SYSTEMATIC LITERATURE REVIEW ON RESOURCE ALLOCATION AND RESOURCE SCHEDULING IN CLOUD COMPUTING

International Journal of Advanced Information Technology (IJAIT) Vol. 6, No.4, August 2016
DOI : 10.5121/ijait.2016.6401 1
SYSTEMATIC LITERATURE REVIEW ON RESOURCE
ALLOCATION AND RESOURCE SCHEDULING IN
CLOUD COMPUTING
B. Muni Lavanya and C. Shoba Bindu
Department of Computer Engineering, JNTUACEP, Pulivendula, AP
ABSTRACT
The objective the work is intend to highlight the key features and afford finest future directions in the
research community of Resource Allocation, Resource Scheduling and Resource management from 2009 to
2016. Exemplifying how research on Resource Allocation, Resource Scheduling and Resource management
has progressively increased in the past decade by inspecting articles, papers from scientific and standard
publications. Survey materialized in three fold process. Firstly, investigate on the amalgamation of
Resource Allocation, Resource Scheduling and then proceeded with Resource management. Secondly, we
performed a structural analysis on different author’s prominent contributions in the form of tabulation by
categories and graphical representation. Thirdly, huddle with conceptual similarity in the field and also
impart a summary on all resource allocations. In cloud computing environments, there are two players:
cloud providers and cloud users. On one hand, providers hold massive computing resources in their large
datacenters and rent resources out to users on a per-usage basis. On the other hand, there are users who
have applications with fluctuating loads and lease resources from providers to run their applications.
Further, delivers conclusions by conferring future research directions in the field of cloud computing, such
as reduce clouds early in the Internet, combining Resource Allocation, Resource Scheduling and Resource
management rather than a Cloud model for providing high quality results, etc.
KEYWORDS
Resource Allocation, Resource Scheduling, Resource Management, Cloud Computing, SaaS, Paas, IaaS,
Private Cloud, Public Cloud and Community Cloud.
1.INTRODUCTION
Cloud computing is a technique which provides a delivery of computing resources over the
Internet. Cloud computing is one of the latest buzzwords in the world of Information Technology,
which means key to the definition of cloud computing is the “cloud” itself, but, the exactly
meaning of the cloud is that clouded cover computing is a set of web based computing resources
that deliver on demand information services to users from any location in the world. The purpose
of using cloud is that cloud is a large group of interconnected computers. These computers can be
personal computers or network servers or they can be public or private. This cloud of computers
extends beyond a single company or enterprise. Cloud storage is a service model in which data is
maintained, managed and backed up remotely and made available to users over a network as web
or typically the Internet. The applications and data served by the cloud are available to wide
group of users, cross-enterprise and cross-platforms. Access is via the Internet. Any authorized
user can access these documents and different applications from any computer over any Internet
connection. Cloud computing transfers different computing resources over the Internet as a

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replacement for keeping data on user own hard disk or updating applications for user needs, to
use a service over the Internet at another location, to store user information or use its applications.
The cloud is web storage where your all data remain store. One can access that data from the
cloud according to user requirement easily. The clouds flexibility is most useful for businesses in
its ease of access, particularly with data sharing. Documents and files can be stored in the cloud
and accessed via an Internet connection, which is ideal solution for businesses. Also in terms of
data backup, the cloud provides peace of mind with securing useful data.
The goal of cloud computing is to concern conventional supercomputing, or high-performance
computing power, normally used by military and research facilities, to perform millions of
computations per second, in user-oriented applications such as financial portfolios, to deliver
personalized information, to provide a large volume of data storage. Cloud computing has been
changing how mostly users use the web and how they store their files of data on web. There are
examples as Orkut, Facebook, Amazon and Twitter and the services like Google Docs and Gmail
etc. Cloud computing technology uses networks of large group of servers typically running low-
cost user PC technology with specialized connections to increase data processing tasks across
them.
The resource is generally depend on computer mainframe and can also be classified into 3 layers
from top to bottom: First, in the application layer – the operation status of network server,
database server and other related servers, Second, Network layer – routing between hosts, data
transmission bandwidth, communication data layer, Third, System Layer – CPU Information,
Memory size. All these are at one side and in other hand cloud computing resources can also
include operation system, disk space, network communication capabilities, data resources,
equipment and other related resources.
In cloud computing, the resource allocation is also one of the processes to assign available
resources for the needed cloud application users, cloud resources can be provisioned on-demand
in a fine-grained, multiplexer manner. In the cloud the resource allocation is based on the
software, platform and infrastructure respectively known as software as a Service(SaaS),
Platform as a Service(PaaS) and Infrastructure as a service (IaaS). In cloud platforms, resource
allocation takes place at two levels as follows: In first level, when the cloud application user
wants to uploaded to the cloud, then the load balancer assigns the requested instances to physical
computers, to balance the computational load of multiple applications across physical computers.
And second level, when an application receives multiple incoming requests, these requests also
should be assigned to a specific instance of application to balance the computational load across a
set of instances of the same application.
Table 1.1 Search Key Terms
Resource Scheduling
“ORed”
and
“ANDed”
Resource Management
Survey on Resource Allocation
Survey on Resource Scheduling
Survey on Resource Management
SLR on Resource Allocation
SLR on Resource Scheduling
SLR on Resource Management

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Table 2 Quality Assessment and Data Extraction template
Extracted Fields Assessment Factors
Author Research Scholar / Experience
Title Relevant / Not Relevant
Year NA (Not Applicable)
Keywords Match / Not Match
Objective(s)/Issue(s) Clear / Not Clear
Research Question(s) (optional) Yes/No
Framework/Model/Method/Technique/Approach Yes/No/Other
Pros & Cons Detailed / Not defined
Case study/Empirical study/Survey Theoretical/ Experimental/None
Future Scope Mentioned / Not Mentioned
Category and Sub-category Belongs to / Not Belongs to any
Published in a relevant journal/conference Yes / No
Cited by other authors Yes/No
Fig. 1 Graphical representation of surveyed papers on Cloud Computing
Fig. 3 Evolution of autonomic computing
(Courtesy: S.Singh and I. Chana)
Fig. 2 Graphical representation for surveyed papers on Resource
allocation and Scheduling

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In table 1 shows that search key word terms and table 2 shows the data extraction fields to assess
the articles using different factors. However, an outline of survey and systematic literature review
(SLR) publications is shown in figure 1 and Figure 2 shows drastically increased research on
resource allocation and resource scheduling in both journal articles and conference papers from
2009 to 2016.
Next section of the paper presents as follows: section 2 speaks about literature survey search on
resource allocation and resource scheduling in which different categories of article authors
opinions followed by section 3 talks about in detailed on SLR, Resource allocation followed by
scheduling and next section 4 gives discussion on the survey and last section 5 conclusion on
resource allocation and scheduling.
2.LITERATURE SURVEY
2.1.Literature Search
A literature search identified 200 plus among which filtered to 15 conferences, 25 journal articles
and Some other papers from different reputed forums, a total of both 100 articles from 2009 to
2016. Figure 2 shows the number of conference papers from standard symposium, workshops,
tutorials like international conference on Cloud Computing (ICCC), Resource Allocation,
Resource Scheduling and Resource management.
Fig. 4 Evolution of Qos in Cloud Computing

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It seems that Resource Allocation to Resource Management, Survey on Resource Allocation,
Scheduling, Management and SLR on Resource Allocation, Scheduling, Management, adoption
and transformation is drastically increasing and shows good attention in research communities
both from academia and industry. Fig. 4 shows the evolution of quality of service in cloud
computing.
2.2.Selection Inclusion And Exclusion Criteria
Each study that was retrieved from the manual search or an automated search is formulated as in
section 2.3 and was evaluated by thesis author and his both supervisors (a total of three
conductors).Divergences in the selection were work out by accord among the three conductors in
the wake of examining the whole paper. The following studies were considered that met atleast
one for the inclusion criteria:
• Studies presenting Resource Allocation that are used for allocating resource in cloud.
• Studies related to Resource Scheduling in cloud computing.
The following studies were excluded that met at least one from the exclusion criteria were:
• Papers that are not focused on the Resource Allocation.
• Papers presenting only ideation without any practical orientation.
• Papers presenting only Cloud computing like reusability, application-specific and
flexibility.
Category Sub-Category Article Author & Year
Survey Cloud Computing
Issues
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Haibo Yang et al (2009)
Haibo Yang et al (2012)
Yaser Ghanam et al (2012)
Sunilkumar S.M et al(2013)
Frederico Durao et al (2014)
Mahmoud.M et al (2014)
F.F.Moghaddam et al (2015)
Anton Backe et al (2015)
Nasrin Hesabian et al (2015
Scheduling 8
Jaspreet kaur et al (2012)
Lovejit Singh et al (2013)
Shabnam Khan et al (2013)
M.Padmavathi et al (2014)
Sukhpal Singh et al (2015)
Soumen Santra et al (2015
A E Mohamed Mosa (2014)
Malathi.P et al (2015)
SLR 5
Yan Hu et al(2014)
Li Liu et al(2014)
Rashmi Rai et al(2015)
Anterpreet Kaur(2015)
Ilango Sriram et al(2015)
Table 3 Different articles used in Cloud Computing Survey

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2.3.Categories on cloud computing
Table 3 shows that different articles used to study survey on cloud computing under the theme of
resource allocation and resource scheduling with the fields authors and corresponding year.
Similar fashion, table 4 shows exact point of articles on allocation and scheduling
Table 4 Different articles on Resource Allocation and Resource Scheduling
Category Sub-Category Article Author & Year
Resource Allocation Resource Utilization 7
Ms. Shubhangi D. et al(2012)
Abirami S.P et al(2012)
Ram Kumar Sharma et al(2013)
N. Asha et al (2013)
Xin Xu et al (2014)
Zhigang Zhou et al (2015)
Hussein S. Al-Olimat et al(2015)
Energy
Consumption
7
A.Y.Khobragade et al (2013)
Wanneng Shu et al (2014)
Qi Zhang et al (2014)
Jayanthi S et al(2014)
Jing-Mian Tang et al(2015)
Devendra Singh Thakur et al
Shilpi Saxena et al
Cloud Computing
Environment 3
Sagar.G et al (2013)
S.Thejeswi et al (2013)
S.Sujan et aL(2015)
Advanced
Methodologies 3
Chien-Hung Chen et al(2015)
D.A. Heger et al
Gunho Lee et al
Resource Allocation
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MS. Pooja .P.Vasani et al (2013)
Shivani Sharma et al(2014)
N.D. Doulamis et al(2014)
H.Wang et al(2015)
Longbin .C et al(2015)
A.Narayan Singh et al (2015)
Hui Wang et al(2015)
K.Beghdad Bey et al
Zhou Fang et al
K.Beghdad Bey et al
Virtual Machine 3 Sharrukh .Z et al(2013)
Pratik P. Pandya et al(2014)
Omar Khedher et al(2015)
Distributed Data
centers
4 M.A.Sharkh et al(2013)
Shaolei Ren et al(2014)
Aziz Mohaisen et al(2014)
Seokho Son et al
Scheduling Scheduling
Algorithm
Shaobin Zhan et al(2012)
Monika Choudhary et al(2012)
Upendra Bhoi et al(2013)
A.Kaleeswaran et al(2013)
Hilda Lawrance et al(2013)
Vijayalakshmi A et al(2013)

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19
Guan Wang et al(2013)
Yogita Chawla et al(2013)
Kavita Bhatt et al(2013)
Xiaomin Zhu et al(2014)
WenAn Tan et al(2014)
Arabi E. Keshk et al(2014)
P Kowsik et al(2014)
Mahesh S. Shinde et al(2014)
Garima Mahajan et al(2014)
Dinesh Kumar et al(2015)
Mala Kalra et al(2015)
Dr.M.Srivenkatesh(2015)
Er. Shelly Sachdeva et al(2015)
Scheduling policy 2 Shachee V Parikh et al(2013)
Gongzhuang Peng et al(2015)
3.OVERVIEW ON SYSTEMATIC LITERATURE REVIEW
3.1. Survey on Cloud Computing
Haibo Yang et al. (2009) [10], Haibo Yang et al. (2012) [11], Sunilkumar S.M et al.(2013) [89],
F.F.Moghaddam et al. (2015) [3], Mahmoud.M et al. (2014) [7], Nasrin Hesabian et al. (2015 [9],
Anterpreet Kaur (2015) [22], K.Beghdad Bey et al. [84], MalaKalraetal(2015) [50] and
Vijayalakshmi A et al.(2013) [70], authors are discussed on cloud computing technological issues
such as network, performance, security, resource allocation, job scheduling, load balancing, data
availability, interoperability, compatibility, scalability, and data management. The new theme of
research works on social and organizational implications of cloud computing and the most
important issue is related to security and privacy subjects in cloud-based environments.
Furthermore, resource allocation and Job scheduling is one of the biggest issues in cloud
computing for dedicating the resources to the system requests. Dedicating resources to the
requests is a NP-complete problem due to requests and resource dynamics. One of the most
pressing issues in cloud computing for IaaS is the resource management. Resource management
problems include allocation, requirement provisioning, discovery, brokering, estimation,
mapping, adaptation and modeling.
Yaser Ghanam et al. (2012) [12], Frederico Durao et al. (2014) [16], Anton Backe et al. (2015)
[15], the authors are focusing on advances issues in the field of cloud computing such as security
and privacy, resource sharing, billing and service layer agreement to opened up new questions
about the real gains of the business model. The authors also suggested conducting intensive case-
studies for understanding challenges related to design of software as well as testing the cloud
environments.
Jaspreet kaur et al. (2012) [13], Lovejit Singh et al. (2013) [5], Sukhpal Singh et al. (2015) [2]
[18], Soumen Santra et al. (2015) [4] authors are presented different strategies for efficient
scheduling and resource allocation to decrease operational cost and time. These strategies solve
the load distributing problem on various nodes of a distributed system to improve both resource
utilization and job response time. It also avoiding a situation where some of the nodes are heavily
loaded while other nodes are idle or doing very little work. Authors give a direction for selection
of a particular scheduling algorithm depends upon various factors such as cost or time, quality of
service, availability of information, etc.

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Shabnam Khan et al. (2013) [6] and A E Mohamed Mosa et al. (2014) [8] focused on the issue
related to disk space management in virtual environment as well as amount of power
consumption in data centers for which great amount of carbon dioxide emissions increases.
Authors suggested that scheduling existing scheduling algorithms gives high throughput and cost
effective but they don’t considered availability and reliability issues.
To achieve good performance, high utilization of resources and less response time, Cloud
computing required to execute many tasks by providing related resources and also for any type of
complicated tasks in different environments of cloud is suggested by M.Padmavathi et al. (2014)
[14] and Malathi.P et al. (2015) [17].
Yan Hu et al. (2014) [20] and Zhi-Hui Zhan et al.(2015) [1] discussed on three types of studies
about cloud computing in eHealth, namely, cloud-based eHealth framework design, security or
privacy control mechanisms of healthcare data in the cloud and applications of cloud computing.
Li Liu et al. (2014) [21] and Rashmi Rai et al. (2015) [19] focused on the several critical issues
such as maintainability, scalability, security, scale and complexity associated with the legacy
system. Among the issues they considered security is prime factor for the adoption in cloud
migration.
Ilango Sriram et al. (2015) [23] and Rahul Sharma et al. (2015) [24] are raised on different
research challenges such as to facilitate industry in building successful clouds and comparing
cloud computing features with pervasive as well as service computing.
3.2.Survey on Resource Allocation
Shubhangi D. et al.(2012) [38], Abirami S.P et al.(2012) [63], Ram Kumar Sharma et al.(2013)
[72] , Xin Xu et al. (2014) [44], Zhigang Zhou et al. (2015) [35],Hussein S. Al-Olimat et
al.(2015) [60] focuses on sharing the knowledge on utlization of resources in cloud computing
environments such as cloud providers and cloud users with a minimal expense for which it meets
performance requirements. The knowledge is applied on different services such as nimbus,
cumulus and demand. N. Asha et al. (2013) [40] and Jayanthi S et al. (2014) [49] suggested on
resource accounting and two distinct alternatives: one is strictly usage-oriented where user has a
limited number of units, which can be connected to CPU and/or Memory usage, time. And
another alternative is capacity pre-allocation.
A.Y.Khobragade et al. (2013) [39],Wanneng Shu et al. (2014) [26] and Jing-Mian Tang et
al.(2015) [62] presented energy consumption offers in various computing utility services to the
day to day life but the demand of Cloud infrastructure has greatly increased the energy
consumption of data centers, which is a major big problem is facing the world today. It is very
hard to evaluate and optimize energy consumption in complex cloud infrastructure architectures.
Devendra Singh Thakur et al. [83] and Shilpi Saxena et al. [85] discussed lot of challenges such
as security of data, server consolidation, resources, tasks, data storage and consumption of energy.
Qi Zhang et al. (2014) [30] and Zhou Fang et al. [47] said that the existing solutions have not
fully considered the heterogeneity of both workload and machine hardware found in production
environments. Failure to consider the heterogeneity of both machines and workloads will lead to
both sub-optimal energy-savings and long scheduling delays, due to incompatibility between
workload requirements and the resources offered by the provisioned machines.
Sagar.G et al. (2013) [41] and S.Thejeswi et al. (2013) [42] focuses on rapid development of
cloud computing environment for mapping applications from large quantity of users. There must

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be a provision that all resources are dynamically made available to satisfy the needs of requesting
users.
S.Sujan et al. (2015) [61] and H.Wang et al.(2015) [31]fixes the issue of computing millions of
transactions a day by considering resource provisioning and scheduling context.
Chien-Hung Chen et al. (2015) [57], D. A. Heger et al. [46], Gunho Lee et al. [48] authors have
given an idea on advanced methodologies used in cloud computing such as MapReduce
scheduling and Data Analytics are required to assess the performance, capacity behavior and
allocation with high fidelity.
Pooja .P.Vasani et al. (2013) [74] and Longbin .C et al. (2015) [36] are represented on different
types of resources in virtualized form for dynamix utilization using scheduling.
N.D. Doulamis et al. (2014) [29], Christina Delimitrou et al. (2015) [52] and Hui Wang et
al.(2015) [34] takeup a challenging issues called QoS (Quality of Service) for hybrid storage
systems in resource allocation and also QoS degradation is expressed through time deviation
metric.
Shivani Sharma et al. (2014) [80] and A.Narayan Singh et al. (2015) [32] sharing on various
software and hardware resources for designing various resource allocation alogrithms in efficient
manner.
Sharrukh .Z et al. (2013) [28], Pratik P. Pandya et al. (2014) [43] and Gruia Calinescu et al.
(2011) [25] authors addressed the issue on dynamica provisioning VM instances in clouds to
generate higher profit by considering various parameters like QoS, cost, time consumption,
carbon effect. Omar Khedher et al. (2015) [59] have empowered cloud computing challenging
task in the academic communities by reducing the IT infrastructure administration and cost by
representing a simplified architecture design, treating a large size Virtual Cloud Laboratory
(VCL) deployment.
Shaolei Ren et al. (2014) [55] and Seokho Son et al. [45] has discussed on wireless cloud
computing services in which service providers keep deploying services in geographically
distributed data centres and provides services based on dynamic price subscribe, Service Level
Agreements to reduce the load on data centers.
M.A.Sharkh et al. (2013) [27] and Aziz Mohaisen et al. (2014) [88] has introduces Data Center
Resource Allocation paradigms for internal and external challenges, computing nodes in social
ties. The paradigms works based on bootstrapping trust-possessing social graph and these
paradigms are different when compared with the paradigms of distributed, grid and conventional
cloud computing.
3.3. Survey on Scheduling
Shaobin Zhan et al.(2012) [65], Kavita Bhatt et al.(2013) [71],WenAn Tan et al.(2014) [54], P
Kowsik et al.(2014) [77], Garima Mahajan et al. (2014) [76] focused on job scheduling issues
such as optimization algorithm, combination optimization,task schedulers, WFS-algorithms,
workflow scheduling and load balancing.
Monika Choudhary et al.(2012) [64], Upendra Bhoi et al. (2013) [68], A. Kaleeswaran et
al.(2013) [66],Hilda Lawrance et al.(2013) [67] discussed on static and dynamic parameters,

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restriction of tasks and resources achieving best performance such as CPU speed, memory,
bandwidth, minimal total time for completion, shortest response time and utilization of resources .
Guan Wang et al.(2013) [87], Yogita Chawla et al.(2013) [69], Arabi E. Keshk et al.(2014) [75],
Mahesh S. Shinde et al.(2014) [78], Dr.M.Srivenkatesh(2015) [81], Er. Shelly Sachdeva et
al.(2015) [82] focused on task scheduling algorithms such as Min-min algorithm, conventional
scheduling concepts. Task scheduling is divided in two types one is on-line mode and batch mode
service.
,Xiaomin Zhu et al.(2014) [53] and Kapil Kumar er al.(2014) [79] presents energy conversation
such as reducing operating costs, increasing system reliability, prompting environmental
protection and energy –aware scheduling. Dinesh Kumar et. al. (2015) [56], discussed
virtualization technology such as efficient virtual machine scheduling and physical machines.
Shachee V Parikh et al. (2013) [73], Antony Thomas et al. (2015) [51] and Gongzhuang Peng et
al. (2015) [58] authors have addressed dynamic and heterogonous nature of resources and tasks in
cloud computing by designing a framework based on scheduling policies. The framework
monitors, detects and scheduling the system resource performance.
4. DISSCUSION ON THE STUDY OF RESOURCE ALLOCATION AND
SCHEDULING
The main aim of the study is how research is moving drastically in different components of cloud
computing such as Resource Utilization, Scheduling Algorithms, Resource Allocation,
Scheduling Policies and Advanced Methodologies. Different issues such as Security, Privacy,
Infrastructure, Data Management, Job Scheduling, Scalability, Time Complexity, Cost,
Maintainability, Migration, Resource Optimization etc were discussed. At the outside, cost and
resource efficiency is playing a vital role in IT organizations and it needs new technologies for
business applications in cloud computing. To endeavor the security issue, one has to propose
deployment methods and service models in cloud computing. To balance the load on cloud
servers and to improve the performance as well as to enhance the resource and Job scheduling,
new algorithms have to develop.
There is a research gap exists in: growing maturity of cloud migration with its evaluation,
achieving interoperability, building successful industry clouds, managing virtual machine images,
storing and processing features of cloud computing with pervasive computing and service
computing, maximization of system utilization with minimizing make span to reduce cost,
optimizing the algorithms to scale effectively with larger workloads, reducing energy
consumptions, time span, revenue in single or multi-objective formulations.
Finally, we are planning to propose a model which deals with efficient allocation of the resources,
analyze performance in the cloud system and also moving towards resource provisioning, task
scheduling, deployment model and methods in cloud environment.
5. CONCLUSION
Cloud computing, the long-held dream of computing as a utility, has the potential to transform a
large part of the IT industry, making software even more attractive as a service and shaping the
way in which hardware is designed and purchased. Cloud Computing can be distinguished from
other distributed paradigms though the provisioning of resources, data and software to users on
demand in a similar fashion to the services provided by the electric power industry.
In the next work of research, we are going to deal with cloud environment, make cloud services
and cloud-oriented applications efficiently. It works with task scheduling inside cloud computing,
among them there is a particular interest in the ones caused by the influence of system volatility
over the scheduling heuristics and in the problems caused by failures in some of the scheduling
platform components.

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13
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14
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15
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Authors
Smt. B. Muni Lavanya is currently working as Adhoc Lecturer at JNTUA College of
Engineering, Pulivendula. She completed her B.Tech Degree in Computer Science and
Engineering from Sree Vidyanikethan Engineering College, Tirupathi in 2009. She
completed her M.Tech Degree in Computer Science and Engineering from JNT
University, Anantapur in 2012. She is pursuing Ph. D. from JNT University Anantapur.
Her specializations are: Big Data, Cloud Computing, Parallel Processing.
Dr. C .Shoba Bindu, received her Ph.D degree in Computer Science and Engineering
from JNTUA, Anantapuramu, Andhra Pradesh. She is currently extending her services
as Associate Professor in department of Computer Science and Engineering, JNTUA.
She has guided many external and internal projects and has good contributions in many
reputed journals. Her research interests are in the areas of Mobile and Adhoc Networks,
Network security, Data Mining and Cloud Computing.

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