Vol. 05, Issue 07,July 2020 Available Online: www.ajeee.co.in/index.php/AJEEE LOAD MANAGEMENT BY MOBILITY AWARE GROUP-BASED
JOB SCHEDULING ALGORITHM Tej Pratap Singh Bhadauriya1,
Co-Guide Name - Mr. Pramod Kumar Rathore2, Guide Name - Dr. A. K. Jhala3
RKDF College of Engineering Bhopal M. P.
Abstract:- Photovoltaic arrays are formed by grouping the modules with several cells connected in series and/or parallel. When the panels are connected in parallel, the output current increases and when connected in series the output voltage increases .Solar cells are made of semiconductor materials. The proposed Mobility Aware Group-Based Job Scheduling Algorithm (MAGJSA) is compared with two existing methods such as battery- aware criteria developed by Matías and Priority-grouping method (Priority). Tables and graphs are employed in order to provide better result analysis on the performance of the proposed and the existing methods. Job scheduling efficiency is measured as the ratio of number of scheduled jobs based on the available grid resources to the total number of user request
Keywords:- Priority-grouping method (Priority). Mobility Aware Group-Based Job Scheduling Algorithm (MAGJSA), Resource Availability (RA).
1. INTRODUCTION
A photovoltaic system converts sunlight into electrical energy. Photovoltaic cells are the core component of a photovoltaic system. PV cells may be grouped to form panels or modules. Photovoltaic arrays are formed by grouping the modules with several cells connected in series and/or parallel. When the panels are connected in parallel, the output current increases and when connected in series the output voltage increases .Solar cells are made of semiconductor materials. When a solar cell is exposed to sunlight it generates electricity. When a particle of light (photon) hits the PV cell, the energy brought by the particle is captured by the semiconductor material.
That energy hits the electrons and allows them to flow freely Wind is a form of solar energy. Wind flows owing to uneven heating of the atmosphere by the sun. The parameters used in the design of wind flow are the functions of earth's terrain, bodies of water, and vegetative cover. This wind flow or motion energy, when harvested by modern wind turbines converts such kinetic energy into mechanical power. Sometimes, the mechanical power is used for household tasks like grinding grain or pumping water.
Usually, a generator is used to convert this mechanical power into electricity to power homes, schools, farms, or business applications on small (residential) or large (utility) scales Natsheh & Albarbar (2013). These growing
trends can be attributed to the multi- dimensional benefits associated with wind energy Natsheh & Albarbar (2013):
Sustainable energy: wind is a renewable energy resource; it is inexhaustible and requires no "fuel" other than the wind that blows across the earth.
2. PROPOSED MOBILITY AWARE GROUP-BASED JOB SCHEDULING ALGORITHM (MAGJSA)
In order to deal with the issues related to the existing job scheduling methods such as reducing the response time and energy consumption for completing the jobs, Mobility Aware Group-Based Job Scheduling Algorithm (MAGJSA) is proposed. Proposed MAGJSA method performs group based job scheduling in mobile grids with less energy consumption of mobile devices to service the given tasks. First, user job requests for accessing the resources are collected and those jobs are grouped by measuring the resource availability. Then, jobs are grouped. Followed by, jobs are divided into number of sub-tasks to execute them based on priorities.
Hence, effective jobs scheduling is achieved with the help of genetic algorithm by considering the mobility, resource availability, job completion time and energy in proposed MAGJSA method.
Figure .1 shows the architecture of proposed MAGJSA method as shown below. As provided in Figure .1, the proposed MAGJSA method performs job
Vol. 05, Issue 07,July 2020 Available Online: www.ajeee.co.in/index.php/AJEEE grouping and job scheduling with the
consideration of mobility, resource availability and job completion time to improve the Grid performance.
Scheduling priorities are obtained with the measurement of resource availability, mobility and job completion time for the service providers.
Therefore, energy consumption for job scheduling and completing the jobs is optimized in an effective manner for grid users. The steps involved in the proposed MAGJSA method such as measurement of resource availability mobility and energy consumption, grouping of user jobs and job scheduling are explained in the following subsections.
2.1 Measurement of Resource availability, Mobility and Energy Consumption
The proposed MAGJSA method estimates Resource Availability (RA) for the grid user to utilize the system at particular time. Resource availability is measured as the ratio of predicted uptime to the addition of predicted uptime and downtime
Figure: 1 Architecture of Proposed MAGJSA Method The proposed MAGJSA method employs
mobility prediction algorithm in order to predict the mobility of a user. Mobility prediction helps for knowing the continuous access on grid resources by considering user's mobility. Hence, mobile devices make use of this mobility prediction in order to interact with grid resources easily.
The steps involved in mobility prediction algorithm with two steps such as pre-processing and definite prediction As shown, pre-processing and definite prediction steps are utilized in order to predict subsequent location based on the probability for each of the resultant locations appearing in history set.
Hence, all mobile devices mobility is predicted to assign the priority for job scheduling. Energy consumption is also measured for mobile grid in the proposed MAGJSA method as follows. Key energy utility sources of mobile grid are
computing devices (CPU) and cooling system.
2.2 Grouping of user Jobs
A job list from user machine is created by the user. Then, resource availability information is obtained. Resources and jobs are sorted in descending order based on their computing capacity and job duration. First Come First Serve (FCFS) order is utilized for the jobs to be executed on particular resources
2.3 Steps in Mobility Prediction Algorithm
1. Input: Past location of mobile devices „ Gi‟
2. Output: User mobility prediction
Step 1: Begin
//Preprocessing
Step 2: Record future and past location of a mobile device to a file using GPS
Vol. 05, Issue 07,July 2020 Available Online: www.ajeee.co.in/index.php/AJEEE
Step 3: For each mobile user
Step 4: Broke recorded large file into separate files
Step 5: Drop the records where access point is sensed by the devices
Step 6: Merge continuous records into sessions (starting time, related access point and session duration)
Step 7: End For
Step 8: Obtained location of a mobile device
//Definite Prediction
Step 9: Construct a set Qi of device with past locations Gi and current location C
Step 10: Compare Qi with history sequence Hi (i.e., Gi, C and subsequent n location)
Step 11: Add the result sequence into Prediction List „PL‟
Step 12: If PL=NULL
Step 13: Remove session with minimum duration from Gi
Step 14: End If
Step 15: Repeat search of H is until PL contains at least one historical sequenc
Step 16: End
Job grouping is achieved effectively by selecting high length jobs from front end and low length jobs from rear end of the job list. Grouped jobs are then partitioned into sub-tasks to assign priority on each job. Hence, jobs are scheduled based on these measured priorities with the help of Genetic Algorithm as follows.
2.4 Job Scheduling
Scheduling is referred as the process of allocating jobs or subtasks to the resources of a grid in order to improve the grid performance. Sub-tasks are scheduled based on priorities which are obtained by considering the parameters such as mobility, resource availability, job completion time, and energy using genetic algorithm. Table .1 illustrates the scheduling priorities with constraints as shown below.
Table: 1 Scheduling Priorities with Constraints
Constraint Priority
If RA is high,
Mobility is low and 1 Job completion time is low If RA is high,
Mobility is high and 2 Job completion time is low If RA is high,
Mobility is high and 3 Job completion time is high If RA is low,
Mobility is high and 4 Job completion time is high In proposed MAGJSA method, first
scheduling priority is given for the maximum RA, minimum mobility and minimum job completion time. In other cases, second scheduling priority is given for the maximum RA, high mobility and minimum job completion time. Third scheduling priority is given if RA is high, mobility is high and job completion time is high. Finally, fourth scheduling priority is given when there is low RA, high mobility and high job completion time.
Based on these assigned priorities, scheduling is performed for the grouped jobs from users in order to effectively process the given jobs with minimum response time from grid. The steps are involved the scheduling of jobs in proposed MAGJSA method.
2.5 Steps Involved in Scheduling of Jobs
1. Input: Number of grouped jobs, available resources, mobility
Vol. 05, Issue 07,July 2020 Available Online: www.ajeee.co.in/index.php/AJEEE 2. Output: Reduced energy
consumption
Step 1: Begin
Step 2: Estimate resource availability using (4.1)
Step 3: Obtain mobility using (4.2) and (4.3)
Step 4: Estimate job completion time using (3.5)
Step 5: Assign scheduling priorities according to Table 4.1
Step 6: Schedule jobs based on above priorities with genetic algorithm
Step 7: End
Genetic algorithm is utilized in proposed MAGJSA method for scheduling the jobs based on priorities.
2.6 The steps involved in Genetic Algorithm as follows.
1. Input: Number of grouped jobs, available resources, mobility, energy Consumption
2. Output: Reduced response time
Step 1: Begin
Step 2: Collect the information about grouped jobs
Step 3: Create initial population that includes randomly generated individuals
Step 4: Evaluate the fitness of each individual using equation (4.7)
Step 5: Select parents from population based on fitness
Step 6: Perform cross over operation to create new offspring from two parents
Step 7: Perform mutation for each bit of an individual
Step 8: Apply scheduling priorities
Step 9: If optimal solution obtained for job scheduling
Step 10: Stop the operation
Step 11: Else go to step 4
Step 12: End
Genetic Algorithm is utilized in proposed MAGJSA method in order to achieve optimal solution on job scheduling with minimum response time. Scheduling
priorities are provided based on the mobility, resource availability and job completion time. Energy consumption is reduced by effectively scheduling the grouped jobs in the proposed MAGJSA method.
3. EXPERIMENTAL EVALUATION
Proposed Mobility Aware Group-Based Job Scheduling Algorithm (MAGJSA) is implemented by utilizing JAVA language.
The proposed MAGJSA method employs the Amazon Simple Storage Service (Amazon S3) dataset for providing better job scheduling in mobile grids. Amazon S3 dataset maintains the data transmission and job grouping of data while it is uploaded in the grid environment.
In order to conduct the experiments, number of users and number user requests are considered as inputs. Number of users is varied from the range of 5 to 50. Similarly, number of user requests are taken from the range of 5 to 50 for analyzing the performance in various iterations. Performance of proposed MAGJSA method is analyzed with the factors such as job scheduling efficiency, response time, and energy consumption.
4. RESULTS AND DISCUSSION
The proposed Mobility Aware Group- Based Job Scheduling Algorithm (MAGJSA) is compared with two existing methods such as battery-aware criteria developed and Priority-grouping method (Priority) developed Tables and graphs are employed in order to provide better result analysis on the performance of the proposed and the existing methods.
4.1 Impact of Job scheduling Efficiency Job scheduling efficiency is measured as the ratio of number of scheduled jobs based on the available grid resources to the total number of user request
A method is said to be more effective when the job scheduling efficiency is maximum.
Table: 2 Tabulation of Job Scheduling Efficiency Job Scheduling Efficiency (%)
Number of
Existing Existing Proposed MAGJSA MAGJSA
user Battery- Priority MAGJSA Vs Vs
Vol. 05, Issue 07,July 2020 Available Online: www.ajeee.co.in/index.php/AJEEE requests
Aware
Battery- Priority
Criteria Aware
Criteria
5 71.8 66.3 77.4 7.8 16.7
10 72.5 68.1 80.1 10.5 17.6
15 74.6 69.5 82.6 10.7 18.8
20 77.8 71.7 85.2 9.5 18.8
25 79.1 72.8 86.4 9.2 18.7
30 80.4 74.5 87.1 8.3 16.9
35 83.8 76.4 89.5 6.8 17.1
40 84.2 77.3 90.3 7.2 16.8
45 85.1 78.9 91.7 7.8 16.2
50 87.6 80.3 92.2 5.3 14.8
Average 79.69 73.58 86.25 8.3 17.3
value
The above Table .2 shows the tabulation of job scheduling efficiency based on number of user requests using proposed MAGJSA, existing battery-aware criteria and Priority methods. The input, number of user requests is taken from the range of 5 to 50 for experimental purpose. From Table .2, it is evident that job scheduling efficiency is increased gradually with respect to the increase in number of user requests for all methods.
But, better performance of improving job scheduling efficiency is achieved in proposed MAGJSA method
when compared to other existing methods. Figure .2 demonstrates the measurement of job scheduling efficiency using different methods such as proposed MAGJSA method. As shown in Figure .2 proposed MAGJSA method provides high job scheduling efficiency when compared to other existing methods. Maximum job scheduling efficiency is provided by the proposed MAGJSA method where scheduling of jobs is performed based on the priorities.
Figure: 2 Measurement of Job Scheduling Efficiency
Vol. 05, Issue 07,July 2020 Available Online: www.ajeee.co.in/index.php/AJEEE 4.2 Impact of Response Time
Response time is defined as the amount of time taken for getting a response from grid after scheduling with respect to number of Grid users.
Table: 3 Tabulation of Response Time Response Time (ms)
Number Existing Existing Proposed MAGJSA MAGJSA of user Battery- Priority MAGJSA Vs Battery- Vs
requests Aware Aware Priority
Criteria Criteria
5 25.1 16.8 -21.5 -33.1 21.4
10 22.8 26.7 18.4 -19.3 -31.1
15 24.5 28.6 20.9 -14.7 -26.9
20 26.9 30.2 21.5 -20.1 -28.8
25 27.5 31.8 23.7 -13.8 -25.5
30 28.1 32.4 24.8 -11.7 -23.5
35 30.4 34.8 25.9 -14.8 -25.
40 31.8 35.7 28.7 -9.7 -19.6
45 32.7 38.1 30.5 -6.7 -19.9
50 34.1 39.7 32.4 -5.0 -18.4
Ave 32.31
24.36 -13.7 -25.2 28.02
These priorities are obtained by considering resource availability, mobility, and job completion time. Then, Genetic Algorithm is utilized to optimize the results on job scheduling. When there are 50 user requests, the job scheduling efficiency by 8.3%when compared to existing battery-aware criteria method and 17.3% when compared to existing Priority method. As a result, the proposed MAGJSA method improved job scheduling efficiency when compared to the existing battery-aware criteria method and existing Priority method. Table.3 shows the tabulation of response time with respect to number of users for different methods.
Number of user is considered as input and varied from the range of 5 to 50 for experimental purpose. As shown in Table 3, response time is increased for the
gradual increase in number of users using all methods. However, the proposed MAGJSA method achieves minimum response time when compared to other existing methods. The measurement of response time for the proposed MAGJSA method which is compared with the existing methods such as battery-aware criteria developed by Matías Hirsch et al.
(2016) and Priority method developed by Good head Tomvie Abraham et al. (2015).
From Figure .3, it is obvious that response time is effectively minimized in proposed MAGJSA method when compared to other methods. This efficient reduction of response time is achieved in proposed MAGJSA method by utilizing Genetic Algorithm to perform effective job scheduling.
Vol. 05, Issue 07,July 2020 Available Online: www.ajeee.co.in/index.php/AJEEE
Figure 3 Measurement of Response Time User request, the response time gets
minimized in MAGJSA method by around 14% when compared to existing battery- aware criteria method and around 25%
when compared existing Priority method.
Therefore, response time gets minimized in proposed MAGJSA method when compared to existing battery-aware criteria method and Priority method respectively.
6. CONCLUSION
A novel Mobility Aware Group-Based Job Scheduling Algorithm (MAGJSA) was developed for reducing the response time and energy consumption for mobile grid users by performing group based job scheduling. Collected jobs are grouped with the consideration of mobility, resource availability and job completion time. Mobility of a device was predicted using mobility prediction algorithm.
Then, grouped jobs were divided into sub-tasks and priorities are assigned to provide order of execution of these sub- tasks. Mobility aware energy efficient job scheduling is achieved in proposed MAGJSA method with the application of Genetic Algorithm where selection, crossover and mutation operations were performed.
Experimental results show that, proposed MAGJSA method improves the job scheduling efficiency nearly 13%
(Average of Battery-Aware Criteria method 8.3% and priority method 17.3%), proposed MAGJSA minimizes response time and energy consumption by around 20% (Average of 13.7% and 25.2%) and approximately 27% (Average of 20.2% and 34.2%) when compared to Battery aware criteria and Priority methods respectively.
However, scalability for addressing number of users is not considered in proposed MAGJSA method. Hence, effective job scheduling and load balancing are performed on grid to improve the scalability
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