17 | 55.39 | 72.19 | 90.06 |
500 (D06) | 64.38 | 60.35 | 79.25 | 94.22 |
1000 (D06) | 68.87 | 70.78 | 82.27 | 98.78 |
Dataset | ACO | GA | PSO |
---|---|---|---|
D01 | 22.96 | 37.45 | 35.39 |
D02 | 24.01 | 24.45 | 27.13 |
D03 | 29.06 | 35.72 | 28.9 |
D04 | 15.75 | 22.25 | 12.01 |
D05 | 31.6 | 45.82 | 41.25 |
D06 | 22.45 | 34.21 | 29.5 |
Dataset | ACO | GA | PSO |
---|---|---|---|
D01 | 64.81 | 63.84 | 63.64 |
D02 | 65.06 | 61.01 | 56.02 |
D03 | 66.56 | 60.36 | 31.11 |
D04 | 51.07 | 64.06 | 69.42 |
D05 | 70.5 | 70.04 | 67.59 |
D06 | 73.88 | 73.6 | 74.88 |
Dataset | ACO | GA | PSO |
---|---|---|---|
D01 | 22.86 | 30.80 | 22.04 |
D02 | 26.03 | 40.88 | 22.31 |
D03 | 19.46 | 37.15 | 23.33 |
D04 | 26.92 | 19.65 | 24.97 |
D05 | 34.62 | 38.56 | 41.13 |
D06 | 70.39 | 47.23 | 35.68 |
Dataset | ACO | GA | PSO |
---|---|---|---|
D01 | 49.5 | 22.86 | 42.41 |
D02 | 46.67 | 39.46 | 14.71 |
D03 | 37.52 | 50.15 | 17.17 |
D04 | 46.16 | 37.08 | 39.02 |
D05 | 72.43 | 38.7 | 41.91 |
D06 | 63.91 | 48.23 | 75.5 |
Dataset | ACO | GA | PSO |
---|---|---|---|
D01 | 49.44 | 52.4 | 54.80 |
D02 | 43.89 | 41.52 | 30.98 |
D03 | 43.17 | 41.28 | 34.3 |
D04 | 43.37 | 51.04 | 17.34 |
D05 | 81.53 | 45.76 | 43.08 |
D06 | 49.42 | 51.76 | 21.11 |
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Mangalampalli, S.; Karri, G.R.; Gupta, A.; Chakrabarti, T.; Nallamala, S.H.; Chakrabarti, P.; Unhelkar, B.; Margala, M. Fault-Tolerant Trust-Based Task Scheduling Algorithm Using Harris Hawks Optimization in Cloud Computing. Sensors 2023, 23, 8009. https://doi.org/10.3390/s23188009
Mangalampalli S, Karri GR, Gupta A, Chakrabarti T, Nallamala SH, Chakrabarti P, Unhelkar B, Margala M. Fault-Tolerant Trust-Based Task Scheduling Algorithm Using Harris Hawks Optimization in Cloud Computing. Sensors. 2023; 23(18):8009. https://doi.org/10.3390/s23188009
Chicago/Turabian StyleMangalampalli, Sudheer, Ganesh Reddy Karri, Amit Gupta, Tulika Chakrabarti, Sri Hari Nallamala, Prasun Chakrabarti, Bhuvan Unhelkar, and Martin Margala. 2023. "Fault-Tolerant Trust-Based Task Scheduling Algorithm Using Harris Hawks Optimization in Cloud Computing" Sensors 23, no. 18: 8009. https://doi.org/10.3390/s23188009