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\title{Hybrid Genetic Swarm Scheduling for Cloud Computing}
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             \author[1]{Dr.  M.Sridhar}

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\date{\small \em Received: 16 December 2014 Accepted: 31 December 2014 Published: 15 January 2015}

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\begin{abstract}
        


Cloud computing ensures access to shared resources and common infrastructure, offering services on demand over a network for operations to meet changing business needs. Scheduling is a prominent activity that is executed in a cloud computing environment. To increase cloud computing work load efficiency, tasks scheduling is performed to get maximum profit. In cloud, high communication cost prevents task schedulers from being applied in large scale distributed environments. Cloud environment system scheduling is NP-complete. To solve the NP complete and NP hard problems heuristic approaches are used. This study proposes a hybrid optimization based on Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for scheduling in cloud environments

\end{abstract}


\keywords{cloud computing, scheduling, particle swarm optimization (PSO), genetic algorithm (GA).}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
loud computing attracts increasing applications to run in remote datacenters. Many complex applications need parallel processing capabilities some of which show a decrease in CPU resources use. Whenever there is a parallelism increase, when jobs are not scheduled correctly, it reduces computer performance. Scheduling allocates tasks to available resources based on tasks' qualities and need \hyperref[b0]{[1,}\hyperref[b1]{2]}. The goal of scheduling is increased resource use without affecting cloud provided services.\par
Scheduling efficiency mechanism in cloud computing depends on how efficiently it manages the processes and increases server performance as well as resources. Scheduling problems involve jobs that should be scheduled on machines subject to constraints to optimize a given objective function \hyperref[b2]{[3]}. The goal is computing a schedule that specifies when and on which machine a job is to be executed. The scheduler in online scheduling receives jobs that arrive over time, and schedules them without any knowledge of the future.\par
Cloud scheduling process is divided into 3 stages namely: a) Resource discovering and filtering-Datacenter Broker discovers resources in a network system collecting status information on resources.\par
In Cloud computing, Task Scheduling algorithms aim to minimize tasks make span with minimum resources efficiently. Cloud computing, uses low-power hosts to achieve high usability. Cloud computing is a class of systems and applications that use distributed resources to perform a decentralized function \hyperref[b3]{[4]}.\par
Clouds computing, uses computing resources (service nodes) in networks to, ensure complicated tasks execution needing large-scale computation. Thus, node selection to execute a task in cloud computing is to be considered. Scheduling algorithms utilize better executing efficiency and maintain system load balancing. The cloud's efficiency depends on algorithms used for task scheduling.\par
The job scheduling algorithm's advantage is achieving high performance computing and best system throughput \hyperref[b4]{[5]}. Traditional job scheduling algorithms cannot provide scheduling in cloud environments. According to simple classification, job scheduling algorithms in cloud computing are categorized into 2 groups; Batch mode heuristic scheduling algorithms (BMHA) and online mode heuristic algorithms.\par
Task scheduling algorithm maps jobs submitted to cloud environment to available resources so that in total response time, make span is minimized. A Max-Min algorithm feature is selecting the largest job and executing it on the fastest resource. The algorithm's drawback is its delaying execution of smaller jobs and indefinitely postponing smaller jobs execution because of the cloud's dynamic nature. The solution to this is improved Max-Min, which works well for a given set of jobs but, the dynamic cloud environment where jobs are submitted any time results in performance degradation.\par
Min-Min algorithm first finds minimum tasks execution time and then chooses one with least execution time. The algorithm assigns the task to a resource with minimum completion time. The same is repeated by Min-Min, till all tasks are scheduled \hyperref[b5]{[6,}\hyperref[b6]{7]}. The algorithm's limitation is that it chooses smaller tasks first using up resources with high computational power.\par
Conventional scheduling is infeasible in a cloud environment due to its dynamic, distributed, and sharable properties. Tasks resource allocation is to meet performance targets. Many jobs need resources while operating simultaneously \hyperref[b7]{[8]}. It is important to balance jobs on appropriate resources for optimal performance and cloud's efficient working. So, varied task parameters are considered for scheduling. Available resources should be effectively used without affecting service parameters.\par
Cloud environment system scheduling is NPcomplete. As users increase, tasks to be scheduled also increase proportionately. So, better algorithms to schedule tasks on such systems are required. Scheduling algorithms are service-oriented and vary in environments. To solve NP complete and NP hard problems, heuristic approaches are used. Heuristic techniques used are local heuristics, meta-heuristics and hyper-heuristics. Hyper-heuristics operate at a higher abstraction levels. Meta-heuristic techniques are expensive techniques needing knowledge in problem domain and heuristic technique.\par
Evolutionary algorithms are based on species origin. Examples are Particle Swarm Optimization (PSO) and Genetic algorithm (GA). PSO is a parallel evolutionary computation technique and a heuristic search method inspired by biological populations swarming behavior \hyperref[b8]{[9]}. Using PSO ensures a good performance. GA is a search heuristic that mimics natural evolution. It is routinely used to generate useful solutions for optimization and search problems. GAs belongs to a larger class of evolutionary algorithms, generating solutions to optimization problems with techniques from natural evolution like inheritance, selection, mutation, and crossover \hyperref[b9]{[10]}.\par
PSO algorithm has many advantages like easy realization, high flexibility, strong robustness, and scalability due to which it solves many combinational problems. But, its disadvantages are low convergence rate when solving large scale optimization problems and easily sinking into local optima due its randomicity \hyperref[b10]{[11]}. PSO is good in an initial phase but when going through iterations convergence rate becomes low and particles lose variety.\par
There is need for an algorithm to offset these issues and so this study proposed a hybrid algorithm where PSO combines with GA i.e. GAPSO algorithm ensuring better results due to the properties of both. The remaining sections of this paper are organized as follows: Section 2 reviews related work, Section 3 explains the methodology. Section 4 discusses experimental results and Section 5 concludes the work. 
\section[{II.}]{II.} 
\section[{Related Works}]{Related Works}\par
A hybrid task scheduling algorithm based on combining plus points of bio-inspired algorithms like Ant Colony Optimization (ACO) and Artificial Bee Algorithm (ABC) was proposed by Madivi and Kamath \hyperref[b11]{[12]}. The strong points of both algorithms are incorporated to optimize task scheduling in a cloud algorithm. It is observed that the new algorithm ensured an improvement of about 19\% compared to default FCFS scheduling strategy, 11\% better than ABC algorithm and was 9\% better than conventional ACO based task scheduling.\par
A Hybrid algorithm combining advantages of ACO and Cuckoo search was proposed by Raju et al., \hyperref[b12]{[13]}. The makespan is lowered by the hybrid algorithm, as jobs were executed in a specified time interval by required resources allocation using the Hybrid algorithm. Results showed that Hybrid algorithm performed well when compared to ACO algorithm regarding performance of algorithm and make span.\par
The advantage of Multi-Agent Genetic Algorithm (MAGA) a hybrid of GA, whose performance is superior to traditional GA was proved by Zhu et al., \hyperref[b13]{[14]}. MAGA solved load balancing in cloud computing by designing a load balancing model based on virtualization resource management. Experiment results comparing MAGA with Minimum strategy proved that MAGA achieved better load balancing performance.\par
The performance of Hadoop schedulers including FIFO and Fair sharing was analyzed by Rasooli and Down \hyperref[b14]{[15]} comparing them with a Classification and Optimization based Scheduler for Heterogeneous Hadoop (COSHH) scheduler, developed by the authors. A hybrid solution was introduced, based on insights which selected appropriate scheduling algorithms for scalable and heterogeneous Hadoop systems regarding number of incoming jobs and available resources.\par
A new parallel hybrid evolutionary algorithm to solve issues of virtual machines subletting in cloud systems was presented by Iturriaga et al., \hyperref[b15]{[16]}. It deals with allocation of a set of Virtual Machine (VM) requests from customers to available pre-booked resources from a cloud broker, to maximize broker profit. The new parallel algorithm used a distributed subpopulations model, and a Simulated Annealing operator. Evaluation analyzed profit and makespan results of the new methods over a set of problem instances accounting for realistic workloads and scenarios with real data from cloud providers. A comparison with greedy heuristics revealed that the new method computed solutions with up to 133.8\% improvement in profit values, while ensuring accurate make span results.\par
A hybrid batch job scheduling method for grid environment combining GA and PSO techniques to reduce makespan and flow time was proposed by Dehghani Zahedani and Dastghaibyfard \hyperref[b17]{[17]}. Results showed a reduced make span in 7 of 12 instances of Braun workload compared to Min-Min, Max-Min, and discrete PSO algorithms.\par
A hybrid scheduling method that computed 5 different schedules, based on a combination of two resource selection rules with 4 job selection rules using the best of the five was proposed by Ashraf and Erlebach \hyperref[b18]{[18]}. Simulation of workflow scheduling in an advance reservation environment conducted with GridSim revealed that the new hybrid scheduling method achieved makespan improvement of up to 25.5\% on benchmark workflows, compared to earlier methods.\par
A hybrid algorithm, ant colony system and GA to solve job scheduling issues was proposed by Alobaedy and Ku-Mahamud \hyperref[b19]{[19]}. The high level hybridization algorithm ensured the identity of the algorithm performing scheduling tasks. The new study focused on static grid computing environment and metrics for optimization are makespan and flow time. Results showed that the new algorithm outperformed other stand-alone algorithms like ant system, GA and ant colony system for makespan. But for flow time, ant system and GA performed better.\par
A new updating mechanism for discrete PSO that directly used discrete solutions from personal and global best particles was proposed by Nguyen and Zhang \hyperref[b20]{[20]}. A new local search heuristic was proposed to refine solutions found by PSO. Results showed that hybrid PSO is more effective than current PSO methods in literature when tested on 2 benchmark datasets. The efficient hybrid method suited handling large-scale problem instances.\par
Map Reduce HPSO-GA based on Map Reduce parallel programming model presented by Sadasivam and Selvaraj \hyperref[b21]{[21]} yielded better results than normal PSO providing better load balancing and resource use in grid environment. It identified the node to which a task is assigned in a Hadoop cluster. So, the new approach could be used in Hadoop resource management system with Hadoop and system parameters to schedule jobs in a Hadoop cluster.\par
A hybrid job scheduling approach, which considered system load balancing and reduced total execution time and execution cost was presented by Javanmardi et al., \hyperref[b22]{[22]}. The proposed work's goal was assigning jobs to resources considering VM MIPS and jobs length. The new algorithm assigned jobs to resources considering job length and resource capacity. Performance was evaluated with famed cloud scheduling models. Results showed the proposed approach's efficiency regarding execution time, execution cost and average degree of imbalance. 
\section[{III.}]{III.} 
\section[{Methodology}]{Methodology}\par
In this work, a hybrid algorithm for cloud scheduling is proposed. It is based on PSO and GA. In the hybrid algorithm PSO combines with GA i.e. GAPSO algorithm ensuring better results. a) Genetic Algorithm (GA) GA is a meta-heuristic technique solving optimization problems by imitating natural selection; i.e., the adaptation to an environment performed by living beings \hyperref[b23]{[23]}. GA is an appealing approach to solve a complex problem. GA determines not one solution but a whole 'population' of 'individuals,' which are candidate solutions to a problem. Each individual's distinctive features are coded into a 'chromosome' which is a string of genes, whose values are chosen from a set of symbols.\par
GAs are stochastic search methods managing a population of simultaneous search positions. A conventional GA has 3 essential elements:\par
? a coding of the optimization problem 
\section[{? a mutation operator}]{? a mutation operator}\par
? a set of information-exchange operators GAs evaluate target function to be optimized at randomly selected points of a definition domain. Considering this information, a new set of points (a new population) is generated. Gradually the population approaches a function's local maxima and minima. The GA's pseudo code is given below \hyperref[b24]{[24]}. PSO, like other evolutionary computation techniques is a population based search algorithm initialized with a population of random solutions, called particles. Unlike other evolutionary computation techniques, a PSO particle is associated with velocity. Particles fly through search space with velocities, dynamically adjusted according to their and swarm's historical behaviors. So, particles fly towards better and better solutions in a search process. PSO algorithm is simple in concept, easy to implement, and efficient computationally. The PSO algorithm's updating rules are listed as \hyperref[b25]{[25]}.\par
The PSO algorithm consists of just three steps, which are repeated until some stopping condition is met \hyperref[b26]{[26]}:\par
1. Evaluate the fitness of each particle 2. Update individual and global best fitness and positions 
\section[{Update velocity and position of each particle}]{Update velocity and position of each particle}\par
The first two steps are fairly trivial. Fitness evaluation is by supplying a candidate solution to an objective function. Individual and global best fitness and positions are updated by comparing newly evaluated fitness against earlier individual and global best fitness, and replacing best fitness and positions as needed. The PSO algorithm is summarized as follows \hyperref[b27]{[27]}:\par
1. Initialize the swarm X i , the position of particles are randomly initialized within the hypercube of feasible space.\par
2. Evaluate the performance F of each particle, using its current position X i (t).\par
3. Compare the performance of each individual to its best performance so far: if F(X i (t))<F(P ibest ):F(P ibest )=F(X i (t )) P ibest = X i (t )\par
4. Compare the performance of each particle to theglobal best particle: if F (X i (t ))<F (P gbest ): F (P gbest )=F (X i (t )) P gbest = X i (t ) 5. Change the velocity of the particle.\par
6. Move each particle to a new position.\par
7. Go to step 2, and repeat until convergence.\par
c) Hybrid Genetic Algorithm Particle Swarm Optimization (GAPSO)\par
GAs were applied successfully to varied problems. But, using them for large-scale optimization is expensive due to its requirement of many function evaluations for convergence, leading to high cost for function evaluations computation \hyperref[b28]{[28]}. Considering the PSO's efficiency and GA and PSO's compensatory property combining searching abilities of both in one algorithm seems logical. Both GA and PSO share common elements \hyperref[b29]{[29]}:\par
1. Both initialize a population similarly.\par
2. Both use an evaluation function to determine a potential solution's fitness.\par
3. Both are generational, repeating same processes for a predetermined time.\par
A hybrid algorithm has operator like enhancement, selection, crossover, and mutation.\par
Enhancement: In every generation, after fitness values of population individuals are calculated, the top-half best performers are marked and regarded as elite. Instead of reproducing the elite directly to next generation as elite GAs do, they are first enhanced.\par
Here, a dynamic decrease of ? value was suggested based on a fraction multiplier (kw). When no improvement was made for a predefined number of consecutive design iterations:1 t t w k ? ? + =\par
It should be noted that elite in a generation can be from both groups of an earlier generation, i.e., enhanced elite or produced offspring. If elite i is an offspring produced by parents of a previous generation, then Selection: In GAPSO, GA operations are performed on the enhanced elite achieved by PSO. To select parents for crossover, a tournament selection scheme is used. Two enhanced elite are selected randomly, and their fitness values compared to select one with better fitness as a parent for placing it in a mating pool. This scheme is the selection operator in GA also.\par
Crossover: Parents are randomly selected from the mating pool in groups of two and two offspring are created through a crossover on parent solutions. This study uses a simulated binary crossover (SBX) \hyperref[b28]{[28]}. SBX operator is suitable as the spread of children solutions around parent solutions is controlled using distribution index, ?c. With this operator, an arbitrary contiguous region can be searched, provided there is enough diversity among feasible parent solutions Mutation: A final genetic operator, it creates new genetic material in the population to maintain the latter's diversity. Mutation operator used is a variable dependent random mutation where a solution is created near the parent solution with uniform probability distribution(1, 1)\textbf{(1, )}\par
(r 0.5) x + outside a specified lower and upper limits.t t i i i i x x + = + ? ? r i is a random number in [0, 1]. Î?" i is 
\section[{IV.}]{IV.} 
\section[{Experimental Results}]{Experimental Results}\par
To verify the proposed algorithm Cloudsim, a simulation software provided by Gridbus project was used. The proposed algorithm is integrated in the cloudsim layer. The simulator models the datacenter components which are designed to handle the service requests. These requests are the tasks which need to be allocated to VMs for processing. The VM is allocated a share of processing power by the Datacenter. Each VM has a pre configured processing capability based on memory, storage and Millions of Instructions it can execute per second. In this work four VMs from two data centers are used. The available Band Width of 128Kbps is dynamically varied. Similarly the memory is also dynamically varied from 256 Mb to 1.5 Gb. In this work, number of tasks were varied from 200 to 1000 in increments of 200 for estimating average schedule length and ratio of successful execution.The results are shown in table \hyperref[tab_3]{1}.  From table 2 and figure  {\ref 2} it is observed that the proposed GAPSO performs better by increasing the execution ratio when compared to Max-Min Scheduling and Minimum Execution time. The GAPSO improved Ratio of successful execution by 6.52\% than Max-Min Scheduling and by 4.3\% than Minimum Execution Time with 400 numbers of tasks.\par
V. 
\section[{Conclusion}]{Conclusion}\par
Cloud computing is a provider of dynamic services using huge scalable and virtualized resources over the Internet. Due to its novelty, there is no standard task scheduling algorithm in a cloud environment. In this study, the GAPSO performs better when compared to Max-Min Scheduling and Minimum Execution time in case of schedule length and execution ratio. GAPSO reduced average schedule length by an average of 5.66\% than Max-Min Scheduling and by an average of 3.83\% than Minimum Execution Time. GAPSO improves execution ratio by an average of 4.45\% than Max-Min Scheduling and by an average of 2.65\% than Minimum Execution Time.\begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.1875\textwidth}P{0.225\textwidth}P{0.25\textwidth}P{0.1875\textwidth}}
Number\tabcellsep Max-Min\tabcellsep Minimum\tabcellsep Hybrid\\
of tasks\tabcellsep scheduling\tabcellsep Execution time\tabcellsep GAPSO\\
200\tabcellsep 679\tabcellsep 664\tabcellsep 641\\
400\tabcellsep 1364\tabcellsep 1337\tabcellsep 1290\\
600\tabcellsep 2072\tabcellsep 2048\tabcellsep 1956\\
800\tabcellsep 2746\tabcellsep 2690\tabcellsep 2592\\
1000\tabcellsep 3438\tabcellsep 3373\tabcellsep 3253\end{longtable} \par
 
\caption{\label{tab_3}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.06908127208480565\textwidth}P{0.17720848056537103\textwidth}P{0.07508833922261485\textwidth}P{0.1441696113074205\textwidth}P{0.15618374558303888\textwidth}P{0.1351590106007067\textwidth}P{0.0931095406360424\textwidth}}
\tabcellsep 4000\tabcellsep \tabcellsep \tabcellsep \\
Average Schedule Length\tabcellsep 500 1000 1500 2000 2500 3000 3500\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep 0\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep 200\tabcellsep 400\tabcellsep 600\tabcellsep 800\tabcellsep 1000\\
\tabcellsep \tabcellsep \tabcellsep Number of tasks\tabcellsep \\
\tabcellsep Max Min scheduling\tabcellsep \multicolumn{2}{l}{Minimum Execution time}\tabcellsep \multicolumn{2}{l}{Hybrid GAPSO}\\
\tabcellsep \tabcellsep \tabcellsep Number\tabcellsep Max Min\tabcellsep Minimum\tabcellsep Hybrid\\
\tabcellsep \tabcellsep \tabcellsep of tasks\tabcellsep scheduling\tabcellsep Execution time\tabcellsep GAPSO\\
\tabcellsep \tabcellsep \tabcellsep 200\tabcellsep 0.91\tabcellsep 0.94\tabcellsep 0.96\\
\tabcellsep \tabcellsep \tabcellsep 400\tabcellsep 0.89\tabcellsep 0.91\tabcellsep 0.95\\
\tabcellsep \tabcellsep \tabcellsep 600\tabcellsep 0.87\tabcellsep 0.89\tabcellsep 0.91\\
\tabcellsep \tabcellsep \tabcellsep 800\tabcellsep 0.87\tabcellsep 0.87\tabcellsep 0.89\\
\tabcellsep \tabcellsep \tabcellsep 1000\tabcellsep 0.85\tabcellsep 0.86\tabcellsep 0.88\end{longtable} \par
 
\caption{\label{tab_4}Table 2 :}\end{figure}
 			\footnote{© 2015 Global Journals Inc. (US) 1} 			\footnote{© 2015 Global Journals Inc. (US)} 		 		\backmatter  			  				\begin{bibitemlist}{1}
\bibitem[Tripathy and Patra]{b0}\label{b0} 	 		\textit{},  		 			L Tripathy 		,  		 			R R Patra 		.  		 	 	 (Scheduling in cloud computing) 
\bibitem[Dehghani Zahedani and Dastghaibyfard (2014)]{b17}\label{b17} 	 		‘A hybrid batch job scheduling algorithm for grid environment’.  		 			S Dehghani Zahedani 		,  		 			G Dastghaibyfard 		.  	 	 		\textit{Computer and Knowledge Engineering (ICCKE)},  				2014. October. 2014. IEEE. p. .  	 	 (4th International e Conference on) 
\bibitem[Nguyen and Zhang (2014)]{b20}\label{b20} 	 		‘A hybrid discrete particle swarm optimisation method for grid computation scheduling’.  		 			S B Nguyen 		,  		 			M Zhang 		.  	 	 		\textit{Evolutionary Computation (CEC)},  				2014. July. 2014. IEEE. p. .  	 
\bibitem[Rasooli and Down (2012)]{b14}\label{b14} 	 		‘A hybrid scheduling approach for scalable heterogeneous hadoop systems’.  		 			A Rasooli 		,  		 			D G Down 		.  	 	 		\textit{High Performance Computing, Networking, Storage and Analysis (SCC), 2012 SC Companion},  				2012. November. IEEE. p. .  	 
\bibitem[Ashraf and &erlebach (2011)]{b18}\label{b18} 	 		‘A hybrid scheduling technique for grid workflows in advance reservation environments’.  		 			J Ashraf 		,  		 			T \&erlebach 		.  	 	 		\textit{High Performance Computing and Simulation (HPCS), 2011 International Conference on},  				2011. July. IEEE. p. .  	 
\bibitem[Khanesar et al. (2007)]{b27}\label{b27} 	 		‘A novel binary particle swarm optimization’.  		 			M A Khanesar 		,  		 			M Teshnehlab 		,  		 			M A Shoorehdeli 		.  	 	 		\textit{Control \& Automation, 2007. MED'07. Mediterranean Conference on},  				2007. June. IEEE. p. .  	 
\bibitem[Sadasivam and &selvaraj (2010)]{b21}\label{b21} 	 		‘A novel parallel hybrid PSO-GA using Map Reduce to schedule jobs in Hadoop data grids’.  		 			G S Sadasivam 		,  		 			D \&selvaraj 		.  	 	 		\textit{Nature and Biologically Inspired Computing (NaBIC), 2010 Second World Congress on},  				2010. December. IEEE. p. .  	 
\bibitem[Iturriaga et al. (2013)]{b15}\label{b15} 	 		‘A parallel hybrid evolutionary algorithm for the optimization of broker virtual machines subletting in cloud systems’.  		 			S Iturriaga 		,  		 			S Nesmachnow 		,  		 			B Dorronsoro 		,  		 			E G Talbi 		,  		 			P \&bouvry 		.  	 	 		\textit{P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC)},  				2013. October. 2013.  	 
\bibitem[Vijayalakshmi et al. ()]{b3}\label{b3} 	 		‘A Study on Task Scheduling Algorithms in Cloud Computing’.  		 			A Vijayalakshmi 		,  		 			Lepakshi 		,  		 			Dr 		,  		 			Prashanth C S R 		.  	 	 		\textit{International Journal of Engineering and Innovative Technology (IJEIT)}  		2013. 2  (11) .  	 
\bibitem[Salot ()]{b4}\label{b4} 	 		‘A Survey Of Various Scheduling Algorithm In Cloud’.  		 			P Salot 		.  	 	 		\textit{Computing Environment. International Journal of Research in Engineering\& Technology (IJRET)}  		2013. 2  (2)  p. .  	 
\bibitem[Patel and Mehta]{b6}\label{b6} 	 		\textit{A survey on various task scheduling algorithm in cloud computing},  		 			G Patel 		,  		 			A P R Mehta 		.  		 	 
\bibitem[Alobaedy and Ku-Mahamud ()]{b19}\label{b19} 	 		 			M M Alobaedy 		,  		 			K R Ku-Mahamud 		.  		\textit{Scheduling jobs in computational grid using hybrid ACS and GA approach. Computing, Communications and IT Applications Conference},  				2014. p. .  	 
\bibitem[Kaur and Verma ()]{b9}\label{b9} 	 		‘An efficient approach to genetic algorithm for task scheduling in cloud computing environment’.  		 			S Kaur 		,  		 			A Verma 		.  	 	 		\textit{International Journal of Information Technology and Computer Science (IJITCS)}  		2012. 4  (10)  p. 74.  	 
\bibitem[Madivi and Kamath (2014)]{b11}\label{b11} 	 		‘An hybrid bioinspired task scheduling algorithm in cloud environment’.  		 			R Madivi 		,  		 			S S Kamath 		.  	 	 		\textit{Computing, Communication and Networking Technologies (ICCCNT), 2014 International Conference on},  				2014. July. IEEE. p. .  	 
\bibitem[Santhosh et al. ()]{b5}\label{b5} 	 		\textit{An Improved Task Scheduling Algorithm based on Max-min for Cloud Computing},  		 			B Santhosh 		,  		 			Dr 		,  		 			D Manjaiah 		.  		2014.  	 
\bibitem[Settles ()]{b29}\label{b29} 	 		\textit{An Introduction to Particle Swarm Optimization},  		 			M Settles 		.  		2005.  		 			Department of Computer Science, University of Idaho 		 	 
\bibitem[Kaur and Kinger]{b1}\label{b1} 	 		\textit{Analysis of Job Scheduling Algorithms in Cloud Computing},  		 			R Kaur 		,  		 			S Kinger 		.  		 	 
\bibitem[Liu et al. ()]{b25}\label{b25} 	 		\textit{Binary Particle Swarm Optimization Based},  		 			W Liu 		,  		 			L Liu 		,  		 			D A Cartes 		,  		 			G K \&venayagamoorthy 		.  		2007.  	 
\bibitem[Azar et al. (2013)]{b2}\label{b2} 	 		‘Cloud scheduling with setup cost’.  		 			Y Azar 		,  		 			N Ben-Aroya 		,  		 			N R Devanur 		,  		 			N Jain 		.  	 	 		\textit{Proceedings of the 25th ACM symposium on Parallelism in algorithms and architectures},  				 (the 25th ACM symposium on Parallelism in algorithms and architectures)  		2013. July. ACM. p. .  	 
\bibitem[Eighth International Conference on]{b16}\label{b16} 	 		\textit{Eighth International Conference on},  				IEEE. p. .  	 
\bibitem[Kaveh and &malakouti Rad ()]{b28}\label{b28} 	 		‘Hybrid genetic algorithm and particle swarm optimization for the force method-based simultaneous analysis and design’.  		 			A Kaveh 		,  		 			S \&malakouti Rad 		.  	 	 		\textit{Iranian Journal of Science and Technology}  		2010. 34  (B1)  p. .  	 	 (Transaction B: Engineering) 
\bibitem[Zhu et al. (2011)]{b13}\label{b13} 	 		‘Hybrid genetic algorithm for cloud computing applications’.  		 			K Zhu 		,  		 			H Song 		,  		 			L Liu 		,  		 			J Gao 		,  		 			G Cheng 		.  	 	 		\textit{Services Computing Conference (APSCC)},  				2011. December. 2011. IEEE. p. .  	 
\bibitem[Aravindh ()]{b24}\label{b24} 	 		‘Hybrid of Ant Colony Optimization and Genetic Algorithm for Shortest Path in Wireless Mesh Networks’.  		 			S Aravindh 		.  	 	 		\textit{Journal of Global Research in Computer Science}  		2012. 3  (1)  p. .  	 
\bibitem[Kumar ()]{b7}\label{b7} 	 		‘Hybrid optimized list scheduling and trust based resource selection in cloud computing’.  		 			V S Kumar 		.  	 	 		\textit{Journal of Theoretical \& Applied Information Technology}  		2014.  (3)  p. 69.  	 
\bibitem[Javanmardi et al. ()]{b22}\label{b22} 	 		 			S Javanmardi 		,  		 			M Shojafar 		,  		 			D Amendola 		,  		 			N Cordeschi 		,  		 			H Liu 		,  		 			A Abraham 		.  		 arXiv:1404.5528.  		\textit{Hybrid Genetic Algorithm for Cloud Computing Applications},  				2014.  	 	 (arXiv preprint) 
\bibitem[Raju et al. (2013)]{b12}\label{b12} 	 		‘Minimizing the makespan using Hybrid algorithm for cloud computing’.  		 			R Raju 		,  		 			R G Babukarthik 		,  		 			D Chandramohan 		,  		 			P Dhavachelvan 		,  		 			T Vengattaraman 		.  	 	 		\textit{Advance Computing Conference (IACC)},  				2013. February. 2013. IEEE. p. .  	 	 (3rd International) 
\bibitem[Kaur and Sharma]{b10}\label{b10} 	 		\textit{Optimized Utilization of Resources Using Improved Particle Swarm Optimization Based Task Scheduling Algorithms in Cloud Computing},  		 			G Kaur 		,  		 			E S Sharma 		.  		 	 
\bibitem[Blondin ()]{b26}\label{b26} 	 		\textit{Particle swarm optimization: A tutorial},  		 			J Blondin 		.  		 \url{http://cs.armstrong.edu/saad/csci8100/psotutorial.pdf}  		2009.  	 
\bibitem[Jacob et al. ()]{b8}\label{b8} 	 		‘Resource Scheduling in Cloud using Bacterial Foraging Optimization Algorithm’.  		 			L Jacob 		,  		 			V Jeyakrishanan 		,  		 			P Sengottuvelan 		.  	 	 		\textit{International Journal of Computer Applications}  		2014. 92  (1)  p. .  	 
\bibitem[Girgis et al.]{b23}\label{b23} 	 		\textit{Solving the Wireless Mesh Network Design Problem using Genetic Algorithm and Tabu Search Optimization Methods},  		 			M R Girgis 		,  		 			T M Mahmoud 		,  		 			B A Abdullatif 		,  		 			A M Rabie 		.  		 	 
\end{bibitemlist}
 			 		 	 
\end{document}
