An Efficient Dynamic-Decision Based Task Scheduler for Task Offloading Optimization and Energy Management in Mobile Cloud Computing
Abstract
:1. Introduction
- The main objective of this system is dynamic decision-making for task scheduling using the decision-based algorithm.
- The task offloading decision is straightforward, using a dynamic decision-based scheduler to predict which task is offloaded to the mobile cloud and which task is executed on the mobile device.
- The controller effectively decides to enhance the efficiency of the decision algorithm by making choices in less time.
- The decision algorithm works collectively with the scheduler to enhance the probability of task-processing decision-making.
- We effectively reduce the power consumed by mobile devices’ task execution through task scheduling decision algorithms and task competition models.
- Finally, for evaluation of the system performance, we analyze the results using mobile offloading through simulation. Our proposed technique indicates that the decision algorithm effectively improves the system decision-making, and less power is consumed through dynamic decision-making for task execution.
2. Related Work
3. Proposed Model
- (a)
- Scheduling Handler: Handle multiple application services access and provide a dynamic scheduling technique for managing and distributing numerous services over the cloud.
- (b)
- Information Collection: Collect all information from the mobile devices accessing the services, like power information, processing, storage, battery information, bandwidth, processing capacity.
- (c)
- Information Processing/Checking: This part of the processor checks the above information to assess that the particular cloud service is suitable for handling the different mobile devices or not.
- (d)
- Scheduling Information Keeper: This Information Collaboration Site (ICS) module is important because it keeps the information related to the cloud services and some other information whilst the service is being used by a specific mobile device. After completion, the ICS automatically deletes the information from its storage.
- (e)
- Decision Support (DS): The DS module decides about the processing and other mobile device capacities and decides whether to allocate the cloud services to the mobile or not. The final allocation is based on the decision of this module.
- (f)
- Data Query Organizer (DQO): The DQO is responsible for data inflow and outflow from cloud services based on what type of request is received, and what kind of service needs to be distributed to the mobile client. Besides, to handle the computational costs and the results of the data query returned from the cloud processors.
System Model
Algorithm 1. Task Scheduling Decision |
Input: Input from Table 1 (LEGENDS Table) Output: Returns the state of the job submitted to the cloud or processed on mobile device/decision about mobile or cloud execution |
|
4. Simulation Environment
= 1.2 ms
ƩToffλ = 0.012 + (1.2 − 0.5) + 0.5 + 2.31
= 3.342 ms
ƩTtotalλ = (3.342 + 1.2) + (1 + 0.3 + 0.5)
ƩTtotalλ = (4.542) + (1.8)
ƩTtotalλ = (6.342) ms
- Battery information
- Bandwidth
- Storage
- Offloading time
- Job completion rate
5. Conclusions
6. Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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S.No | Proposed Papers | Algorithm Used | Fault Rate | Makespan Time | Energy Optimization | Offloading | Heterogeneity | Control Messages | Storage | % of Task Executed |
---|---|---|---|---|---|---|---|---|---|---|
1 | Lee et al. [30] | Group based fault tolerance | ✓ | ✓ | - | ✓ | ✓ | ✓ | - | - |
2 | Raju et al. [31] | Disease Resistance Approach | ✓ | ✓ | - | - | ✓ | - | - | ✓ |
3 | Abd et al. [26] | k-out-of-n framework (denoted by KNF) | ✓ | ✓ | ✓ | - | ✓ | - | - | ✓ |
4 | Park et al. [24] | MARKOV chain based monitoring Model | ✓ | ✓ | ✓ | ✓ | - | ✓ | ✓ | ✓ |
5 | Al-Sayed et al. [32] | Dynamic Grouping Technique | ✓ | ✓ | - | - | - | - | - | - |
6 | Kashanchi et al. [33] | A genetic method for task scheduling | ✓ | - | - | ✓ | - | - | ✓ | - |
7 | Peng et al. [34] | Reliability-compliant and Energy-aware Data Storage | ✓ | - | ✓ | - | ✓ | - | ✓ | - |
8 | Tang et al. [35] | Energy-Efficient Task Scheduling | ✓ | - | ✓ | - | - | - | - | ✓ |
9 | Lin et al. [36] | Performance-Aware Task Scheduling | - | - | ✓ | ✓ | - | - | - | ✓ |
10 | Guo et al. [37] | EETS. Model for Task Scheduling | - | - | ✓ | ✓ | - | ✓ | - | - |
11 | Wei et al. [38] | MLMCM for Task Scheduling | - | ✓ | - | - | ✓ | - | - | - |
12 | Nawrocki et al. [39] | M L through Adoptive service | - | ✓ | - | - | - | ✓ | ✓ | - |
13 | Akki et al. [40] | N.N. based optimization methods | - | - | ✓ | ✓ | - | - | - | ✓ |
14 | Shakarami et. al. [41] | stochastic-based offloading approaches | - | ✓ | ✓ | ✓ | - | ✓ | - | ✓ |
S.No | Lagend | Description |
---|---|---|
1 | K | Number of cloud virtual machines that are representing the cloud {kmax, kmin} |
2 | J | Job from anywhere on android phone, task request rate (customarily considered as per mobile device) |
3 | Poff | Mobile task offloading probability |
4 | Nt(j) | Number of tasks that forms a job (j) |
5 | Nc | The average number of cores of the CPU for the mobile |
6 | A | Clock frequency ratio |
7 | B | The bandwidth available to the mobile |
8 | C | Job size in terms of instructions |
9 | Sc | Cloud machine speedup |
10 | C(B,C,Sc) | Mobile device energy balance |
11 | M | Instructions/second (job/task execution speed) |
12 | D | Data transfer amount (in bytes) |
13 | R | RAM required on memory (in bytes) |
14 | Wm | Average power used by mobile device |
15 | Wi | Power used by mobile when idle |
16 | Woff | Power used by mobile when it is offloading a job to the cloud VM |
17 | Won | Power used when network enabled on the mobile device |
18 | Eon | Energy to turn the network interface |
19 | ΔTon | Average time for turning on the network interface |
20 | ΔTe | Average time for task/job execution |
21 | ΔTm | Average mobile job execution time |
22 | ΔToff | Average time required for the offloading process |
23 | ΔTec | Average execution time on the cloud |
24 | ΔTret | Average job return time from cloud VM |
25 | Øtc | The ratio between waiting and execution time on the mobile or cloud |
26 | S | Setpoint for Øtc |
27 | D | Parameter for the adaptive cloud controller |
28 | ƒct | Tasks completed on the cloud |
29 | Q | Probability of tasks at low-parallelism |
30 | F | Cloud speed-up is estimated using the formula. |
31 | Mb | Mobile battery information |
32 | ML | Mobile location information |
33 | Mstore | Mobile storage information |
34 | M(b)threshold | Estimated battery required for backup for the offload of a task |
35 | Mloc | Mobile current location |
36 | Mnew-loc | New location of the mobile |
S.No. | Tasks | ΔTm (ms) | Battery Information (mAh) | Location | More (Storage, Mb) | ΔTtotal Mobile (ms) | B (Bandwidth, Kb/s) | CPU Cores | RAM (Gb) | Kmin, Kmax |
---|---|---|---|---|---|---|---|---|---|---|
1 | J1 | 0.5 | 0.2 | 33.9944073 72.9335021 | 5 | 1.2 | 131 | 1 | 2 | 5, 20 |
2 | J2 | 0.8 | 0.4 | 31.25440053 70.5335021 | 8 | 1.4 | 131 | 1 | 2 | 5, 20 |
3 | J3 | 1.8 | 0.7 | 32.2356291 69.7629013 | 12 | 3.2 | 131 | 1 | 2 | 5, 20 |
4 | J4 | 199 | 1.47 | 33.9944073 72.9335021 | 82 | 310.21 | 131 | 1 | 2 | 5, 20 |
5 | J5 | 2000.2 | 15.2 | 33.9944073 72.9335021 | 503 | 2821.4 | 131 | 3 | 2 | 5, 20 |
6 | J6 | 5.77 | 12.6 | 33.9944073 72.9335021 | 9 | 10.42 | 131 | 1 | 2 | 5, 20 |
7 | J7 | 789.45 | 14.6 | 33.9944073 72.9335021 | 392 | 834.91 | 131 | 2 | 2 | 5, 20 |
8 | J8 | 43.2 | 6.8 | 33.9944073 72.9335021 | 34 | 65.23 | 131 | 1 | 2 | 5, 20 |
9 | J9 | 122 | 11.5 | 33.9944073 72.9335021 | 61 | 210.41 | 131 | 1 | 2 | 5, 20 |
10 | J10 | 450.81 | 28.6 | 33.9944073 72.9335021 | 242 | 602.31 | 131 | 2 | 2 | 5, 20 |
Tasks | ΔTtotalλ (ns) | Decision | Decision Value (Flag 0/1) |
---|---|---|---|
J1 | 6.342 | Mobile | 0 |
J2 | 8.422 | Mobile | 0 |
J3 | 12.362 | Mobile | 0 |
J4 | 838.482 | Cloud | 1 |
J5 | 7796.932 | Cloud | 1 |
J6 | 29.494 | Mobile | 0 |
J7 | 2498.302 | Cloud | 1 |
J8 | 182.702 | Mobile | 0 |
J9 | 356.802 | Mobile | 0 |
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Ali, A.; Iqbal, M.M.; Jamil, H.; Qayyum, F.; Jabbar, S.; Cheikhrouhou, O.; Baz, M.; Jamil, F. An Efficient Dynamic-Decision Based Task Scheduler for Task Offloading Optimization and Energy Management in Mobile Cloud Computing. Sensors 2021, 21, 4527. https://doi.org/10.3390/s21134527
Ali A, Iqbal MM, Jamil H, Qayyum F, Jabbar S, Cheikhrouhou O, Baz M, Jamil F. An Efficient Dynamic-Decision Based Task Scheduler for Task Offloading Optimization and Energy Management in Mobile Cloud Computing. Sensors. 2021; 21(13):4527. https://doi.org/10.3390/s21134527
Chicago/Turabian StyleAli, Abid, Muhammad Munawar Iqbal, Harun Jamil, Faiza Qayyum, Sohail Jabbar, Omar Cheikhrouhou, Mohammed Baz, and Faisal Jamil. 2021. "An Efficient Dynamic-Decision Based Task Scheduler for Task Offloading Optimization and Energy Management in Mobile Cloud Computing" Sensors 21, no. 13: 4527. https://doi.org/10.3390/s21134527