RESOURCE ALLOCATION STRATEGIES IN CLOUD … for resource allocation strategies in cloud computing environment such as ... A resource allocation is defined to be robust ... Resource

RESOURCE ALLOCATION STRATEGIES IN CLOUD COMPUTING

Pankaj Sareen1 Assistant Professor,

Computer Science Department, SPN College Mukerian

[email protected]

Parveen Kumar2 Assistant Professor,

Computer Science Department, SPN College Mukerian

[email protected]

Dr. Tripat Deep Singh3 Assistant Professor,

Computer Applications Department, GNIMT Ludhiana [email protected]

Abstract -Cloud computing is an attractive computing model since it allows for the provision of resources on-demand. Such a process of allocation and reallocation of resources is thekey to accommodating unpredictable demands and improving the return on investment from the infrastructure supporting the Cloud. Cloud computing booming area and emerging trends in information communication technology domain. Resource allocation is to allocate the resource based on infrastructure as a service (IaaS) is one of the keys for large-scale Cloud applications. Therefore, performance evaluation of workload models and Cloud resource allocation and algorithms in a repeatable manner under different configurations and requirements is difficult. There is still lack of tools that enable developers to compare different resource allocation strategies in IaaS regarding both computing servers and user workloads. To fill this gap in tools for evaluation and modeling of Cloud environments and applications, we propose Cloud computing environment can help developers identify and explore appropriate solutions considering different resource allocation strategies [1]. We proposed for resource allocation strategies in cloud computing environment such as Cloud data centers, and results by applying the proposed system. However, despite the recent growth of the Cloud Computing market, several problems with the process of resource allocation remain unaddressed. In this paper we introduce essential concepts and technologies regarding resource allocation in Cloud Computing.

KEYWORDS-Cloud computing, Deployment model. RAS, Service Model.

I. INTRODUCTION

Cloud Computing offers an interesting solution for software development and access of content with transparency of the underlying infrastructure locality. The Cloud infrastructure is usually composed of several datacenters and consumers have access toonly a slice of the computational power over a scalable network. The provision of these computational resources is controlled by a provider, and resources are allocated in anelastic way, according to consumers’ needs. The use of Clouds as a type of infrastructure for running software is quite different than traditional practices, where software runs over infrastructures often dimensioned according to the worst case use and peak scenarios. To accommodate unforeseen demands on the infrastructure in a scalable and elastic way, the process of allocation in Cloud Computing must be dynamic. Furthermore, another essential feature of the resource allocation mechanisms in Cloud Computing is toguarantee that the requirements of all applications are suitably met. A resource allocation is defined to be robust against perturbations in specified system parameters if degradation in the performance feature is limited when the perturbations occur within a certain range. To achieve this requirement, any allocation mechanism in Cloud Computing should be aware of the status of each element/resource in the infrastructure. Then, themechanism should apply algorithms to better allocate physical or virtual resources toconsumers’ applications, according to the requirements pre-established with the cloudprovider.Beyond the benefit of elastic services, Cloud Computing allows consumers toreduce or eliminate costs associated with internal infrastructure for the provision of theirservices. This opportunity of cost reduction makes Cloud Computing a very attractivealternative for consumers, especially for

Pankaj Sareen et al, International Journal of Computer Science & Communication Networks,Vol 5(6),358-365

IJCSCN | Dec 2015 Available [email protected]

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business initiatives. Enterprises can effectivelyoffload operational risks to cloud providers. From the perspective of cloud providers,the model offers a way for better utilization of their own infrastructure[2].

Cloud Computing is an essential ingredient of modern computing systems. Computing concepts, technology and architectures have been developed and consolidated in the last decades; many aspects are subject to technological evolution and revolution. Cloud Computing is a computing technology that is rapidly consolidating itself as the next step in the development and deployment of increasing number of distributed application. Cloud computing is nothing but a specific style of computing where everything from computing power to infrastructure, business apps are provided as a service. It’s a computing service rather than a product.In cloud, shared resources, software and information is provided as s metered service over the network. When the end user accesses some service is cloud, he is not aware of where that service is coming from or what is platform being used or where it is being stored.

Cloud computing platforms, such as those provided by Google, IBM Microsoft, Amazon,etc., let developers deploy applications across computers hosted by a central server. So these all applications can access a large network of computing resources that are deployed and managed by a cloud provider. Software Developers obtain the advantages of a managed computing platform, without having to commit resources to build and maintain the network. One important problem that must be addressed effectively in the cloud is how to manage QoS and maintain SLA for cloud users that share cloud resources.

Figure 1. Challenges in SLA-based resource allocation

The cloud computing paradigm makes the resource as a single point of access to the number of clients and is implemented as pay per use basis. Though there are number of advantages of cloud computing such as virtualized environment, equipped with

dynamic infrastructure, pay per consume, totally free of software and hardware installations, prescribed infrastructure and the major concern is the order in which the requests are satisfied which evolves the scheduling of the resources. Allocation of resources has been made efficiently that maximizes thesystem utilization and overall performance. Cloud computing is mainly sold or rented on demand on the basis of time constrains basically specified in hours or minutes. So the scheduling has to be done in such a way that the resource utilization has need done efficientlyIn cloud computing environment, resource allocation or load balancing takes place at two levels. First, when an application is uploaded to the cloud, the load balancer assigns the requested process to physical computers, attempting to balance the computational load of multiple applications across physical computers.

Second, when an application receives multiple incoming requests, these requests should be each assigned to aspecific requested application instance to balance the computational load across a set of instances of the same requested application. For example Amazon EC2 uses elastic load balancing (ELB) to control how incoming requests are handled. Application designers can direct requests to instances in specific availability zones, to instances demonstrating the shortest response times or to specific instances.

II. THE EMERGENCE OF CLOUD COMPUTING

Nowadays, there are several definitions for Cloud Computing[3] in literature, covering common terms like IaaS (Infrastructure as a Service), PaaS (Platform as a Service) and SaaS (Software as a Service). The main reason for theexistence of different perceptions of Cloud Computing is that it is not a new technology,but rather a new model that brings together a set of existing technologies to develop and run applications in a different way. In fact, technologies such as virtualization and service oriented provisioning are not new, however Cloud Computing uses them to offer a new service to its consumers and, at the same time, to meet new business requirements. As well as contributions from commercial interests, the Open SourceCommunity has been very active in the development of Cloud Computing. They havesupplied numerous contributions in related areas such as tools for interaction withexistent Clouds, software for automated use of available Clouds, alternatives forstandardization, and virtualization.



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III.THE VALUE OF CLOUD FOR BUSINESS

Many organizations have invested in Cloud Computing, and to date more than 150enterprises have entered the industry as Cloud providers. On the side of Cloud consumers, a recent study of more than 600 companies by Information Week reported that the number of companies using Cloud Computing increased from 22% in February 2012 to 44% in October 2013, 67% in November 2014 increase. A 76% Moreover, an interesting worldwidesurvey by Gartner identified Cloud Computing as the top technology priority for CIOs in 2015.

Fig 2. Percentage increase in number of companies using Cloud

Computing over the year

The cited business research on Cloud Computing demonstrates financial interest and reveals the increasing credibility of Cloud Computing. Such growth is motivated by its ongoing consolidation as well as by the revenues that customers and operators are observing with Cloud Computing. It is interesting to notice the different viewpoints of both parties: from the side of enterprises that are using Cloud Computing, it is very attractive since it provides opportunity for cost reductions with internal infrastructure, as well as other advantages. Alternately, from the providers’ viewpoint, Cloud Computing makes it possible to increase revenues using their own IT infrastructure.

Fig3. Distribution of resources allocation in cloud

However, such investment should be carefully dimensioned, since consumers have high expectations about the elasticity of their applications. Metaphorically, one can say that consumers expect that provider’s resources be infinite. Questions like “How many physical machines are necessary to accommodate my unpredictable demand?” and “How many consumers are necessary to obtain financial returns in a reasonable time?” should be taken into consideration by providers. IV. DEFINITIONS FOR RESOURCE ALLOCATION

Resource allocation is a subject that has been addressed in many computingareas, such as operating systems, grid computing, and datacenter management. AResource Allocation System (RAS) in Cloud Computing can be seen as any mechanismthat aims to guarantee that the applications’ requirements are attended to correctly bythe provider’s infrastructure. Along with this guarantee to the developer, resourceallocation mechanisms should also consider the current status of each resource in theCloud environment, in order to apply algorithms to better allocate physical and/orvirtual resources to developers’ applications, thus minimizing the operational cost of thecloud environment.

An important point when allocating resources for incoming requests is how there sources are modeled. There are many levels of abstraction of the services that a cloud can provide for developers, and many parameters that can be optimized during allocation. The modeling and description of the resources should consider at least these requirements in order for the RAS works properly.

Generally, resources are located in a datacenter that is shared by multiple clients, and should be dynamically assigned and adjusted according to demand[4]. It isimportant to note that the clients and developers may see those finite resources as unlimited and the tool that will make this possible is the RAS. The RAS should deal with these unpredictable requests in an elastic and transparent way. This elasticity should allow the dynamic use of physical resources, thus avoiding both the under-provisioning and over-provisioning of resources.

Resource allocation is process of assigning the available resources in an economic way and efficient and effective way Resource allocation is the scheduling of the available resources and available activities required by those activities while taking into consideration both the resource availability and the project time. Resource provisioning and allocation solves that problem by allowing the service providers to manage the resources for each individual

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request of resource. Resource Allocation Strategy (RAS) is all about the number of activities for allocating and utilizing inadequate resources within the limit of cloud environment so as to meet the needs of the cloud application. It requires the type and amount of resources needed by each application in order to complete a user job.

Cloud providers can share their resources over the internet during resource scarcity. Four different modes of hiring the computing capacities from a cloud provider have been considered: 1. Advance Reservation (AR): Resources are

reserved in advance. They should be available at a specific time

2. Best-effort: Resources are provisioned as soon as possible. Requests are placed in a queue.

3. Immediate: When a client submits a request, either the resources are provisioned immediately, or the request is rejected, based on the resource availabilities.

4. Deadline sensitive: assumed to be pre-emptible but there is a limitation to their preempt ability. It is pre-emptible only if the scheduling algorithm of Haizea can assure that it can be completed before its deadline.

V. PROSAND CONS OF RESOURCE ALLOCATION STRATEGIES:

PROS: A. The first major benefit of resource allocation

is that user neither has to install software nor hardware to access the applications, to develop the application and to host the application over the internet.

B. The next major benefit is that there is no limitation of place and medium. We can reach our applications and data anywhere in the world, on any system.

C. The user does not need to expend on hardware and software systems.

D. Cloud providers can share their resources over the internet during resource scarcity.

CONS: A. Since users rent resources from remote

servers for their purpose, they don’t have control over their resources.

B. Migration problem occurs, when the users wants to switch to some other provider for the better storage of their data. It’s not easy to transfer huge data from one provider to the other.

C. In public cloud, the clients’ data can be susceptible to hacking or phishing attacks. Since the servers on cloud are

interconnected, it is easy for malware to spread.

D. Peripheral devices like printers or scanners might not work with cloud. Many of them require software to be installed locally. Networked peripherals have lesser problems.

E. More and deeper knowledge is required for allocating and managing resources in cloud, since it mainly depends upon the cloud service provider.

VI. TOPOLOGY AWARE RESOURCE ALLOCATION (TARA)

TARA is composed of two major components: a prediction engine and a fast genetic algorithm-based search technique. The prediction engine is the entity responsible for optimizing resource allocation. When it receives a resource request, the prediction engine iterates through the possible subsets of available resources (each distinct subset is known as a candidate) and identifies an allocation that optimizes estimated job completion time[5]. However, even with a lightweight prediction engine, exhaustively iterating through all possible candidates is infeasible due to the scale of IaaS systems. We have therefore developed a genetic algorithm-based search technique that allows TARA to guide the prediction engine through the search space intelligentlyPrediction EngineThe prediction engine maps resource allocation candidates toscores that measures their “fitness” with respect to a given objectivefunction, so that TARA can compare and rank different candidates.The inputs used in the scoring process can be seen in Figure 4. Wedescribe these three inputs in greater detail below, show how theyare obtained without manual input, and then describe how they areused within a lightweight Map Reduce simulator.

A. Objective Function

The objective function defines the metric that TARA should optimize.For example, given the increasing cost and scarcity of powerin the data center, an objective function might measure the increasein power usage due to a particular allocation. Our prototype’s objective function uses MapReduce job completion time as the optimizationmetric because it indirectly maps to the monetary costof executing the job on an IaaS system. The output value for theobjective function is calculated using the Map Reduce simulator



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B. Application Description

The application description consists of three parts:

1. The frameworktype that identifies the framework model to use

2. Workloadspecificparameters that describe the particular application’s resourceusage

3. A request for resources including the number of VMs,storage, etc.

The prediction engine uses a model-based approach to predictthe behavior of the given application on the selected framework.As each framework behaves differently, it requires a model for theframework being optimized, and the user specifies the frameworktype.

C. IaaS Information on Available Resources The final input required by the prediction engine is

a resourcesnapshot of the IaaS data center. This includes information derivedfrom both the virtualization layer and the IaaS monitoring service.The information gathered ranges from a list of available servers,current load and available capacity on individual servers to datacenter topology and a recent measurement of available bandwidthon each network link

Fig. 4 TARA Algorithm

VII. SEARCH ALGORITHM

In any large IaaS system, a request for r VMs will have a large number of possible resource allocation candidates. If n servers are available to host at most one VM, the total number of possible combinations is nCr. Given that n> r, exhaustively searching throughall possible candidates for an optimal solution is not feasible in a computationally short period of time. To efficiently identify an approximate solution, we chose a genetic algorithm (GA) [7] to generate possible candidates for the prediction engine to evaluate. GA is a search technique inspired by evolutionary biology for finding solutions to

optimization and search problems. Candidates are represented as genes and they evolve toward better solutions. In comparison to other search techniques, we found that GA was a good match for the resource allocation problem. It was natural to map server selection in an IaaS system to GA’s gene representation, and to apply operations during the GA’s evolution process. To represent each possible candidate, we use a bit string with the length of n, the number of servers available to host a single VM.

The binary value of each bit means whether the corresponding server is selected or not. For each bit in the string, a value of 1 represents the physical server being selected for hosting a VM anda 0 represents the server being excluded. To evaluate a candidate, the prediction engine described above is used. Once we have the genetic representation and the fitness function, GA initializes a population of candidates. It then goes through theevolution process of reproduction and selection until it terminates. In the reproduction step, mutation, swap, or crossover operations are applied at random to the candidate population to create offspring, i.e., the next generation of candidates. Once each candidatehas been evaluated, a stochastic process is used to select a majority of the “fitter” candidates along with a small percentage of “weak” candidates to maintain population diversity. For our implementation, the population size and the offspring ratio were selected after performing a sensitivity analysis. VIII. RESOURCE ALLOCATION POLICIES

We used four different allocation policies for comparison. These policies that can be used as manually-generated hints are included in [7]:

A. RR-R: allocates VMs in a round-robin (RR) manner across racks (-R).

B. RR-S: allocates VMs in a round-robin (RR) manner across servers (-S). This is the default policy used by Eucalyptus [14] and, based on work by Ristenpart et al. [15], we also believe that it is closest to what is used by Amazon’s EC2 for a single job. In order to positively bias RR-S results, we also enabled this policy to select racks with the highest available bandwidth first

C. H-1: A hybrid policy that combines RR-S and RR-R with a preference for selecting servers in the rack with the greatest available bandwidth



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but will only select a maximum of 20 servers per rack.

D. H-2: A hybrid policy similar to H-1 but only selects a maximum of 10 servers per rack.

E. TARA: uses the best allocation found by TARA. As stated earlier, each physical node never contains more than one benchmark VM for all of the above allocation policies. Further, input data for all benchmarks is only copied into the VMs after they are launched but before the benchmarks are executed.

IX. DYNAMIC RESOURCE ALLOCATION TECHNIQUES

A. Dynamic Optimization of Multi-Attribute Resource Allocation in Self-Organizing Clouds (SOC)

The Existing system generate the more messages for a single request. The proposed system use SOC and it achieves the maximized resource utilization and it also delivers optimal execution efficacy.

1. SOC

SOC connect a large number of desktop computers on the internet by P2P network. Each participating computer acts as a resource provider and resource consumer. SOC having two main issues:

• Locating a qualified node to satisfy a user task’s resource demand with bounded delay

• To optimize a task’s execution time by determining the optimal shares of the multiattribute resources to allocate to the tasks with various QoS constraints, such as the expected execution time.

2. Algorithm:

This algorithm[10] used to redistribute available resources among running tasks dynamically, such that these tasks could use up the maximum capacity of each resource in a node, while each task’s execution time can be further minimized. Procedures:

a) Slice handler:It is activated to equally scale the amount of resources allocated to tasks.

b) Event handler: It is used for resource redistribution upon the events of task arrival and completion.

B. Dynamic Resource Allocation Using VirtualMachines for Cloud Computing Environment:

Cloud computing allows business customers to scale up and down their resource usage based on needs. In this paper, using virtualization technology to allocate data center resources dynamically[11] based on application demands and support green computing by optimizing the number of servers. Goals:

1. Overload avoidance:The capacity of a PM should be sufficient to satisfy resource needs of all VMs running on it.

2. Green computing:The number of PMs used should be minimized as long as they can still satisfy the needs of all VMs. Idle PMs can be turned off to save energy.

3. Virtualization technology: This technology used to allocate datacenter resources based on the application demands.

4. Skewness:This is used to measure the unevenness multidimensional resource utilization of a server. To minimizing skewness, we can combine different types of workloads. Skewness can be measured based on-

5. Hot spot:If the utilization of any of its resources is above a hot threshold. This indicates that the server is overloaded and hence some VMs running on it should be migrated away.

6. Cold spot:If the utilizations of all its resources are below a cold threshold. This indicates that the server is mostly idle and a potential candidate to turn off to save energy. Achieve the goals to make the following contributions:

a) Develop a resource allocation system that can avoid overload in the system

b) Skewness to measure the uneven utilization of a server.

c) Design a load prediction algorithm that can capture the future resource usages of applications accurately without looking inside the VMs.

X. PRIORITY BASED RESOURCE ALLOCATION MODEL FOR CLOUD COMPUTING

Cloud computing is a model which enables on demand network access to a shared pool computing resources. A Cloud environment [12]consists of multiple customers requesting for resources in a dynamic environment with possible constraints. In



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existing system cloud computing, allocating the resource efficiently is a challenging job.

A. Algorithm

Priority algorithm that mainly decides priority among different user request based on many parameters like cost of resource, time needed to access, task type, number of processors needed to run the job or task.

In this model client send the request to the cloud server. The cloud service provider runs the task submitted by the client. The cloud admin decides the priority among the different users request. Each request consists of different task and it have the different parameters such as -

1. Time:computation time needed to complete the particular task.

2. Processor request:refers to number of processors needed to run the task. More the number of processor, faster will be the

completion of task. 3. Importance:refers to how important the

user to a cloud administrator (admin) that is whether the user is old customer to cloud or new customer.

4. Price:refers to cost charged by cloud admin to cloud users.

XI.CONCLUSION:

Cloud computing technology is increasingly being used in enterprises and business global markets. A evaluate shows that dynamic resource allocation is growing need of cloud providers for more number of users and with the less response time. In cloud paradigm, an effective resource allocation strategy is required for achieving user satisfaction and maximizing the profit for cloud service providers. This paper summarizes the main resource allocation strategies and its impacts in cloud system. Some of the strategies discussed above mainly focus on memory resources but are lacking in other factors. Hence this survey paper will hopefully motivate future researchers to come up with smarter and secured optimal resource allocation algorithms and framework to strengthen the cloud computing paradigm.

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[6] Gunho Leey, Byung-Gon Chunz, Randy H. Katzy,” Heterogeneity-Aware Resource Allocation and Scheduling in the Cloud”, IJERT-2012.

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RESOURCE ALLOCATION STRATEGIES IN CLOUD … for resource allocation strategies in cloud computing environment such as ... A resource allocation is defined to be robust ... Resource