Elastic Resource Provisioning for Cloud Data Centerpsrcentre.org/images/extraimages/10 214360.pdfCloudSim supports the time-shared and space-shared resource allocation policies for

Abstract—Cloud data centers promises flexible, scalable,

powerful and cost-effective executing environment to users. There

are still challenges of cloud systems while there are several

advantages of cloud computing infrastructures such as on-demand

resources scalability. The amount of resources needed in cloud data

centers is often dynamic due to its dynamic workload demand.

Resource provisioning with the right amount of dynamic resource

demand while meeting service level objectives (SLOs) becomes a

critical issue in cloud data centers. Elastic resource provisioning

mechanism for the Cloud Data Center is proposed by applying time-

shared policy for Virtual Machines (VMs) and tasks. It is focused to

maximize the utilization of resources and minimizing the cost

associated with the resources. The proposed system is simulated and

evaluated with real world workload traces. The evaluation results

show that the proposed provisioning system achieves high utilization

of resources for the cloud data center to allocate the resources.

Keywords—Data Center, Resource Provisioning, Service Level

Objective, Time-Shared Policy

I. INTRODUCTION

CLOUD is a type of parallel and distributed system

consisting of a collection of interconnected and

virtualized computers that are dynamically provisioned and

presented as one or more unified computing resources based

on service-level agreements established through negotiation

between the service provider and consumers. Cloud computing

is a model for enabling ubiquitous, convenient, on-demand

network access to a shared pool of configurable computing

resources (e.g., networks, servers, storage, applications and

services) that can be rapidly provisioned and released with

minimal management effort or service provider interaction.

Cloud provider offers services as Software as a Service (SaaS),

Platform as a Service (PaaS) and Infrastructure as a Service

(IaaS). When a cloud provider accepts a request from a

customer, it must create the appropriate number of virtual

machines (VMs) and allocate resources to support them [10].

Cloud service providers are facing many challenges of

resource demands as cloud computing grows in popularity and

usage. Data centers resource needs are often dynamic, varying

as a result of changes in overall workload. A key problem

when provisioning virtual infrastructures is how to deal with

Thant Zin Tun, University of Computer Studies, Yangon, Myanmar. (e-

mail: [email protected]).

Thandar Thein, University of Computer Studies, Yangon, Myanmar. (e-

mail: [email protected]).

situations where the demand for resources. Resource

Provisioning is the mapping and scheduling of VMs onto

physical Cloud servers within a cloud. Cloud providers must

ensure utilizing and allocating scare resources within the limit

of cloud environment so as to meet the needs of dynamic

resource demand. Cloud data center providers either do not

offer dynamic resource provisioning or support any

performance guarantee leads to inefficient utilization of

resources and occurs SLO violations.

The cloud provider’s task is, therefore, to make sure that

resource allocation requests are satisfied with specific

probability and timeliness. These requirements are formalized

in infrastructure SLAs between the service owner and cloud

provider, separate from the high-level SLAs between the

service owner and its end users. SLA-oriented capacity

planning guarantees that there is enough capacity to guarantee

service elasticity with minimal over-provisioning. Thus, the

IaaS providers make the Service Level Objectives to grantee

the SLA for the dynamic workload demand for different

resources. In order to avoid the under-provision, which leads

to compensation costs for the provider, the cloud providers

plan to predict the dynamic workload demand in advance by

different methods. In this paper, the SLO Granted Resource

Prediction (SGERP) is used to predict the CPU resource usage

[9]. At the same time, the IaaS cloud provider strives to

minimally over-provision capacity, thus minimizing the

operational costs.

In this paper, we propose resource provisioning system that

makes the resource provision for the IaaS cloud data center to

achieve high utilization of data center resources. The rest of

the paper is organized as follow. The proposed architecture is

presented in the next section. Then, the detail design of

provision strategies is discussed. The experimental results are

also conducted. And then we discuss related work, concluding

remarks and future work are provided.

II. SYSTEM ARCHITECTURE

An elastic resource provisioning system is proposed by

using time-shared allocation policy for both VMs and tasks. It

is tried to achieve the high utilization of data center resources

while preventing the over provisioning of resources. In the

proposed provision system, two different provisioning

strategies are used to make the decision to create the hosts and

VMs.

Elastic Resource Provisioning for

Cloud Data Center

Thant Zin Tun, and Thandar Thein

A

3rd International Conference on Computational Techniques and Artificial Intelligence (ICCTAI'2014) Feb. 11-12, 2014 Singapore

45

Resource Usage

PredictorElastic Resource

Provisioning System

Predicted Resource

Usage

IaaS Cloud Provider

VM1

VM2

VMn

Host 1

VM1

VM2

VMn

Host 2

VM1

VM2

VMn

Host n

Resource Allocator

Provisioning

Information

Workload

Traces

Resource

Usage Data

Fig. 1 The Architecture of Resource Provisioning System

The architecture of the proposed system is shown Figure 1.

In the resource provisioning system, SLO Granted Resource

Prediction (SGERP) results are used to predict the CPU

resource workload in order to avoid the under provisioning of

dynamic resource demand. The predicted resource usages are

used by the resource provisioning system. The provisioning

information is sent to resource allocator of the IaaS cloud

provider for the allocation of the resources requested by the

cloud customers. A cloud data center is composed by a set of

hosts, which are responsible for managing VMs during their

life cycles.

Host is a component that represents a physical computing

node in a cloud which is assigned a pre-configured processing

capability and a scheduling policy for allocating processing

cores to virtual machines. The Host component implements

interfaces that support modeling and simulation of both single-

core and multi-core nodes. In this paper, we focus on the CPU

resource usage to provision for the tasks. The predicted CPU

usage by SGERP is used in our provisioning system. The CPU

resource usage is predicted as the batch mode. Firstly, the real

world workload traces are clustered based on the deadline of

the requests to handle non uniform cases of execution time and

wait time of the data centers’ requests.

III. RESOURCE PROVISIONING MODEL

The resource provisioning system is developed for handling

dynamic workload nature resource provisioning ahead of the

needs in the cloud data centers. In this provision system we use

SGERP prediction model which integrate signal processing

approach and statistical learning approach to predict both

repeating pattern and non-repeating pattern workload [8]. To

overcome the under provisioning, SLO analysis is conducted

in the prediction system. By increasing 5% of the maximum

predicted value, it can almost eliminate under provisioning of

the predictor and can meet SLOs of the cloud provider.

SGERP and the time sharing resource allocation are used in

the provisioning system to achieve the right amount of

resource provisioning. In this paper, we focus on the resource

allocation strategies of the data centers.

A. RESOURCE PROVISIONING OF CLOUDS

One of the key advantages of a Cloud computing

infrastructure is the immense deployment of virtualization

technologies and tools. Hence, as compared to Grids, Clouds

have a virtualization layer that acts as an execution and hosting

environment for Cloud-based application services. The hosts

component in cloud data centers implements interfaces that

support modeling and simulation of both single-core and

multi-core nodes.

The data center entity manages a number of host entities.

The hosts are assigned to one or more VMs based on a VM

allocation policy that should be defined by the Cloud service

provider. The control policies of the operations related to VM

life cycle such as: VM creation, VM destruction, and VM

migration stands for provisioning of a host to a VM. Similarly,

one or more application services can be provisioned within a

single VM instance, referred to as application provisioning in

the context of Cloud computing.

Hence, the amount of hardware resources available to each

VM is constrained by the total processing power and system

bandwidth available within the host. The critical factor to be

considered during the VM provisioning process, to avoid

creation of a VM that demands more resource than is available

within the host, is referred to as the resource provisioning. In

order to allow simulation of different provisioning policies

under varying levels of performance isolation, we apply the

time sharing allocation policy as CloudSim supports. Two

different allocation policies are conducted in the resource

provisioning system. VM provisioning at two levels: first, at

the host level and second, at the VM level. At the host level, it

is possible to specify how much of the overall processing

power of each core will be assigned to each VM. At the VM

level, the VM assigns a fixed amount of the available

resources to the individual task units that are hosted within its

execution engine.

B. TIME-SHARED ALLOCATION POLICY

CloudSim supports the time-shared and space-shared

resource allocation policies for the VMs and tasks. The time-

shared allocation example for both VMs and task units is

shown in Fig. 2. In this figure, a host with two CPU cores

receives request for hosting two VMs, such that each one

requires two cores and plans to host four tasks units. More

specifically, tasks T1, T2, T3, and T4 to be hosted in VM1,

whereas T5, T6, T7, and T8 to be hosted in VM2. The CPU

resources of the host are concurrently shared by the VMs and

the shares of each VM are concurrently divided among the

task units assigned to each VM. In this case, there are no

queues either for virtual machines or for task units. We

proposed two provisioning scenarios based on allocation of the

tasks to each VM while using the time-shared provisioning

policy.

We assume each VM characteristic is homogeneous for both

scenarios. In provisioning strategy 1, the tasks are assigned to

their corresponding VMs that the tasks use the resources of the

VMs which are hosted. In the provisioning strategy 2, the

available resources of the VM are shared for the tasks.


46

T1

T2

T5

T6

T3

T4

T7

T8

VM1

VM1

VM2

VM2

1

2

Cores

Time

Fig. 2 Time-shared allocation for VMs and Tasks

C. PROVISIONING STRATEGY 1

A host with two CPU cores receives request for hosting two

VMs, such that each one requires two cores and plans to host

four tasks units. The tasks are assigned to their corresponding

VMs which are hosted. The resource requests for each task are

varied depending on the type of tasks. The resource

provisioning strategy 1 by using time-shared allocation policy

is shown in algorithm 1. Figure 3 presents the example of

provisioning strategy 2 with the sixteen tasks.

Algorithm 1: Elastic Resource Provisioning Strategy1

Input : x //Resource Usage data

Output : y //Number of Host

1. Classify Resource Usage data into clusters //K-means

2. for each cluster in k clusters

3. total CPU=Calculate the total number of CPU requests

4. y= Calculate_total_number_of_host(total CPU, total

task)

5. end for

Calculate_total_number_of_host(total CPU, total task)

Input : total CPU, total task

Output : Nhost

1. Nhost =(total task* Nhosts(each task))/ Ntasks(each

host)))+((total CPU-total task)* Nhosts(each task))/ Ntasks(each

VM)

2. if the number of host for the tasks is not the multiple of

four

3. Nhost=the upper adjacent multiple of four

4. end if

In this example, there are sixteen tasks which are assigned

to their corresponding VMs, where T7 (task 7) requests four

CPU cores and T15 requests two CPU cores and the other

tasks request one core. Each task gets the one fourth of a core

according to the policy such that task 1 requests one core and

it needs four VMs to complete the task.

Fig. 3 Example of Resource Provisioning Strategy 1

By allocating VMs and tasks as shown in Fig. 3, the host

need for these tasks is calculated as shown in (1), where the

result is needed to check the multiple of four. If the number of

host for the tasks is not the multiple of four, it is increased to

the upper adjacent multiple of four. The symbols and notations

of the equations are shown in Table I.

(1)

TABLE I

SYMBOLS AND NOTATIONS

Symbols Definition

Nhost Number of host for the tasks

Total task Total no of tasks

Total CPU Total no of CPU resource

Nhosts(each task) Number of hosts for each task

Ntasks(each host) Number of tasks in each host

Ntasks(each VM) Number of tasks in each VM

D Deadline

Rt Run time of each job

Wt Wait time of each job

wf Waiting factor for each job

The requests are processed in batch mode for both

prediction and provision. We do not consider for each request

to provision. Hence, (1) is used for all the requests in batch

mode and the number of host calculated by (1) is the maximum

possible hosts for any requests in batch.

D. PROVISIONING STRATEGY 2

The tasks are assigned to the available VMs in the hosts by

using the time-shared allocation policy for both VMs and

tasks. The resource provisioning strategy 2 by using time-

shared allocation policy is shown in algorithm 2. Example of

provisioning strategy 2 is shown in Fig 4.


47

Algorithm 2: Elastic Resource Provisioning Strategy2

Input : x //Resource Usage data

Output : y //Number of Host

1. Classify Resource Usage data into clusters //K-means

2. for each cluster in k clusters

3. totalCPU=Calculate the total number of CPU requests

4. y= Calculate_total_number_of_host(totalCPU, totaltask)

5. end for

Calculate_total_number_of_host(total CPU, totaltask)

Input : totalCPU, totaltask

Output : Nhost

1. Nhost = (totalCPU*Nhosts(each task))/Ntasks(each host)

2. if the number of host for the tasks is not the multiple of

four

3. Nhost=the upper adjacent multiple of four

4. end if

Fig. 4 Example of time-shared allocation for VMs and Tasks for

Strategy 2

The provisioning scenario in Fig. 3 changes to the Fig. 4 by

using the strategy 2. T7 requests four CPU cores and T15

requests two CPU cores and the other tasks request one core.

T7 and T15 are assigned at all the available VMs in a host.

The host needed for the tasks is calculated as shown in (2),

where the result is needed to check the multiple of four. If the

number of host for the tasks is not the multiple of four, it is

increased to the upper adjacent multiple of four.

(2)

IV. PERFORMANCE EVALUATION

A. SIMULATION SET UP

The simulated model is composed of one Cloud data center

containing hosts. Each host has two CPU cores receives

request for hosting two VMs, such that each one requires two

cores and plans to host four tasks units as discussed in the

above. The time-shared policy for resource provisioning is

conducted where new VMs are created because resource

provisioning decision is the main goal of this work.

TABLE II

SELECTED IMPORTANT FEATURES OF THREE WORKLOAD TRACES

HPC2N CEA -Curie Anon

Job Number Job Number JobId

Submit Time Submit Time Submit Time

Wait Time Wait Time Wait Time

Run Time Run Time Run time

Number of Allocated

Processors

Number of Allocated

Processors Nproc

Average CPU Time

Used Average CPU Time Used UsedMemory

Used Memory Used Memory ReqNProcs

Deadline Deadline Deadline

- Request Number of

Processor ReqTime

- - Status

Output metrics collected for each scenario is the average

resources utilization rate, which we define as the rate between

the actual resource usage and the maximum available resource

of hosts in data center. In this paper, we use three workload

traces from Parallel Workload Archives [1]. The important

selected features of three workload traces are shown in Table

II. Simulation of each scenario was repeated 10 times for three

workload traces, and we report the average for each output

metric.

B. SIMULATION SCENARIO

Depending on the nature of the workload we varied the total

capacity of the data centers because the workloads are with the

non uniform execution time and wait time of the requests. In

this case the workload can be decomposed according to their

associated deadline. The deadline D for each request is

calculated as in (3). In our experiment, the waiting factor is set

as five seconds. The capacity of the data centers is more

efficient by using the clustered workloads.

(3)

Clustering is the process of partitioning or grouping a given

set of patterns into disjoint clusters and view as an

unsupervised method for data analysis. K-means clustering is

a method commonly used to automatically partition a data set

into k groups. The process flow of k-means clustering is shown

in Fig 5.

TABLE III

CLUSTER SIZE AND DEADLINE RANGE OF CEA-CURIE WORKLOAD

Cluster Deadline Range No of Tasks

(Size of Cluster)

1 6-281 9246

2 284-1241 741

3 1264-3253 124

4 3282-8345 73

5 9033-21619 31

6 36006-132249 32

We use k-means clustering to classify the workloads into 10

groups based on the deadline of the requests. 100000 records


48

of each workload are set to group according to their deadlines.

The characteristics of each cluster of Anon workload traces are

described in Table III.

Start

Number of cluster K

Centroid

Distance objects to centroids

Grouping based on minimum distance

No objects move? End

Fig. 5 The Flow Diagram of K-means Clustering

C. EXPERIMENTAL RESULTS

We test the simulation of provisioning strategies with the

clusters that we mentioned in the previous section. The

utilization of resource for each strategy is calculated in (4).

The requests are processed in batch mode for both prediction

and provision. We do not consider for each request to

provision.

(3)

Figure 6 shows the comparison of the utilization in

percentage of both strategies of provisioning of Anon Grid

workload. According to Fig 6, we can see that provisioning

strategy 1 scores higher utilization rate than the strategy 2.

Fig. 6 The Utilization Rate of Anon Workload for both Strategies

The utilization in percentage of both policies CEA-Curie

workload trace is shown in Fig 7. The maximum utilization

rate is 52% for strategy 1 and approximately 99% of utilization

for strategy 2.

Fig. 7 The Utilization Rate of CEA-Curie Workload for both Policies

The maximum utilization rate of 73% is achieved for

strategy 1 and 98% is achieved for the strategy 2 on HPC2N

workload as shown in Fig 8.

Fig. 9 The Utilization Rate of HPC2N Workload for both Policies

The average resource utilization rate of three workload

traces, 100000 records for each workload with 6 clusters, is

described in Fig 9. We test ten times of 10000 records and

calculate average for all output metric. According to Fig 9, we

can see that the strategy 2 achieves high utilization rate of

resource provisioning with the batch prediction of the resource

usages.

Fig. 9 Average Resource Utilization of three Workload traces (6

clusters)

The average resource utilization rate of three workload

traces, 100000 records for each workload with 5 clusters, is

described in Fig 10. We test ten times of 10000 records and

calculate average for all output metric. According to Fig 10,


49

we can see that the strategy 2 achieves high utilization rate of

resource provisioning with the batch prediction of the resource

usages.

Fig. 10 Average Resource Utilization of three Workload traces (5

clusters)

V. RELATED WORK

B. Urgaonkar et. al. [3] have used virtual machines (VM) to

implement dynamic provisioning of multi-tiered applications

based on an underlying queuing model. For each physical host,

however, only a single VM can be run. T. Wood et. al. [7] use

a similar infrastructure as in [3]. They concentrate primarily on

dynamic migration of VMs to support dynamic provisioning.

They define a unique metric based on the consumption data of

the three resources: CPU, network and memory to make the

migration decision.

R. N. Calheiros et al. [5] presented a provisioning technique

that automatically adapts to workload changes related to

applications for facilitating the adaptive management of

system and offering end users guaranteed Quality of Services

(QoS) in large, autonomous, and highly dynamic

environments. They model the behavior and performance of

applications and Cloud-based IT resources to adaptively serve

end-user requests. To improve the efficiency of the system, we

use analytical performance (queuing network system model)

and workload information to supply intelligent input about

system requirements to an application provisioner with limited

information about the physical infrastructure.

S. K. Garg et al. [6] proposed admission control and

scheduling mechanism to maximize the resource utilization

and profit and ensures the SLA requirements of users [4]. They

use an artificial Neural Network based prediction model by

using standard Back Propagation (BP) algorithm for

prediction. The number of hidden layers is varied to tune the

performance of the network and through iterations it was found

to be optimum at the value of 5 hidden layers. In their

experimental study, the mechanism has shown to provide

substantial improvement over static server consolidation and

reduces SLA Violations.

R. Buyya et al. [4] presented vision, challenges, and

architectural elements for energy efficient management of

Cloud computing environments. They focus on the

development of dynamic resource provisioning and allocation

algorithms that consider the synergy between various data

center infrastructures (i.e., the hardware, power units, cooling

and software). Unlike our system their provisioning scheme

holistically works to boost data center energy efficiency and

performance.

X. Kong et al. [9] presented a fuzzy prediction method to

model the uncertain workload and the vague availability of

virtualized server nodes by using the type-I and type-II fuzzy

logic systems. They also proposed an efficient dynamic task

scheduling algorithm named SALAF for virtualized data

centers.

VI. CONCLUSION

A key problem when provisioning virtual infrastructures is

how to deal with situations where the demand for resources.

Resource Provisioning is the mapping and scheduling of VMs

onto physical Cloud servers within a cloud. In this paper, we

presented design and implementation of resource provision

system for cloud data centers by using two provisioning

strategies based on time-shared allocation policy for both VMs

and tasks. The provisioning system is simulated and evaluated

with real world workload traces. The evaluation results show

that the proposed provisioning system achieve high utilization

of resources of the cloud data center.

REFERENCES

[1] http://www.cs.huji.ac.il/labs/parallel/workload/

[2] B. Sotomayor, R. S. Montero, I. M. Llorente, and I. Foster, “Virtual

infrastructure management in private and hybrid clouds,” IEEE Internet

Computing, 13(5):14_22, September/October, 2009.

[3] B. Sotomayor, R. S. Montero, I. M. Llorente, and I. Foster, “Virtual

infrastructure management in private and hybrid clouds,” IEEE Internet

Computing, 13(5):14_22, September/October, 2009.

[4] R. Buyya, A. Beloglazov, J. Abawajy, “Energy-Efficient Management

of Data Center Resources for Cloud Computing: A Vision, Architectural

Elements, and Open Challenges,” Proceedings of the 7th High

Computing and Simulation (HPCS 2009) Conference, Leipzig,

Germany, June 21-24,2009 [5] R. N. Calheiros, R. Ranjany, and R. Buyya, “Virtual Machine

Provisioning Based on Analytical Performance and QoS in Cloud

Computing Environments,” in Parallel Processing (ICPP), International

Conference September, 2011, pp. 295-304

[6] S. K. Garg, S. K. Gopalaiyengar, R. Buyya, “SLA-Based Resource

Provisioning for Heterogeneous Workloads in a Virtualized Cloud

Datacenter,” in Proceedings of the 11th International Conference on

Algorithms and Architectures for Parallel Processing (ICA3PP 2011),

Melbourne, Australia, October, 2011.

[7] T. Wood, P. J. Shenoy, A. Venkataramani, and M. S. Yousif, “Black-

box and gray-box strategies for virtual machine migration,”in NSDI,

2007.

[8] T. Z. Tun, T. Thein, “SLO Granted Elastic Resource Prediction in Cloud

Data Center,” International Journal of Information Engineering, Jeju

Island, Korea, December 2013.

[9] X. Kong, C. Lin, Y. Jiang, W. Yan, X. Chu, “Efficient dynamic task

scheduling in virtualized data centers with fuzzy prediction,” in Journal

of Network and Computer Applications, 34(4), 2010, 1068-1077.

[10] Z. Gong, X. Gu, and J. Wilkes, "PRESS: PRedictive Elastic ReSource

Scaling for cloud systems," in Proceeding of CNSM’10, Niagara Falls,

Canada, 2010, pp. 9-16.


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http://www.cs.huji.ac.il/labs/parallel/workload/

Documents

Elastic Resource Provisioning for Cloud Data Centerpsrcentre.org/images/extraimages/10 214360.pdfCloudSim supports the time-shared and space-shared resource allocation policies for