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\title{Simulator for Resource Optimization of Job Scheduling in a Grid Framework}
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\begin{document}

             \author[1]{Sumit  Mittal}

             \author[2]{Dr. P.K.  Suri}

             \affil[1]{  M.M. University}

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\date{\small \em Received: 11 December 2011 Accepted: 2 January 2012 Published: 15 January 2012}

\maketitle


\begin{abstract}
        


Traditionally, computer software?s has been written for serial computation. This software is to be run on a single computer with a single Central Processing Unit (CPU). A problem is broken into a discrete serial of instructions that executed in the exact order, one after another. Only one instruction can be executed at any moment of time on a single CPU. Parallel computing, on the other hand, is the simultaneous use of multiple computer resources to solve a computational problem. The program is to be run using multiple CPU?s. A problem is broken into discrete parts that can be solved concurrently and executed simultaneously on different CPU?s. The purpose of this proposed work is to develop a simulator using Java for the implementation of Job scheduling and shows that Parallel Execution is efficient with respect to serial execution in terms of time, speed and resources.

\end{abstract}


\keywords{grid computing, grid framework, job scheduling, parallel computing, resource optimization.}

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\let\tabcellsep& 	 	 		 
\section[{I. Introduction}]{I. Introduction}\par
mong the many disciplines of computer science, parallel processing is a discipline that deals with system structure and software processes related to the contingency performance of computer programs. It has been an area of active research interest and application for many fields, mainly the focus on powerful processing, but is now growing as the frequent processing model due to the semiconductor industry's move to multi-core processor chips.\par
Typically, software has been programmed for sequential computation, i.e., to be run on just one computer having just one main processing unit; where Instructions are implemented one after another, a problem is divided into distinct sequence of guidelines and only one instruction is executed at any instant. Although simple and economical serial computing is far much slower as compared to parallel computing. In essence, parallel computing is the simultaneous use of more than one processor or computer to solve a problem. Problems are run on multiple processors where each problem is broken into discrete parts which are further broken down into series of instructions to be executed simultaneously on different processors.\par
In the recent days, parallel computing has become popular based on multi-core processor chips.\par
Most desktop computer and laptop computer systems are now delivered with dual-core micro-processors with quad-core processor chips which becomes easily available. Processor producers have started to increase overall computing performance by including additional CPU cores to provide the maximum parallelism in a program. The reason is that improving performance through parallel computing can be far more energyefficient than improving micro-processor time wavelengths. In a world which is progressively mobile and energy aware, this has become essential. 
\section[{II.}]{II.} 
\section[{Parallel computing}]{Parallel computing}\par
Parallel computing is an outline of computation in which many data operations functions are carried out simultaneously \hyperref[b0]{[1]}.\par
Parallel computing takes four different forms: The first one is data parallelism / loop-level parallelism. In this computational form, a single thread controls all the data operations and in other situations, multiple threads control the execution, but they execute the same code. Second is Instruction level parallelism where instructions can be re-ordered and then, they are combined into groups and executed in parallel without affecting the result of the program \hyperref[b1]{[2]}. The third form is task parallelism which is in contrast with data parallelism where the processor executes different threads in same or different sets of data. It focuses on allocating the processes on different parallel computing nodes. Task parallelism does not generally changes with the size of a problem \hyperref[b1]{[2]}. The last form of parallel computation is bit level parallelism in which processors have to execute an operation on variables whose sizes are larger than the size of word.\par
Parallel computing has some advantages that make it attractive for certain types of problems that are suitable for use of multiprocessors, especially given limited computer memory, provides concurrency, saves money -Parallel computing resources can be built from cheap commodity components as shown in the figure \hyperref[fig_0]{1}, uses resources from a wide area network and saving time -Allocating more resources for a task shortens, it's time for completion with potential cost savings.\par
Conversely, parallel programming has also some disadvantages. By increasing the processors, memory in parallel computers, hence, produces a lot of data (I/O) and require parallel file system, need more space and more power, which leads to load imbalance. Parallel computer programs are more difficult to write than sequential ones, \hyperref[b4]{[7]} because concurrency introduces several new classes of potential software bugs, of which race conditions are the most common. Communication and synchronization between the different subtasks are typically some of the greatest obstacles to getting good parallel program performance. 
\section[{III. Association with Grid Framework}]{III. Association with Grid Framework}\par
Grid computing combines computers from multiple administrative domains to solve a single task \hyperref[b2]{[4]}. The grid can be thought of as a distributed system with non-interactive workloads that involve a large number of files. Grid computing tends to be more loosely coupled, heterogeneous and geographically dispersed \hyperref[b2]{[4]}. On a single-processing machine, testing one model can take as long as five days. Using grid framework, the model can be distributed into different number of processing segments, each of which goes to its own processor; a task that normally takes five days can be completed in several hours. 
\section[{IV. Comparison of Grids and Conventional Supercomputers}]{IV. Comparison of Grids and Conventional Supercomputers}\par
Distributed or grid computing in general is a special type of parallel computing that relies on complete computers resources (with onboard CPU's, storage, power supplies, network interfaces, etc.) connected to a network (private, public or the Internet) by a conventional network interface, such as Ethernet. This is in contrast to the traditional notion of a supercomputer, which has many processors connected by a local high-speed computer bus \hyperref[b3]{[6]}. The size of a grid may vary from small network of workstations within a corporation to large public collaborations across many companies and networks. The notion of a confined grid may also be known as intra-nodes cooperation whilst the notion of a larger, wider grid may thus, refer to internodes cooperation \hyperref[b4]{[7]}.\par
The primary advantage of distributed computing is that each node can be purchased as commodity hardware, which, when combined, can produce a similar computing resource as multiprocessor supercomputer, but at a lower cost. The primary performance disadvantage is that the various processors and local storage areas do not have high-speed connections. This arrangement is thus well-suited to applications in which multiple parallel computations can take place independently, without the need to communicate intermediate results between processors [8].\par
V. 
\section[{Simulation of Resource Optimization}]{Simulation of Resource Optimization}\par
The purpose of this research work is to develop an algorithm for the resource optimization of job scheduling in a grid framework and shows that parallel execution is efficient in terms of time, speed and throughput. In addition to total time taken to execute all jobs, we will also calculate the CPU's usage efficiency using the following equation:???????????????????? = ? ???????????????? ???? ?????? ?? ???????????? ???? ???????? ??=1 ?????????? ???????????? ???? ???????? × ???????? ?????? 1 ?????? × 100\%\par
The proposed application consists of 3 different classes:\par
1. Main Class: It contains a method that is being executed at program startup. The main flow occurs in this method. 2. Processor Class: This class contains methods to assign a job, check whether processor is free and ready for the next job and to calculate the amount of time it was busy. 3. Scheduler Class: It assigns jobs to the processors using the Round Robin scheduling. When a job needs to be assigned to the CPU, scheduler searches for the free processor and assigns the job to it. If at some moment, all CPUs are busy, it waits until one of the processors becomes free. 
\section[{VI.}]{VI.}\par
Algorithm to Compute the Efficiency of Job Scheduling in Grid Framework\par
Step 1. Read one line of data from the input file:\par
i.\par
Test serial number ii.\par
Number of jobs iii.\par
Number of processors.\par
Step 2. Create an array of instances of the Processor class. Create Job scheduler instance.\par
Step 3. Run Job Scheduler. Step 4. When work is finished (all jobs have been executed); and calculate the results (time taken).\par
Step 5. Write results to the output file.\par
Step 6. Go to step no. 1) unless input file is fully processed. 
\section[{VII. Simulation Results and Discussions}]{VII. Simulation Results and Discussions}\par
In order to compare serial and parallel implementations, a few series of test has been performed. For a fixed number of CPU's (2, 5, 10, 20, 30, 40, 50 in different series of tests), we ran 200 tests with different number of Jobs (5, 10, 15, etc up to 1000).\par
The input file is a simple text file that can be created in any text editor as shown in figure \hyperref[fig_0]{1}. Each line of this file should consist of 3 values separated by pipes ("|"). First value is a string that is a serial number of the test, second value is an integer that is number of jobs and third value is an integer that is number of processors.  Test Case 1: Table \hyperref[tab_0]{1} shows the execution time (in microseconds) taken by the processors in serial and parallel computation and efficiency of the system for the 5 CPU's.  Test Case 2: Table  {\ref 2} shows the execution time taken by the processors in serial and parallel computation and efficiency of the system for 10 CPU's. The graph 3 depicts the relationship between the number of jobs and the execution time estimation for the test case 2 (number of CPU's = 10) and graph 4 shows the efficiency vs. number of jobs in terms of the time and resources. 
\section[{Jobs}]{Jobs} 
\section[{Jobs}]{Jobs} 
\section[{Graph No. 3 Graph No. 4}]{Graph No. 3 Graph No. 4}\par
Test Case 3: Table \hyperref[tab_3]{3} shows the execution time taken by the processors in serial and parallel computation and efficiency of the system for 20 CPU's.      
\section[{Jobs}]{Jobs} 
\section[{Discussion and Conclusion}]{Discussion and Conclusion}\par
After analyzing the generated results, it has been concluded that parallel execution is very efficient in terms of execution time and resources. Parallel execution on N processors is almost N times faster than serial execution. It's not exactly N times faster because of the job scheduler that needs some time to delegate tasks to the processors available. For large number of tasks, it approaches to 98\% and 2\% is that time when CPU's are unused waiting for a task from the job scheduler.\par
For a fixed number of jobs, when we are increasing the number of processors, the efficiency will be decreasing as depicts by graph no. 7, because when all processors are busy all the time; efficiency will be equal to 100\%. When some of the processors were free (without a job assigned) some of the time, efficiency will be less than 100\%.\par
As multi-core processors bring parallel computing to mainstream customers, the key challenge in computing today is to transition the software industry to parallel programming. Future capabilities such as photorealistic graphics, computational perception and machine learning rely heavily on highly parallel algorithms. Enabling these capabilities will advance a new generation of experiences that will expand the scope and efficiency of what users can accomplish in their digital lifestyles and work place. These experiences include more natural, immersive and increasingly multisensory interactions that offer multi-dimensional richness and context awareness.  
\section[{Execution Time}]{Execution Time} 
\section[{Number of Jobs}]{Number of Jobs}\begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-3.png}
\caption{\label{fig_2}Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{22}\includegraphics[]{image-4.png}
\caption{\label{fig_3}Figure 2 Figure 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5}\includegraphics[]{image-5.png}
\caption{\label{fig_4}CPU = 5 )}\end{figure}
  \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.11130952380952382\textwidth}P{0.2765873015873016\textwidth}P{0.2091269841269841\textwidth}P{0.25297619047619047\textwidth}}
\tabcellsep \tabcellsep Time\tabcellsep \\
\tabcellsep \multicolumn{2}{l}{(In Microseconds)}\tabcellsep \\
\tabcellsep Serial\tabcellsep Parallel\tabcellsep Efficiency\\
5\tabcellsep 500\tabcellsep 105\tabcellsep 95.24\\
10\tabcellsep 1000\tabcellsep 206\tabcellsep 97.09\\
15\tabcellsep 1500\tabcellsep 307\tabcellsep 97.72\\
20\tabcellsep 2000\tabcellsep 408\tabcellsep 98.04\\
25\tabcellsep 2500\tabcellsep 509\tabcellsep 98.23\\
50\tabcellsep 5000\tabcellsep 1014\tabcellsep 98.62\\
100\tabcellsep 10000\tabcellsep 2024\tabcellsep 98.81\\
150\tabcellsep 15000\tabcellsep 3034\tabcellsep 98.88\\
200\tabcellsep 20000\tabcellsep 4044\tabcellsep 98.91\\
250\tabcellsep 25000\tabcellsep 5054\tabcellsep 98.93\\
500\tabcellsep 50000\tabcellsep 10104\tabcellsep 98.97\\
750\tabcellsep 75000\tabcellsep 15154\tabcellsep 98.98\\
1000\tabcellsep 100000\tabcellsep 20204\tabcellsep 98.99\end{longtable} \par
 
\caption{\label{tab_0}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.85\textwidth}}
CPU = 20)\end{longtable} \par
 
\caption{\label{tab_3}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.2512430939226519\textwidth}P{0.2441988950276243\textwidth}P{0.19019337016574586\textwidth}P{0.1643646408839779\textwidth}}
\tabcellsep \multicolumn{3}{l}{shows the efficiency for different}\\
\multicolumn{4}{l}{number of CPU's for a given set of jobs executed in the}\\
grid framework.\tabcellsep \tabcellsep \tabcellsep \\
Jobs\tabcellsep \tabcellsep Efficiency\tabcellsep \\
\tabcellsep 5 CPU\tabcellsep 10 CPU\tabcellsep 20 CPU\\
5\tabcellsep 95.24\tabcellsep 47.62\tabcellsep 23.81\\
10\tabcellsep 97.09\tabcellsep 90.91\tabcellsep 45.45\\
15\tabcellsep 97.72\tabcellsep 72.82\tabcellsep 65.22\\
20\tabcellsep 98.04\tabcellsep 94.79\tabcellsep 83.33\\
25\tabcellsep 98.23\tabcellsep 81.43\tabcellsep 60.68\\
50\tabcellsep 98.62\tabcellsep 97.28\tabcellsep 80.13\\
100\tabcellsep 98.81\tabcellsep 98.14\tabcellsep 95.42\\
150\tabcellsep 98.88\tabcellsep 98.43\tabcellsep 91.8\\
200\tabcellsep 98.91\tabcellsep 98.57\tabcellsep 97.18\\
250\tabcellsep 98.93\tabcellsep 98.66\tabcellsep 94.55\\
500\tabcellsep 98.97\tabcellsep 98.83\tabcellsep 98.27\\
750\tabcellsep 98.98\tabcellsep 98.89\tabcellsep 97.48\\
1000\tabcellsep 98.99\tabcellsep 98.92\tabcellsep 98.64\end{longtable} \par
 
\caption{\label{tab_4}Table 4}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.85\textwidth}}
CPU's\end{longtable} \par
 
\caption{\label{tab_5}Table 4 :}\end{figure}
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