“Dynamic Collaboration” from Scientists’ Eyes · “Dynamic Collaboration” from...

Special Issue

NEC Journal of Advanced Technology, Vol. 1, No. 1 17

Advanced Technologies Driving “Dynamic Collaboration” — Featuring System Technologies

“Dynamic Collaboration” from Scientists’ EyesBy Takashi EGAWA,* Masayoshi KOBAYASHI,* Kenji YAMANISHI,†Akira ARUTAKI* and Junji NAMIKI‡

* System Platforms Research Laboratories†Internet Systems Research Laboratories‡Vice President

Considering SCM (Supply Chain Management) as an example, we formulate ‘Dynamic Collabora-tion,’ NEC’s business vision, as a concept of control theory. This enables us to build business

relationships in more flexible manner. Control theory tells us that such deeds make a system unstable if thenumber of business partners is more than a few, but our experience tells us they do not. We believe that in thereal world there are self-organizing processes under which lie dynamics to make structure, and that thanks tothis dynamics, systems become simple enough to be stable. It is therefore more important for us to understandand to control the dynamics than merely to discuss surface architecture. Ubiquity is another importantcomponent for system stability. It enables us quick sense and reaction, thus suppressing internal systemdelays and contributing to stability. We believe this clearly shows that our future lies in ubiquitous computing,and therefore in Dynamic Collaboration.

KEYWORDS Dynamic Collaboration, Control theory, Self-organization

1. INTRODUCTION

At Geneva Telecom 2003 and at every opportunity,NEC has proposed to create new business opportuni-ties through computer-network integration, espe-cially the opportunities created during computer- andnetwork-aided collaboration among ASPs, contentscreators and providers, and a wide range of end users.

The symbol of this proposal is “Dynamic Collabora-tion.” This represents the meat of NEC’s growthstrategy. What is then “Dynamic Collaboration”?

Many of you may say that you have heard of IBM’s“on-demand business.” This TV-famous concept is, inshort, a proposal to provide service resources or aservice itself in an adaptive, agile manner as sup-plies, demands and consumers of businesses changewith the times. This enables their corporate custom-ers to be free of wasteful investment, excessive main-tenance fees, fear of demand outbreaks, and enablescustomers to concentrate on their own businesses.This is absolutely a natural concept for a computerservice company.

Then what will be the concept of NEC, a uniquecompany in the world whose former corporate iden-tity was ‘C&C,’ and which has a wide range of prod-ucts in both the computer and network area, distin-guished track records in maintaining them, andmany satisfied customers? Yes, we can provide cus-tomers not only with basic components such as serv-

ers and storages, but also the ability to integratethem with networks, which optimizes the distributionof shared resources and eventually optimizes the re-lationship among their service partners. This conceptis ‘Dynamic Collaboration.’ In the networks area thisconcept includes corporate networks, mobile commu-nications in which we are the No. 1 in Japan and animportant player in the world, wireless LAN, MetroEthernet, WAN, carrier networks, ADSL with re-markable progress, ITS (Intelligent TransportationSystems) — anything useful is included, of course.

2. HOW CAN WE MODEL DYNAMIC COL-LABORATION?

The left part of Fig. 1 shows a situation wherecompany A is providing a service based on SCM

Fig. 1 Dynamic Collaboration in SupplyChain Management.

ABSTRACT

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(Supply Chain Management) system with companyB, C and D. This SCM system is established in morethan a second, thus usually forming a long-lasting,reliable partnership.

However, the current movement towards open en-vironment and a horizontal division of labor enablesthe company A to procure company B, C and D’sfunctions from all over the world. Company X, Y andZ can be the alternative for B, C and D, for example.The right half of Fig. 1 shows such a situation. Com-pany A hires component functions of their servicefrom all over the world at the speed of light, andprovides their services and enjoys the service forthemselves that is the best regarding cost and perfor-mance at the moment. This right half can be definedas a dynamic SCM if we define the left half a staticSCM.

We will use this an example scenario of DynamicCollaboration, and formulate it and analyze it in ascientific manner.

3. FORMULATING DYNAMIC COLLABORA-TION

Let us assume a collaboration of N companies (asupply chain composed of N components).

In the upper-left of Fig. 2, N black circles (herein-after denoted as ‘nodes’) represents N companies, andthe arcs between them represents their relationships.These arcs can be unidirectional or bidirectional, butwe assume every arc is bidirectional for the simplicityof figures and discussions. Node X1 therefore inter-acts with node Xj through the arc between node X2and node X3, though one might think they are inde-pendent because they belong to node X2 and node X3,

respectively.The lower right of Fig. 2 shows the changed state of

collaboration among companies by applying DynamicCollaboration. What is important is that though thearcs have changed and are changing, each node takesover its internal state (e.g., capital, the surplus, as-sets and liabilities). This is quite natural because thechange in the collaboration does not cause immediatechange of assets and liabilities of each company. Theychange gradually as a result of inputs and outputs ofthe new arcs. In modern control theory these Xs arecalled ‘state parameters.’ In electronic circuits arcsand state variables correspond to memory-less resis-tance R, and capacitance C and inductance L thatmemories electrical charges and magnetic field, re-spectively.

If we thus describe the collaboration between Ncompanies using the N state parameters and the arcsbetween them, the dynamics of the system can berepresented as the result of differential equation X’ =AX as shown in the upper right of Fig. 2. A denotes amatrix of N times N that represents the state of arcsthat connect state parameters. In other words, A is akind of list that shows the status of collaborationamong companies.

As described in the lower left of Fig. 2, DynamicCollaboration enables frequent changes of the arcsbetween companies. This can be described as changesof matrix A as time goes by. Therefore we can formu-late Dynamic Collaboration as a differential equationX’ = A(t)X.

4. DO TRIALS AND ERRORS IN DYNAMICCOLLABORATION CAUSE SYSTEM INSTA-BILITIES?

Analyzing the behavior of a system becomes easierif we formulate it like Fig. 2, however complex thesystem is. Here we analyze the most important prop-erty, stability.

First we consider a ‘modest’ system, where all com-ponents of SCM are obedient to an atmosphere andnever launch severe counterattacks. The curve α inthe upper right of Fig. 3 shows a potential curve ofsuch system, and the system stays at a point wherethe potential is local minimum. The pendulum of aclock at standstill is an example. If the arcs of thesystem are suddenly changed and the matrix A movesto A’, the stable point immediately changes to aninclined point because the balance of forces among Nstate parameters changes. A sudden move of a pendu-lum clock can be a rough figure of speech. If thesystem is modest enough it will converge to a new

Fig. 2 Control theory’s view of Dynamic Col-laboration in Supply Chain Management.

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stable point; how large the variance is. Such a systemis called ‘globally stable’ in control systems.

Let us consider the high-performance systems thatour modern computerized society is based on. Shownin Fig. 4 is a common property of the so-called high-performance systems, e.g., modulation, coding, multi-parameter control mechanisms and so on. The hori-zontal axis shows the conditions for a system andvertical axis shows the performance of the system.The linear line whose inclination is 45 degree repre-sents a natural system without any device, and theperformance increases linearly as conditions becomebetter. High-performance systems provide drastically

better performance than the natural system underthe same conditions. However, the performance im-provement is achieved using various devices withmany assumptions on its conditions, and if the condi-tions go out of these assumptions, system failure oc-curs and the performance becomes worse than thenatural system. This is generally called ‘the effect ofthreshold value,’ and in general the more elegant thedevice becomes, the narrower the acceptable environ-ment becomes. This is the reason why an extremelyhigh-performance system requires extremely precisetuning.

While we are on this subject, if we improve theconditions for natural systems such as analogrecords, there is no limit to its performance improve-ments. On the other hand, the modern digital proc-essing system usually has its limits so that the lim-ited amount of processing power is used efficiently.

We can therefore conclude that the conditions ap-propriate for high-performance system are not global,but local.

Let us go back to the lower right of Fig. 3. If thepotential curve of matrix A changes to A’ as a result ofDynamic Collaboration, the system can successfullygo back to a stable state though the stable region isjust a local one. However, if the potential curvechange to A”, the system will diverge. In general, thematrix A must have a certain property in order toguarantee the stability of an X’ = AX system.

If Dynamic Collaboration really changes the sys-tem in a trial and error manner, it becomes virtuallyimpossible to guarantee the system stability; unlessthe number of companies is only a few, the systemmust become unstable ... but this is not recognized asan important problem. Why?

We are now approaching the essence of DynamicCollaboration.

5. APPROACHING THE ESSENCE OF DY-NAMIC COLLABORATION

As we have mentioned, the stable region of a gen-eral, multi-parameter system is very limited. Can wereally change dynamically the configuration of suchsystems? It looks like a very difficult problem. Wetherefore move our focus of discussions from whethera given system is stable to what is the secret of exist-ing stable systems.

In the center of Fig. 5 many small pendulumsswing in a dependent manner to a large pendulum.This system has many parameters, but in reality thisis a simple system whose behavior is decided solely bythe large pendulum in the center. In the natural

Fig. 3 Potential curve on global stability andlocal stability.

Fig. 4 What is a high-performance system, ingeneral? (modulation, coding, multi-parameter control, …)

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world and in physical phenomena we often observethat many components give up their original indepen-dence and form a simple system. In physics this iscalled ‘pull-in’ or ‘mode.’ This is a phenomenon wherea complicated multi-parameter system degeneratesto a simple system. Many components abandon theirfreedom to acquire total stability and eternity, andsimplify the structure by themselves. Fireflies of akind start to blink on and off one by one towardsevening, but after a while innumerable flies synchro-nize their timing of on and off. Scientists think ithelps each firefly to make fireflies of the opposite sexsense its existence from a long distance if it abandonsits independence and shares the frequency and phaseof the blink. This is the secret that enables the frailfirefly to survive, and this synchronization mecha-nism is built into its DNA and behaves as a key toform ‘structure.’

Forming structure is not a unique property of fire-flies. The upper left of Fig. 6 shows the scenery of aski slope, and we can see many large bumps thatbecome a headache for novice skiers. These bumps donot come from the lay of the slope. Large snowmobilesflatten these bumps every morning. But after a while,free sliding of each skier generates these regularbumps. The wind pattern on the sand in the lowerright is the same kind. All that generate this beauti-ful pattern are gentle sandhill slopes and the windblowing over them. The right picture is a formation ofbirds’ migratory flight. They make such a formationeven though this is not an air show. It is interestingthat the ring of Saturn is thin like a record disk. Thisis the sustainable shape under friction caused bycrashing among the ring’s rocks.

Generally speaking, regularity in gatherings dis-appears as time goes by, as the entropy and the sec-ond law of thermodynamics tells us. But this law

applies only when there is no energy exchange withthe outside. In the ordinary natural world or in abiological environment, there are many exchanges ofenergies such as wind, waves, rain, thermal changescaused by the radiation of the Sun, eruption of volca-noes and hot springs caused by the heat from theearth’s core. Synergetics[1] tells us that in such cir-cumstances, forming a structure leads to energeticstability and sustainability. In other words, the sus-tainable shape corresponds to a particular structure.Initial form may vary, but once the shape has reachedits final form it requires more energy than the finalform and the form becomes the stable point of thesystem.

In business, we can think of maintenance cost asthe energy to operate and maintain a system. If wehave to do something we prefer a simple and cheapsystem. A slightly superior system cannot survive if itis expensive and difficult to use.

In other words, if Dynamic Collaboration makesbusinesses flourish and sustainable, it will nevermake chaotic gatherings of various components. Itmust make a structured group of components. If suchstructure decays it is the time when the life of thatgroup ends and energy exchange stops.

We will next discuss what is the key of the struc-ture.

6. STRUCTURAL CHANGES IN NETWORK AR-CHITECTURE

Let us consider networks as an example of struc-tured group discussed in the previous section, andanalyze its dynamics focusing on the key parameters

Fig. 6 Growth makes structure. Death bringsdecomposition.

Fig. 5 Why not diverge?

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in forming the structure. A network is a much morecomplicated system than fireflies or bumps on skislopes, and it is of course impossible to describe thedynamics to form the structure with limited param-eters, but we focus on them to extract the essence ofthe structure.

6.1 Preparatory Network (1): Hierarchical Network(Traditional Telephone Carrier Networks)

The left part of Fig. 7 shows a hierarchical net-work carefully designed by a designer. A typical ex-ample is a traditional telephone carrier network. Thecharacteristics of this network are in its carefullydesigned planning method. To build a nationwide net-work, for example, a five-year, ten-year, or sometimeseven longer investment plan is made, and each yearswitches, access lines and trunks are installed andservice areas are expanded. This planning method isthe ‘dynamics’ of this network. A typical planningmethod is based on a queuing theory, that is, a prob-ability theory such as random walk. As a result ofthat, a traffic (this is called ‘a call’) generation patternthat comes into an access switch is assumed to be-come a Poisson distribution, a typical distribution outof random process, and its destination also followsanother random process, which means that the prob-ability that the traffic is sent to a trunk switch followsa certain distribution. For example 12.5% of incomingtraffic typically goes to the trunk switch. This formedthe baseline for planning and building of hieraticalnetworks.

The numbering plan also followed the same rule,preparatory planning. If we daringly simplify the de-tails, in Japan all area codes form a hierarchy foreach region though the number of digits varies, as youknow, and if we see the beginning of a phone numberwe can know the destination. In the US the areacodes are always three digits whether its destinationis a big city or countryside, and we can distinguishthe destination access switch with these first three

digits.In old days in Japan and in developed countries,

this dynamics based on preparatory planning wasreally efficient in building a completely new networkfrom scratch. However, if a large housing complexwas suddenly developed, or traffic coming from satel-lite cities was larger than expected, customers wouldhave to wait for lines to be installed or to put up withfrequently busy lines, which would frustrate them. Inreality the preparatory plan was therefore slightlyadjusted every year, e.g., by adding a slant link.

6.2 Preparatory Network (2): Mesh Network (Tradi-tional Computer Networks)

The geodesic network theory that emerged in thelatter half of the ’80s created a stir in the networkplanning theory. Geodesic is an arc that connects twolocations, and this dynamics aims at connecting twolocations by the shortest distance. This resulted inmesh-type networks in which the component nodesare basically of the same kind, and intimate nodes areconnected directly as shown in the right part of Fig. 7.

In the ’90s the stir grew into the discussion on howto build access networks, and on the monopoly ofaccess carrier. However, we think the most directeffect of this dynamics can be found in the Internet inits initial stage. The original Ethernet created byMetcalfe and his colleagues in the ’70s was based onbroadcasting. It was realized by tapping into coaxcables, and this was the most typical method to buildfull-mesh networks. In the ’90s when the 10baseTEthernet was introduced in which a dedicated UTPcable was used to connect each terminal to a hub, adiscussion on how to build virtual full-mesh networkwith this dedicated cables drew wide attention.

It was also in the early ’90s that the purely aca-demic Internet became open to commercial ISPs(Internet Service Providers) and the number of usersexploded. Yes, a network service based on geodesicrouting started; it used already established telephonenetworks, but it succeeded in getting independentbandwidth from voice by using modems and wasbased on the area- and central-independent logicaladdress (IP address). Because of this difference indynamics in physical telephone networks and logicalIP networks, ‘IP networks will corrupt at the end of1996,’ the above-mentioned Internet pundit Metcalfepredicted in 1995. But in reality the Internet nevercorrupted, but flourished even more, and Metcalfehad to eat his prediction paper in front of the public tokeep his vow; some say it was just an American stylestage-managed affair, though.

Fig. 7 Traditional preparatory networks.

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6.3 Naturally Growing Network: Scale-Free Net-works (Can be Seen in Natural World)

The reason why Metcalfe’s prediction of Metcalfewas not realized will be discussed later. Please look atFig. 8 first. This is dynamics called ‘Scale-Free’ thatwas proposed in 1999[2]. We can find many examplesin the natural world and it tries to explain the struc-ture of the World Wide Web, human body, and eventhe structure of the universe, the origin of life andpersonal relationships. The basic idea is that archi-tecture is not the starting point, but that each nodeself-evolves according to dynamics.

Assuming that a firefly is a node, let us explainthis idea using the synchronized blink mentioned pre-viously.

In the world of preparatory planning there is afirefly favored with great charisma, and in a prepara-tory and forceful manner it synchronizes the blink ofits group, e.g., by eating unsynchronized fireflies. Ingeodesic networks the blink never synchronizes andfrom far away a dim light can be seen by the oppositesex fireflies. In a scale-free world, dynamics thatforces each firefly to ‘blink on when I receive anotherfirefly’s light’ is installed in its DNA, and even if allfireflies are of the same kind, they abandon theirindependence and the frequency and phase of blinkssynchronizes.

Readers may have experienced that handclaps tocall for an encore start in a random manner, synchro-nize immediately, and become louder but randomagain if the conductor appears in the wing of thestage. According to the scale-free concept, a once ef-fective synchronization parameter has become inef-fective because the energy order is increased by theappearance of the conductor, and the local stable

point in the potential field has changed.Then why did Metcalfe’s prediction not come true?

The left part of Fig. 9 shows geodesic networks, thestructure of the Internet in its early days. The nodesfrom A to K are routers and switches in IP networksand Web servers in World Wide Web networks plushuman resources such as SIers and operators thatmaintain these computer resources. If the dynamicsthat dominates this network is a purely geodesic one,the structure of the network never changes. Even ifthe number of users drastically increases, the currentservers remain unchanged. But what happened in thereal world? ‘The CPU load is getting higher,’ ‘OK Iwill set up one more server soon,’ a server administra-tor decides, his server response time improves, andthe number of accesses from users increase because ofthe quick response, and so on. This circle makes aWeb server stronger, and eventually that server willbecome a hub of many attended servers. This is whathappened to node E/C/H/J in the right part of Fig. 9.This dynamics also applies to router administrators.As the result of this process a router site with largerouting tables and bandwidth emerges as a hub.

At first glance the right half of Fig. 9 may seem tobe a preparatory, hierarchical network. But the simi-larity lies only in the results and there is no resem-blance in their dynamics. The most important thinghere is that we must understand that what comesfirst is not networks or system architectures, butdynamics that characterizes networks or computersystems, an environment such as markets that ener-gize the dynamics, the system implementation thatrealizes the dynamics.

7. ESSENCE OF DYNAMIC COLLABORATION

As described above, the essence of Dynamic Col-laboration must not be understood as a ready-madearchitecture, but as a ‘container of dynamics’ thateventually forms a certain structure. No matter howbeautifully we draw a pattern on a sandhill, windsand sands will replace them with their own pattern

Fig. 9 Growth toward scale-free networks.Fig. 8 Scale-free networks (growing net-

works common in natural world).

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the next day. The container of the dynamics is, in thisexample, the curve of the sandhill, the kind of sand,its size, the pattern that wind blows and so on. If weapply this way of thinking to business, this is cer-tainly the market environment. The taste of custom-ers, financial environment, season, event, interna-tional relationships, security and so on must all beconsidered, and a system that minimizes energy ex-penses will survive.

This is the essence of our Dynamic Collaboration,and you will now understand that this is far frombeing a discussion of a sort of almighty architecture.

8. THE DIRECT LOAD FROM DYNAMIC COL-LABORATION TO UBIQUITOUS NET-WORKS; DELAY

We have discussed the behavior of large systemsfrom various points of view. Here we focus on anotherimportant parameter for system stability: delay.

Figure 10 is the same as Fig. 2 except that eachinput contains delays (by information transmission,on judgments, on action and so on) and delay τi isconnected in series. This is the situation in real sys-tems.

Figure 11 shows the convergence of a simple, one-parameter system on a X-X’ phase plane. If τ = 0 thesystem converges linearly to stable point, but as τincreases from zero it starts to overshoot during theconvergence, and eventually diverges. It resembles adrunken driver weaving his way and finally crashinginto a guardrail. The delay effect becomes severe inproportion to the ratio between the delay and the timeconstant of the system, that is, how agile the systemis against the control. This means that even a

drunken driver seldom has serious troubles if hedrives at 10km/h speed, but at 60 to 80km/h speed aslight delay of steering will cause a severe crash.

The speed of everything is increasing today. If com-puter- and network-aided Dynamic Collaborationspeeds up everything, for example by enabling thedynamic SCM we used to formulate Dynamic Collabo-ration, we cannot neglect various delays related tothe system any more.

9. WHY DO WE HAVE TO CARE ABOUT DE-LAYS?

Let us analyze this delay problem from a differentpoint of view. The upper left of Fig. 12 shows echoes.The lower left shows the ghosts in TV signals. Thesetwo examples show the reason why delay matters; itgenerates multiple images of the past and makes theappearance of the system more complicated. Theright figure shows a still more different aspect ofdelays (we show these pictures because they are themost impressive accidents). About 50% of traffic acci-dents start as a delay in noticing the danger, whichcauses overshooting and eventually results in errone-ous fatal operation.

Reader might think that the inconsistency of infor-mation, i.e., data error or data loss, causes moretroubles. But in real systems, especially in theInternet, the end-to-end TCP sessions guarantees theintegrity of data by using retransmission. If we canwait long enough such inconsistency can be avoided.We consider this problem that a long delay causes theabove-mentioned inconsistency in the delay, and thisis the main cause of the system instability.Fig. 10 Effect of delay against system stability.

Fig. 11 Effect of delay on convergence.

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If the world of ubiquitous networks we mentionlater arrives, we expect that the number of connec-tions among CPUs in an electronic home applianceswill explode to 10,000 times that of today, as shown inFig. 13. In such large systems, the question of how wecan minimize the delay with minimum cost becomes atechnological challenge.

10. WHY UBIQUITOUS NOW?

As we have mentioned the most important issuesfor the stability of large systems are, first, the num-ber of components and second, delay. Generallyspeaking, if delay is small and if the number of com-ponents is small, a system becomes stable. As theperformance of Dynamic Collaboration improves, op-erational delays by its members and other delaysbecome fatal for system stability. The way to savethis situation is the ‘anytime, anywhere and withanything’ ubiquitous technology. It is indispensableto solve the delay instability of Dynamic Collabora-tion in the future.

Metcalfe’s ominous prediction was avoided by thesuperb operations of SIers and administrators. How-ever, when we create a ubiquitous world in which thenumber of connections increases by a factor of 10,000,we cannot dream that we will have 10,000 timesSIers and administrators. In fact, people have startedto worry about the lack of SIers these days. It is nottoo much to say that the aim of ubiquitous technolo-

Fig. 12 Delay makes a system appear more complicated than it really is(and often leads to fatal erroneous operations).

gies is to solve the system instability caused by theexplosion of the number of system parameters and bythe delay among data exchange coming from the ex-plosion of the number of internal states. Fig. 14shows this claim.

Figure 15 depicts our overall remarks; one lineshows our first findings that a stable system has itsstructure, that is, the so-called self-organization

Fig. 13 Explosive increase in number ofstates in ubiquitous world.

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suppresses the system instability caused by the com-plexity. The second curve shows the other findings forwhich the internal delay suppression is also indis-pensable for stability. Let us assume that a key per-son who contributes to the simplification of thedecision-making process is on a business trip or isabsent from his office. If such a situation happens,even if important changes occur in its surroundingenvironment the system cannot make a proper deci-sion, and it becomes impossible to optimize systemparameters. This becomes a problem not only for thesystem itself, but for every system that has a relation-ship with the first system. Moreover, the typical timeto make decisions is seconds in the financial systemsof today. If we try to make a collaborative system thatinvolves such systems, the stability depends whollyon the system’s internal delay. Therefore, for the fu-ture wide-area Dynamic Collaboration that forms an

infrastructure, we must have these two items “self-organization” and “internal delay” in mind.

11. CONCLUDING REMARKS

In the introduction we described the aim of IBM’sOn Demand Business as a proposal to provide serviceresources or service itself in an adaptive, agile man-ner for supply and demand of the business, changeswith the times, and changes of consumers. This en-ables their corporate customers to be free of wastefulinvestment, excessive maintenance fees, and out-breaks of demand, and enables customers to concen-trate on their own businesses.

If you compare this with the sentence we declaredas the essence of Dynamic Collaboration, the essenceof Dynamic Collaboration must not be understood asa ready-made architecture, but as a ‘container of dy-namics’ that eventually forms certain structure. Nomatter how beautifully we draw a pattern on a san-dhill, winds and sands will replace them with theirown pattern the next day. The container of the dy-namics is, in this example, the curve of the sandhill,the kind of sands, its size, the pattern that the windblows and so on. If we apply this way of thinking tobusiness this is certainly the market environment.The taste of customers, financial environment, sea-son, events, international relationships, security andso on must all be considered, and a system that mini-mizes energy expenses will survive.

This is the essence of our Dynamic Collaborationand you will now understand that this is far frombeing a discussion of a sort of almighty architecture.

If you compare these two sentences you will under-stand that Dynamic Collaboration has wider perspec-tive and includes on-demand business. If these sys-tems grow to nationwide or worldwide ones and cometo include quickly fluctuating systems such as finan-cial systems, internal delay becomes the most impor-tant issue. With the declaration that NEC is the onlycompany in the world that can solve this issue, weconclude this paper.

REFERENCES

[1] Herman Haken, Synergetics, “An Introduction:Nonequilibrium Phase Transitions and Self-Organizationin Physics, Chemistry, and Biology,” Springer-Verlag Ber-lin and Heidelberg GmbH & Co. KG, 1983.

[2] Albert-Laszlo Barabasi, “Linked: How Everything Is Con-nected to Everything Else and What It Means,” PerseusPublishing, 2002.

Fig. 14 “Ubiquitous” is indispensable in sup-pressing internal system delay.

Received January 28, 2004

Fig. 15 Wide-area Dynamic Collaborationtherefore requires both!

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* * * * * * * * * * * * * * *

* * * * * * * * * * * * * * *

Takashi EGAWA received his B.Sc. and M.Sc.from the University of Tokyo in 1989 and 1991,respectively, and joined NEC Corporation in1991. He studied self-healing and QoS, andstarted research activities on active networks.He is now an assistant manager and is respon-sible for the research of network traceability.

From 1999 to 2000 Mr. Egawa was a visiting researcher atthe Swiss Federal Institute of Technology Zurich and since2003, he has been a technical editor of the Institute of Elec-tronics, Information and Communication Engineers (IEICE)Transaction on Communications.

Masayoshi KOBAYASHI received his B.E. de-gree in Applied Mathematics and Physics andhis M.E. degree in Applied Systems Sciencefrom Kyoto University, Japan, in 1995 and1997, respectively. He joined NEC Corporationin 1997 and has been engaged in research on

high-speed routers and content delivery networks.Mr. Kobayashi received the Young Investigators Award

from the IEICE in 2002. He is a member of the IEEE.

Kenji YAMANISHI received his M.Sc. andPh.D. degree from the University of Tokyo in1987 and 1992, respectively. He joined NECCorporation in 1987, and is currently a re-search fellow there. From 1992 to 1995 hestayed at the NEC Research Institute Inc. in

Princeton as a visiting researcher. His former research inter-ests were in coding theory and information theory, and cur-rently are in machine learning, data mining and text mining.

Dr. Yamanishi received the best paper award of the Insti-tute of Electronics, Information and Communication Engi-neers (IEICE) in 1990. He chaired IEICE Information-BasedInduction Sciences Technical Group (IBIS-TG) and organizesmany committees, including Computational Learning Theory(COLT).

Akira ARUTAKI received his B.E. degree inelectrical engineering and M.E. in electronicsand communications engineering from TohokuUniversity in 1978 and 1980, respectively.Joining NEC Corporation in 1980, he was en-gaged in research and development of PCM,

ISDN and ATM systems. From 1987 to 1990 he stayed atWashington University in St. Louis, MO, as a scholarshiprecipient, and was transferred to NEC America, Inc. in 1990.After coming back to Japan in 1994, he managed research anddevelopment of IP based communication systems such as rout-ers, Ethernet switches and VoIP, and is currently the GeneralManager of System Platforms Research Laboratories coveringR&D activities in Broadband, Mobile, Computer, and Storagearenas.

Mr. Arutaki received the best paper award of the Instituteof Electronics, Information and Communication Engineers(IEICE) Network Systems Technical Group in 1999. He is a co-author of “Multimedea ATM-LAN (in Japanese)” publishedfrom Tricepts in 1994. The author is a member of the IEICE ofJapan and the IEEE.

Junji NAMIKI received his B.E., M.E. andPh.D. degree in communication engineeringfrom WASEDA University, Tokyo in 1970,1972 and 1985 respectively. He joined NECCorporation in 1972. Since then he has beendoing research and management of satellite,

mobile, high capacity digital microwave, VSAT, satellite LANand optical communications systems. He is currently a vicepresident there, supervising the research and developmentdivision. He is also a director for conference and education ofthe Institute of Electronics and Communication Engineers(IEICE) of Japan.

Dr. Namiki received the young investigators award fromthe IEICE in 1978, national patent prize in 1990 and wasreceived an award as an excellent researcher from the Ministryof Science and Technology of Japan in 1996.

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