Cloud computing is an on-demand service resource which includes applications to data centers on a
pay-per-use basis. In order to allocate these resources properly and satisfy users’ demands, an efficient
and flexible resource allocation mechanism is needed. Due to increasing user demand, the resource
allocating process has become more challenging and difficult. One of the main focuses of research
scholars is how to develop optimal solutions for this process. In this paper, a literature review on proposed
dynamic resource allocation techniques is introduced.
Ant Colony Optimization for Load Balancing in CloudChanda Korat
here the presentation gives the natural behavior of ants and how the that logic is applicable to cloud for load balancing is discussed here with detailed literature survey.
Evolution of Distributed computing: Scalable computing over the Internet – Technologies for network based systems – clusters of cooperative computers - Grid computing Infrastructures – cloud computing - service oriented architecture – Introduction to Grid Architecture and standards – Elements of Grid – Overview of Grid Architecture.
Ant Colony Optimization for Load Balancing in CloudChanda Korat
here the presentation gives the natural behavior of ants and how the that logic is applicable to cloud for load balancing is discussed here with detailed literature survey.
Evolution of Distributed computing: Scalable computing over the Internet – Technologies for network based systems – clusters of cooperative computers - Grid computing Infrastructures – cloud computing - service oriented architecture – Introduction to Grid Architecture and standards – Elements of Grid – Overview of Grid Architecture.
On demand delivery of IT resources through the internet with payment depending on the use of the service is known as cloud computing.
The term cloud refers to a network or the internet.
It gives a solution for infrastructure at low cost.
Cloud computing refers to manipulating, configuring, and accessing the applications online. It offers online data storage, infrastructure and application.
Cloud computing is both a combination of software and hardware based computing resources delivered as a network service.
This presentation will help you all a lot.
because this is not from a particular text book or a reference guide it is a collection of several web sites.
Cloud deployment models: public, private, hybrid, community – Categories of cloud computing: Everything as a service: Infrastructure, platform, software - Pros and Cons of cloud computing – Implementation levels of virtualization – virtualization structure – virtualization of CPU, Memory and I/O devices – virtual clusters and Resource Management – Virtualization for data center automation.
Hybrid Based Resource Provisioning in CloudEditor IJCATR
The data centres and energy consumption characteristics of the various machines are often noted with different capacities.
The public cloud workloads of different priorities and performance requirements of various applications when analysed we had noted
some invariant reports about cloud. The Cloud data centres become capable of sensing an opportunity to present a different program.
In out proposed work, we are using a hybrid method for resource provisioning in data centres. This method is used to allocate the
resources at the working conditions and also for the energy stored in the power consumptions. Proposed method is used to allocate the
process behind the cloud storage.
A survey on various resource allocation policies in cloud computing environmenteSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
On demand delivery of IT resources through the internet with payment depending on the use of the service is known as cloud computing.
The term cloud refers to a network or the internet.
It gives a solution for infrastructure at low cost.
Cloud computing refers to manipulating, configuring, and accessing the applications online. It offers online data storage, infrastructure and application.
Cloud computing is both a combination of software and hardware based computing resources delivered as a network service.
This presentation will help you all a lot.
because this is not from a particular text book or a reference guide it is a collection of several web sites.
Cloud deployment models: public, private, hybrid, community – Categories of cloud computing: Everything as a service: Infrastructure, platform, software - Pros and Cons of cloud computing – Implementation levels of virtualization – virtualization structure – virtualization of CPU, Memory and I/O devices – virtual clusters and Resource Management – Virtualization for data center automation.
Hybrid Based Resource Provisioning in CloudEditor IJCATR
The data centres and energy consumption characteristics of the various machines are often noted with different capacities.
The public cloud workloads of different priorities and performance requirements of various applications when analysed we had noted
some invariant reports about cloud. The Cloud data centres become capable of sensing an opportunity to present a different program.
In out proposed work, we are using a hybrid method for resource provisioning in data centres. This method is used to allocate the
resources at the working conditions and also for the energy stored in the power consumptions. Proposed method is used to allocate the
process behind the cloud storage.
A survey on various resource allocation policies in cloud computing environmenteSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A survey on various resource allocation policies in cloud computing environmenteSAT Journals
Abstract Cloud computing is bringing a revolution in computing environment replacing traditional software installations, licensing issues into complete on-demand services through internet. In Cloud computing multiple cloud users can request number of cloud services simultaneously. So there must be a provision that all resources are made available to requesting user in efficient manner to satisfy their need. Resource allocation is based on quality of service and service level agreement. In cloud computing environment, to allocate resources to the user there are several methods but provider should consider the efficient way to guarantee that the applications’ requirements are attended to correctly and satisfy the user’s need This paper survey different resource allocation policies used in cloud computing environment. Keywords: Cloud computing, Resource allocation
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Allocation Strategies of Virtual Resources in Cloud-Computing NetworksIJERA Editor
In distributed computing, Cloud computing facilitates pay per model as per user demand and requirement.
Collection of virtual machines including both computational and storage resources will form the Cloud. In
Cloud computing, the main objective is to provide efficient access to remote and geographically distributed
resources. Cloud faces many challenges, one of them is scheduling/allocation problem. Scheduling refers to a
set of policies to control the order of work to be performed by a computer system. A good scheduler adapts its
allocation strategy according to the changing environment and the type of task. In this paper we will see FCFS,
Round Robin scheduling in addition to Linear Integer Programming an approach of resource allocation.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
PROPOSED ONTOLOGY FRAMEWORK FOR DYNAMIC RESOURCE PROVISIONING ON PUBLIC CLOUDIAEME Publication
Cloud computing is an essential ingredient of today’s modern information technology. Cloud computing is totally based on internet. With the use of cloud computing resources can be shared from anywhere and anytime. In cloud computing there are multiple users simultaneously requests for the number of services and its important to provision all resources to user in efficient manner to satisfy their requirements. To come out this problem in this paper we had reviwed the different types of resource allocation strategies and proposed an ontology based resource management framwork for dynamic resource allocation. Ontology Framework contain four sections, each section equipped with functionality to collect information regarding all resources available in actual cloud deployment based on signed SLA agreement, and then replies to the user with appropriate allocation.
The concept of Genetic algorithm is specifically useful in load balancing for best virtual
machines distribution across servers. In this paper, we focus on load balancing and also on
efficient use of resources to reduce the energy consumption without degrading cloud
performance. Cloud computing is an on demand service in which shared resources, information,
software and other devices are provided according to the clients requirement at specific time. It‟s
a term which is generally used in case of Internet. The whole Internet can be viewed as a cloud.
Capital and operational costs can be cut using cloud computing. Cloud computing is defined as a
large scale distributed computing paradigm that is driven by economics of scale in which a pool
of abstracted virtualized dynamically scalable , managed computing power ,storage , platforms
and services are delivered on demand to external customer over the internet. cloud computing is
a recent field in the computational intelligence techniques which aims at surmounting the
computational complexity and provides dynamically services using very large scalable and
virtualized resources over the Internet. It is defined as a distributed system containing a
collection of computing and communication resources located in distributed data enters which
are shared by several end users. It has widely been adopted by the industry, though there are
many existing issues like Load Balancing, Virtual Machine Migration, Server Consolidation,
Energy Management, etc.
Efficient Resource Sharing In Cloud Using Neural NetworkIJERA Editor
In cloud computing, collaborative cloud computing(CCC) is the emerging technology where globally-dispersed cloud resource belonging to different organization are collectively used in a cooperative manner to provide services. In previous research, Harmony enables a node to locate its desired resources and also find the reputation of the located resources, so that a client can choose resource providers not only by resource availability but also by the provider’s reputation of providing the resource. In proposed system to reform resource utilization based on optimal time period to allocate resources to the neural network training and to load factor calculation the dynamic priority scheduling technique is used to assign the priority to the cloud users according to their load. The dynamic priority scheduling algorithm strikes the right balance between performance and power efficiency.
Review and Classification of Cloud Computing Researchiosrjce
IOSR journal of VLSI and Signal Processing (IOSRJVSP) is a double blind peer reviewed International Journal that publishes articles which contribute new results in all areas of VLSI Design & Signal Processing. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & Signal Processing concepts and establishing new collaborations in these areas.
Design and realization of microelectronic systems using VLSI/ULSI technologies require close collaboration among scientists and engineers in the fields of systems architecture, logic and circuit design, chips and wafer fabrication, packaging, testing and systems applications. Generation of specifications, design and verification must be performed at all abstraction levels, including the system, register-transfer, logic, circuit, transistor and process levels
ANALYSIS OF THE COMPARISON OF SELECTIVE CLOUD VENDORS SERVICESijccsa
Cloud computing refers to a location that allows us to preserve our precious data and use computing and
networking services on a pay-as-you-go basis without the need for a physical infrastructure. Cloud
computing now provides us with powerful data processing and storage, exceptional availability and
security, rapid accessibility and adaption, ensured flexibility and interoperability, and time and cost
efficiency. Cloud computing offers three platforms (IaaS, PaaS, and SaaS) with unique capabilities that
promise to make it easier for a customer, organization, or trade to establish any type of IT business. We
compared a variety of cloud service characteristics in this article, following the comparing, it's
straightforward to pick a specific cloud service from the possible options by comparison with three chosen
cloud providers such as Amazon, Microsoft Azure, and Digital Ocean. By using findings of this study to not
only identify similarities and contrasts across various aspects of cloud computing, as well as to suggest
some areas for further study.
A FRAMEWORK FOR SOFTWARE-AS-A-SERVICE SELECTION AND PROVISIONINGIJCNCJournal
As cloud computing is increasingly transforming the information technology landscape, organizations and
businesses are exhibiting strong interest in Software-as-a-Service (SaaS) offerings that can help them
increase business agility and reduce their operational costs. They increasingly demand services that can
meet their functional and non-functional requirements. Given the plethora and the variety of SaaS
offerings, we propose, in this paper, a framework for SaaS provisioning, which relies on brokered Service
Level agreements (SLAs), between service consumers and SaaS providers. The Cloud Service Broker (CSB)
helps service consumers find the right SaaS providers that can fulfil their functional and non-functional
requirements. The proposed selection algorithm ranks potential SaaS providers by matching their offerings
against the requirements of the service consumer using an aggregate utility function. Furthermore, the CSB
is in charge of conducting SLA negotiation with selected SaaS providers, on behalf of service consumers,
and performing SLA compliance monitoring
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
A latency-aware max-min algorithm for resource allocation in cloud IJECEIAES
Cloud computing is an emerging distributed computing paradigm. However, it requires certain initiatives that need to be tailored for the cloud environment such as the provision of an on-the-fly mechanism for providing resource availability based on the rapidly changing demands of the customers. Although, resource allocation is an important problem and has been widely studied, there are certain criteria that need to be considered. These criteria include meeting user’s quality of service (QoS) requirements. High QoS can be guaranteed only if resources are allocated in an optimal manner. This paper proposes a latency-aware max-min algorithm (LAM) for allocation of resources in cloud infrastructures. The proposed algorithm was designed to address challenges associated with resource allocation such as variations in user demands and on-demand access to unlimited resources. It is capable of allocating resources in a cloud-based environment with the target of enhancing infrastructure-level performance and maximization of profits with the optimum allocation of resources. A priority value is also associated with each user, which is calculated by analytic hierarchy process (AHP). The results validate the superiority for LAM due to better performance in comparison to other state-of-the-art algorithms with flexibility in resource allocation for fluctuating resource demand patterns.
ABSTRACT
Cloud computing utilizes large scale computing infrastructure that has been radically changing the IT landscape enabling remote access to computing resources with low service cost, high scalability , availability and accessibility. Serving tasks from multiple users where the tasks are of different characteristics with variation in the requirement of computing power may cause under or over utilization of resources.Therefore maintaining such mega-scale datacenter requires efficient resource management procedure to increase resource utilization. However, while maintaining efficiency in service provisioning it is necessary to ensure the maximization of profit for the cloud providers. Most of the current research works aims at how providers can offer efficient service provisioning to the user and improving system performance. There are comparatively fewer specific works regarding resource management which also deals with the economic section that considers profit maximization for the provider. In this paper we represent a model that deals with both efficient resource utilization and pricing of the resources. The joint resource management model combines the work of user assignment, task scheduling and load balancing on the fact of CPU power endorsement. We propose four algorithms respectively for user assignment, task scheduling, load balancing and pricing that works on group based resources offering reduction in task execution time(56.3%),activated physical machines(41.44%),provisioning cost(23%) . The cost is calculated over a time interval involving the number of served customer at this time and the amount of resources used within this time
Similar to A Survey on Resource Allocation in Cloud Computing (20)
ANALYSIS OF ATTACK TECHNIQUES ON CLOUD BASED DATA DEDUPLICATION TECHNIQUESneirew J
ABSTRACT
Data in the cloud is increasing rapidly. This huge amount of data is stored in various data centers around the world. Data deduplication allows lossless compression by removing the duplicate data. So, these data centers are able to utilize the storage efficiently by removing the redundant data. Attacks in the cloud computing infrastructure are not new, but attacks based on the deduplication feature in the cloud computing is relatively new and has made its urge nowadays. Attacks on deduplication features in the cloud environment can happen in several ways and can give away sensitive information. Though, deduplication feature facilitates efficient storage usage and bandwidth utilization, there are some drawbacks of this feature. In this paper, data deduplication features are closely examined. The behavior of data deduplication depending on its various parameters are explained and analyzed in this paper.
SUCCESS-DRIVING BUSINESS MODEL CHARACTERISTICS OF IAAS AND PAAS PROVIDERSneirew J
ABSTRACT Market analyses show that some cloud providers are significantly more successful than others. The research on the success-driving business model characteristics of cloud providers and thus, the reasons for this performance discrepancy is, however, still limited. Whereas cloud business models have mostly been examined comprehensively, independently from the distinctly different cloud ecosystem roles, this paper takes a perspective shift from an overall towards a selective, role-specific and thereby ecosystemic perspective on cloud business models. The goal of this paper is specifically to identify the success-driving business model characteristics of the so far widely neglected cloud ecosystem’s core roles, IaaS and PaaS provider, by conducting an exploratory multiple-case study. 21 expert interviews with representatives from 17 cloud providers serve as central data collection instrument. The result is a catalogue of generic as well as cloud-specific, subdivided into role-overarching and role-specific, business model characteristics. This catalogue supports cloud providers in the initial design, comparison and revision of their business models. Researchers obtain a promising starting and reference point for future analysis of business models of various cloud ecosystem roles.
Strategic Business Challenges in Cloud Systemsneirew J
For the past few years, the evolution of cloud computing has been potentially becoming one of the major
advances in the history of computing. But is cloud computing the saviour of business? Does it signal the
demise of the corporate IT functionality entirely? However, if cloud computing has to achieve its potential,
there is a need to have a clear understanding of various issues involved, both from the perspectives of the
providers and the consumers related to the technology, management and business aspects. Objective of this
research is to explore the strategic business, management and technical challenges existing in cloud
systems. It is believed that adopting a methodology and suggesting a corresponding architectural
framework would serve as a potential comprehensive conceptual tool, which shows path for mitigating
challenges and hence effort are put in bringing in by mentioning a suitable methodology and its brief
description. It concludes that International Business Machine Common Cloud Management Platform is one
way to realize the combined features of various models such as Hub & Spoke Model as a quality of
Governance model; Gen-Spec Research Methodology design for semantic and quality research studies into
one in the form of Reference Architecture. However in order to realize the full potential of the CustomerRespond-Adapt-Sense-Provider
(conceptual) methodology for dealing with semantics, it is important to
consider Internet of Things Architecture Reference Model where in the resources are translated into
Services.
Laypeople's and Experts' Risk Perception of Cloud Computing Services neirew J
Cloud computing is revolutionising the way software services are procured and used by Government
organizations and SMEs. Quantitative risk assessment of Cloud services is complex and undermined by
specific security concerns regarding data confidentiality, integrity and availability. This study explores how
the gap between the quantitative risk assessment and the perception of the risk can produce a bias in the
decision-making process about Cloud computing adoption.
The risk perception of experts in Cloud computing (N=37) and laypeople (N=81) about ten Cloud
computing services was investigated using the psychometric paradigm. Results suggest that the risk
perception of Cloud services can be represented by two components, called “dread risk” and “unknown
risk”, which may explain up to 46% of the variance. Other factors influencing the risk perception were
“perceived benefits”, “trust in regulatory authorities” and “technology attitude”.
This study suggests some implications that could support Government and non-Government organizations
in their strategies for Cloud computing adoption.
Factors Influencing Risk Acceptance of Cloud Computing Services in the UK Gov...neirew J
Cloud Computing services are increasingly being made available by the UK Government through the
Government digital marketplace to reduce costs and improve IT efficiency; however, little is known about
factors influencing the decision making process to adopt cloud services within the UK Government. This
research aims to develop a theoretical framework to understand risk perception and risk acceptance of
cloud computing services.
Study’s subjects (N=24) were recruited from three UK Government organizations to attend a semi
structured interview. Transcribed texts were analyzed using the approach termed interpretive
phenomenological analysis. Results showed that the most important factors influencing risk acceptance of
cloud services are: perceived benefits and opportunities, organization’s risk culture and perceived risks.
We focused on perceived risks and perceived security concerns. Based on these results, we suggest a
number of implications for risk managers, policy makers and cloud service providers
A Cloud Security Approach for Data at Rest Using FPE neirew J
In a cloud scenario, biggest concern is around security of the data. “Both data in transit and at rest must
be secure” is a primary goal of any organization. Data in transit can be made secure using TLS level
security like SSL certificates. But data at rest is not quite secure, as database servers in public cloud
domain are more prone to vulnerabilities. Not all cloud providers give out of box encryption with their
offerings. Also implementing traditional encryption techniques will cause lot of changes in application as
well as at database level. This paper provides efficient approach to encrypt data using Format Preserving
Encryption technique. FPE focuses mainly on encrypting data without changing format so that it’s easy to
develop and migrate legacy application to cloud. It is capable of performing format preserving encryption
on numeric, string and the combination of both. This literature states various features and advantages of
same.
Error Isolation and Management in Agile Multi-Tenant Cloud Based Applications neirew J
Management of errors in multi-tenant cloud based applications remains a challenging problem. This
problem is compounded due to (i) multiple versions of application serving different clients, (ii) agile nature
in which the applications are released to the clients, and (iii) variations in specific usage patterns of each
client. We propose a framework for isolating and managing errors in such applications. The proposed
framework is evaluated with two different popular cloud based applications and empirical results are
presented.
Locality Sim : Cloud Simulator with Data Localityneirew J
Cloud Computing (CC) is a model for enabling on-demand access to a shared pool of configurable
computing resources. Testing and evaluating the performance of the cloud environment for allocating,
provisioning, scheduling, and data allocation policy have great attention to be achieved. Therefore, using
cloud simulator would save time and money, and provide a flexible environment to evaluate new research
work. Unfortunately, the current simulators (e.g., CloudSim, NetworkCloudSim, GreenCloud, etc..) deal
with the data as for size only without any consideration about the data allocation policy and locality. On
the other hand, the NetworkCloudSim simulator is considered one of the most common used simulators
because it includes different modules which support needed functions to a simulated cloud environment,
and it could be extended to include new extra modules. According to work in this paper, the
NetworkCloudSim simulator has been extended and modified to support data locality. The modified
simulator is called LocalitySim. The accuracy of the proposed LocalitySim simulator has been proved by
building a mathematical model. Also, the proposed simulator has been used to test the performance of the
three-tire data center as a case study with considering the data locality feature.
Benefits and Challenges of the Adoption of Cloud Computing in Businessneirew J
The loss of business and downturn of economics almost occur every day. Thus technology is needed in
every organization. Cloud computing has played a major role in solving the inefficiencies problem in
organizations and increase the growth of business thus help the organizations to stay competitive. It is
required to improve and automate the traditional ways of doing business. Cloud computing has been
considered as an innovative way to improve business. Overall, cloud computing enables the organizations
to manage their business efficiently. Unnecessary procedural, administrative, hardware and software costs
in organizations expenses are avoided using cloud computing. Although cloud computing can provide
advantages but it does not mean that there are no drawbacks. Security has become the major concern in
cloud and cloud attacks too. Business organizations need to be alert against the attacks to their cloud
storage. Benefits and drawbacks of cloud computing in business will be explored in this paper. Some
solutions also provided in this paper to overcome the drawbacks. The method has been used is secondary
research, that is collecting data from published journal papers and conference papers.
Intrusion Detection and Marking Transactions in a Cloud of Databases Environm...neirew J
The cloud computing is a paradigm for large scale distributed computing that includes several existing
technologies. A database management is a collection of programs that enables you to store, modify and
extract information from a database. Now, the database has moved to cloud computing, but it introduces at
the same time a set of threats that target a cloud of database system. The unification of transaction based
application in these environments present also a set of vulnerabilities and threats that target a cloud of
database environment. In this context, we propose an intrusion detection and marking transactions for a
cloud of database environment.
An Approach to Reduce Energy Consumption in Cloud data centers using Harmony ...neirew J
Fast development of knowledge and communication has established a new computational style which is
known as cloud computing. One of the main issues considered by the cloud infrastructure providers, is to
minimize the costs and maximize the profitability. Energy management in the cloud data centers is very
important to achieve such goal. Energy consumption can be reduced either by releasing idle nodes or by
reducing the virtual machines migrations. To do the latter, one of the challenges is to select the placement
approach of the migrated virtual machines on the appropriate node. In this paper, an approach to reduce
the energy consumption in cloud data centers is proposed. This approach adapts harmony search
algorithm to migrate the virtual machines. It performs the placement by sorting the nodes and virtual
machines based on their priority in descending order. The priority is calculated based on the workload.
The proposed approach is simulated. The evaluation results show the reduction in the virtual machine
migrations, the increase of efficiency and the reduction of energy consumption.
Data Distribution Handling on Cloud for Deployment of Big Dataneirew J
Cloud computing is a new emerging model in the field of computer science. For varying workload Cloud
computing presents a large scale on demand infrastructure. The primary usage of clouds in practice is to
process massive amounts of data. Processing large datasets has become crucial in research and business
environments. The big challenges associated with processing large datasets is the vast infrastructure
required. Cloud computing provides vast infrastructure to store and process Big data. Vms can be
provisioned on demand in cloud to process the data by forming cluster of Vms . Map Reduce paradigm can
be used to process data wherein the mapper assign part of task to particular Vms in cluster and reducer
combines individual output from each Vms to produce final result. we have proposed an algorithm to
reduce the overall data distribution and processing time. We tested our solution in Cloud Analyst
Simulation environment wherein, we found that our proposed algorithm significantly reduces the overall
data processing time in cloud.
Cloud Computing is an attractive research area for the last few years; and there have been a tremendous
grows in the number of educational institutions all over the world who have either adopted or are
considering migrating to cloud computing. However, there are many concerns and reservations about
adopting conventional or public cloud based solutions. A new paradigm of cloud based solution has been
proposed, namely, the private cloud based solutions, which becomes an attractive choice to educational
Institutions. This paper presents the adjustment and implementation of private-based cloud solution for
multi-campus educational institution, namely, Al-Balqa Applied University (BAU) in Jordan.
Implementation of the Open Source Virtualization Technologies in Cloud Computingneirew J
The “Virtualization and Cloud Computing” is a recent buzzword in the digital world. Behind this fancy
poetic phrase there lies a true picture of future computing for both in technical and social perspective.
Though the “Virtualization and Cloud Computing are recent but the idea of centralizing computation and
storage in distributed data centres maintained by any third party companies is not new but it came in way
back in 1990s along with distributed computing approaches like grid computing, Clustering and Network
load Balancing. Cloud computing provide IT as a service to the users on-demand basis. This service has
greater flexibility, availability, reliability and scalability with utility computing model. This new concept of
computing has an immense potential in it to be used in the field of e-governance and in the overall IT
development perspective in developing countries like Bangladesh.
A Broker-based Framework for Integrated SLA-Aware SaaS Provisioning neirew J
In the service landscape, the issues of service selection, negotiation of Service Level Agreements (SLA), and
SLA-compliance monitoring have typically been used in separate and disparate ways, which affect the
quality of the services that consumers obtain from their providers. In this work, we propose a broker-based
framework to deal with these concerns in an integrated mannerfor Software as a Service (SaaS)
provisioning. The SaaS Broker selects a suitable SaaS provider on behalf of the service consumer by using
a utility-driven selection algorithm that ranks the QoS offerings of potential SaaS providers. Then, it
negotiates the SLA terms with that provider based on the quality requirements of the service consumer. The
monitoring infrastructure observes SLA-compliance during service delivery by using measurements
obtained from third-party monitoring services. We also define a utility-based bargaining decision model
that allows the service consumer to express her sensitivity for each of the negotiated quality attributes and
to evaluate the SaaS provider offer in each round of negotiation. A use-case with few quality attributes and
their respective utility functions illustrates the approach.
Comparative Study of Various Platform as a Service Frameworks neirew J
Cloud computing is an emerging paradigm with three basic service models such as Software as a Service
(SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). This paper focuses on
different kinds of PaaS frameworks. PaaS model provides choice of cloud, developer framework and
application service. In this paper, detailed study of four open PaaS frameworks like AppScale, Cloud
Foundry, Cloudify, and OpenShift are explained with the architectural components. We also explained
more PaaS packages like Stratos, mOSAIC, BlueMix, Heroku, Amazon Elastic Beanstalk, Microsoft Azure,
Google App Engine and Stakato briefly. In this paper we present the comparative study of PaaS
frameworks.
Neuro-Fuzzy System Based Dynamic Resource Allocation in Collaborative Cloud C...neirew J
Cloud collaboration is an emerging technology which enables sharing of computer files using cloud
computing. Here the cloud resources are assembled and cloud services are provided using these resources.
Cloud collaboration technologies are allowing users to share documents. Resource allocation in the cloud
is challenging because resources offer different Quality of Service (QoS) and services running on these
resources are risky for user demands. We propose a solution for resource allocation based on multi
attribute QoS Scoring considering parameters such as distance to the resource from user site, reputation of
the resource, task completion time, task completion ratio, and load at the resource. The proposed algorithm
referred to as Multi Attribute QoS scoring (MAQS) uses Neuro Fuzzy system. We have also included a
speculative manager to handle fault tolerance. In this paper it is shown that the proposed algorithm
perform better than others including power trust reputation based algorithms and harmony method which
use single attribute to compute the reputation score of each resource allocated.
A Proposed Model for Improving Performance and Reducing Costs of IT Through C...neirew J
Information technologies are affecting the big business enterprises of todays from data processing and
transactions to achieve the goals efficiently and effectively, affecting creates new business opportunities
and towards new competitive advantage, service must be enough to match the recent trends of IT such as
cloud computing. Cloud computing technology has provided all IT services. Therefore, cloud computing
offers an alternative to adaptable with technology model current , creating reducing cost (Fixed costs and
ongoing), the proliferation of high speed Internet connections through Rent, not acquisitions, cheaper
powerful computing technology and effective performance. The public and private clouds are characterized
by flexibility, operational efficiency that reduces costs improve performance. Also cloud computing
generates business creativity and innovation resulted from collaborative ideas of users; presents cloud
infrastructure and services; paving new markets; offering security in public and private clouds; and
providing environmental impact regarding utilizing green energy technology. In this paper, the main
concentrate the cloud computing.
Secure cloud transmission protocol (SCTP) was proposed to achieve strong authentication and secure
channel in cloud computing paradigm at preceding work. SCTP proposed with its own techniques to attain
a cloud security. SCTP was proposed to design multilevel authentication technique with multidimensional
password generations System to achieve strong authentication. SCTP was projected to develop multilevel
cryptography technique to attain secure channel. SCTP was proposed to blueprint usage profile based
intruder detection and prevention system to resist against intruder attacks. SCTP designed, developed and
analyzed using protocol engineering phases. Proposed SCTP and its techniques complete design has
presented using Petrinet production model. We present the designed SCTP petrinet models and its
analysis. We discussed the SCTP design and its performance to achieve strong authentication, secure
channel and intruder prevention. SCTP designed to use in any cloud applications. It can authorize,
authenticates, secure channel and prevent intruder during the cloud transaction. SCTP designed to protect
against different attack mentioned in literature. This paper depicts the SCTP performance analysis report
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A Survey on Resource Allocation in Cloud Computing
1. International Journal on Cloud Computing: Services and Architecture (IJCCSA) Vol. 6, No. 5, October 2016
DOI: 10.5121/ijccsa.2016.6501 1
A SURVEY ON RESOURCE ALLOCATION IN
CLOUD COMPUTING
Samah Alnajdi, Maram Dogan, Ebtesam Al-Qahtani
Faculty of Computing and Information Technology, King Abdul-Aziz University
Jeddah, Saudi Arabia
ABSTRACT
Cloud computing is an on-demand service resource which includes applications to data centers on a
pay-per-use basis. In order to allocate these resources properly and satisfy users’ demands, an efficient
and flexible resource allocation mechanism is needed. Due to increasing user demand, the resource
allocating process has become more challenging and difficult. One of the main focuses of research
scholars is how to develop optimal solutions for this process. In this paper, a literature review on proposed
dynamic resource allocation techniques is introduced.
1. INTRODUCTION
Resource management is a major task in cloud computing and in any other computing
environments. Cloud computing attempts to provide cheap and easy access to computational
resources, which include servers, networks, storage, and, possibly, services. Cloud providers have
to efficiently manage, provide, and allocate these resources to provide services to cloud
consumers based on service level agreements (SLAs) which both sides agree to prior to the
consumer using the services. Therefore, providers must maintain a reliable allocating mechanism
in order to satisfy the cloud users’ requirements, while stabilizing an appropriate profit margin for
themselves. Due to the increasingly high use of the Internet, and thereby cloud services, the
typically static allocation and management of resources have become impractical, and the
development of dynamic mechanisms have become more appropriate and worth studying.
Nonetheless, even these dynamic mechanisms present issues and challenges to be overcome and
solutions to be found. Many researchers have tried, and are still trying, to provide the optimal
solutions for the resource allocation and management problem in cloud computing environments.
In this paper, we did categorize the existed solutions of resource allocation problem and compare
them in each category.
The contents are organized as follows: section 2 presents a brief background on cloud computing
and resource allocation challenges, section 3 discusses the existing dynamic resource allocation
techniques and solutions, and, finally, section 4 gives a summary and suggestions for future
research directions.
2. BACKGROUND
There are two types of cloud models: the cloud service model and the cloud deployment model.
A. Cloud Computing Services
The cloud offers its services in the form of three types:
2. International Journal on Cloud Computing: Services and Architecture (IJCCSA) Vol. 6, No. 5, October 2016
2
1) Software-as-services (SaaS):
The cloud provider offers the user SaaS through the Internet to use when he wants without having
to install it on his PC This removes the work of updating and maintenance from the end user,
cutting the price of buying the software and removing the need for inconvenient software
licenses. The provider and the user will pay-as-used to gain access from any terminal with an
operating system and web browser without physical restrictions do this automatically.
2) Platform-as-a services(PaaS):
With PaaS, the cloud provides the customer a complex IT environment for the development of
applications the consumer uses without having control of the network or server. These services
provide the company with a development platform to create their own applications (e.g., Google
App Engine).
3) Infrastructure-as-a-services (IaaS):
With IaaS, the cloud provides the end user with the storage memory, server, and network on a
pay-per-use basis, which reduces the cost and is advantageous to the business. These services
provide the company with the ability to add or delete services easily, such as Amazon S3.
B. Cloud Deployment Model
There are various cloud models and classification systems that determine how the services are
provided to the end user:
1) Public cloud: This service is provided to public users and is open to the end user, but the
end user cannot control the infrastructure. Examples of public clouds are IBMs’ Blue
Cloud, Sun Cloud, and Google App Engine[1].
2) Private cloud: This is any service that is provided exclusively to the end user or to a
single organization. This offers more security than the public cloud and is more
expensive [1].
3) Hybrid cloud: This cloud is composed of two or more different clouds which may be
public or private. More important data can be kept in the private cloud, and the other
types of data can be kept in the public cloud. This hybrid arrangement is more secure and
less expensive than the purely private cloud [1].
4) Community cloud: In this cloud, the services are provided exclusively to a group of
persons or companies who share the same interests.
C. Resource Allocation In Cloud Computing
In cloud computing, resource allocation (RA) is a field that is taken into account in many
computing areas such as datacenter management, operating systems, and grid computing. RA
deals with the division of available resources between cloud users and applications in an
economic and effective way. It is one of the challenging tasks in cloud computing based on the
IaaS. Furthermore, RA for IaaS in cloud computing provides several benefits: it is cost effective
because users do not need to install and update hardware or software to access the applications,
its flexibility allows access applications and data on any system in the world, and there are no
limitations of the medium or usage site.
In addition, there are two major processes of RA via cloud computing
3. International Journal on Cloud Computing: Services and Architecture (IJCCSA) Vol. 6, No. 5, October 2016
3
D. Static Allocation
Static Allocation schemes: assign fixed resources to the cloud user or application. In this case, the
cloud user should know the number of resource instances needed for the application and what
resources are requested and should aim to confirm the application’s peak load requests. But the
limitation for static allocation is usually affected by the over-utilization or under-utilization of
computing resources based on the normal workload of the application. This is not cost-effective
and is related to insufficient use of the resource during off-peak periods.
E. Dynamic Allocation
Dynamic Allocation schemes provide cloud resources on the fly when the cloud user or
application is requested, specifically to avoid over-utilization and under-utilization of resources.
A possible drawback when needed resources are requested on the fly is that they might not be
accessible. Thus, the service supplier must allocate resources from different participating cloud
data centers [2].
Resource allocation strategy (RAS) is related to combining cloud provider functions for utilizing
and assigning scarce resources within the boundaries of the cloud system in order to suit the
demand of the cloud application.
As cloud computing has its characteristics, the RAS should avoid the following situations as
much as possible:
1) Resource contention: This situation occurs when multiple users and applications attempt
to allocate the same resource simultaneously.
2) Resource fragmentation: This occurs when applications cannot allocate resources due to
isolated resources being small items.
3) Scarcity: This occurs when multiple applications’ requirements for the resources are high
and there are limited resources, for example, requests for memory, I/O devices, CPUs,
and the techniques that should serve that demand.
4) Over provisioning: This occurs when the users and applications obtain more resources
than those that are requested to fit the quality of service (QoS) requirements.
5) Under provisioning: This occurs when the users and applications obtain fewer resources
than those requested to fit the QoS requirements [2].
From the perspective of cloud users, RA should be achieved at a lower cost and in as little time as
possible. However, for the cloud provider, it is impractical to predict the dynamic of user
demands, nature of users, and application demands. Therefore, resource diversity, limited
resources, locality restrictions, dynamic nature of resource requests, and environmental
necessities require an efficient and dynamic RAS that is suitable for cloud environments. Since
the dynamic and uncertainty of resource demand and supply are unpredictable, different strategies
for dynamic resource allocation are suggested. This research presents different RAS that are
utilized in cloud environments.
3. MODELS FOR DYNAMIC RA IN CLOUD COMPUTING
The quality and cost of the services in cloud computing are based on their RA process, and the
resource provider should assign the resource to the clients in an optimal way. Yet, there are many
RA techniques and proposed models that are used in the area of cloud computing. We are going
to present some of the dynamic RA techniques, classifying them based on the main strategy that
they use to allocate resources. The result of any optimal RAS must consider certain parameters
4. International Journal on Cloud Computing: Services and Architecture (IJCCSA) Vol. 6, No. 5, October 2016
4
such as latency, throughput, and response time. In this paper, we address some of the commonly
used strategies: service level agreement-based, utility-based, market-based, and priority-based
strategies.
A. SLA-Based Dynamic RA Models
The SLA is an agreement that specifies the QoS between the service provider and the service
consumer, and it includes the service price with the level of QoS adjusted by the price of the
service [3]. Most of the RA models in cloud computing environments focus on satisfying the
agreed specifications of the SLA for the cloud user. Some other models’ strategies in RA focus
on achieving the objectives of the cloud provider, which could negatively affect some of the
users’ requirements and the level of QoS provided. One such model is proposed by Popovici et
al.[4]. They investigated the QoS parameters such as offered load and price on the SaaS
provider’s side but did not consider the user’s side.
For a multi-cloud environment, Soodeh Farokhi [5] developed a framework for resource
allocation in a multi-cloud system from the perspective of the SaaS level, agreed SLA, and
service provider conditions. The proposed model utilizes a selection engine, construction engine,
and SLA violation detection and monitoring with the use of the service provider’s QoS
parameters.
There are few models that focus on both the cloud provider and consumer perspectives. One such
model was proposed by Wu et al.[6], and it focuses on the QoS parameters of both the SaaS
provider and the consumer through proposed RA algorithms aimed at minimizing SLA violations
and infrastructure costs, as well as controlling the dynamic change of customers, by specifying
customer demands to the infrastructure level aspect and managing dissimilarity of virtual
machines (VMs). The two proposed algorithms perform well by decreasing costs by about 50%
with fewer VMs and optimizing the means to avoid SLA violations. Also, another work proposed
by Lee et .al [7] addresses the issue of profit basis on service request scheduling in cloud
computing by taking into account the purposes of both the consumers and service providers.
Zhu et al. [8] proposed architecture to solve virtualized RA problems for multi-tier applications.
Their model improved overall performance, reduced the cost, maximized the profit for SaaS
providers, and aimed to meet the user’s performance requirements.
EARA [9] is an efficient agent-based resource allocation framework designed by Kumar et al.
EARA uses agent computing as several agents collect available resource information to allocate it
for user requests based on the signed SLA agreement and, therefore, balancing the performance
and controlling the cost; however, this model only considers the SaaS level of cloud
environments.
Buyya et al. [10] proposed a market-oriented RA scheme by integrating both the customer-driven
service management and provider-driven risk management to promote SLA-based RA.
Nevertheless, [10] it requires a market maker and a market registry to bring the consumer and
providers together and to publish the cloud services and discover their providers.
Pawar et. al. cite{sla8}, proposed a priority-based allocation model by considering various SLA
parameters such as bandwidth, memory, and execution time. Using a preemption mechanism
associated with the benefit of parallel processing, their model improved utilization, especially in a
resource contention situation.
5. International Journal on Cloud Computing: Services and Architecture (IJCCSA) Vol. 6, No. 5, October 2016
5
B. Market-Based Dynamic RA Models
Using the market economy to manage RA has been studied extensively in the past, and several
researchers have investigated the economic aspects of cloud computing from different points of
view. To deal with dynamically fluctuating resource demands, market-driven RA has been
proposed, and it has been implemented by many public IaaS providers such as Amazon EC2 [12].
In this environment, the cloud provider could follow the commodity market approach or
auction-based mechanisms, with the main goal of achieving maximum revenue while minimizing
the cost.
Zaman et al. [13] proposed a combinatorial auction-based mechanism for resource management
in clouds. Their algorithm is based on the users’ valuation concept, which is that each user desires
a specific bundle of VM instances and bids on it. It represents efficient allocation and high profits
for the provider, but it still allows users to pay a minimum cost [13].
In addition, Zhang et. al. [14] introduced a mechanism for spot markets, which addressed the
problem of allocating resources for different VM types in Amazon spot instances using the model
predictive control (MPC) algorithm. The proposed model [14] insures high revenue for the
providers over time by changing the price depending on the level of demand, meeting customer
expectations, and minimizing energy consumption; however, future or forward markets are not
included in the model. From there, Fujiwara et al. [15] improved the market-based allocation by
developing a double-sided combinatorial auction-based model, which allows both the users and
providers to trade their current and future services in the spot and forward market.
There are other market-based models, such as the one proposed by You et al. [16], the RAS-M
model, that define the equilibrium theory and use the GA-based price adjusted algorithm. Their
model [16] is efficient and improves utilization and profit; nevertheless, it only considers the
physical level of the cloud environment and is limited to CPU resources only.
Lin et al. [17] proposed an RA model for clouds based on a sealed-bid auction, where the users
submit their bids to the cloud service providers who collect the bids and determine the price. This
mechanism provides efficient allocation of resources, but no profit maximization is ensured due
to its truth telling property.
C. Utilization-Based RA Models
In order to overcome the under-utilization of resources that results from allocating fixed resources
to applications and services, the main approach of methods that fall into this category is to
dynamically manage VMs to maximize utilization of resources and minimize costs. A model that
adjusts the VMs according to an application’s actual needs has been developed by Lin et al. [18],
and it is based on the threshold. The proposed algorithm uses monitoring and predicting the needs
of cloud applications, which leads to increased resource utilization and decreased costs.
Yin et al. [19] focus on RA at the application level. The authors proposed a multi-dimensional
RA (MDRA) schema using a framework of application allocation to minimize the cost of the data
center by assigning small-sized nodes to the processors of users’ programs.
Simulated annealing-based RA has been performed by Pandit et al. [20] using a bin packing
algorithm with multi-parameters to decrease the unallocated part of resource parameters. The
proposed model has improved utilization of cloud resources at the multi-level in the cloud system
and has decreased the cost.
6. International Journal on Cloud Computing: Services and Architecture (IJCCSA) Vol. 6, No. 5, October 2016
6
The topology aware resource allocation (TARA) schema was introduced by Lee et .al [7]. This
schema deals with unconcerned of the hosted application’s demands for IaaS system. They
proposed a prediction engine and genetic algorithm-based search for minimum latency and proper
confidence. The authors showed that the TARA experiment could result in a decrease in job
compilation time by up to 59%.
Li and Qiu [21] suggested an adaptive RA algorithm for cloud a computing model with
preemptable jobs. The authors defined two algorithms, adaptive list scheduling (ALS) and
adaptive min-min scheduling (AMMS), which apply to task scheduling [21], and they proved that
the proposed algorithm is effective and efficient for use with resources. Younge et al.[22]
proposed a resource management model to improve job scheduling using a green cloud
framework. Their model maximizes utilization as it reduces performance overload and energy
consumption and provides an overall efficiency for data-centers in the cloud computing
environment. An online optimization for scheduling preemptable tasks on IaaS cloud system
models was proposed by J. Li et al.[23], using a min-min algorithm, and the scheduling process is
based on feedback information about actual task execution. Their solution proved to reduce RA
execution time and energy consumption; however, the strength of their model is based on the
reliability of the feedback information.
Rammohan and Baburaj [24] developed an RA in a cloud computing model based on the
interference-aware resource allocation (IARA) technique, providing optimal energy consumption,
and it is practical for a resource-constrained environment and supports special hardware.
There are a number of other models. Some of these models mainly focus on improving resource
utilization, such as the ones by Minarolli et al. [25], Buyya et al. [26], and Ergu et al. [27].
4. RESEARCH CHALLENGES
The research on RA in cloud systems is still at an early stage. Several existing issues have not
been fully addressed while new challenges keep emerging. Some of the challenging research
issues are given as follows:
1) Migration of VM: This migration problem occurs due to the need of the user to switch to
another provider in order to get better data storage.
2) Control: There often is a lack of control mechanism over the resources as they are rented
by the users from the remote server.
3) Energy Efficiency: Due to the emergence of huge data centers that have various
computing operations, there is a need for energy efficient allocation. These centers lead
to the release of large quantities of carbon emission.
4) The Scheduling of Parallel Jobs: Parallel jobs in the field of computing increase the job
that is serve. There are two types of jobs: dependent and independent. The first type must
be done very carefully. These jobs include communication issues. Independent jobs can
be performed using several VMs at the same time.
5) Reduction of Cost and Maximizing of Resources: It is important to handle the constraints
that must be met in the allocation of resources in terms of cloud operating costs and to
maximize the use of all resources. In other words, the service provider must provide users
with low-cost services.
6) Maintaining High Availability: The availability of resources in the cloud must be
guaranteed in case there is a job with long running computations that can take many
hours. Thus, there is a need for some techniques to automatically handle any interruption
or unavailability in resources and switch the jobs to an available resource. Moreover,
7. International Journal on Cloud Computing: Services and Architecture (IJCCSA) Vol. 6, No. 5, October 2016
7
these techniques should support the transparency property by which the user cannot
observe the unavailability or any failure problem.
7) Elasticity: In the cloud, elasticity refers to what extent resource requirements can be
handled dynamically. Demand for resources may increase over time, and the cloud
should automatically detect the size of these demands to be met and the necessary
resources required to meet them.
5. FINDINGS AND FUTURE RESEARCH DIRECTIONS
A great deal of research has been done, and many solutions have been presented in the area of
cloud computing in respect to the RA problem; however, there are still some issues and
challenges that need further research, and an optimal solution that is practical for most cloud
environments has still not been found.
Some of the findings based on our literature of previous studies are as follows:
1) There is a need for reducing the user’s SLA violations when maximizing the RA
utilization because most of the models affect the QoS in order to reduce the cost and
keep high utility algorithms.
2) There is a need for an RA framework that is practical for different cloud environments
in order to ease the complexity of allocation in heterogeneous clouds.
3) There is a need for RA to minimize the cost for cloud consumers and maximize the
profit for cloud providers. It is very important for cloud providers to offer efficient
utilization and management of the limited amount of resources available.
4) There is a need to consider load balance in cloud resources and scheduling the workload
in optimal ways in order to satisfy the QoS requirements of users and maximize profit
by enhancing the use of resources.
The future direction of research into resource allocation in cloud computing should address each
of the above-named challenges and try to implement best practices models.
Table 1 presents the comparative study between various resource allocation techniques in cloud
environment and their merits and demerits.
CONCLUSION
Cloud computing technology is increasingly being used in enterprises and business markets.
Subsequently, an effective RAS is required for achieving user satisfaction and maximizing the
profit for cloud service providers.
This paper provides a survey of some of the models and solutions for the RA problem in the
cloud computing environment. These models are classified based on their strategies, and a
discussion of their strengths and limitations is supported by a comparison table. Finally, research
directions and findings from our literature review are included, and hopefully they will help in
motivating future research to determine optimal RA solutions for cloud environments.
ACKNOWLEDGMENT
First of all, we would like to express our sincere thanks to Allah,then we would like to express
our gratitude to D.Muhammed Monowar for the continuous support us and for his patience,
motivation, and immense knowledge.
8. International Journal on Cloud Computing: Services and Architecture (IJCCSA) Vol. 6, No. 5, October 2016
8
TABLE I
Reference Methodology Used Strengths Limitations
[7] Multi-dimensional
resource allocation
(MDRA) schema
The proposed algorithm
improves resource
utilization and reduces cost
of data center.
It is inefficient at saving
power when user demand
increases in a long-run
situation.
[20] Proposed bin packing
algorithm use for
simulated annealing
It helps to reduce the costs
and solves the multi-layer
resource allocation
problems.
It lacks in handling dynamic
resource requests.
It relays request availability
and restricts bin size.
U4 Topology aware
resource allocation
(TARA) schema
based in IaaS cloud
This minimizes the job
completion time of
application.
The functions used in this
schema are not considered
complicated objective
functions related to power,
infrastructure costs, and
reliability.
[23] Min-min and list
scheduling algorithm
in pre-emptible tasks
This provides significant
improvement in the fierce
resource contention, and
improves load balancing.
This algorithm have a
shorter average execution
time.
It reduces energy
consumption.
The algorithm’s predictions
may not be precise and can
then lead to over provisioning
or under provisioning.
There is a risk whether the
feedback information is
reliable enough for the
scheduler to rely on.
[5] Proposed framework
assists SaaS providers
in multi-cloud system
It helps to find proper
infrastructure resources
which best satisfy the user
requirements while
monitoring SLA and
detection violation.
It does not consider the
latency and data traffic
included in selected services
in a multi-cloud at run time.
[6] Proposed resource
allocation algorithms
for SaaS providers
The proposed algorithms
reduce the SaaS provider’s
cost and the number of
SLA violations.
It manages the dynamic
change of customers.
The total profit of the
algorithms needs to be
enhanced to improve customer
satisfaction levels.
It should monitor penalty
limitations by considering
system failures.
[9] Efficient agent based
resource allocation
(EARA) framework
This provides appropriate
resources for the users.
It serves large number of
users.
If any agent fails, the system
does not function properly.
[11] Priority and heuristic
scheduling algorithms
This provides effective
utilization of cloud
resources to meet the SLA
objective.
The method cannot predict
VMs which will be free earlier
and based on its capability
selecting the task from
waiting for the queue for
execution on that VM.
[28] Robust cloud resource
provisioning
(RCRP) algorithm
used for reservation
plan in cloud system
The algorithm minimizes
the total provisioning costs.
It is implemented on the
agent side as opposed to the
cloud broker side.
It strictly considers a
reservation plan for resource
provisioning.
[29] Proposed resource
allocation algorithm
The algorithm maximizes
the overall profit of the
It’s requirements of VMs.
9. International Journal on Cloud Computing: Services and Architecture (IJCCSA) Vol. 6, No. 5, October 2016
9
for infrastructure
providers
cloud provider.
It minimizes infrastructure
costs and SLA violations.
[24] Interference-aware
resource allocation
(IARA) technique
based model
It provides optimal energy
consumption.
It is practical for a
resource-constrained
environment.
It supports special
hardware.
Minimum performance and
QoS.
[18] A threshold-based
dynamic allocation of
system resources
using CloudSim
toolkit.
This maximizes resource
utilization and minimizes
costs.
It lacks of considering the
overhead of physical
resources.
[21] Adaptive list
scheduling (ALS) and
adaptive min-min
scheduling
The scheme maximize the
utilization and benefit for
the cloud provider.
Minimizes the control over
SLA’s violations for users.
[22] A green cloud
framework -based
scheme
It maximizes the utilization.
It reduces overload and
energy consumption.
It provides an overall
efficiency for data-centers.
The setup phase of the system
is high in cost.
[4] Economics-based
approach using utility
function
It investigated the QoS
parameters such as offered
load and price on the SaaS
provider’s side.
It does not consider SLA’s
violations toward service
customers.
[8] Agent-based
algorithm
It consider allocation for
multi-tier applications.
It improves the overall
performance and reduces
the cost.
It reduces SLA’s violations
for both user and provider.
The algorithms is only
efficient for SaaS level
environment.
[10] Market-oriented
mechanism
It provides both
customer-driven and
provider-driven resource
management.
Many requirements, i.e.
market maker and registry
maker.
[13] Combinatorial auction
mechanism
It provides efficient
allocation and high profit
for the provider.
Allows users to pay a
minimum cost.
Not ensured profit for services
users.
Users can not trade their
services with the providers.
[14] Model Predictive
Control (MPC)
algorithm
It provides revenue
maximization
Allows meeting customer
expectations.
It provides energy
consumption minimization.
Lack future profit prediction
because of unconsidering of
forward markets.
[15] Double-sided
combinatorial
auction-based model
Allows both the users and
providers to trade their
current and future services
in the spot and forward
market.
The high requirements of the
model.
Complex management of
resources.
[16] Equilibrium theory
and GA-based price
This provides efficient
utilization.
It only considers the physical
level of the cloud environment
10. International Journal on Cloud Computing: Services and Architecture (IJCCSA) Vol. 6, No. 5, October 2016
10
adjusted algorithm. It maximizes the profit for
cloud service providers.
and is limited to CPU
resources only.
[17] Sealed-bid auction It provides efficient
allocation of resources.
Not ensured profit
maximization due to its truth
telling property.
REFERENCES
[1] Abdulkader, S. J., & Abualkishik, A. M. Cloud Computing And E-Commerce In Small And Medium
Enterprises (Sme’s): The Benefits, Challenges
[2] K Delhi Babu, D.Giridhar Kumar "Allocation Strategies Of Virtual Resources In Cloud Computing
Networks" Journal Of Engineering Research And Applications,201,Pp.51-55.
[3] Son, Seokho, Gihun Jung, And Sung Chan Jun. "An Sla-Based Cloud Computing That Facilitates
Resource Allocation In The Distributed Data Centers Of A Cloud Provider.” The Journal Of
Supercomputing 64.2 (2013): 606-637.
[4] I. Popovici, And J. Wiles, {Em “Proitable Services In An Uncertain World”} In Proceeding Of
The18th Conference On Supercomputing (Sc 2005), Seattle, Wa.
[5] S. Farokhi, "Towards An Sla-Based Service Allocation In Multi-Cloud Environments," Cluster,
Cloud And Grid Computing (Ccgrid), 2014 14th Ieee/Acm International Symposium On, Chicago, Il,
2014, Pp. 591-594.
[6] Wu, Linlin, Saurabh Kumar Garg, And Rajkumar Buyya.{Em"Sla-Based Resource Allocation For
Software As A Service Provider (Saas) In Cloud Computing Environments." }Cluster, Cloud And
Grid Computing (Ccgrid), 2011 11th Ieee/Acm International Symposium On. Ieee, 2011.
[7] Lee, Gunho, Et Al. "Topology-Aware Resource Allocation For Data-Intensive Workloads."
Proceedings Of The First Acm Asia-Pacific Workshop On Workshop On Systems. Acm, 2010.
[8] Kumar, Ajit, Emmanuel S. Pilli, And R. C. Joshi. "An Efficient Framework For Resource Allocation
In Cloud Computing. Computing, Communications And Networking Technologies (Icccnt), 2013
Fourth International Conference On. Ieee, 2013.
[9] Jiao, Jianxin Roger, Xiao You, And Arun Kumar. "An Agent-Based Framework For Collaborative
Negotiation In The Global Manufacturing Supply Chain Network.Robotics And Computer-Integrated
Manufacturing 22.3 (2006): 239-255.
[10] Garg, Saurabh Kumar, And Rajkumar Buyya. "Market-Oriented Resource Management And
Scheduling: A Taxonomy And Survey. Cooperative Networking (2011): 277-306.
[11] Pawar, Chandrashekhar S., And Rajnikant B. Wagh."Priority Based Dynamic Resource Allocation In
Cloud Computing With Modified Waiting Queue." Intelligent Systems And Signal Processing (Issp),
2013 International Conference On. Ieee, 2013.
[12] Amazon, E. C. Amazon Elastic Compute Cloud (Amazon Ec2). Amazon Elastic Compute Cloud
(Amazon Ec2), 2010.
[13] Zaman, Safdar; Grosu, Daniel. "A Combinatorial Auction-Based Mechanism For Dynamic Vm
Provisioning And Allocation In Clouds" Cloud Computing, Ieee Transactions On, 2013, 1.2: 129-141.
[14] Zhang, Qi, Quanyan Zhu, And Raouf Boutaba. "Dynamic Resource Allocation For Spot Markets In
Cloud Computing Environments" Utility And Cloud Computing (Ucc), 2011 Fourth Ieee
International Conference On. Ieee, 2011.
[15] Fujiwara, Ikki, Kento Aida, And Isao Ono. "Market-Based Resource Allocation For Distributed
Computing" Vol. 2009. Ipsj Sig Technical Report, 2009.
[16] You, Xindong, Et Al. "Ras-M: Resource Allocation Strategy Based On Market Mechanism In Cloud
Computing" In: 2009 Fourth Chinagrid Annual Conference. Ieee, 2009. P. 256-263.
[17] Lin, Wei-Yu, Guan-Yu Lin, And Hung-Yu Wei. "Dynamic Auction Mechanism For Cloud Resource
Allocation." Cluster, Cloud And Grid Computing (Ccgrid), 2010 10th Ieee/Acm International
Conference On. Ieee, 2010.
[18] Lin, Weiwei, James Z. Wang, Chen Liang, And Deyu "A Threshold-Based Dynamic Resource
Allocation Scheme For Cloud Computing", Procedia Engineering, 2011.
[19] Yin, Bo, Et Al. "A Multi-Dimensional Resource Allocation Algorithm In Cloud Computing" Journal
Of Information And Computational Science (2012):3021-3028.
11. International Journal on Cloud Computing: Services and Architecture (IJCCSA) Vol. 6, No. 5, October 2016
11
[20] D. Pandit, S. Chattopadhyay, M. Chattopadhyay And N. Chaki,"Resource Allocation In Cloud Using
Simulated Annealing," Applications And Innovations In Mobile Computing (Aimoc), 2014, Kolkata,
2014, Pp. 21-27.
[21] Li, Jiayin, Et Al. "Adaptive Resource Allocation For Preemptable Jobs In Cloud Systems." Intelligent
Systems Design And Applications (Isda), 2010 10th International Conference On. Ieee, 2010.
[22] Younge, Andrew J., Et Al. "Efficient Resource Management For Cloud Computing Environments."
Green Computing Conference, 2010 International. Ieee, 2010.
[23] Li, Jiayin, Et Al. "Online Optimization For Scheduling Preemptable Tasks On Iaas Cloud Systems."
Journal Of Parallel And Distributed Computing 72.5 (2012): 666-677.
[24] Rammohan, N. R., And E. Baburaj. "Resource Allocation Using Interference Aware Technique In
Cloud Computing Environment." International Journal Of Digital Content Technology And Its
Applications 8.1 (2014): 35.
[25] Minarolli, Dorian, And Bernd Freisleben. "Utility-Based Resource Allocation For Virtual Machines
In Cloud Computing." Computers And Communications (Iscc), 2011 Ieee Symposium On. Ieee,
2011.
[26] Buyya, Rajkumar, Anton Beloglazov, And Jemal Abawajy. "Energy-Efficient Management Of Data
Center Resources For Cloud Computing: A Vision, Architectural Elements, And Open Challenges."
Arxiv Preprint Arxiv:1006.0308 (2010).
[27] Ergu, Daji, Et Al. "The Analytic Hierarchy Process: Task Scheduling And Resource Allocation In
Cloud Computing Environment." The Journal Of Supercomputing 64.3 (2013): 835-848.
[28] Vishnupriya, S., P. Saranya, And P. Suganya. "Effective Management Of Resource Allocation And
Provisioning Cost Using Virtualization In Cloud." Advanced Communication Control And
Computing Technologies (Icaccct), 2014 International Conference On. Ieee, 2014.
[29] Yuan, Haitao, Et Al. "Sla-Based Virtualized Resource Allocation For Multi-Tier Web Application In
Cloud Simulation Environment." Industrial Engineering And Engineering Management (Ieem), 2012
Ieee International Conference On. Ieee, 2012.