On the Impact of Fractal Organization on thePerformance of Socio-technical Systems

Vincenzo De Florio∗, Hong Sun†, Jonas Buys‡, Chris Blondia§∗PATS research group

University of Antwerp & iMinds Research InstituteMiddelheimlaan 1, 2020 Antwerpen, Belgium

Email: vincenzo.deflorio@uantwerpen.be†AGFA Healthcare

100 Moutstraat, Gent, BelgiumEmail: hong.sun@agfa.com

‡PATS research groupUniversity of Antwerp & iMinds Research Institute

Middelheimlaan 1, 2020 Antwerpen, BelgiumEmail: jonas.buys@uantwerpen.be

∗PATS research groupUniversity of Antwerp & iMinds Research Institute

Middelheimlaan 1, 2020 Antwerpen, BelgiumEmail: chris.blondia@uantwerpen.be

Abstract—Fractal organizations are a class of bio-inspired dis-tributed hierarchical architectures in which control and feedbackinformation are allowed to flow independently of the position theparticipating nodes have in the system hierarchy. In this paper wediscuss the adoption of a fractal organization in a class of socio-technical systems characterized by a centralized architecture. Wepresent the key architectural traits of the resulting Fractal SocialOrganization and put forward our conjecture that services basedon the presented solution may exhibit significant improvements,e.g., in terms of scalability and performance. In order to provideelements to justify our conjecture we describe how we envision theuse of the new organization in two different cases: a frameworkfor semantic service description-and-matching and a low-costtelemonitoring service.

I. INTRODUCTION

In our past research we proposed a concept called MutualAssistance Community (MAC) [1], [2], [3]. In a nutshell, aMAC is a socio-technical system coupling services providedby assistive cyber-physical things with collaborative servicessupplied by human beings into an alternative social organi-zation for the ambient assistance of the elderly population.Later said concept was extended into a so-called Service-oriented Community (SoC) [4] so as to include other classesof services—for instance crisis management and civil defense.Both said concepts are based on similar architectural “axioms”:

• Social actors are modeled as peer entities. No pre-defined classification is introduced; in particular rolessuch as clients and servers or service requesters andservice providers are replaced by the simpler role ofmember. Members are not locked in [5] a requesteror provider role. A member’s actual behavior is onlydecided by the current context. As an example in thedomain of healthcare members may be care-givers ata given time and care-takers at another time.

• Semantically annotated services and requests for ser-vices are published into a service registry and triggersemantic discovery of optimal responses [6].

• Responses are constructed making use of the availablesocial resources as well as the current context knowl-edge with the goal of optimizing both individual andsocial concerns.

A major aspect of both MAC and SoC is given by theassumption of a “flat” society: a cloud of social resourcesare organized and orchestrated under the control of a central“hub”—a so-called service coordination center (SCC).

As common to any centralized architecture, the center ofthe system is likely to become a single-point-of-failure anda single-point-of-congestion. Evidence to the above statementwas brought by analyzing the performance of our systemunder increasingly turbulent conditions [6]. In particular in thecited reference we showed how service matching when dealingwith more than 10,000 entries implied severe performance andscalability failures (results were obtained with a SPARQL / N3architecture on a conventional PC).

Due to the above limiting result we set to consider alterna-tive solutions beyond the pure centralized approach. Lessonswere learned by modeling the social activity that characterizesflat societies of roles [7], [8]. We showed how the dynamicevolution of the enacted social elements could be modeled as adynamic system governed by a simple combinatorial function.By defining geometrical representations for said system wecould observe how the flat society gives raise to noteworthytraits, among which the spontaneous emergence of hierarchicalstructures, modularization, and self-similarity (patterns or rolesself-replicating at different scales.)

Inspired by the above result, in the cited references weintroduced the above traits into a novel social organization. Byconstruction, the new design adopts a hierarchical architecturein which a same node—modeled as our original Service-oriented Community—is repeated at different scale throughoutthe layers of the hierarchy. A same set of rules is enacted ateach layer so as to govern inter-layer and intra-layer socialcollaboration. The resulting architecture is that of a fractalorganization [9], [10], [11] that we called Fractal SocialOrganization [8].

Aim of this paper is reporting on some preliminary resultsand lessons learned while making use of our Fractal SocialOrganizations (FSO). This is done first by recalling in Sect. IIthe major characteristics of FSO. After this we consider twoongoing experiences. In the first case, reported in Sect. III,we focus on SSDM and provide the elements of a novelsemantic framework to manage service matching according tothe FSO principles. Preliminary experiments conducted withcomputer-generated activity graphs show that the FSO mayhave a significant impact on reducing the performance andscalability limitations that we experienced with the MAC andSoC. Section IV introduces our second experience by brieflydescribing a recently started Flemish research project that aimsat the design of a low-cost, non-intrusive monitoring solutionfor tele-monitoring services. Such solution shall be based on apredefined and static fractal social organization. In particularwe report how we envisage the FSO to play a key role inoptimizing quality vs. costs dynamic trade-offs. Conclusionsand a view to some future work are finally drawn in Sect. V.

II. FRACTAL SOCIAL ORGANIZATIONS

Fractal Social Organizations (FSO) is the name of a novelclass of socio-technical systems characterized by a distributed,bio-inspired, hierarchical architecture [7], [8]. Though funda-mentally hierarchical, FSO is not based on the classic top-downflow of control and bottom-up flow of feedbacks (autocracy)but rather on a more peer-to-peer approach where every nodein the hierarchy may play both management and subordinateroles depending on the situation at hand (sociocracy). Nodes inFSO hierarchies are in fact similar to sociocratic circles [12] orto the members of Service-oriented Communities and MutualAssistance Communities [4], in that they allow control andinformation to flow in any direction of the hierarchy. A fixedset of rules (called “canon” in fractal organizations [13], [10],[11]) regulates the spontaneous emergence and in general thelife-cycle of “social overlay networks” (SON). Said SON aremade of those nodes in the FSO hierarchy that are “electri-fied” [14] by the onset of some novel condition s—for instancethe awareness of a new threat or opportunity. In other words,SON represent dynamic aggregates of entities, both physicaland computer-based, that unite to enact a collective responseto s. In what follows we shall refer to those responses as to aSON’s “fired activities”.

As an example scenario, an elderly woman falling in hersmart house may call for the service of a detecting device—typically an accelerometer. This triggers the creation of aninitial SON: S0 = {elderly woman, accelerometer}. The newlycreated SON may deal with the fall event, e.g., through thefollowing fired activity: “trigger an alarm and enrol the serviceof a general practitioner”. This leads to changing the initial S0

Fig. 1. Space of all sub-communities of a society consisting of 3 rolesplayed respectively by 1, 2, and 3 individuals. The rendering is done with thePOV-Ray raytracer [16].

into an S1 = S0∪{GP}. The GP then may in turn request theintervention of other entities, e.g., a nurse and an ambulance,which then leads to a S2 = S1 ∪ {nurse, ambulance}. As aresult of this dynamic process and the enacting of the corre-sponding fired activities, SON may change their compositionand may shrink or grow in number. A formal way to representthis process is that of a random walk through the space of allpossible social elements in the current node. Figure 1 showssuch space for a society of six nodes (for instance, six people)1.

Enrollment is in fact the process by means of whichthe above mentioned SON self-develop. It may be conciselydescribed as the action of locating and appointing roles tothe available cyber-physical entities. A formal description ofactivities, roles, and enrollment processes is out of the scopeof this paper and may be found in [8]. Enrollment is carriedout in FSO, MAC, and SoC, via semantic service descriptionand matching (SSDM) as described in [6], [7]. SSDM is in factthe “architectural cornerstone” all the socio-technical systemsour paper focuses on are built upon.

Let us refer to either SoC or MAC as to a Community. Amajor difference of the FSO with respect to both Communitiesis the way said enrollment process is carried out. In SoC andMAC this is done through a central entity (the SCC) thatworks as a “hub” receiving and servicing all the availableand requested services published by its members. In particulareach new submitted entry triggers a semantic match with allthose related entries that are already known to the SCC. Ifa satisfactory match can be found within the Community theactivities requiring the found role can be launched. If that isnot the case the SCC just re-enters its main processing loopand waits for a new publication.

Enrollment in the FSO takes place through inter- andintra-layer collaboration. In the FSO we have a hierarchy oflayers each node of which is organized as in a Communitywhose SCC (predefined or elected by the participating nodes)

1Videoclips and pictures of this and other societies may be accessed via [15].

Fig. 2. Exemplary Fractal Social Organization. Note how the shape repro-duces the well known Sierpinski triangle [18].

represents the whole node2. When executing the enrollmentphase in an FSO such as the one exemplified in Fig. 2 a missingrole in one node triggers a so-called “exception” [8]: theSCC realizes that the sought role is currently unavailable andpropagates the event to the next level upward in the hierarchy.This goes on until the first suitable candidate member forplaying the required role is found or until some “floodingthreshold” is met. This creates a sort of inter-layered, orbi-dimensional social overlay network whose nodes are notrestricted to a single layer but can span across multiple layersof the FSO. This rule corresponds to the Double Linking ruleof sociocracy [12] in that it allows the restrictions of purehierarchical organizations to be overcome. This is done bycreating a temporary means for entities situated at differentlayers to cooperate by creating a new structure complementaryto the FSO and its nodes. The new structure is in fact anew ad hoc Service-oriented Community whose objective andlifespan are determined by the fired activity.

In the following section we shall focus on the impact thatthe fractal organization of the FSO has on the performance ofSSDM in “flat” (viz., single-layered) centralized architectures,namely our Communities.

III. FIRST CASE: FRACTAL ORGANIZATION OFSEMANTIC SERVICE MATCHING

In [6] we introduced the design of a mutual assistancecommunity in which service publication and service discoveryare executed with a SPARQL [19] endpoint. A simple servicedescription is exemplified in Fig 3. The SPARQL endpoint isbuilt with Fuseki [20], which allows services to be publishedeither in memory (through the in-memory graph store) or ondisk (via TDB [21]). Setting up a SPARQL endpoint withFuseki using in-memory graph store has several advantages;in particular it avoids the necessity to set up a dedicated graphstore. On the other hand, the use of in-memory graph storealso places a restriction on the size of the graph that may bemanaged by the single SCC of the MAC. As a consequence ofthis, the amount of services that can be effectively accommo-dated by the endpoint is limited (as discussed in Sect.III-A).

2This process is called personization and is known in Actor-Network Theoryas “punctualization” [17].

Through the fractal organization of the FSO the abovementioned limitation can be reduced, if not fully overcome,thanks to the fact that services are not published globallybut only in the originating layer. Each layer has its ownSCC that manages only a portion of the total amount ofservices published in the system. This inherent partitioningalso reduces the workload of the SCC and therefore alsothe probability that it turns into a single-point-of-congestion.Moreover the availability of multiple autonomous SCC reducesthe consequences of failures, as a failed SCC results in a(temporary3) network partitioning instead of a global failure.

Figure 4 shows the semantic framework that we used tointroduce the FSO concept in our MAC. As can be seen fromthat picture, the Community is decomposed into a distributedhierarchy of sub-communities whose members may also in-clude other sub-communities. An important consequence ofthis reorganization is that service requests are propagatedupward in the hierarchy only if results are not found in thelocal sub-community.

SPARQL endpoints are set up for those sub-communities atthe bottom layer of the hierarchy tree, exemplified by the layer-1 communities in Fig. 3. Service publications and discoveryactions is done through the SPARQL endpoints to explore theresources in the related community.

For the sub-communities on a higher layer, a virtualSPARQL endpoint is set up. In so doing the services publishedin the sub-communities can be queried through a SPARQLfederated query. Figure 5 shows a sample federated query tolook for services published in two sub-communities. Lines9–21 and 23–36 specify queries to two sub-communities viatheir SPARQL endpoint respectively. The results from the twospecified endpoints are aggregated together by the UNIONstatement in Line 22. The aggregated results are returnedwith the construct statements listed in Lines 3–7. The virtualSPARQL endpoint may also access context information exter-nal to the Communities by querying so-called Live Data [23]SPARQL endpoints.

A. Preliminary experiments and a few remarks

The already mentioned Fuseki is a Jena SPARQL serverwhich supports a range of operations on RDF graph. Fusekihas been used to build the SPARQL endpoint to manage thematching services of our Communities. Services are describedas RDF graphs with N3 syntax and are managed throughthe SPARQL endpoint. In order to test the performance ofthe service matching algorithm we generated sets of sam-ple activity graphs corresponding to a different number ofactivities and we run those graphs on the Fuseki SPARQLendpoint. Two different methods have been used: the in-memory data set and TDB [21] (which persists the data-seton disk). As can be seen from Fig. 6, the in-memory methodconsiderably outperforms TDB. On the other hand we foundthat in-memory could only be used for data sets of up to about230,000 services (corresponding to approximately 2.8 millionsN3 triples), beyond which we consistently experience a Javaheap space error. We observe how FSO inherently results in

3Mechanisms such as the “mutual suspicion” algorithm in [22] may be usedto seamlessly tolerate crash failures of the SCC.

Fig. 3. Exemplary service description.

Fig. 4. Semantic framework for a Community organized as FSO.

a graph partitioning whose blocks may be designed so as toguarantee the adoption of the faster in-memory method.

A missed opportunity for improved performance derivesfrom a technological limitation. In fact in its current imple-mentation of federated queries Fuseki executes queries sent toremote services in sequence. As an example, in the federatedquery expressed in Fig. 5, the query expressed in Lines 9–21 isexecuted first while the query in Lines 23–36 is only executedafter the first query is finished. On the contrary a concurrentexecution of federated queries would enable activities to bepropagated much faster through the FSO hierarchy. In otherwords constructing a virtual SPARQL endpoint to run feder-ated queries does not allow the parallelism intrinsic in the FSOto be properly exploited.

Additional benefits from the introduction of the FSO mayderive from the following two properties:

1) By dividing the nodes into a set of sub-communitiesrepresenting physical entities the FSO allows domain-specific “priorities” to be introduced. In particularresources that are (physically or logically) “closer”to the service requester may be explored first. Weconjecture this to result in a reduction of the costs ofservice delivery.

2) As a consequence of introducing the FSO eventsand service requests are either sunk or propagateddepending on their criticality and the resources avail-able at each layer. The FSO allows nodes and cor-responding roles to be decomposed according to thenature of the monitored events: low-level, machine-

Fig. 5. Exemplary SPARQL federated query.

oriented context changes may thus be associatedto and managed in the lower layers while higherlevel, human-oriented situation identification may beappointed to the higher layers. This matches wellwith modern techniques for situation identificationin pervasive computing [24] and—we conjecture—may be used to set up cost-effective services couplingquality-of-service and quality-of-experience designrequirements. One such service is the subject of thefollowing section.

IV. SECOND CASE: FRACTAL ORGANIZATION OF ATELEMONITORING SERVICE

The proposed concept of FSO will be applied in the designand implementation of the software components developedwithin the scope of Little Sister, an ICON project financed by

Fig. 6. Performance of SPARQL endpoints with services published in memory and on disk. A Java heap space exception is experienced when data sets reachabout 230,000 services.

iMinds and the Flemish Government Agency for Innovation byScience and Technology (IWT). The project aims to deliver alow-cost telemonitoring [25] solution for home care. As canbe seen in Fig. 2, the system may be described as a multi-tier,distributed systems architecture, in which specially designedlow-resolution sensors [26] and RFID readers are individuallywrapped and exposed as manageable web services. Theseservices are then structured within a hierarchical federationreflecting the architectural structure of the building in whichthey are deployed [27]. More specifically, the system maintainsdedicated, manageable service groups for each room in thebuilding, each of which contains references to the web serviceendpoint of the underlying sensors (as depicted in layers 0and 1 in Fig. 2). These “room groups” are then aggregatedinto service groups representative of individual housing units.Finally, at the highest level of the federation, all units per-taining to a specific building are again exposed as a singleresource (layer 3). All services and devices situated at layers 0–3 are deployed and placed within the building and its housingunits; all services are exposed as manageable web services andallow for remote reconfiguration. The system was designed toseamlessly integrate with external applications developed andoffered by our industrial project partners (layer 4).

Information between different web services in the architec-ture is exchanged by means of a standardised, asynchronouspublish-and-subscribe mechanism [28]; subscriptions are auto-matically setup while the service group federation is initialised.

Events are raised by the sensors (proxy software) at thelower tier, and can only “flow” upward. A dedicated softwaremodule is available within each resource to 1) accept events,2) verify if actuation logic is available for the event to bedealt internally by some module contained within the resourcelogic, or 3) to propagate the event to the next level. Each eventis annotated with a topic identifier when it is published, suchthat the system can decide on whether to trigger local actuationlogic or propagate the event to the next tier [29].

In order to exemplify this approach, let us consider theapplication of this service-oriented architecture in the contextof an elderly home. In this setting, one may reasonablyexpect permanent surveillance by mean of, e.g., a warden whointeracts with the system by means of a user interface thatconnects to a back-end web service hosted at layer 3. If afall is detected, the appurtenant software modules in the hubdeployed in that room, fed with raw data from the underlying

sensor set, will raise an event. The corresponding fired activitycalls for a warden to go and inspect the flat where the eventoriginated. As no such role can be found neither in the roomnor in the flat ambient, the event propagates to layer 3. Here thewarden is notified and therefore he goes to the flat to providethe necessary assistance and get a first idea of the situation. Aninter-layered social overlay network is set in motion for as longas it is necessary for it to deal with the fall. As the fired activityalso calls for other higher level services, e.g., an ambulance andits driver, the event is also propagated upward until those assetsare located. The driver in particular is instructed to expect acall from the warden within a certain time interval. The callmay for instance inform the driver that 1) his/her service isindeed required; or 2) it is a case of a false alarm; or 3) extraroles are necessary (e.g., a specialist in certain treatments). Inabsence of a call the driver initiates his/her standard serviceprocedure.

We conjecture that the dynamic adaptation of the involvedsocial overlay networks now exemplified will play a key role infacilitating the expression and the management of the qualityvs. costs dynamic trade-offs mandated by Little Sister.

V. CONCLUSIONS

The choice of the organizational structure is a key designfactor as it determines the emergence of important designproperties including, e.g., responsiveness to altered environ-mental conditions, timeliness, determinism, scalability, andperformance—or the lack thereof. This paper focused on acase study—our Communities, socio-technical systems bothcharacterized by a “flat” and centralized organization. Severalshortcomings of these systems. were highlighted. After thiswe provided a high level description of the key elements ofa second organization—the Fractal Social Organization. TheFSO constitutes a natural evolution of our Communities inthat it introduces a new, vertical “dimension”: Communitiesbecome the nodes of a distributed, hierarchical organization.As in sociocracy, said nodes are free to overcome the typicalflaws of the hierarchic and centralized scheme by creatingSocial Overlay Networks that span across the hierarchy so asto provide reliable and cost-effective responses to the onset ofchange. Preliminary evidence of the effectiveness of FSO isreported through two ongoing experimentations.

In the first case we argued that fractal organization maybe beneficial in the framework for semantic description and

matching of our Communities. In particular we showed howdividing a big monolithic SPARQL endpoint for a flat com-munity into a set of SPARQL endpoints responsible for a setof sub-communities avoids single points of failure and allowsservices to be queried with smaller target graphs. The reducedsize of graphs enhances maintainability and allows services tobe published through an in-memory graph store rather than ondisk. We showed how this results in considerable improvementand conjectured that further enhancement shall be reachedwhen technology will allow the intrinsic parallelism of theFSO to be exploited.

A qualitative argument is put forward in the secondcase, which focuses on the design of a novel low-cost tele-monitoring service that is being devised in the frameworkof Flemish ICON-program project “LittleSister”. A key re-quirement for this project is the definition of a service com-bining hard safety guarantees with low cost and low energyconsumption. The fractal organization discussed in this papermatches well with those requirements in that it allows themonitoring and analysis processes to be partitioned accordingto the level of criticality and according to the complexity ofthe reflected information. Simple context changes may then beappointed to the comparably simpler lower layers of the FSOhierarchy while more and more complex and human-orientedsituations may be assigned to the more advanced higher layerscapable to enact complex high-order predictive behaviours asexemplified, e.g., in [30]. In turn—we conjecture—this maypave the way towards future effective architectures for theoptimal self-adaptive reconfiguration of system resources [31].

ACKNOWLEDGMENT

This work was partially supported by iMinds—Interdisciplinary institute for Technology, a research institutefunded by the Flemish Government—as well as by theFlemish Government Agency for Innovation by Science andTechnology (IWT). The iMinds LittleSister project is a projectco-funded by iMinds with project support of IWT. Companiesand organizations involved in the project are UniversiteitAntwerpen, Universiteit Gent, Vrije Universiteit Brussel,Xetal, Christelijke Mutualiteit vzw, Niko Projects, JF OceansBVBA, and SBD NV.

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