Resource Allocation Techniques in Cloud Environment: A Study
Deepesh Kumar
M.Tech (CSE), Galgotias University, India Email: [email protected]
Dr.Ajay Shanker Singh Professor, Galgotias University, India
Email:[email protected]
Abstract—Cloud Computing is a growing area in the world of Internet, it is the next step to the evolution of Internet. It offers dynamic ways to provide services to the users and serves to a large number of users using a concept of virtualization. Services are delivered to users in a wherever-and-whenever they need on pay-on-demand manner. But now, the demand for cloud services is increasing sharply and it becomes hard to satisfy users requests for cloud resources with an agreement called SLA (service level agreement). In this paper a survey is done on different techniques of resource allocation.
Keywords—Virtualization,Resource allocation,SLA.
I. INTRODUCTION
Today, cloud computing becomes the most popular and a new growing technology in the success of Internet. NIST defines cloud computing as follows ,”cloud computing is a model for enabling convenient on-demand network access to a shared pool of configurable computing resources (eg.
Networks, servers, storage , applications and services) that can rapidly provisioned and released with minimal management effort or service provider intraction”[1].
Everything in cloud environment offered as a service and users of cloud access the services as cost pay per usage. The all the available resources are exits at a single point means pool of resources and application are dynamically scalable in the cloud. A cloud has following basic characteristics:
Resources are elastic and scalable in nature.
Automated and self-service provisioning
Application program interfaces (API) for each resource.
A pay-as-you-go model for resource metering and billing
Two kinds of models exist in cloud computing environment:
1) Cloud Deployment Model: These models are public cloud, private cloud, community cloud and hybrid cloud.
Public cloud: This is general public cloud everyone can access the services from it because it is open and cheaper, the main problem within it
Private cloud: A cloud used within an organization or company called private cloud. It is expensive and a secure cloud e.g. cloud used by concur technology.
Community cloud: A sharable cloud that is shared by two or more company or different communities to fulfill their requirements.
Hybrid cloud: A mixture of nay if two or three clouds discussed earlier and the data and applications are permitted to share from one cloud to other.
2) Cloud Service Model: In cloud environment everything is offers as a service. The three main service models are:
SaaS (Software as a service): GoogleDocs, Salesforcee.com etc. are the examples of a SaaS model these models provides user the capacity to access applications by a web browser.
PaaS (platform as a service): This model makes user to able to create and install own operating system and applications e.g. Google Appengine.
IaaS (infrastructure as a service): It allow a cloud user to deploy and run software, and also provisioning of data storage, network, cpu and other resources e.g. Amazon EC2
The main features of the cloud computing are virtualization and scalability. Virtualization is used to build the illusion that two or more things are present for a single thing, when there is a single physical entity exists in the system. The Common virtualization forms are: server virtualization, desktop virtualization, network and virtual storage. The scalability means increase and decrease in resource allocation according to the change in users demand automatically.
Figure: An overview of cloud computing
The major characteristics of cloud systems are described as follows:
Dynamic computing infrastructure and IT service centric scheme.
Self-service base usage model.
Self-managed platform and consumption base selling.
Elasticity and large network access.
In section II, we have discussed various techniques of resource allocation and section III includes a brief summary on results analysis.
II. RELATED WORKS
A good resource allocation technique is needed to satisfy the customer requirements and to gain maximum revenue to service provider. In [4] Q.zhang et al. Models the problem of customer demand in terms of both supply and price in order to maximize the service provider revenue and customer satisfactions as a constrained-discrete-time optimal control problem and use MPC (model predictive control) to find solution. In this paper resources are dynamically allocates data center resources to spot markets to the best match customer demand in terms of both supply and price in order to maximize the provider revenue and customer satisfaction.
In [5], Vinothina et al. provides a technique called RAS (resource allocation strategy). This technique integrates all activities of cloud service provider to the resources that are scare resource and to utilize them. This strategy also gives classification summary and effects in cloud environment, and satisfy the application of a cloud system.
In [6], YingSong et.at. proposed a two tiered on demand resource allocation technique, including both local and global resource allocation that based on two level control model. The wastages of resources can be minimized and quality of an application can be guaranteed.
Zhan Xion et al [7], proposed virtualization technique to dynamic resource allocation in data center based on application demands and support green computing by
optimization the number of servers by using technique called virtualization and skew-ness. The goal is to avoid overload and green computing. The disadvantages of this technique is future load prediction algorithm does not always give the actual result.
Thangraj et.at [8], proposed resource allocation strategy based on predefined resource allocation polices for IaaS (Infrastructure as a service). The main focus of this paper is on deadline sensitive policy for resource allocation by reducing the request rejections using Hazea (an open source lease manager and can be used as a scheduler for cloud toolkit Nebula).
In [15], J.Espadas et al. proposed a model using multi- tenancy characteristic to solve the problem of over and under provisioning of cloud resources. The approach used by researcher is a tenant-based isolation approach, isolation approach, this approach work for each tenant and services of each tenant are encapsulated with its execution.
In [16], A.Calatrava.et al formulates a model by combining and integrates the cloud a grid resources. Solution is given by outsourcing workload to the cloud when resources on the grid become exhausted. A Meta scheduling analysis considered to find current state of the grid and schedule integration with the cloud. This approach had utilized the resources in hybrid infrastructure (cloud and grid).
In [9] Sowmya Koneru et al. proposed a model on increasing the efficacy of real time cloud computing services.
This paper makes the use of round robin scheduling that utilizes the turnaround time utility efficiently making differentiation it into gain and loss function for one task with maximum efficiency gain. Advantage of proposed model is that it reduces the cost of processing and improves the utilization of resource to satisfy user requirements.
In [10] Makhlouf et al. used a technique that based on game theory. In this research paper a theoretical model that based on stackelberg game is proposed and solution is based on Stackelberg/Nash equilibrium solution. The solution provides maximum revenue and also maximizes user satisfaction.
In [11], Rajkumar et al. proposed a Rule based Resource manager that improves the scalability of a private cloud with low cost and on-demand. They also set time for public cloud and private cloud to provide services within time and to full fill the user requests.
In [12], Boyin et al. consider a strategy for resource allocation based on the client’s SLA. The proposed algorithm is force directed graph which provide solution to the SLA based resource allocation problem for multitier application in cloud system.
In [13], Gihum et al. presents adaptive resource allocation model for resource allocation of customer to a data center using customer geographical location and workload of data center in cloud environment. This model shows a good
response time for allocation of resources and the model is tested by an agent based test bed.
In [14], Ikki Fujiwara et al. presented a market-based resource allocation technique which allows participants to trade services using by a mean of double-sided combination auction. Paper proposed an efficient market-place for cloud computing and allocate services in a effective manner. It provides an integration of services for co-allocation and workflows and enables all participants to trade future and current services in the Spot-market and also in forward market.
In [17], Hadi et al. proposed a general way to optimize resource allocation utilization. One is arriving of application at application level and second is in the application running period. This Multi-D resource allocation (MDRA) approach allocates the virtual resources dynamically in cloud applications to reduce cost by making use of fewer nodes to process application.
In [18], Zhenhang et al. Presented Statistic Based Load balance (SLB) for resource allocation to solve load imbalance problem in cloud computing environment. This method makes use of on-line historical data analysis for forecasting the each virtual machine demand of resources.
In [19], D.Worneke et al. provides different possible opportunities and challenges regarding efficient parallel data processing framework called Nephell to exploit the dynamic resource allocation offered by job scheduling and job execution. This paper also proposes a performance comparision to the Hadoop framework.
In [20], Siva Theja et al. Presents a stochastic model for load balancing and scheduling in cloud environment. The task arrives according to stochastic process and requests for resources like CPU, memory and storage. This model takes advantages of Just the Shortest Queue (JSQ) routing and two choice routing algorithm with maximum weight scheduling policy that have optimal throughput. But these algorithms request in the heavy traffic limit with optimal queue length.
In [21], Gandhi krishan et al. proposed an Intelligent Resource Allocation (IRA) technique For Desktop-as-a- Service in cloud environment. This technique assures the parameters such as minimum SLA violation, scalability, user satisfaction etc. by assigning priority to the users request based on SLA factor. The greater advantage of this technique is the maximum usability and utilization of cloud resources
III. RESULT ANALYSIS
S.
no
Title Author Method Advantage
1 Dynamic Resource Allocation for Spot Markets in Cloud Computing
Environments
Q.zhang et al[4].
Model Predictive Control
Best match customer demand in terms of both supply and price 2 Resource Allocation
Strategies in Cloud Computing
Vinothin a et al.[5]
RAS(resourc e allocation strategy)
Meet the need of the cloud application and classification summary 3 A 2-tiered on
Demand Resource Allocation
mechanism for vm- based data centers
YingSon g et.at.[6]
2-tiered resource allocation
Wastages of resources can be minimized and quality of an application can be guaranteed 4 Dynamic Resource
Allocation using Virtual Machine for cloud computing environment
Zhan Xion et al[7]
Virtualizatio n technique
Avoid overload and support green
computing
5 Resource allocation policy for IaaS in Cloud computing
Thangraj et.at[8]
Predefined resource allocation policies
Reduce the request rejection
6 Resource Allocation Method using Scheduling methods for Parallel Data Processing in Cloud
Sowmya Koner et al.[9]
Use round robin scheduling
Reduce processing cost and
improvement in resource utilization 7 Constrained Pricing
for Cloud Resource Allocation
Makhlou f et al.[10]
Stackelberg game theory
Provide maximum revenue and user satisfaction
8 A Rule based
Approach for Effective Resource Provisioning In Hybrid Cloud Environment
Rajkuma r et al[11].
Rule based resource manager
Improves scalability with low cost
9 A Multi-dimensional Resource Allocation Algorithm in Cloud Computing
Boyin et al.[12]
Force directed graph
Solve the problem of multitier application 10 Agent-based
Adaptive Resource Allocation on the Cloud Computing Environment
Gihum et al.[13]
Adaptive resource allocation model
Good response time
11 Applying Double- sided Combinational Auctions to Resource Allocation in Cloud Computing
Ikki Fujiwara et al.[14]
Market based technique
Integration of services and workflow
12 A tenant-based resource allocation model for scaling software-as-a-service applications over cloud computing infrastructures
J.Espada s et al[15]
Tenant based isolation approach
Solve the problem o over and under provisioning of resources
13 Combining grid and cloud resources for
. A.Calatr
Meta scheduling
Combine and integrate loud
hybrid scientific computing
executions
ava.et al[16]
analysis and grid
resources
14 Multi-dimensional SLA based Resource Allocation for Multi- tier Cloud Computing Systems
Hadi et al.[17]
MDRA(mult i-D resource allocation)
Reduce the cost
15 A Statistical based Resource Allocation Scheme in Cloud
Zhenhan g et al[18]
Statistic load balance(SLB )
Solve load imbalance problem
16 Exploiting Dynamic Resource Allocation for Efficient Parallel Data Processing in the Cloud
D.Worne ke et al[19]
Nephell framework
Exploit the dynamic resource allocation
17 Heavy Traffic Optimal Resource Allocation
Algorithms for Cloud Computing Clusters
Siva Theja et al.[20]
Stochastic model
Takes
advantages of just the shortest queue
18 Intelligent Resource Allocation (IRA) technique For Desktop-as-a-Service
Gandhi krishan et al.[21]
Intelligent Resource Allocation (IRA)
Maximum utilization and utilization of cloud resources
IV. CONCLUSION
As cloud computing is a new technology, there are a number of challenges faced by various researchers in resource allocation. In this paper, we have discussed an overview of various resource allocation techniques.
ACKNOWLEDGMENT
I would like to thank and acknowledge Prof. Ajay Shanker Singh program chair B.Tech (CSE + IBM) GALGOTIAS UNIVERSITY, G.NOIDA (INDIA) for supporting this research work.
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