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Prof.
Sarangkumar s. Dubey

Assistant
Professor in Department of (IT)

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Anantrao
Pawar College of Engineering & Research

Pune,
India

[email protected]

 

 

Abstract— Cloud Computing is a standard computing pattern. It
aims to share data, calculations, and service fairly over a scalable network of
end system. In storage management is required to separate the active local workload
evenly across all the end system. It helps to get a high user satisfaction and
resource utilization ratio by providing an efficient and fair allocation of
every computing resource. Proper management aids in optimize resource damage ,
implementing overcome on fail server , allow scalability, avoiding bottlenecks
and over-provisioning etc. It helps in optimal utilization of resources and
hence in enhancing the performance of the system. A few existing scheduling
algorithms can maintain load balancing and provide better strategies through
efficient job scheduling and resource allocation techniques as well. In order
to gain maximum profits with optimized load management algorithms, it is
necessary to utilize resources properly. This paper discusses some of the
existing load management algorithms in cloud computing and also their
challenges.

 

Keywords— Storage Management, cloud computing

 

                                                                                                                                                               
I.      Introduction

Cloud computing is emerging as
a new standard of  large scale
distributed computing. It has moved computing and data away from desktop to compact
PCs into large data centers 1. It has the ability of power on Internet and
wide area network to resources that are accessible globally, thereby, providing
cost effective solution to the most of the real life requirement. It provides
the globally accessible  IT resources
such as applications and service, as well as infrastructure on which they
operate, over the Internet, as on demand pay-per- use basis to adjust the
capacity quickly and easily. It helps to changes in demand. Thus cloud computing
is a framework for providing suitable, and resource utility of the system. It
also ensures for the fair distribution of work and resources.

Load balancing in computer
networks is a technique used to storage management across multiple network
links of computers 2. by providing a maximum output result with less time,
thus it helps to improve feasibility by optimally using available resources and
helps in optimize latency and completion 
time. Storage management  is
getting  by using multiple resources that
is, number of  servers that are able to
fulfill a requirment or by having number of 
paths to a resource. Storage  management
helps to get a high user satisfaction and resource utilization. When one
of  the components  service fail, load management provide continuation
of the service by implementing overcome service, that is, it helps in setup and
de-setup of instances of applications without fail. It also monitoring every
computing resource is distributed efficiently and fairly 3.

Damage of resources and
conservation of energy is not always a prime focus of discussion in cloud
computing. However, resource damage can be kept to minimum with proper load
management which is not only helps to reducing costs but making enterprise
greener. , One of the very important features of cloud computing, is also
enabled  load management . Hence,
improving resource utility and the performance of a distributed system in such
a way will optimize the energy damage  and carbon footprints to achieve Green computing
. example of  storage management can be a
website which has thousands of users at the same time. If not balanced then the
users have to face the problem of  timeouts,
response delays and long processing time. The solutions involve making use of duplicate
servers to make the website available by balancing the network traffic.

 

                                                                                                                                             
II.    STORAGE MANAGEMENT

For storage
management  is technique that provide  networks and resources to a max –output result
with min- completion time 5. Dividing the load between servers, data can be
sent and received without major delay. Different kinds of algorithms are
available that helps traffic loaded between available servers. A basic example
of load management in our daily life can be related to websites. If there is no
load management, users could experience timeouts and possible long system
responses. Storage management solutions usually apply redundant servers which
help a better distribution of the communication traffic so that the website
availability is conclusively settled 5.

There are many
different types of load management algorithms available, which can be
categorized into two groups. The following section will discuss these two main
categories of load management algorithms, those categories are as follows.

The load
management algorithms categories depending up on who initiated the process as
given in 6:

·        
Sending
Instigate: If storage
management or balancing is instigating by the sending end then the algorithm is
called sending instigate.

·        
Receiving
Instigate: If storage
management is instigating by the receiving end then the algorithm is called
receiving instigate.

·        
Balanced: It is the combination of both
sender instigate and receiver instigate. The load balancing algorithms can be
divided into two categories depending on the current state of the system, as
given in 6:

·        
Fixed: The fixed load  balancing algorithms always depend on the previous
system .rather than. Present knowledge of the system .

·        
Active: In the active load balancing
algorithms decisions on storage management will be based on present  state of the system. No backup knowledge is
needed. So it is better than fixed approach.

 

                                                                                                                                  
III.   storage management methods

A.    Corton

R. Stanojevic 7
proposed  a CARTON for cloud control the
unifies the use of storage management  and distributed process. SM is used to
distribute the jobs equally  to numbers
of Servers so that they minimize the cost and DL is used monitor  that the resources are distributed in a way to
keep a fair resource allocation. With very low computation and communication
overhead, this algorithm is simple and easy to implement.

B.    Test
and stability

 The problem of internal-cloud storage
management amongst physical hosts by live migration of virtual machines. A load
balancing  is designed and implemented to
reduce virtual machines’ relocation on time by shared storage to balance load
amongst servers according to their processor or IO usage and to keep virtual
machines zero-downtime in the process. A distributed storage management
algorithm Test and stability is also based on sampling and reaches very fast.
This algorithm assures that the moving of VMs is always from high-cost physical
hosts to low-cost host but assumes that each physical host has enough memory
which is a weak assumption.

C.    Event-Directect

V. Nae9
presented an event-directed  LB algorithm
for large time  multiplayer online games .
This algorithm after receiving capacity events as input, checks its components
in context of the resources and the global state of the game session, thereby
generating the game session load balancing actions. It is capable of scaling up
and down a game session on multiple resources according to the variable user
load but has occasional QoS breaches.

D.    Planing on LB of V-M

A planning management
on storage management of Virtual Machine  resources that uses previous data and active  state of the system. This strategy gives the
best storage management and reduced active changes by using a genetic
algorithm. It helps in resolving the issue of overload ing and high cost of
migration thus achieving better resource utilization 11.

 

E.    Honey-bee
hunt

M.Randles
investigated a decentralized honey-bee based load management technique that is
a nature- inspired algorithm for self -organization. It achieves global load
management through local server actions. Flexibility of the system is enhanced
and increase system diversity but output is not increased with an increase in size
of system. It is best suited for the conditions where the diverse population of
service types is required 10.

 

F.    Tendentious
Alternate Sampling

M. Randles
investigated a distributed and scalable load management  approach that uses Alternate sampling of the
system domain to achieve automatic organization thus managing  the load across all end  system. The performance of the system is upto
mark  with high and same population of
resources thus resulting in an increased output or result by effectively
utilizing the increased system resources 10.

G.    Message
supportive Model

Zenon Chaczko
presented a model that uses XMPP for loadmanagement . This technology is open
for real time communication between various partners. XMPP clients send
presence information to XMPP presence servers and XML streams containing
details of presence information of clients produced by these servers. Using a
load balancer on the top of an XMPP server allowed incoming requests to be
prioritized and handled by a generic service 2.

H.    Open
Flow Model

Hardeep Uppal
presented a model in which open flowswitch is used. Open flow switches are like
a standard switch with a flow table performing packet lookup and forwarding.
The difference lies in how flow rules are inserted and updated inside the
switch’s flow table 12.

I.      Self-organized
Algorithm

Giuseppe has
presented a new method for load balancing in which a node with highest capacity
serves as super peers. At first level, algorithm find out the capacity of every
peer, i.e., the amount of service requests that peer is able to fulfill in a
client time unit. This, in turn, is reflected in the Myconet overlay as the
target Bs of peers maintained by a super peer. This way, super peers are well
positioned to effectively balance their neighbors’ request queues 13.

 

 

 

J.     Ant
Colony minimize

14. Ratan  Mishra has proposed a model in which
Individual ants are behaviorally much straightforward insects. They have a very
limited memory and exhibit individual behavior that appears to have a more
alternative component. Acting as a collective however, ants manage to perform a
variety of complicated tasks with great reliability and consistency

 

                                                                                                                                     
IV.   tricks for load management

Various tricks
available for  storage management techniques
in cloud computing are discussed below-

·        
Scalability
is the ability of an
algorithm to perform storage management on a system with any finite number of
ends. This tricks should be improved for efficient load management.

 

·        
Resource
exertion is used to
check the utilization of re-sources. It should be optimized for an efficient
load balancing.

 

·        
Pursuance
is used to check the
flexibility of the system. This has to be improved at reasonable cost, e.g.,
reduce job completion time

 

·        
completion
Time is the amount
of time taken to performed  particular storage
management algorithm in a different level of  system. This parameter should be minimized.

 

·        
On
high overload determines
the amount of high overload while implementing a load management algorithm. It
is collected of overload due to numbers of  tasks, intra-processor and inter process communication.
This should be optimizing so that a load management  technique can work efficiently.

 

V.   
STORAGE MANAGEMENT CHALLENGES IN THE     CLOUD COMPUTING

Although cloud computing  widely adopted and Research in cloud
computing is still in its previous stages, and some technical challenges remain
unsolved .

·        
Automated
service setup: The resources can be
allocated automatically this is a key feature of cloud computing called
elasticity. How then can we use or release the resources of the cloud, by
keeping the same  performance as
traditional systems and using optimal resources?

 

·        
Moving of
Virtual Machines: With virtualization,
an entire machine can be seen as a file or set of files, to unload a physical
machine overloaded, it is possible to move a virtual machine between physical
machines. The main objective is to distribute the load in a datacenter or set
of datacenters. How then can we actively distribute the load when moving the
virtual machine to avoid bottlenecks in Cloud computing systems?

 

Management of Energy: The
benefits that advocate the adoption of the cloud is the economy of scale.
Energy saving is a key point that allows a global economy where a set of
global resources will be supported by reduced providers rather that each
one has its own resources. How then can we use a part of datacenter while
keeping acceptable performance?

 

 Data management: In the last decade
data stored across the network has an exponential increase even for
companies by outsourcing their data storage or for individuals, the
management of data storage or for individuals, the management of data
storage becomes a major challenge for cloud computing. How can we distribute
the data to the cloud foroptimum storage of data while maintaining fast access?

 

Evolution of small data centers for cloud computing: Small
datacenters can be more beneficial, cheaper and less energy consumer than large
datacenter. Small providers can deliver cloud computing services leading
to geo-diversity computing. Load balancing will become a problem on a
global scale to ensure an adequate response time with an optimal
distribution of resources.

 

Conclusion

Cloud
Computing has been accepted by the industry, though there are many issues like
storage management, Server overload, Energy Management, etc. which have not
been fully addressed. To overcome these issues storage management, that is
required to distribute the excess active local workload evenly to all the end
system. The whole Cloud get a high user satisfaction and resource utilization. It
also ensures that every computing resource is distributed properly and fairly.
This paper presents a concept of Cloud  along with research challenges in storage
management. Major thrust is given on the study of load balancing algorithm,
followed by survey of these above mentioned algorithms in cloud computing with
respect to output, resource utilization, performance, completion time and
overloading.

 

 

 

 

 

 

 

 

 

 

 

 

 

References

 

1 Nidhi Jain Kansal,
Inderveer Chana, Cloud Load balancing techniques:A step towards green
computing, IJCSI International Journal of Computer Science Issues, Vol. 9,
Issue 1, No 1, January 2012.

2 Zenon Chaczko,
Venkatesh Mahadevan, Shahrzad Aslanzadeh and Christopher Mcdermid, Availabilty
and load balancing in cloud computing, 2011 International Conference on
Computer and Software

 

3 Tanveer Ahmed, Yogendra
Singh, Analytic study of load balancing techniques using tool cloud analyst

 

4 Peter Mell, Timothy Grance,
“The NIST Definition of Cloud Computing”, NIST Special Publication 800-145,
September 2011.

 

5 R. Shinmonski. Windows 2000
& Windows Server 2003 Clustering and Load balancing. Emeryville.
McGraw-Hill professional publishing, CA, USA (2003), p2, 2003.

 

6 David Escalante and Andrew J.
Korty, Cloud Services: Policy and Assessment, EDUCASE Review, Vol. 46,
No.4(2011).

7 Stanojevic R and Shorten
R(2009) IEEE ICC,1-6.

 

8 Zhao Y. and Huang W. (2009)
5th International Joint Conference on INC, IMS and IDC, 170-175.

 

9 Nae V., Prodan R. and
Fahringer T.(2010) 11th IEEE/ACM International Conference on Grid Computing
(Grid), 9-17.

 

 10 Randles M., Lamb D. and Taleb-Bendiab A.
(2010) 24th International Conference on Advanced Information Networking and
Applications orkshops, 551-556.

 

11 Meenakshi Sharma, Pankaj
Shrama, Dr. Sandeep Sharma, Efficient Load 6 Hardeep Uppal, dane Brandon,
OpenFlow based load balancing.

 

12 Hardeep Uppal, dane Brandon,
OpenFlow based load balancing.

 

13 Giuseppe Valetto, Paul
Snyder, Daniel J. Dubois, Elisabetta Di Nitto and Nicolo M. Calcavecchia, A
self-organized load balancing algorithm for overlay based decentralized service
networks.

 

14 Ratan
Mishra, Anant jaiswal, Ant colony optimization: A Solution of load balancing in
cloud, International Journal of Web & Semantic Technology.

 

15 A. Khiyaita, M. Zbakh, H. El Bakkali and Dafir El Kettani,
“Load Balancing Cloud Computing: State of Art” , 9778-1-4673-1053-6/12/$31.00,
2012 IEEE.

 

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