2013 International Conference on Computational and Information Sciences
QoS-oriented Monitoring Model of Cloud Computing Resources Availability
WANG En Dong
WU Nan
LI Xu
State Key Laboratory of High-end Server & Storage
Technology
Beijing, China
{wangend,wunan}@inspur.com
Inspur(Beijing) Electronic Information Industry Co.,
Ltd.
Beijing, China
[email protected]
Abstract—With the development of cloud computing, many
critical applications have been supported to provide many key
services in the cloud computing. So the availability of cloud
computing services turns to be higher and higher. Because
resources of cloud computing are distributed, dynamic and
heterogeneous, traditional research on availability cannot be
good to adapt to the cloud computing new features. This paper
does research on QoS-oriented cloud computing resources
availability. First, a monitoring model of cloud computing
resources availability is created. Then, according to the
dynamic process of the cloud computing service, the
availability of cloud computing resources is analyzed from QoS
of a single cloud resource node which is described by common
attribution and special attribution to QoS of some cloud
resources which are connected by series model, parallel model
and mix model to provide service. According to the three
models and the analysis of the single cloud service resource, the
availability of cloud computing service is monitored.
RELATED WORK
The math models on the research of the availability are
classified into probability model and statistics model
according to failure probability of the devices. Probability
model is that according to system structure, distribution of
assemblies’ life-span and distribution of the repair time, the
parameters about the life-span of the system are forecasted.
Using the probability model, the optimal design of the
system is funded and maintenance strategy is decided.
Depending on the distribution of time, probability models are
classified into Markov models and non-Markov models. The
statistical model is starting from the observation data and the
life of a component or system then to estimate and inspect
the reliability index.[2]
AVAILABILITY ORIENTED QOS
In cloud computing, providers use cloud computing
resources to provide services to customers. With the rise of
the service model, functions which the cloud computing
services can complete are more and more complex. And
more and more key applications are supported in the cloud
computing. So the QoS of services in the cloud computing is
increasingly important. According to the characteristics of
cloud computing, the availability of cloud computing
resources is studied. Traditional studies of the availability are
not suitable to the dynamic and distributed cloud computing.
QoS of the services of the cloud computing resource does
not consider the availability. In the thesis, a kind of QoS
oriented availability monitoring model is invented to monitor
the availability of cloud computing resource.
The availability means the probability that the system can
complete a predetermined function under a predetermined
condition.
The resources in the cloud computing have the
heterogeneity that different resources complete different
function and have different features and attribute. So a kind
of QoS model is designed to describe the service quality of
the resources in cloud computing. Using the QoS model, the
quality of the service in the cloud computing can be
described. At the same time, customers can use the QoS
model to describe the quality of the service which they want.
In this thesis, that the resources in the cloud computing are
available to the customers means that the services which the
Keywords-availability; QoS; cloud computing; service;
INTRODUCTION
With the development of the technology, the software
and hardware of the computer is making progress rapidly.
And this also makes the computing model changing. After
the appearance of the distributed computing, parallel
computing and gridding computing, the concept of cloud
computing is introduced. Cloud computing is developed
rapidly and becomes very common. According to the
different level of cloud computing, it is classified into the
classes which are infrastructure as a service-IaaS, platform as
a service-PaaS and software as a service-SaaS.[1]
At present, there are two trends of mass computing. One
is high performance computing. The other is high availability
computing. High performance computing is very important
to the science computing. On the other hand, with the
development of the cloud computing, many key applications,
especially some business applications are supported by cloud
computing. So the availability of cloud computing turns to
be more and more important. The research on the availability
comes from the RAS theory in the industry which include
reliability, availability and serviceability. Now RAS theory is
widely used in the research on the system of devices. It is
rarely used in the cloud computing area.[8]
978-0-7695-5004-6/13 $26.00 © 2013 IEEE
DOI 10.1109/ICCIS.2013.404
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resource in cloud computing can meet the need of the
customers.
Different resources in cloud computing have different
features. So it is not scientific to use the static QoS index to
describe the resources. In this thesis, a QoS model which can
be configured dynamically to describe the QoS of resources
in cloud computing is introduced. While cloud computing
resources are heterogeneous, but services that they provide
have common attributes. For example, the common attributes
are service time, cost of the service, the credit rating of the
service and reliability which the service resource can offer.
On the other side, different cloud computing resources have
different attributes. For example, storage resources in the
cloud computing may be more concerned with the storage
capacity of the storage resources. A description model of
cloud computing resources is designed to describe the quality
of cloud computing resource service, as shown in Figure 1.
Cloud computing resources QoS model on the one hand be
able to describe the quality of service provided by the cloud
computing resources, on the other hand for customers it can
describe the demand of customers.
When Parallel model complete the services, a branch of
resources complete the service to meet the availability at
least.
As shown in Figure 4, the hybrid model is that when
services are completed, some cloud computing resources
nodes provide services in series model and some are
connected in parallel model.
Figure 4. The hybrid model
This QoS-oriented cloud computing resource availability
monitoring model is able to combine the characteristics of
cloud computing and the customers' demand for the quality
of the services they need to monitor service availability.
RESOUCE SELECTION
According the thesis-The Bilateral Resource Integration
Service System, as shown in Figure 5, there is a model how
the cloud computing resources to provide services to meet
the customers' demand.[6]
Figure 1. The description model of cloud computing resources
Figure 5. The process to complete the service
Cloud computing services are rarely provided by a
separate resource node, but a series of cloud computing
resource nodes. According different link ways, collaborative
services are divided into series model, parallel model, mixed
model.
As shown in Figure 2, series model is that when the
cloud computing resources to complete the services, they
provide services in order. In order to complete the services,
all cloud computing resources must be available in series
model.
In cloud computing, each service node in the service
process is done by a kind of service resources. The same
kind of cloud computing resources are able to accomplish the
same function, but quality and the value of the services
attributes which are described in the part 3 are different. It is
necessary to select and match the specific service resources
to meet the customers' need. For one service node, there are
many cloud computing resources which can complete the
service. As shown in Figure 6, the resource selection is to
select and match the specific cloud computing resource for
each service node according the process of the services to
meet the customers’ need.
Figure 2. The series model
As shown in Figure 3, the parallel model is that the
service resource nodes are parallel to provide services at the
same time.
Figure 6. The selection of cloud computing resource
Figure 3. The parallel model
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RE stands for the service’s reliability.
ܳ௜௝ means that the ݆௧௛ quality parameter when using
resource ܴ௜ to finish the service.
ܸ௜௝ means that ܳ௜௝ after adjustment by the weights of the
parameter.
ܹ௝ means that the weight of the ݆௧௛ quality parameter,
ܹ௝ ‫ א‬ሾͲǡͳሿ.
…‘”‡௜ ሺܴ௝ ሻ stands for the score when using the resource ܴ௝
to finish the service ܵ௜ .
The process is consisted of n service nodes. the service
on each service node can be completed by a number of
candidate cloud computing resources. The object is to find a
solution to meet the customers’ need.
There are two kinds of customers’ need. One of the
customers’ needs is the demand of the whole process of the
service. The other is the demand of the single service node.
In this thesis, only the local demand is discussed.
ALGORITHM OF SELECTION OF RESOURCE
As part 4 has said, customer demand for services rarely
cannot be met by a single cloud computing resources, but
often by some cloud computing resources together. The
demands of customers can constraint the overall process of
the services or the local service nodes. In the thesis, only the
local demand is discussed. A kind of local optimization
algorithm which is suitable to this local situation is
introduced to solve the selection of cloud computing
resources problem. The local optimization algorithm is to
match the cloud computing resources on the service node to
meet the needs of the customers to the greatest extent.
Customer demand includes many needs, and the degree of
importance of the different needs is not the same. Some
aspects of property values when they become bigger turn to
be better for the needs of customers, these kinds of properties
are defined to be positive attributes. On the other side, some
aspects of property values when they become smaller turn to
be better for the needs of customers, these kinds of properties
are defined to be negative attributes. The object of the
selection resources is to meet the customers’ needs. Other
aspects about the service quality are constraints.
After the analysis above, the selection of resources for
the whole process turns to be selection of resources for a
single service node. In the situation, every service node
selects the best quality of the cloud computing resource. So
the local optimization algorithm is used to solve the problem.
A local optimization algorithm model is introduced to
explain how to solve this selection problem. In the local
optimization algorithm model, descriptions of variables,
objective function and constraints are discussed.[2]
B.
Objective Function and Constraints
Objective function is that the score of quality of service is
the highest. The object is to find the resources to provide the
services to make the score of the services highest. Equation
(1) is the objective function which means that the highest
score of the service ୧ which is provided by the candidate
cloud computing service.
‹ሺ…‘”‡௜ ሺܴଵ ሻǡ …‘”‡௜ ሺܴଶ ሻǡ ǥ ǡ …‘”‡௜ ሺܴ௡ ሻሻ (1)
Constraints are about the non-quality attributes from the
demands of customers. Using the QoS model, the demands
of customers are transformed to the constraints. Before the
selection of the cloud computing resources, they must be
checked whether they can meet the customers' demands to
turn to be candidate resources. According the situations,
there are three types constraints-greater formula, equal
formula and less formula. Equation (2) is the greater formula
which means that the value of the service which the cloud
computing resources provide is greater than the value which
the customers expect. Equation (3) is the equal formula
which means that the value of the service which the cloud
computing resources provide is equal to the value which the
customers expect. Equation (4) is the less formula which
means that the value of the service which the cloud
computing resources provide is less than the value which the
customers expect.
ሺܵଵ ǡ ܵଶ ǡ ǥ ǡ ܵ௡ ሻ ൐ Ͳ
ሺܵଵ ǡ ܵଶ ǡ ǥ ǡ ܵ௡ ሻ ൌ Ͳ
ሺܵଵ ǡ ܵଶ ǡ ǥ ǡ ܵ௡ ሻ ൏ Ͳ
A. Descriptions of Variables
Service Resource
SR=(SRID,PROVIDERID,{ATTRVALUE},SRTYPE,{
SRTYPE_ATTRVALUE})
SRID is ID of service resources to uniquely identify a
resource.
PROVIDERID refers to the id of service resources
providers, used to represent the relationship of the service
resources and service providers.
{ATTRVALUE} is a collection of attributes to describe
the service resources.
SRTYPE are the attribute types of service resources.
{SRTYPE_ATTRVALUE} is a collection of values of
service resources SRTYPE.
Customer Demand
P stands for the price of the service.
D stands for the time that the service costs.
REP stands for the service’s reputation.
(2)
(3)
(4)
C.
The basic idea of the algorithm
A service can be provided by many different cloud
computing services. But different resources provide different
quality services. There are many attributes about the quality
of service. Different attributes have different units of
measurement. So it is necessary to make the resources to be
at the same level of measurement. Then according the weight
of different attributes, the score of the service which the
cloud computing resources provide is calculated. At last, the
resources which have the highest score are selected to
provide the service.
D. The Description of Algorithm
Making the value of different attributes at the same level of
measurement
As it is said above, some aspects of property values when
they become bigger turn to be better for the needs of
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model failed to choose cloud services resources for the
overall demand of the customer.
customers, these kinds of properties are defined to be
positive attributes. On the other side, some aspects of
property values when they become smaller turn to be better
for the needs of customers, these kinds of properties are
defined to be negative attributes.
Equation (5) is that how to make the value of positive
properties at the same level of measurement. In equation (5),
ܳ௜ǡ௝ stands for the value of the ݆௧௛ parameter when the
resource ܴ௜ provide the service. ܸ௜ǡ௝ is the value after ܳ௜ǡ௝ is
made at the same level of measurement. Similarly, equation
(6) is about the negative properties.
REFERENCES
[1]
[2]
[3]
ொ೔ǡೕିொೕ ೘೔೙
ܸ௜ǡ௝ ൌ ൞
݂݅ܳ௝ ௠௔௫ െ ܳ௝ ௠௜௡ ് Ͳ
(5)
௠௔௫
௠௜௡
െ ܳ௝
ൌ Ͳ
ͳ݂݅ܳ௝
ொೕ ೘ೌೣ ିொೕ ೘೔೙
[4]
ொೕ ೘ೌೣ ିொ೔ǡೕ
[5]
݂݅ܳ௝ ௠௔௫ െ ܳ௝ ௠௜௡ ് Ͳ
(6)
ܸ௜ǡ௝ ൌ ቐ
௠௔௫
௠௜௡
െ ܳ௝
ൌ Ͳ
ͳ݂݅ܳ௝
ொೕ ೘ೌೣ ିொೕ ೘೔೙
[6]
Calculating the score according to the weight
The importance of the different attributes of the service
quality is not the same. Different customers focus on
different quality of service attributes. So the weight of the
attributes comes from the customers. ܹ௝ stands for the
weight of the ݆௧௛ parameter. Equation (7) is the score of the
service ‫ݏ‬௜ .
…‘”‡ሺ‫ݏ‬௜ ሻ ൌ σ௡௝ୀଵ ܸ௜ǡ௝ ȉ ܹ௝
[7]
[8]
[9]
(7)
[10]
Because local optimization algorithm mathematical
model turns the selection problem to be a single service
node’s selection problem. Local optimization algorithm
mathematical model can quickly match cloud computing
resources to meet customer demand.
[11]
[12]
[13]
CONCLUSIONS
In this paper, according to the status of cloud computing,
a kind of availability oriented QoS is introduced. A kind of
QoS model is created to describe the quality of the services
which the cloud computing resources provide. Then the
resource selection problem is analyzed and expressed in a
formal way. According to the oriented QoS availability and
the process of resource selection, a kind of local optimization
algorithm mathematical model is designed to solve the
resource selection problem. Because the feature of the
mathematical model, it can quickly and easily select the
suitable cloud computing resources to meet the customers’
demands. But local optimization algorithm mathematical
[14]
[15]
[16]
[17]
1540
1541
Z.J. Wang and X.F. Xu,"Bilateral resource integration service mode
for value innovation," Computer Integrated Manufacturing
Systems,vol. 15,no. 11,Nov. 2009,pp.2216-2225.
Xu LI,"Design and Implementation of Resource Services System
Based on ICES-SSP," Master's thesis of Harbin Institute of
Thechology,2011.
Liu, F., Yu, C.”Personalized web search for improving retrieval
effectiveness,”
IEEE Transaction on knowledge and data
engineering, 16(1), 2004,pp.28-40.
R Calinescu, L Grunske, Marta Kwiatkowska,Raffaela
Mirandola,Giordano Tamburrelli.”Dynamic QoS Management and
Optimisation in Service-Based Systems,” IEEE TRANSACTIONS
ON SOFTWARE ENGINEERING, 2011,37(3).
D.Ardagna, B.Pernici,”Adaptive service composition in flexible
processes,” IEEE Trans. Softw. Eng. 2007. vol.33, no. 6, pp.369-384.
ZHANG Dong, WU Nan, LI Xu." The Bilateral Resource Integration
Service System," The fourth International Conference on
Computational and Information Sciences,2011.
R. Hull, M. Benedikt, V. Christophides, and J. Su, “E-services: a look
behind the curtain,” In Proc. 22nd ACM SIGMOD-SIGACT-SIGART
symposium-Principles of database system, Jun. 2003, pp. 1-14,
doi:10.1145/773153.773154
Fashandi A.System availability and operation support modeling. The
28th International Electronics Manufacturing Tech-nology
Symposium . 2003
Nebel S,Bertsche B.Modeling and simulation methodology ofthe
operational availability and logistics using extended coloredstochastic
petri nets:an astronautics case study. 2008 Re-liability and
Maintainability Symposium . 2008
Zhao JF,,Xie B,Zhang L,Yang FQ.A Web services composition
method supporting domain feature. The Chinese Journal . 2005
Shalil M,,Walker DW,Gray WA.A framework for automated service
composition in service-oriented architectures. Proc.of the
ESWS2004.LNCS3053 . 2004
Mike P. Papazoglou,Willem-Jan Heuvel.
Service oriented
architectures: approaches, technologies and research issues[J] ,2007
Arsanjani A.Service-Oriented modeling and architecture:How to
identify,specify,and realize services for your SOA. Whitepaper from
IBM . 2004
Papazoglou
MP,van
den
Heuvel
WJ.Service
oriented
architectures:Approaches,technologies and research issues. The
VLDB Journal,the Int‘l Journal on Very Large Data Bases . 2007
Cui XX,,Lin C.A constrained quality of service routing algorithm
with multiple objectives. Journal of Computer Research and
Development . 2004
Oh SC,,Lee D,Kumara SRT.Web service Planner(WsPr):An effective
and scalable Web service Web composition algorithm. Int’l Journal of
Web Services Research . 2007
Gao AQ,Yang DQ,Tang SW,Zhang M.Web service composition
using Markov decision processes. Proc.of the6th Int’l Conf.on WebAge Information Management . 2005