 Open Access
 Total Downloads : 418
 Authors : Neha Kapoor, Parveen Kakkar
 Paper ID : IJERTV3IS080924
 Volume & Issue : Volume 03, Issue 08 (August 2014)
 Published (First Online): 28082014
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Resource Matching Job Scheduling Algorithm in Mobile Cloud Computing Environment
Resource Matching Job Scheduling Algorithm
Neha Kapoor
Research Scholar, CSE
DAV Institute of Engineering & Technology Jalandhar, India
Parveen Kakkar
Assistant Professor, CSE
DAV Institute of Engineering & Technology Jalandhar, India
Abstract Mobile Cloud Computing (MCC) is a new emerging technology that has revolutionized the way in which mobile subscribers across the globe can enjoy abundant multimedia applications on the go. The scheduling of massive multimedia flows is a complicated task in the mobile cloud environment. The goal of this paper is to propose a model for joboriented resource scheduling in a mobile cloud computing environment in which the workflow is allocated or scheduled to the process which gives the available resources such as RAM, Bandwidth, MIPS etc. In this paper we did the analysis of job scheduling algorithms i.e. Blind Online Scheduling Algorithm (BOSA) and Resource Matching with Priority (RMP) on the basis of the performance metrics such as TurnAround Time, Waiting Time, Response Time and Throughput. From the results, it has been computed that Resource Matching with Priority Algorithm (RMP) has the lowest time parameters and maximum throughput and proves to be the most efficient algorithm for resource scheduling.
Keywords Job Scheduling, Process, Mobile Cloud Computing.

INTRODUCTION
Mobile computing is based collectively on three major concepts: hardware, software and communication. The hardware can be considered as mobile devices, such as Android phone and laptop. The mobile computing software is comprised of the numerous mobile applications installed the devices, such as the antivirus software, browser, gmail and games. The communication is comprised of the infrastructure, protocols and data delivery techniques which must be transparent to the end users.
MCC is defined as "a mobile computing technology that makes use of elastic resources of various clouds and network technologies providing unrestricted functionality, storage, and mobility to mobile devices anywhere, anytime based on the payasyougo principle."
Job scheduling is defined as the process to schedule the jobs to the available resources by finding out a sequence order in which the jobs can be executed considering various transaction logic constraints. The effective scheduling algorithm is one that spreads the load evenly on the available
processors thus maximizing the processors utilization while minimizing the total execution time.

SCHEDULING CRETERIA
CPU scheduling [8] is the process of allocating a process to perform its task. Scheduling of the process is based on certain criteria of the algorithm. Criteria include:

CPU utilization: CPU should be kept as busy as possible. That is, it should involve a maximum usage.

Throughput: Time in one measure of work that the numbers of processes are completed per unit time, is supposed to be maximum.

Turn Around time: The time that is involved from the time of submission of a process till its completion, is supposed to be minimum.

Waiting time: The addition of the time that the process is waiting for its execution, is supposed to be minimum.

Response time: The time that the process is submitted for a request until the first response is produced, is supposed to be minimum.


PROBLEM FORMULATION
The previous work done in this area assumes that the all the service requests in the cloud computing are linear in nature even if the request load is heavy , It assumes it is linearly increasing and there is not much nonlinearity however , this is not the case in real life scenarios. Any service request will have some peak and off peak time with request to day hours, week hourly and may even have seasonal peaks and valleys as a matter of fact, but at the same it is also known fact it will have some pattern which may be recognizable to some extend by using machine learning algorithms or by using some statistical tests etc. There is strong case where there may be need to find upper and lower bounds to find sub optimal solution locally of the complete dataset sample considered at particular time and then come the some logical conclusion especially when there is need to find new correlation based finding is to be determined as in the previous work which works on considering service rate ,
resource allocation , service routing to match resources in mobile cloud environment , therefore , there is need to work in realistic function designs which can incorporate the patterns that arise of real time rather than linear.

METHODOLOGY ADOPTED
The methodology that we will follow is that first of all the cloud simulated environment would be developed using simulator CloudSim in NetBeans IDE 7.4. After that workload distribution policy is to be designed. Then workload will be distributed according to the policy framed considering the following attributes such as arrival rate, service request rate, and available resources. After the distribution of the workload the next step is to design the scheduling policy and an algorithm. Then the workload will be executed using the scheduling algorithm proposed. At last the performance of a designed algorithm will be evaluated.
To Develop Cloud Simulated Environment
To Design a workload distribution policy
To distribute the workload among various processes according to the following attributes
Arrival Rate
Service Request Rate
Available Resources
Design a Scheduling Policy and algorithm

The arrival of jobs follows Poisson probability law and service time has an exponential distribution.

There are k virtual machines in n data centers in r region, each of which provides identical service.

The arrival rate is smaller than the combined service rate of all k service facilities.
ALGORITHM 1: BOSA
STEP 1: Consider u = {1, ., U} represent set of user classes and s = { 1, , S} represent set of server classes.
STEP 2: Get the Task List according to an independent Poisson Processes in the firstcome firstserved basis based on the calculations done by applying PoissonExponential Multiple Channel Queuing Model where, Pu, u U.
STEP 3: Now, considering the assumption 3 & assumption 4, BOSA satisfies an exponential function and the server from the same class i.e. s S has the same average service time.
STEP 4: Let be a specific scheduling scheme and t >= 0, be a time slot, the waiting time of a class u U is given by Wu (t; ).
STEP 5: Find the appropriate cloudlet/task providing the service for each R in Regions, DC in Data Center, and JOB in Task List.
STEP 6: Get the state of a virtual machines say VM from a list of a virtual machines.
STEP 7: If VM_STATE = IDLE, then add the job to the scheduled list.
STEP 8: Calculate the service time for the job and compare it with the minimum service time given by the formula in [3]
Run the workload based on the Scheduling Policy designed in the previous step
U Pu T W
min u,k
Evaluate the merits and Performance of the designed algorithm
(1)
u1
k 1
Pu T
Y t ;
s.t. P s 1
Y Y t ;
Fig1: Flow Diagram of Research Methodology Adopted
Where
s

The input of jobs is infinite.

The service is on firstcome first served basis.
(0,1) is the venture level
Pu(T) shows the recorded user request over time period T
Wu, k represents the waiting time up to T by the kth class u that has requested the service at the beginning of time.
STEP 9: If the service time of a job < Min Time Formula as given above in the equation (1), add the job to the cloudlet.
STEP 10: If no Virtual Machine available, job must wait in the waiting queue.

Resource Matching With Priority Scheduling Algorithm (RMP)
Blind Online Scheduling Algorithm (BOSA)
BOSA algorithm is based on a multiplechannel queuing system in which two or more channels (servers) are available to handle jobs that arrive for service. In the base paper, author has considered the most basic multiple channel system that contains parallel data centers serving a single queue on a firstcome first served (FCFS) basis. The data centers all provide the same service. The single queue may separate into shorter queues in front of the respective data centers. BOSA algorithm is based on the following assumptions:
Priority of jobs is an important issue in scheduling because some jobs should be serviced earlier than other those jobs cant stay for a long time in a system. So, we have proposed a prioritized resource matching scheduling algorithm and implemented in a simulated environment.
ALGORITHM 2: RMP
STEP 1: Find the appropriate Task List for each R in regions and for each DC in data centers.
STEP 2: Find the state of each virtual machine.
STEP 3: If (VM == Idle), find the minimum sized cloudlet ().
STEP 4: Calculate the Score for a Current available resources as given by formula:
Score of Current available resources = Current Total Million Instructions per Second * Current RAM * Current Bandwidth;
STEP 5: Calculate the Score of Required Resources as given by formula:
Score of Required Resource = Required Total MIPS * Required RAM * Required Bandwidth;
STEP 6: If the Score of current available resources is less than Score of the requested resources then assign the first task to the first available Virtual Machine.
STEP 7: If Virtual Machine is busy then wait in the waiting queue, else perform step 6.


EXPERIMENTAL SETUP
The scheduling algorithms have been executed and resulted in a simulated environment using a simulator named CloudSim. CloudSim is a java based tool especially for the execution of cloud based application or simulation.

Simulation Description
CloudSim version 3.0.2 is used to implement scheduling algorithms in Mobile Cloud Computing. The computer running Window 7 operating system is used for simulation.
We have implemented the Prioritized Resource Matching Scheduling model and compared it with Blind Online Scheduling Algorithm (BOSA) initially by taking into account the various parameters like Turnaround time, Waiting time, Response Time and Throughput.

Configuration of Data Centers

Configuration of Processing Elements
TABLE I: CONFIGURATION OF PROCESSING ELEMENTS
PARAMETERS
VALUE
No of Processing Element
2
Processing Power
2000

Configuration of Hosts
TABLE II: CONFIGURATION OF HOSTS
PARAMETERS
VALUE
No of Host
1
RAM
2048 MB
Storage
1000000 MB
Bandwidth
10000
VM SCHEDULING
TimeShared

Configuration of Virtual Machines

TABLE III: CONFIGURATION OF VIRTUAL MACHINES
PARAMETERS
VALUE
No of VM
5
Image Size
10000 MB
RAM
512 MB
VM name
XEN


RESULTS AND ANALYSIS
We have done the comparison of turnaround time, waiting time, response time and throughput of jobs when executed by applying Resource Matching with Priority Scheduling Algorithm (RMP) versus the Blind Online Scheduling Algorithm (BOSA). Figure 2 shows a graphical representation of comparison between two algorithms considering waiting time.
Fig.2. Comparison of BOSA and RMP Algorithm based on their TurnAround Time
Fig. 3 shows a graphical representation of comparison between two algorithms considering waiting time.
Fig.3. Comparison of BOSA and RMP Algorithm based on their waiting Time
Fig. 4 shows a graphical representation of comparison between two algorithms considering response time.
Fig.4. Comparison of BOSA and RMP Algorithm based on their Response Time
Fig 5 shows a graphical representation of comparison between two algorithms considering Throughput per unit time.
Fig.5. Comparison of BOSA and RMP Algorithm based on their Throughput

CONCLUSION
Scheduling is one of the most important tasks in mobile cloud computing environment. In this paper, we have analyzed two scheduling algorithms i.e. Blind Online Scheduling Algorithm (BOSA) and Resource Matching with Priority Algorithm (RMP) and tabulated various parameters.
From the observed results, it is concluded that:

The Resource Matching with Priority (RMP) in mobile cloud computing has reduced the turnaround time of jobs when compared with Blind Online Scheduling Algorithm (BOSA).

The waiting time for the jobs has been reduced in case of Resource Matching with Priority Scheduling Algorithm as compared to Blind Online Scheduling Algorithm (BOSA).

The response time has also been improved using Resource Matching with Priority Algorithm.

The throughput per unit time has been maximized in case of Resource Matching with Priority Scheduling Algorithm (RMP) as compared to Blind Online Scheduling Algorithm (BOSA).
Hence, RMP Algorithm proves to be a more efficient Scheduling algorithm for allocating mobile cloud computing resources to the jobs submitted by the users. Despite of giving least execution time and cost effective, the existing scheduling algorithms do not consider reliability issue. In future, we will enhance our algorithm by considering reliability issue using failure generator and failure monitor component of workflow management system for resource scheduling.


ACKNOWLEDGEMENT
This research paper is made possible through the help and support of my guide and mentor Mr. Parveen Kakkar. I would like to thank him for his utmost support and encouragement.
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