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Cell[TextData[{
 "Rudolf J. ",
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  FormBox["Strijkers", TraditionalForm]],
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Cell[TextData[{
 "Robert J. ",
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  FormBox["Meijer", TraditionalForm]],
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Cell["\<\
University of Amsterdam, Amsterdam, The Netherlands
TNO Informatie- en Communicatietechnologie, Delft, The Netherlands
strijkers@uva.nl, robert.meijer@tno.nl\
\>", "Address",
 CellChangeTimes->{{3.4161290798333273`*^9, 3.41612915471878*^9}, {
  3.4172904518571167`*^9, 3.417290548041507*^9}}],

Cell["\<\
We have developed a concept that considers network behavior as a collection \
of software objects, which can be used or modified in computer programs. The \
interfaces of these software objects are exposed as web services and enable \
applications to analyze and manipulate networks, e.g. to find articulation \
vertices or configure non-default routes. This article describes the software \
that allows Mathematica to optimize networks and applications in a continuous \
cycle of monitoring, analysis and adaptation. Here, the full collection of \
Mathematica algorithms becomes available to calculate the next optimal \
configuration. These algorithms use parameters such as energy consumption, \
application performance or bandwidth consumption as input. As a result, \
Mathematica can automatically adapt the network to maintain or provide (sub-) \
optimal or \[OpenCurlyQuote]better-than-best-effort\[CloseCurlyQuote] \
services. Furthermore, integration of networks with Mathematica allows a \
multi-scale optimization approach, where local behavior is adapted to support \
global goals. The presented work makes extensive use of Mathematica\
\[CloseCurlyQuote]s Dynamics and Combinatorica Package, but also of web \
services and J/Link.\
\>", "Abstract",
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Cell["Introduction", "SectionFirst"],

Cell["\<\
Next-generation networks open up service interfaces of individual network \
elements to allow distributed and networked applications more control over \
network behavior. The foremost reason to allow more control is that the \
resource consumption of these applications should be in balance with the \
resources networks can provide. Any imbalance directly impacts network \
performance and perceived quality of experience (QoE). Typical examples that \
rely on good QoE include VoIP and video streaming, but also communication \
intensive supercomputing tasks. Therefore, networks need to orchestrate \
traffic to achieve 'better than best-effort' services, i.e. optimize resource \
utilization to meet robustness or efficiency requirements.\
\>", "Text",
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Cell["\<\
The need to have more control over network behavior can also be found in \
recent developments in photonic and hybrid networks [1]. Photonic switches \
connect ports to form light paths and have no knowledge of traffic flows. One \
of the main issues is how to orchestrate the configuration of photonic paths \
according to traffic flows that enter the network. This issue motivates \
research in multi-scale optimization, where technologies at different layers \
in the OSI reference model have to cooperate to fully utilize the network \
infrastructure.\
\>", "Text",
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Cell["\<\
Our research focuses on the development of architectures and models that \
facilitate multi-scale adaptation and \[OpenCurlyQuote]better than \
best-effort\[CloseCurlyQuote] services. In [2], we presented User \
Programmable Virtualized Networks (UPVN), an architectural framework that \
enables interworking between applications and networks. In this framework, \
application-specific network services can be implemented in the form of \
component-based software objects, potentially modifying or adding behavior at \
different layers of the OSI reference model. These objects are typically \
accessible through web service interfaces and can be included as part of \
distributed and networked programs. Using these objects, such programs can \
orchestrate network resources to meet their needs. Furthermore, the framework \
allows not only applications to orchestrate network resources, but also \
enables the network to run self-adaptation programs.\
\>", "Text",
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Cell["\<\
Mathematica\[CloseCurlyQuote]s environment is well suited for solving the, \
often combinatorial, problems in networks. In cases where combinatorial \
solutions are unfeasible or not possible due to the unpredictable nature of \
the network, trial and error algorithms can be used. However, trial and error \
algorithms require middleware support to maintain front-end interactivity \
with the Mathematica kernel. By providing this middleware and integrating it \
with Mathematica and UPVNs, Mathematica programs can continuously adapt \
network behavior.\
\>", "Text",
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Cell["\<\
This article is organized in three parts. The first part describes the use of \
web services to facilitate the integration between network services and \
applications in current state-of-the-art networks and utility computing \
environments. It also presents the concepts and implementation of UPVN and \
describes the software architecture for integration with Mathematica. The \
second part demonstrates experimental results by example and the last part \
reflects on the results and provides directions for future work.\
\>", "Text",
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Cell[CellGroupData[{

Cell["\<\
Web Services in Telecommunications and Virtual Operating Systems\
\>", "Section",
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   3.416567092028542*^9}],

Cell["\<\
Service Oriented Architectures (SOA) and web services as its implementation \
technology is becoming increasingly common in networks [3]. The devices \
within the network expose their functions as web services and by combining \
these; new, application-specific services can be composed. Web services \
enable high flexibility in network infrastructures and give the operator the \
ability to adapt services when needed. The usefulness of this approach can be \
observed in the many web service based deployments of large-scale hybrid \
networks that offer users the ability to control parts of the infrastructure \
[4, 5]. In these hybrid network deployments, however, the network functions \
remain static. Their primary service is to facilitate circuits between \
applications or computing clusters.\
\>", "Text",
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Cell["\<\
Web services are also becoming available in utility computing environments to \
orchestrate resources, the most notable examples are Xen [6] and VMware [7]. \
VMware allows monitoring and control of the whole spectrum of virtual and \
physical resources through web services. Consequently, the whole utility \
computing environment can be optimized by specialized applications such as \
Mathematica.\
\>", "Text",
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}, Open  ]],

Cell[CellGroupData[{

Cell["Software Framework", "Section",
 CellChangeTimes->{{3.416567140211376*^9, 3.4165671437620277`*^9}}],

Cell["\<\
The UPVN model (Figure 1) describes architectural principles for programmable \
network services. UPVNs enable applications to upload code to network \
elements (NE) in the form of software objects called application components \
(AC). ACs allow implementation of not foreseen and application-specific \
behavior and allow computer programs to access their service interfaces \
through network components (NC). NCs act as proxies that provide redirection, \
virtualization or composition of multiple AC service interfaces. The manner \
in which NCs are exposed to applications such as Mathematica is \
application-specific, and technology choices and AC to NC interfacing depend \
on the network\[CloseCurlyQuote]s application domain. For example, when the \
network is under full control of a single owner, it is not necessary to \
support multi-user mechanisms or a complex security infrastructure. NCs can \
offer AC services through a synchronous or asynchronous interface. With the \
synchronous interface, applications interact with UPVNs by instantiating the \
appropriate NCs that act as remote procedure call proxies. In the \
asynchronous interface, the application, network or middleware implements an \
event mechanism that continuously polls ACs and fires events in case of state \
changes.\
\>", "Text",
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Cell[TextData[{
 StyleBox["Figure 1. ", "SB"],
 "The UPVN model defines the elementary functions to get information from, \
use, and adapt network resources in computer programs. "
}], "NumberedFigureCaption",
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Cell["\<\
Figure 2 shows a practical implementation architecture for integrating \
Mathematica in UPVNs. From Version 5 on, Mathematica supports synchronous \
interaction with web service enabled networks. The kernel architecture, \
however, does not allow simultaneous monitoring, visualization, and \
manipulation. Therefore, Java middleware implements an event mechanism that \
supports asynchronous updates. It enables the frond-end to remain responsive \
while providing continuous updates to the kernel. With this mechanism, a user \
can write a network visualization program with Dynamics in one notebook, for \
example, to display a continuously updated graph with marked articulation \
vertices while calling NC functions to manipulate the network in another.\
\>", "Text",
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Cell[TextData[{
 StyleBox["Figure 2. ", "SB"],
 "Notebooks (1) allow users to visualize, write and prototype programs for \
network manipulation or analysis in an interactive manner. The programs \
interact with the network through a UPVN Package (2), which implements the \
basic functions, such as data and type conversion to interact with \
Mathematica. The UPVN Package communicates through web services (3) with ACs, \
such as a topology AC (4). Through the same interface (2), asynchronous \
interaction is handled. Java middleware takes care of the updates in the \
MathKernel (5) through MathLink (6). Notebooks can update or programs can \
adapt the network in a continuous cycle with Dynamics."
}], "NumberedFigureCaption",
 CellChangeTimes->{{3.416659954041294*^9, 3.416659971646822*^9}, 
   3.416660008761888*^9, {3.416819384332294*^9, 3.416819416849841*^9}, {
   3.417369996487113*^9, 3.41736999928832*^9}, {3.417428169762226*^9, 
   3.4174282067634172`*^9}},
 CellTags->"Fig 2"],

Cell["\<\
We have built a test bed to conduct experiments and to validate the UPVN \
model [2] on a real network. In the test bed, ACs act and operate on physical \
resources. Because an application interact with the network through NCs, \
simulated or real network behavior is transparent to it. Therefore, \
simulating a network, which would otherwise be difficult to setup or build \
physically, can be useful in prototyping adaptation algorithms for example. \
However, as in any simulation environment care should be taken when making \
assumptions.\
\>", "Text",
 CellChangeTimes->{3.417369982803359*^9}]
}, Open  ]],

Cell[CellGroupData[{

Cell["Network Modelling", "Section",
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For any visualization, analysis or adaptation problem there is an underlying \
model that transforms the measured properties into a useful representation. \
It is not necessary to model all the intricate details and the full scope of \
the network for the representation to be useful and the scope of what control \
is limited by what can be successfully managed. For example, a large \
collection of NEs can be load-balanced to macroscopic network properties such \
as throughput. However, when adapting NEs to service requirements of many \
applications at once the amount of NEs under control of one application will \
be relatively small. \
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In many adaptation problems, knowing the current topology is a primary \
requirement. This information can be static and provided as input, discovered \
or a combination of both, and the UPVN may facilitate different methods of \
retrieval. A basic discovery strategy consists of three parts, which may be \
combined to improve efficiency.\
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1.\tDiscover connected NEs
2.\tQuery NEs for neighbors
3.\tConstruct Mathematica representation\
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Reachable NEs can be found by a breadth-first search. To support the \
breadth-first search, every NE in the UPVN test bed provides a topology AC, \
which implements neighbor discovery with Nmap [8]. By scanning the UPVN web \
services port, it also detects if a NE offers AC functions. Another topology \
AC function takes the discovered NEs as input and queries their IP links. \
When needed, a conversion routine translates the IP addresses to id\
\[CloseCurlyQuote]s and back. With these functions, the full IP topology can \
be discovered and used in Mathematica. The code snippet below shows the \
breadth-first search code and illustrates how the network calls are embedded.\
\
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DiscoverNetworkElements finds the neighbor UPVN NEs of the calling NE. Remote \
is a special function in the topology AC. It calls a remote web service from \
the host it executes on. This way, the AC supports a recursive search from a \
single NE. Note that disconnected elements will not be found. The web service \
calls return strings and need to be converted to the correct Mathematica data \
types. Type conversion can be abstracted away from the programmer in the NCs.\
\
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Once the network topology is acquired and a representation is constructed, \
the full range of Mathematica\[CloseCurlyQuote]s environment becomes \
available for network visualization, analysis and adaptation. We consider two \
common categories for network related problem solving. The first category \
uses combinatorial methods. Mathematica\[CloseCurlyQuote]s Combinatorica \
Package provides many combinatorial algorithms that can be applied without \
modification. The second category uses a trial and error approach. It uses \
historical data to converge to an (sub-) optimum and requires a continuous \
cycle of monitoring, analysis and adaptation. In addition, visualization of \
information flows and network configurations can aid in problem solving.\
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The edge weights were obtained by performing a bandwidth probe on the \
discovered IP links. The topology AC uses Iperf [10] for performance \
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Cell["\<\
Consider the network given by Figure 3. It shows the discovered IP topology \
of the UPVN test bed together with results of bandwidth measurements. Now, \
with the aid of the Combinatorica Package it is possible to route traffic in \
exotic ways. For example, to broadcast a stream to all nodes in a network the \
minimum spanning tree can be used. The result of the calculation can be \
configured in the network in various ways. In the test bed, it was done by \
tagging application traffic and adding forwarding rules for the tags. The \
following code shows some examples and how forwarding rules are generated and \
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Now generate the minimum spanning tree and show the NC calls to configure the \
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The program defines a list of routes and the method of path finding. All the \
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The next program shows a straightforward way to find edge disjoint shortest \
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Cell["\<\
The previous examples calculated the precise network configuration. In the \
trial and error approach the adaptations ultimately converge to a better \
state. This approach can be divided in three stages and correspond with an \
elementary feedback loop well known in control theory (Figure 4). The first \
stage builds a representation of the problem space by retrieving the \
necessary information from the network. The second stage applies a decision \
process to find a next better state and the third stage reflects its results \
by adapting the network. 
This example illustrates the stages with a trial and error approach to \
load-balance traffic in a routed network. It introduces the idea of \
attractors and repulsors, which indirectly modify forwarding rules. A \
decision model decides where to place specific attractors or repulsors based \
on throughput measurements. This is an approach to load-balancing inspired by \
physics [11].\
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 "The trial and error approach corresponds with an elementary feedback loop. \
Monitoring functions and configuration options provide input to form a state \
representation. The result of a decision process based on the state is \
actuated with control functions. Monitoring and control functions can be part \
of different ACs."
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Cell["\<\
In the Java middleware, one thread polls the network and another updates \
variables in Mathematica using Set[]. The following code simulates network \
throughput by generating two shortest paths and at each NE summing the \
crossing paths. The generated list is normally updated by Java.\
\>", "Text",
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The decision model is straightforward. It finds the NE with the maximum \
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The following code puts it all together and the result is visualized in \
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 "The first picture shows the ititial state, and the second picture shows the \
state after ten iterations. The area around NE four and five have become \
unattractive enough to change the path of (1,8)."
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Cell[TextData[{
 "It is out of the scope of this paper to provide a full analysis of this \
approach. However, it does demonstrate that network modelling can almost \
entirely be done in ",
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 "and use ACs for monitoring and control only."
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Cell["\<\
When networks support the elementary functions of the UPVN framework, users \
can develop programs to change and optimize network behavior. Network \
services are implemented as software objects that expose their interfaces as \
web services, which can be consumed to develop new or modify existing network \
behavior. In this article, the emphasis was on the communication between the \
network and Mathematica.\
\>", "Text",
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Cell["\<\
In our experiments, we have found three limiting factors in centralized \
network adaptation. The first limitations are the scope and time-scale of \
adaptations. In the UPVN test bed, a single Mathematica instance controls \
network behavior. Since in practice communication between ACs and Mathematica \
was in the range of seconds, it puts a bound to what can be monitored and \
adapted simultaneously. Third, to support real-time interactions with \
Mathematica, the J/Link interface provided a work-around by offloading \
complex real-time network communication from the kernel. It remains to be \
seen if this approach allows large and frequent updates and how this affects \
the front-end responsiveness. Tests upon now were successful in update cycles \
of seconds, which is already good enough to address many problems.\
\>", "Text",
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Cell["\<\
The next step is to embed Mathematica in NEs and explore distributed \
adaptation algorithms. It allows Mathematica to orchestrate the network at \
different levels and scopes and allow the development of adaptation \
hierarchies each with their own adaptation algorithm. The question here is: \
what if network adaptation programs are distributed over the network?\
\>", "Text",
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Cell["\<\
The presented concepts and software framework are currently being applied in \
two applications. The first application integrates Mathematica with \
StarPlane's middleware for light path visualization and control. Using our \
software framework, Mathematica can proactively switch light paths based on \
traffic measurements at the edges of a computing cluster. Shortest edge \
disjoint paths calculated from Mathematica will be used to switch and \
aggregate additional paths when clusters overload one path. The second \
application uses Mathematica to visualize, analyze and automatically adapt a \
VMware infrastructure. In a continuous loop, a linear program will calculate \
the optimal placement of VMwares considering their power consumption and \
adapt the VMware infrastructure to reflect the new optimal state.\
\>", "Text",
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Cell[CellGroupData[{

Cell["Conclusion", "Section"],

Cell["\<\
Mathematica\[CloseCurlyQuote]s powerful symbolic programming language, large \
collection of algorithms and its ability to interface with Java and web \
services makes it a suitable research tool for the development of \
\[OpenCurlyQuote]better-than-best-effort\[CloseCurlyQuote] network services. \
By utilizing UPVNs, Mathematica programs can move applications, reprioritize \
traffic or reroute connections to achieve optimal network performance in a \
single integrated environment. This aspect enables interaction between \
different adaptation programs and opens the way to multi-scale network \
optimization.\
\>", "Text",
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Cell[CellGroupData[{

Cell["References", "Section"],

Cell["\<\
[1]\tC. Riziotis and A. V. Vasilakos, \"Computational intelligence in \
photonics technology and optical networks: A survey and future \
perspectives\", Information Sciences, vol. 177, pp. 5292-5315, 2007.\
\>", "Reference",
 CellChangeTimes->{{3.417370629161675*^9, 3.417370652584778*^9}},
 CellTags->"Ref1"],

Cell["\<\
[2]\tR. J. Meijer, R. J. Strijkers, L. Gommans, and C. de Laat, \"User \
Programmable Virtualized Networks\", in Proceedings of the Second IEEE \
International Conference on e-Science and Grid Computing: IEEE Computer \
Society, 2006.\
\>", "Reference",
 CellChangeTimes->{{3.4173706447831593`*^9, 3.417370660744657*^9}},
 CellTags->"Ref2"],

Cell["\<\
[3]\tD. Griffin and D. Pesch, \"A survey on web services in \
telecommunications\", IEEE Communications Magazine, vol. 45, pp. 28-+, Jul \
2007.\
\>", "Reference",
 CellChangeTimes->{{3.417370672405847*^9, 3.4173706802564173`*^9}},
 CellTags->"Ref3"],

Cell["\<\
[4]\tE. Grasa, G. Junyent, S. Figuerola, A. Lopez, and M. Savoie, \"UCLPv2: A \
network virtualization framework built on web services\", IEEE Communications \
Magazine, vol. 46, pp. 126-134, Mar 2008.\
\>", "Reference",
 CellChangeTimes->{{3.417370690389435*^9, 3.417370694650339*^9}},
 CellTags->"Ref4"],

Cell["\<\
[5]\tP. Grosso, L. Xu, J. P. Velders, and C. de Laat, \"StarPlane - a \
national dynamic photonic network controlled by grid applications\", Internet \
Research: Electronic Networking Applications and Policy, vol. 17, pp. \
546-553, 2007.\
\>", "Reference",
 CellChangeTimes->{{3.417370704225327*^9, 3.417370711631448*^9}, {
  3.4174257470140667`*^9, 3.417425814839634*^9}, {3.4174259019673758`*^9, 
  3.417425922156917*^9}, {3.417426390188649*^9, 3.417426405978011*^9}},
 CellTags->"Ref5"],

Cell["\<\
[6]\tXen Hypervisor, http://xen.org (accessed at 20 April 2008)\
\>", "Reference",
 CellChangeTimes->{{3.417370720203577*^9, 3.417370724857542*^9}},
 CellTags->"Ref6"],

Cell["\<\
[7]\tVMWare, http://www.vmware.com (accessed at 2 August 2007)\
\>", "Reference",
 CellChangeTimes->{{3.417370734928172*^9, 3.417370738350718*^9}},
 CellTags->"Ref7"],

Cell["\<\
[8]\tNetwork Mapper, http://nmap.org (accessed at 7 April 2008)\
\>", "Reference",
 CellChangeTimes->{{3.41737074587119*^9, 3.4173707494784117`*^9}},
 CellTags->"Ref8"],

Cell["\<\
[9]\tIperf, http://dast.nlanr.net/Projects/Iperf/ (accessed at 20 April 2008)\
\
\>", "Reference",
 CellChangeTimes->{{3.4173707557358637`*^9, 3.417370759230041*^9}},
 CellTags->"Ref9"],

Cell["\<\
[10]\tJ. W. Suurballe, \"Disjoint paths in a network\", Networks, vol. 4, pp. \
125-145, 1974.\
\>", "Reference",
 CellChangeTimes->{3.4173707660792313`*^9},
 CellTags->"Ref10"],

Cell["\<\
[11]\tS. Toumpis, \"Mother nature knows best: A survey of recent results on
wireless networks based on analogies with physics\", Computer Networks, vol. \
52, pp. 360-383, 2008.\
\>", "Reference",
 CellChangeTimes->{{3.417410927804837*^9, 3.417410931251976*^9}},
 CellTags->"Ref11"],

Cell["\<\
Rudolf J. Strijkers (1981) received a BSc and MSc degree in computer science \
from the University of Amsterdam. He currently pursues a PhD degree at the \
same university and works for the Dutch institute for applied scientific \
research TNO. His scientific interests are in adaptive, programmable and \
intelligent networks. He uses Mathematica to implement and test new concepts \
and ideas.\
\>", "TextAboutAuthor",
 CellChangeTimes->{{3.416567929019237*^9, 3.4165681214207993`*^9}, {
   3.416568155716578*^9, 3.4165681626924877`*^9}, 3.416568483212173*^9, {
   3.416670982728285*^9, 3.416671067505764*^9}, {3.416671097624679*^9, 
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   3.4173574187763977`*^9, 3.4173574252992973`*^9}, 3.4173672848197937`*^9}],

Cell["\<\
Robert J. Meijer (1959) received a PhD degree in experimental nuclear physics \
of the University of Utrecht. He switched to ICT research at TNO in 1991 and \
became professor at the University of Amsterdam in 2002. His scientific \
interests are next generation networks and computer technologies with \
application-specific dynamic adaptation capabilities.\
\>", "TextAboutAuthor",
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