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Abstract –
With the failure of grid computing, cloud computing relieved not only in terms
of large upfront investment but also in the area of effective resource
allocation. Balancing this cloud computing cloud providers have very large
number of computing resources which they make them available on pay per use
basis to the cloud users with high resource utilization and maximum profit.
Cloud users want to run their application having varieties of resource
consumption with lower expenses because of this in balancing between these two
giver and taker. Resource management in term of effective allocation becomes most
critical issue of cloud computing. In this survey we investigated in AI and
strategic based algorithms which will make this work very efficient and
definitely balancing the two nature of cloud computing.


Keywords— Artificial Intelligence,
Strategic Based Resource Allocation, Cloud Computing, Grid Computing.

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I.     Introduction

There are many similarities between cloud computing and
grid computing as most of the problem faced are same. According to Foster et al.
1 both have common needs for making balance between methods by which  consumer consumption of resource provided and
to implement highly parallel computation to manage these resources where user
requests are very less for large amount of data to be used 2.

When it is the matter of managing ideas, grid requires
sophisticated policies for resource allocation because of which grid reaches to
its saturation level which make resource management one of the major critical
issue in IaaS of cloud computing 3.

Researches S.H.H et al. 3 elaborated that in a resource allocation
problem there are two actors cloud provider and cloud user cloud users who
wants to utilize the resources with minimum cost and maximum performance .In
the other hand cloud providers want to maximize revenue and get best
utilization of the resources.

In cloud computing resource management is accomplished with
effective resource allocation. It is done by distributing accessible resources
required by cloud application on demand basis 4, 5.

and resources provisioning in cloud computing make the resource allocation a
challenging issue in IaaS layer. Numerous methodologies have been devised by
researchers such as on-demand resource allocation, resource heterogeneity,
locality limitations, limited requirement and environmental requirements 6-13.




         The provider
in the cloud doesn’t include storage facilities alone, but provides hardware
and software services too for the general public and the market oriented work.
The services provided by the service provider can be anything or everything,
i.e. infrastructure, the platform or the software resources. Each service is
respectively called Infrastructure as Service (IaaS), Platform as a Service
(PaaS) and Software as a Service (SaaS). 13

In cloud computing, services can be offered in terms of
resources. Resource Allocation (RA) is the process of providing the available
resources to the needed cloud applications in the presence of internet 16. If
no proper management of resources are held, the application starves for
resources. Resource provisioning is the solution to the problem that allows the
service providers to manage the resources for each application.

Researcher Madni et al. 3 has classified the resource
allocation strategies into two major categories i.e. strategic based resource
allocation and parametric based resource allocation. In the next session of
this survey strategic based resource allocation will be discussed. Mainly this
survey is focused on Artificial Strategy based resource allocation in cloud

II.    Methodology

Before presenting methodology survey, motivation is needed
so that we can understand the importance of resource management. According to
researchers 3 resource allocation is necessary for cloud computing because it
helps to understand the inference of resource allocation, it enhances the
benefits for both cloud providers and cloud users in IaaS management.

The contribution and penetration of artificial
intelligence not only into every field of computer science but also into inter
disciplinary fields.

Use of this power creates immediate effects, increases in
productivity and cost reduction 14.In cloud computing AI helps in optimizing
and minimizing the make-span of allocation process by creating an intelligent
methodology that works like human being 15.

Genetic Algorithm 17,18 is a technique of searching
which works randomly based on Darwin theory.  For analyzing the future it uses historical
data and it is mainly helps in VM scheduling. GA is based on the biological
concept of generating the population. According to Darwin’s theory, term
“Survival of the fittest” is used as the method of scheduling in which the
tasks are assigned to resources according to the value of fitness function for
each parameter of the task scheduling process.

Predefined allocation policies are described by the author
in 19, Thangaraj for infrastructure as a services (IaaS). The author
mentions deadline sensitive policy for resource allocation by reducing the
rejection of the request by the special tool. Haizea is the toll used for the
policy, which is an open source manager that can be used in Nebula.

Koneru 20, researchers mentions that the efficiency of the scheduling
algorithm directly affects the efficiency of the resource allocation. RR
algorithm of scheduling is used for the better turnaround time for
differentiating the profit and the loss and also used to maximize the
efficiency. Processing cost is also reduced and an overall improvement in the
resource allocation.

 In 21 Ikki
Fujiwara by a double-sided combinational auction, propose a market-based
resource allocation mechanism that allows participants to trade their services.
Providing a perfect market space for cloud computing environment it proposes a
market-based resource allocation mechanism to allocate services to participants
in effective manner. It provides a combination of services for workflows and
co-allocations and enables participants to carry future and current services in
the forward market as well as spot market.

CRAA/FA (Cloud Resource Allocating Algorithm via
Fitness-enabled Auction) is introduced in 22, by Zuling et. al, a new cloud
resource allocating algorithm, which creates a market for cloud resources and
makes the resource agents and service agents fix the price by the means of
bargaining in that market.

Apart from that the researchers have devised many AI based
methods or algorithms such as Genetic Algorithm (GA), Simulated annealing (SA),
Tabu Search (TS), Ant Colony Optimization (ACO), Particle Swarm Optimization
(PSO), Artificial Immune System (AIS), Bacterial Foraging Algorithm (BF), Fish
Swarm Optimization Algorithm (FS), Cat Swarm Optimization Algorithm (CS),
Firefly Algorithm (FF), Cuckoo Search Algorithm (CS), Artificial Bee Colony
(ABC), Bat Algorithm (BA).


this brief survey on artificial intelligence strategy based resource allocations
are given so that the better approach can be selected for the new research
work.  There are many challenges that the
cloud computing is facing. This survey paper leads to the resource allocation
which can be a path finder for many researchers. It also acts as a key factor
in balancing in a better way for service providing to the end users.


1     Ian
Foster et al., “Cloud Computing and Grid Computing 360-Degree Compared,” in Grid
Computing Environments Workshop, Austin, TX, USA, 2008.  

2     Jennifer
Schiff. (Jan 21, 2010). Grid Computing and the Future of Cloud Computing Online,
Available: /outsourcing/features/article.php/3859956/Grid-Computing-and-the-Future-of-Cloud-Computing.htm

S.H.H., Latiff, M.S.A., Coulibaly, Y. et al., “Recent advancements in resource
allocation techniques for cloud computing environment: a systematic review,” in
Cluster Comput (2017) 20: 2489.

4     Zhang,
Q., Cheng, L., Boutaba, R.: Cloud computing: state-ofthe-art and research
challenges. J. Internet Serv. Appl. 1(1), 7–18 (2010)

5     Madni,S.H.H.,Latiff,M.S.A.,Coulibaly,Y.,Abdulhamid,S.I.M.:
An appraisal of meta-heuristic resource allocation techniques for IaaS Cloud.
Indian J. Sci. Technol. 9(4), 1–14 (2016)

6     Mustafa,S.,Nazir,B.,Hayat,A.,Madani,S.A.:Resourcemanagement
in cloud computing: taxonomy, prospects, and challenges. Comput. Electr. Eng.
47, 186–203 (2015)

7     Hameed,
A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji,
P., Zeadally, S., Malluhi, Q.M., Tziritas, N., Vishnu, A.: A survey and
taxonomy on energy ef?cient resource allocation techniques for cloud computing
systems. Computing 98, 751–774 (2014)

8     Elghoneimy,
E., Bouhali, O., Alnuweiri, H.: Resource allocation
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Networking and Communications (ICNC), pp. 309–314. IEEE (2012)

9     Mohan,
N., Raj, E.B.: Resource Allocation Techniques in Cloud Computing–Research
Challenges for Applications. In: 2012 Fourth International Conference on
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10   Ma,
T., Chu, Y., Zhao, L., Ankhbayar, O.: Resource allocation and scheduling in
cloud computing: policy and algorithm. IETE Tech. Rev. 31(1), 4–16 (2014)

11   Parikh,
S.M.: A survey on cloud computing resource allocation techniques. In: 2013
Nirma University International Conference on Engineering (NUiCONE), pp. 1–5.
IEEE (2013)

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S.S., Shyam, G.K.: Resource management for Infrastructure as a Service (IaaS)
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13   Chana,
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15   Naveen
Kumar Gondhi and Ayushi Gupta. “Survey on Machine Learning  based scheduling in Cloud Computing,” in ISMSI
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17   Ipsita
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Genetic Algorithm in Cloud Data Centers” International Conference on
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18   Aleena
Xavier T, Rejimoan R 2016 “Survey on Various Resource          Allocation Strategies in Cloud”
International Conference on Circuit, Power and Computing Technologies ICCPCT

19   Thangaraj
P, Soundarrajan S, Mythili A, “Resource allocation policy for IaaS in Cloud
computing”, International Journal of Computer Science and Management Research
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20   Sowmya
Koneru, V N Rajesh Uddandi, Satheesh Kavuri, “Resource Allocation Method using
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21   Ikki
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Allocation in Cloud Computing”, 10th Annual International Symposium on
Applications and the Internet, 2010.

22   Zuling
Kang, Hongbing Wang, “A Novel Approach to Allocate Cloud Resource with
Different Performance Traits”, IEEE 10th
International Conference on Services Computing, 2013, pp. 8-13.

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