A review on various optimization techniques of resource provisioning in cloud computing

International Journal of Electrical and Computer Engineering (IJECE)
Vol.9, No.1, February 2019, pp. 629~634
ISSN: 2088-8708, DOI: 10.1159 1/ijece.v91i1 .pp629-634 o 629

A review on various optimization techniques of resource
provisioning in cloud computing

K. Sumalatha!, M. S. Anbarasi?
Department of Computer Science and Engineering, Annamacharaya Institute of Technology and Sciences, India
*Department of Information Technology, Pondicherry Engineering College, India

Article Info ABSTRACT

Article history: Cloud computing is the provision of IT resources (IaaS) on-demand using a
. pay as you go model over the internet.It is a broad and deep platform that

Received Jan 4, 2018 helps customers builds sophisticated, scalable applications. To get the full

Revised Jul 17, 2018 benefits, research on a wide range of topics is needed. While resource over-

Accepted Aug 2, 2018 provisioning can cost users more than necessary, resource under provisioning

hurts the application performance. The cost effectiveness of cloud computing

highly depends on how well the customer can optimize the cost of renting

Keywords: resources (VMs) from cloud providers. The issue of resource provisioning

IaaS optimization from cloud-consumer potential is a complicated optimization

aa : issue, which includes much uncertainty parameters. There is a much research

Optimization techniques avenue available for solving this problem as it is in the real-world. Here, in

Resource provisioning this paper we provide details about various optimization techniques for
Uncertinity resource provisioning.

Virtual machines
Copyright © 2019 Institute of Advanced Engineering and Science.

All rights reserved.

Corresponding Author:

K. Sumalatha,

Department of Computer Science and Engineering,
Annamacharaya Institute of Technology and Sciences,
Tirupati, India.

Email: sumalathacs@yahoo.com

1. INTRODUCTION

Cloud Computing is a broad and deep platform that helps customers build sophisticated scalable
applications. Cloud provisioning is the allocation of a cloud provider's resources to a customer [1]. When a
cloud provider accepts a request from a customer, it must create the appropriate number of virtual machines
(VMs) [2] and allocate resources to support them. In this context, the term provisioning simply means “to
provide”.

The Provisioning has been done in several different ways. a) Advance Provisioning: The customer
requests the provider for services and the provider prepares the appropriate resources [2] in advance. The
customer is charged a flat fee or is billed on a monthly basis. b) Dynamic Provisioning: The provider
allocates more resources when they are needed and removes them [3] when they are not needed. The
customer is billed on a pay-per-use basis.

Provisioning allows optimal allocation of resources to consumers in a finite time to achieve desired
quality of service. Here, the problem is either the user gets over-provisioning or under-provisioning.
Formally, provisioning problem involves uncertainty parameters while choosing resources subject to some
constraints to optimize some objective function. The aim is to develop an optimized method that reduces over
provisioning and under provisioning problems. It has remained a topic of research in various fields for
decades, may it be supply of electricity or water to consumers [4].

In recent years, distributed computing paradigm [5] has gained much attention due to high
scalability, reliability, and flexibility. Thus stochastic based techniques deal with these problems by

Journal homepage: http:/iaescore.com/journals/index.php/IJECE

630 o ISSN:2088-8708

providing near optimal solutions within the reasonable amount of time. In this paper, we present a review of
various optimization techniques based on various parameters.

2. RESOURCE PROVISIONING PRICING MODELS

Reserved instances are cheapest resources. Price is based on the period of subscription (static).
Customer must reserve the resources in advance. Customer might overpay for the resources reserved if he/she
doesn’t use them extensively and he might under pay for the resources reserved for long time [6].

On-demand instances are the highest priced resources. Price is set by the service provider and
remains constant. Consumer has to pay per use. Customer is aware of the exact price to be paid. Resources
are reserved for the customer for the paid period of time [7]. Service provider might reserve the resources for
longer than the customer’s utilized. Service provider cannot raise the price when demand is high; when
demand is low, the user pays higher than the market price.

Spot Instances [8] allow you to specify the maximum hourly price that you are willing to pay to run
a particular instance type, usually lower than the On-Demand rate. These are suitable for both customers and
the service provider because the price is set according to the level of supply and demand. Less scalability of
high demand in the market than fixed pricing, the spot instances are the unused on-demand instances.

The Spot Price fluctuates based on supply and demand for instances, but customers will never pay
more than the maximum price they have specified. If the Spot Price moves higher than a customer’s
maximum price, the customer’s instance will be shut down by the cloud provider [9]. Figure 1 shows three
pricing models.

= Reserved On-demand
Instances Instances

$0.01 /Hour

. One time low e Pay as you go * auction-based
upfront fee * no long-term * Unreliable
+pay as you go commitment |

$23 for 1 year * Start from | * $0.005 as of
term and $0.02 /Hour | today at 9 am

l
|

Figure 1. Pricing models

3. OPTIMIZATION TECHNIQUES

In Computer Science and operation research, Optimization is referred to as the selection of best
element from a set of alternatives with regard to some criteria. Two types of entities involved in cloud are
cloud service provider and cloud consumer. Cloud service providers provision their resources on rental basis
to cloud consumers and cloud consumers submit their requests for provisioning the resources. They both
have their own motivations when they become part of cloud environment. Consumers are concerned with the
performance of their applications, whereas providers are more interested in efficient utilization of their
resources. Thus these Optimization Techniques [10] can be classified into two types: Static techniques and
Dynamic techniques. Following are some of the optimization criteria followed while provisioning resources
in cloud environment as shown in Figure 2.

peade Determini- Integer
Vision- stic Programmin;
ing Approach | g

Figure 2. Optimization techniques

Combinato- Ge i a) (Stochastic | ( j Benders ) )
rial enee Approxi-mate é Average
Optimiza- Algorithm | | Approach P amie Vee Approximation

tion Programming |

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3.1. Static provisioning

Cloud service provider allocates resources to consumer in advance. That is cloud consumer has to
decide how much capacity of resources he/she wants statically means before using them. The deterministic
approach, integer programming, lineare programming and deadline provisioning algorithm show how
resources are allocated statically for deadline based workflows.Here we consider only the expected values for
all the parameters. In static provisioning we may get over provisioning or under provisioning problem.

3.2. Dynamic provisioning

Cloud service provider allocates resources to consumer as needed and when required and they have
to pay per use. This is also called as pay as you go model. Here, genetic algorithm, stochastic approach,
approximate dynamic programming, benders decomposition and average approximation are used for
provisioning resources dynamically.

3.2.1. Genetic algorithm

Genetic algorithms are commonly used to generate high-quality solutions tooptimizationandsearch
problemsby relying on bio-inspired operators such asmutation,crossoverandselection. Genetic algorithms do
not scale well with complexity, operating on dynamic data sets is difficult.

3.2.2. Stochastic approach

In this section, the stochastic programming with multistage recourse is presented as the core
formulation. First, the original form of stochastic integer programming formulation is derived. Then, the
formulation is transformed into the deterministic equivalent formulation (DEF) which can be solved by
traditional optimization solver software.

3.2.3. Approximate dynamic programming

Approximate Dynamic Programming (ADP) is a powerful technique to solve large scale discrete
time multistage stochastic control processes, i.e., complex Markov Decision Processes (MDPs). These
processes consists of a state space S, and at each time step t, the system is in a particular state St € S from
which we can take a decision xt from the feasible set Xt . This decision results in rewards or costs, typically
given by Ct(St , xt), and brings us to a new state St+1 with probability P(St+1|St , xt), i.e., the next state is
conditionally independent of all previous states and actions. Therefore, the decision not only determines the
direct costs, but also the environment within which future decisions take place, and hence influences the
future costs. The goal is to find a policy. A policy a € II can be seen as a decision function Xa (St) that
returns a decision xt E Xt for all states St E€ S, with II being the set of potential decision functions or policies.
The problem of finding the best policy can be written as (1).

min TEI E*{ (X T t=0 yCt(St , Xr t (St)))} (1)
where y is a discount factor, and T denotes the planning horizon, which could be infinite.

3.2.4. Benders decomposition

The goal of this algorithm is to break down the optimization problem into multiple smaller problems
which can be solved independently and parallel. The Benders decomposition algorithm can decompose
integer programming problems with complicating variables into two major problems: master problem and
sub problem.

3.2.5. Sample average approximation

It may not be efficient to get the solution of the algorithm if the number of scenarios is more by
solving the stochastic programming formulation defined in directly if all scenarios in the problem are
considered. The sample-average approximation (SAA) approach is used to address this complex problem.
This approach is applied on a set of scenarios.

4. COMPARISON OF RESOURCE PROVISIONING TECHNIQUES

In [11], proposed an algorithm called optimal cloud resource provisioning algorithm (OCRP) to
overcome resource under provisioning and over provisioning. Authors applied various optimization
techniques to minimize the user‘s cost. This approach includes an algorithm called optimal cloud resource
provisioning algorithm (OCRP) to overcome resource under provisioning and over provisioning. Authors
applied various optimization techniques to minimize the user‘s cost with more scenarios. Minimizing both

A review on various optimization techniques of resource provisioning in cloud ... (K. Sumalatha)

632 o

ISSN:2088-8708

under -provisioning and over provisioning problems under the demand and price uncertainty in cloud
computing environments is our motivation to explore a resource provisioning strategy for cloud consumers.
The following Table 1 shows proposed optimization techniques and their merits and demerits.

Table 1. Comparison of Various Optimized Provisioning Techniques based on Uncertainty, QoS, Time, Cost,
Heterogeneity and SLA violations

Optimization Technique

Parameter Problem Proposed Applied Inputs Merits Challenges
Cost [12] Resource Particle Swarm Workflows Well suited for It Takes More
&QOoS provisioning and Optimization (PSO) & Meta work-flows with Cost to meet the
scheduling on IaaS Coud Heuristic Optimization. smaller size deadlines of larger
workflows.
[13] Resource Integer Programming Bandwidt,C Maximize Revenue Implement on
provisioning for adaptive PU cycles cloud environment
applications in Grid
[14] Autonomic Resource Match Making Technique User Provides QoS More Energy
Provisioning requests consumption
[15] Dynamic Resource CPU Allocation Algorithm CPU Reduce the Need an
Provisioning for Data Cycles processing time implementation on
Streaming Applications and budget real scenario
Optimization of Resource © OCRP Algorithm with Resource Minimize the cost More Scenarios
Provisioning Cost Stochastic Optimization requests & avoid over
&under
provisioning
[16]Optimization Combinatorial auction Network, QoS& Maximize Not suitable for all
Approach for Resource approach Data-base profit users
Allocation for IoT
[17] Joint Optimization of Stochastic Optimization, Bandwidth Reduce over and Need to consider
Resource Provisioning Sensitivity Analysis & Virtual Under provisioning network delay and
Machines VM migration.
[18] Joint Virtual Machine Deterministic formulation Bandwidth Minimize user’s Applicable for
and Bandwidth Allocation & Virtual provision cost only Network
in Software Defined Machines intensive
Network (SDN) applications
Resource provisioning Particle Swarm Workflows Well suited for It Takes More
and scheduling on IaaS Optimization (PSO) & Meta work-flows with Cost to meet the
Coud[19] Heuristic Optimization. smaller size deadlines of larger
workflows.
Heuristics [19] Adaptive Resource Static Algorithm based on Workflows Low cost for Not suitable for
& Time Provisioning Heuristics execution of each complete
task. workflow structure
[20]SLA-Based Resource Customer driven heuristic Customer Less cot & reduce Applicable only
Provisioning for Hosted algorithms profile and SLA violations for SaaS
Software-as-a-Service response providers.
Applications time
[21]Dynamic Provisioning Map Reduce model, User Reduces total Applicable only
in Hybrid Cloud using Deadline Priority requests, execution time for hybrid clouds.
Aneka Platform Provisioning algorithm
[22]Application of min-min or max-min File sizeof | Minimize make Dependencies
Selective Algorithm for algorithm user span among tasks can
Effective Resource requests be modeled
Provisioning
[23]An Efficient Efficient resource Use Reduces the Applicable for
Architecture and allocation(ERA) Algorithm Requests response time only reserved
Algorithm for Resource for instances
Provisioning in Fog resources
Computing
[24] Towards stochastic scheduling Sources of Introduced various Need
Understanding uncertainty solutions to resolve implementation on
Uncertainty in Resource uncertainty real Scenarios
Provisioning

5. CONCLUSION AND FUTURE WORK

The ultimate goal of cloud computing is to satisfy both cloud consumer and cloud service provider.
Broad research is needed to find best optimal provisioning technique. This paper presents various optimized
provisioning variants with their merits and challenges while considering different parameters and different
algorithms. The efficient dynamic optimization provisioning is one of the primary challenges in cloud
environment, because a tradeoff between professional SLA [25] and QoS constraint like max resource

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Int J Elec & Comp Eng ISSN: 2088-8708 o 633

utilization, cost etc. In future, we will propose a method for Dynamic optimized resource provisioning. It has
dynamic nature so that the cost of provisioning resources may be reduced without SLA violations.

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BIOGRAPHIES OF AUTHORS

Ms.K.Sumalatha is currently working as Assistant Professor in Annamacharya Institute of Technology
and Sciences and Research Scholar in the Department of Computing Science and Engineering at
Pondicherry Engineering College. Her research work focuses Cloud Computing, Optimization
Techniques, and Networks.

Dr. M.S.Anbarasi is currently working as Assistant Professor in the Department of Information
Technology at Pondicherry Engineering College. Her area of interest mainly focuses Data Mining,
Databases and Cloud Computing.

Int J Elec & Comp Eng, Vol. 9, No. 1, February 2019 : 629-634