=Paper=
{{Paper
|id=Vol-548/paper-9
|storemode=property
|title=Remote Sensing Service chain Self-Evolution Method
|pdfUrl=https://ceur-ws.org/Vol-548/paper9.pdf
|volume=Vol-548
}}
==Remote Sensing Service chain Self-Evolution Method==
https://ceur-ws.org/Vol-548/paper9.pdf
Remote Sensing Service chain Self-Evolution Method
Haifeng Li
Supervised by Qing Zhu
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote
Sensing, Wuhan University
430079 Wuhan, China
hfli6135@lmars.whu.edu.cn
Abstract. In order to facilitate continuous and quick adaptation to the change of
environment and use requirement, this paper presents a self-evolution method
for the remote sensing information service chain to keep effective and robust of
service chain with lesser modification, and to maximize all users’ utilization
under concurrent user requirements. According to the driver forces of service
chain change, we partition self-evolution method into three levels: (1) a fuzzy
semantic based relatedness analysis and min-conflict heuristic based service
chain reconfigure methods to adapt to user requirement change; (2) a sensitivity
analysis and robust optimization based method to keep maximum stability of
service chain in dynamic environment; (3) a non-cooperative game approach
for multi-service chain cooperation optimization under concurrent tasks
concurrent condition.
1 Introduction
As satellites of Earth Observing System (EOS) currently beam down several hundred
terabytes annually, the inconsistent between powerful data instruments and
incompetent data process become ever more standing, which are making this field
“data-rich but analysis-poor” [1]. The key reason lead to this is not that we lack of
applications to handle with these data, but mechanisms how to aggregate the
applications which distributing in internet extensively (hence be looked as remote
sensing services) together and cooperate them to satisfied the need of the data analysis.
This is so-called remote sensing service chain[2] through service composition.
Compared with generic Web service composition, remote sensing services have some
typical features as follows:
Complex in user requirements, for examples: real-time monitor for forestry fire,
coast, and flood; concurrent in user requirements, for instance, in Sichuan Wenchuan
Earthquake, we must evaluate earthquake damage and monitor coast, landslides, and
barrier lakes at the same time.
Rich in data dimensions. Data in remote sense with dimensions of spatiality,
temporal, image resolution, sensor type, and image spectrum, makes it harder to be
descript and more complicated in processing flow. What’s more, mass remote sensing
images make the service chain more sensitivity to response time.
Complicated in remote sensing processing. Remote sensing service composition
has been constrained by more strict process semantic; Computation-intensive feather
in remote sensing also make remote sensing processing more time consumed.
So, remote sensing service chain should be flexible enough to effectively adapt to
fast change of use requirements and environment, through frequently refine their
structure. The existent methods to generate services have some disadvantages as
following:
Lack of mechanism to adapt user requirements change via local reconfigure at
function level which are known as abstract services[3]. The state-of-art service
composition approaches[4] are facing more and more serious bottlenecks of
effectiveness and stability, since new service chain must be generated from “scratch”
for each requirement. Those methods are also known as “first principle”. Distinguish
with it, another way is how to make use of relativity between remote sense service
chains and reuse knowledge about similar, already solved problems successfully. This
methods are always known as “second principle”[5], which aim to make service chain
generate more effectively and execute more stably. Although there are some
researches generate service chain by case based reasoning[6, 7], but they all do not
take into account strict process constraints in remote sense. What’s more how to
measure similarity between cases accurately and to refine service chain effectively are
still open questions.
Lack of robust adapting to dynamic environment at capability level known as
concrete services[3] which usually modeled as QoS constraint based optimization.
The service chains are more sensitivity to services and transport network performance,
because data-intensive and computing-intensive are essential features in remote
sensing. A small perturbation in QoS dimension of services and transport network
will make former optimization solution becomes infeasible. There are many
researches dynamic modify service chain through runtime monitor and re-planning
technology[8]. But, because of high dynamic and uncertain of services and transport
network in nature, the dynamic modification may be too frequency, and lead to
unstable and decrease of performance of service chain. Therefore, we still are short of
quantization model to estimate the influence of QoS perturbation on service chain.
The mechanism how to keep service chain robust in dynamic and uncertain
environment is unclean.
Lack of optimal mechanisms to deal with concurrent user requirements. The
existing optimal composition approaches search optimization solution[9] under QoS
constraints (such as response time, cost, stability and available) via “selfish” way. Yet,
these methods only take single used requirements into account, not adapt to
applications like remote sensing emergency and disaster response where concurrent
task happened frequently. Concurrent tasks competing optimal services lead to
conflict problem and decreased performance of all service chains, which are known as
“tragedy of the commons”. A key problem here is how to reduce the conflict cause by
concurrency tasks to make all service chain reach optimization at the same time.
In conclusion, in the face of high dynamic environment and user requirements, and
high concurrent of user requirements, the challenge of remote sensing service chain
generation is: how service chains adapt to user requirements and environment to keep
effective and robust of service chain with lesser modification and how to implement
multiple service chain cooperation optimization under concurrent tasks to maximize
all user’s utilization. Hence, we put forward a novel self-evolution method to solve it.
2 Remote Sensing Service chain Self-Evolution Method
The basic conception behind remote sensing service chain self-evolution method is: it
is a self-adaptive behavior responding to exterior dynamics factors, through frequent
revise structure, function and capability of service chain, with completeness,
minimization and consistency.
Exterior factors dynamics refer to user requirements, services runtime environment
which including service temporarily disabled, modification of services QoS and
network QoS, and so on.
Completeness, refer to if it is feasible to change from current service chain to
others, then, we always can find the post-evolution service chain.
Minimization, refer to achieve the evolution process with minimum service chain
changing. The minimization has two means here: maximum reuse existent service
chain and least revision that establish the upper and lower limits of the sensitivity
interval and find a robust solution with lesser sensitivity to dynamic environment.
Consistency, include function consistency and capability consistency, i.e. evolution
process must satisfied constraints such as function constraints and QoS constraints.
We first analyze driving forces of service chain evolution to understand which
factors make it change.
2.1 Driving Forces of Service chain Evolution
We classify driving forces into two categories: user which provides information
requirements and preference, and runtime environment of services, shown as fig. 1.
Fig. 1. Driving forces of service chain evolution
The driving forces of user are decomposed into information requirements which
describe function demands and preference which describe non-function demands. The
former associate with abstract and the latter associate with concretion service chain[3].
The information requirements describe the function about use demand. A typical
requirement can be described as following:”2008-8-8 Beijing Olympic Country 1m
geospatial resolution panchromatic IKONOS image”. Another requirement changes
to:”2008-8-8 Beijing Olympic Country 0.5m geospatial resolution panchromatic
image”. Now, the abstract service chain must be modified.
The preference describes the non-function about user demand. The preference can
also divide into QoS preference and QoS constraint. The former describes how
important QoS dimension means to user, the latter describes anticipant upper or lower
limit of QoS dimension. A typical preference can be described as following:”response
time less than ten minutes and weight equal to 0.5, cost less than 100 dollars and
weight equal to 0.3, successful execution rate more than 80% and weight equal to 0.3”.
The non-function here mainly refer to QoS dimensions such as five dimensions model
presented by[9]. The concrete service chain should modify with preference changing.
Runtime environment of services include network QoS (the response time is
computed by the sum of the processing time and the transmission time) and services
QoS such as the response time of service change from 20 minutes to 30 minutes. The
alterations of them make performance (object function in optimization) of concretion
service chain fluctuate frequently and irregularly.
What’s more, concurrent tasks will lead to value of network and services QoS
change more severity because of “completion of best resource”.
2.2 Service chain Self-Evolution Method
We reduce self-evolution method to two levels and three hierarchies according to the
driving forces motioned above, shown as table 1. The proposed research methods
consist of three aspects as follows, also shown as fig. 2:
Table 1. The basic idea of service chain evolution.
Service chain level Question Basic idea
How to adapt user Choosing a most similarity service chain by
requirement changing user requirements relatedness analysis, and
fast reconfiguring by local revising based on
reuse knowledge about similarity, already
Single service solved problems successfully.
chain How to keep service Analysis the influence of QoS perturbation
chain robust in dynamic including QoS preference, QoS constraints,
and uncertain and services QoS on service chain; set up a
environment robust optimization model to keep service
composition optimal solution more stability.
How to make all service Modeling competition relationship between
Multi-service chain reach optimization tasks by non-cooperative game, which assures
chain at the same time in maximizing all tasks’ utilization under multi-
concurrent situation. task conflict condition.
Fig. 2. Architecture of service chain self-evolution method
(1) Adapt to user requirement
We model remote sense requirements as and/or graph. To estimate relativity
between two requirements, we proposal fuzzy semantic distance based method on
node level, and Hausdorff distance based method on graph level.
After relatedness analyses, we accomplish service chain explanation by
quantitative analysis the influence domain of each service. Finally a min-conflict
heuristic based regression algorithm is presented to search a minimal influence
domain solution to achieve service chain reconfigure, and prove be A*.
(2) Robust optimization in dynamic environment
We quantitative analysis the influence of QoS perturbation on service chain
performance via mix integer linear programming model. Based on this, we cast QoS
preference, QoS constraints, and QoS of services and network to profit parameters,
left-hand side and left-hand side of constraints, respectively, and establish the upper
and lower limits of the sensitivity interval through sensitivity analysis.
Then, we set up robust optimization model via minimax criterion[10] and
decompose service chain to three execution stage (executed, executing, un executed ) .
Based on this, we decrease service chain sensitivity to dynamic environment and
reduce re-planning frequency by finding robust optimization solution.
(3) Concurrent tasks optimization
A non-cooperative game based mathematics model is proposed to analysis
competition relationship between tasks through best reply function, which is defined
to quantize conflict between tasks to assure each task finds optimal composition
strategy adapting to other tasks’. Based on this, we present an iteration algorithm
converging to Nash equilibrium, which maximizing all task’s utilization under multi-
task conflict condition.
3 Conclusion
Remote sensing service chain self-evolution method is a self-adaptive behavior to
exterior factors dynamics, through frequent revise structure, function and capability of
its. We have done some experiments on user requirements change and tasks
concurrent scenarios and our methods show good performance in former and good
convergence and better practice utility of all tasks in latter.
Next step, we will focus on our sensitivity analysis and robust optimization based
method to test capability of keeping service chain stability in dynamic environment.
References
1. Clery, D. and Voss, D.: All for one and one for all. Science, 308(6):757, (2005)
2. Alameh, N.: Chaining geographic information Web services. IEEE Internet Computing,
7(5):22-29, (2003)
3. Canfora, G., Penta, M.D., Esposito, R. and Villani, M.L.: A framework for QoS-aware
binding and re-binding of composite web services J. Syst. Softw., 81(10):1754-1769, (2008)
4. Sirin, E., Parsia, B., Wu, D., Hendler, J. and Nau, D.: HTN Planning for Web Service
Composition Using SHOP2. In Proc. Proceedings of 2nd International Semantic Web
Conference(ISWC'03). pp. 20-23. Springer, Sanibel Island, Florida, USA (2003)
5. Koehler, J.: Planning from second principles. Artificial Intelligence, 87(1-2):145-186, (1996)
6. Klusch, M. and Renner, K.-U.: Fast Dynamic Re-Planning of Composite OWL-S Services.
In Proc. Proceedings of the 4th IEEE European Conference on Web Services (ECOWS 2006)
pp. 134-137. IEEE CS Press, Zurich, Switzerland (2006)
7. Recio-García, J.A., Diaz-Agudo, B. and González-Calero, P.A.: A Distributed CBR
Framework through Semantic Web Services. In: Bramer, M., Coenen, F.andAllen, T. (eds.):
In Proc. Proceedings of the Twenty-fith SGAI International Conference on Innovative
Techniques and Applications of Artificial Intelligence, AI 2005. pp. 88-101. Springer (2005)
8. Canfora, G., Penta, M.D., Esposito, R. and Villani, M.L.: QoS-Aware Replanning of
Composite Web Services. In Proc. Proceedings of the IEEE International Conference on
Web Services (ICWS'05). pp. 121-129. IEEE Computer Society (2005)
9. Zeng, L., Benatallah, B., Ngu, A.H.H., Dumas, M., Kalagnanam, J. and Chang, H.: QoS-
Aware Middleware for Web Services Composition. IEEE Transactions on Software
Engineering, 30(5):311-327, (2004)
10.Beyer, H.-G. and Sendhoff, B.: Robust optimization – A comprehensive survey. Comput.
Meth. Appl. Mech. Eng., 196(33-34): 3190-3218, (2007)