On the Impact of Fractal Organization on the Performance of Socio-technical Systems Vincenzo De Florio∗ , Hong Sun† , Jonas Buys‡ , Chris Blondia§ ∗ PATS
research group University of Antwerp & iMinds Research Institute Middelheimlaan 1, 2020 Antwerpen, Belgium Email:
[email protected] † AGFA Healthcare 100 Moutstraat, Gent, Belgium Email:
[email protected] ‡ PATS research group University of Antwerp & iMinds Research Institute Middelheimlaan 1, 2020 Antwerpen, Belgium Email:
[email protected] ∗ PATS research group University of Antwerp & iMinds Research Institute Middelheimlaan 1, 2020 Antwerpen, Belgium Email:
[email protected] Abstract—Fractal organizations are a class of bio-inspired distributed hierarchical architectures in which control and feedback information are allowed to flow independently of the position the participating nodes have in the system hierarchy. In this paper we discuss the adoption of a fractal organization in a class of sociotechnical systems characterized by a centralized architecture. We present the key architectural traits of the resulting Fractal Social Organization and put forward our conjecture that services based on the presented solution may exhibit significant improvements, e.g., in terms of scalability and performance. In order to provide elements to justify our conjecture we describe how we envision the use of the new organization in two different cases: a framework for semantic service description-and-matching and a low-cost telemonitoring service.
I.
I NTRODUCTION
In our past research we proposed a concept called Mutual Assistance Community (MAC) [1], [2], [3]. In a nutshell, a MAC is a socio-technical system coupling services provided by assistive cyber-physical things with collaborative services supplied by human beings into an alternative social organization for the ambient assistance of the elderly population. Later said concept was extended into a so-called Serviceoriented Community (SoC) [4] so as to include other classes of 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 predefined classification is introduced; in particular roles such as clients and servers or service requesters and service providers are replaced by the simpler role of member. Members are not locked in [5] a requester or provider role. A member’s actual behavior is only decided by the current context. As an example in the domain of healthcare members may be care-givers at a given time and care-takers at another time.
•
Semantically annotated services and requests for services are published into a service registry and trigger semantic discovery of optimal responses [6].
•
Responses are constructed making use of the available social resources as well as the current context knowledge with the goal of optimizing both individual and social concerns.
A major aspect of both MAC and SoC is given by the assumption of a “flat” society: a cloud of social resources are 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 of the system is likely to become a single-point-of-failure and a single-point-of-congestion. Evidence to the above statement was brought by analyzing the performance of our system under increasingly turbulent conditions [6]. In particular in the cited reference we showed how service matching when dealing with more than 10,000 entries implied severe performance and scalability failures (results were obtained with a SPARQL / N3 architecture on a conventional PC). Due to the above limiting result we set to consider alternative solutions beyond the pure centralized approach. Lessons were learned by modeling the social activity that characterizes flat societies of roles [7], [8]. We showed how the dynamic evolution of the enacted social elements could be modeled as a dynamic system governed by a simple combinatorial function. By defining geometrical representations for said system we could observe how the flat society gives raise to noteworthy traits, among which the spontaneous emergence of hierarchical structures, modularization, and self-similarity (patterns or roles self-replicating at different scales.)
Inspired by the above result, in the cited references we introduced the above traits into a novel social organization. By construction, the new design adopts a hierarchical architecture in which a same node—modeled as our original Serviceoriented Community—is repeated at different scale throughout the layers of the hierarchy. A same set of rules is enacted at each layer so as to govern inter-layer and intra-layer social collaboration. The resulting architecture is that of a fractal organization [9], [10], [11] that we called Fractal Social Organization [8]. Aim of this paper is reporting on some preliminary results and lessons learned while making use of our Fractal Social Organizations (FSO). This is done first by recalling in Sect. II the major characteristics of FSO. After this we consider two ongoing experiences. In the first case, reported in Sect. III, we focus on SSDM and provide the elements of a novel semantic framework to manage service matching according to the FSO principles. Preliminary experiments conducted with computer-generated activity graphs show that the FSO may have a significant impact on reducing the performance and scalability limitations that we experienced with the MAC and SoC. Section IV introduces our second experience by briefly describing a recently started Flemish research project that aims at the design of a low-cost, non-intrusive monitoring solution for tele-monitoring services. Such solution shall be based on a predefined and static fractal social organization. In particular we report how we envisage the FSO to play a key role in optimizing quality vs. costs dynamic trade-offs. Conclusions and a view to some future work are finally drawn in Sect. V. II.
F RACTAL S OCIAL O RGANIZATIONS
Fractal Social Organizations (FSO) is the name of a novel class of socio-technical systems characterized by a distributed, bio-inspired, hierarchical architecture [7], [8]. Though fundamentally hierarchical, FSO is not based on the classic top-down flow of control and bottom-up flow of feedbacks (autocracy) but rather on a more peer-to-peer approach where every node in the hierarchy may play both management and subordinate roles depending on the situation at hand (sociocracy). Nodes in FSO hierarchies are in fact similar to sociocratic circles [12] or to the members of Service-oriented Communities and Mutual Assistance Communities [4], in that they allow control and information to flow in any direction of the hierarchy. A fixed set of rules (called “canon” in fractal organizations [13], [10], [11]) regulates the spontaneous emergence and in general the life-cycle of “social overlay networks” (SON). Said SON are made of those nodes in the FSO hierarchy that are “electrified” [14] by the onset of some novel condition s—for instance the awareness of a new threat or opportunity. In other words, SON represent dynamic aggregates of entities, both physical and computer-based, that unite to enact a collective response to s. In what follows we shall refer to those responses as to a SON’s “fired activities”. As an example scenario, an elderly woman falling in her smart house may call for the service of a detecting device— typically an accelerometer. This triggers the creation of an initial SON: S0 = {elderly woman, accelerometer}. The newly created SON may deal with the fall event, e.g., through the following fired activity: “trigger an alarm and enrol the service of a general practitioner”. This leads to changing the initial S0
Fig. 1. Space of all sub-communities of a society consisting of 3 roles played respectively by 1, 2, and 3 individuals. The rendering is done with the POV-Ray raytracer [16].
into an S1 = S0 ∪ {GP}. The GP then may in turn request the intervention of other entities, e.g., a nurse and an ambulance, which then leads to a S2 = S1 ∪ {nurse, ambulance}. As a result of this dynamic process and the enacting of the corresponding fired activities, SON may change their composition and may shrink or grow in number. A formal way to represent this process is that of a random walk through the space of all possible social elements in the current node. Figure 1 shows such space for a society of six nodes (for instance, six people)1 . Enrollment is in fact the process by means of which the above mentioned SON self-develop. It may be concisely described as the action of locating and appointing roles to the available cyber-physical entities. A formal description of activities, roles, and enrollment processes is out of the scope of this paper and may be found in [8]. Enrollment is carried out in FSO, MAC, and SoC, via semantic service description and matching (SSDM) as described in [6], [7]. SSDM is in fact the “architectural cornerstone” all the socio-technical systems our paper focuses on are built upon. Let us refer to either SoC or MAC as to a Community. A major difference of the FSO with respect to both Communities is the way said enrollment process is carried out. In SoC and MAC this is done through a central entity (the SCC) that works as a “hub” receiving and servicing all the available and requested services published by its members. In particular each new submitted entry triggers a semantic match with all those related entries that are already known to the SCC. If a satisfactory match can be found within the Community the activities requiring the found role can be launched. If that is not the case the SCC just re-enters its main processing loop and waits for a new publication. Enrollment in the FSO takes place through inter- and intra-layer collaboration. In the FSO we have a hierarchy of layers each node of which is organized as in a Community whose SCC (predefined or elected by the participating nodes) 1 Videoclips
and pictures of this and other societies may be accessed via [15].
Through the fractal organization of the FSO the above mentioned limitation can be reduced, if not fully overcome, thanks to the fact that services are not published globally but only in the originating layer. Each layer has its own SCC that manages only a portion of the total amount of services published in the system. This inherent partitioning also reduces the workload of the SCC and therefore also the probability that it turns into a single-point-of-congestion. Moreover the availability of multiple autonomous SCC reduces the consequences of failures, as a failed SCC results in a (temporary3 ) network partitioning instead of a global failure.
Fig. 2. Exemplary Fractal Social Organization. Note how the shape reproduces the well known Sierpi´nski triangle [18].
represents the whole node2 . When executing the enrollment phase in an FSO such as the one exemplified in Fig. 2 a missing role in one node triggers a so-called “exception” [8]: the SCC realizes that the sought role is currently unavailable and propagates the event to the next level upward in the hierarchy. This goes on until the first suitable candidate member for playing the required role is found or until some “flooding threshold” is met. This creates a sort of inter-layered, or bi-dimensional social overlay network whose nodes are not restricted to a single layer but can span across multiple layers of the FSO. This rule corresponds to the Double Linking rule of sociocracy [12] in that it allows the restrictions of pure hierarchical organizations to be overcome. This is done by creating a temporary means for entities situated at different layers to cooperate by creating a new structure complementary to the FSO and its nodes. The new structure is in fact a new ad hoc Service-oriented Community whose objective and lifespan are determined by the fired activity. In the following section we shall focus on the impact that the fractal organization of the FSO has on the performance of SSDM in “flat” (viz., single-layered) centralized architectures, namely our Communities.
Figure 4 shows the semantic framework that we used to introduce the FSO concept in our MAC. As can be seen from that picture, the Community is decomposed into a distributed hierarchy of sub-communities whose members may also include other sub-communities. An important consequence of this reorganization is that service requests are propagated upward in the hierarchy only if results are not found in the local sub-community. SPARQL endpoints are set up for those sub-communities at the bottom layer of the hierarchy tree, exemplified by the layer1 communities in Fig. 3. Service publications and discovery actions is done through the SPARQL endpoints to explore the resources in the related community. For the sub-communities on a higher layer, a virtual SPARQL endpoint is set up. In so doing the services published in the sub-communities can be queried through a SPARQL federated query. Figure 5 shows a sample federated query to look for services published in two sub-communities. Lines 9–21 and 23–36 specify queries to two sub-communities via their SPARQL endpoint respectively. The results from the two specified endpoints are aggregated together by the UNION statement in Line 22. The aggregated results are returned with the construct statements listed in Lines 3–7. The virtual SPARQL endpoint may also access context information external to the Communities by querying so-called Live Data [23] SPARQL endpoints. A. Preliminary experiments and a few remarks
In [6] we introduced the design of a mutual assistance community in which service publication and service discovery are executed with a SPARQL [19] endpoint. A simple service description is exemplified in Fig 3. The SPARQL endpoint is built with Fuseki [20], which allows services to be published either in memory (through the in-memory graph store) or on disk (via TDB [21]). Setting up a SPARQL endpoint with Fuseki using in-memory graph store has several advantages; in particular it avoids the necessity to set up a dedicated graph store. On the other hand, the use of in-memory graph store also places a restriction on the size of the graph that may be managed by the single SCC of the MAC. As a consequence of this, the amount of services that can be effectively accommodated by the endpoint is limited (as discussed in Sect.III-A).
The already mentioned Fuseki is a Jena SPARQL server which supports a range of operations on RDF graph. Fuseki has been used to build the SPARQL endpoint to manage the matching services of our Communities. Services are described as RDF graphs with N3 syntax and are managed through the SPARQL endpoint. In order to test the performance of the service matching algorithm we generated sets of sample activity graphs corresponding to a different number of activities and we run those graphs on the Fuseki SPARQL endpoint. Two different methods have been used: the inmemory data set and TDB [21] (which persists the data-set on disk). As can be seen from Fig. 6, the in-memory method considerably outperforms TDB. On the other hand we found that in-memory could only be used for data sets of up to about 230,000 services (corresponding to approximately 2.8 millions N3 triples), beyond which we consistently experience a Java heap space error. We observe how FSO inherently results in
2 This process is called personization and is known in Actor-Network Theory as “punctualization” [17].
3 Mechanisms such as the “mutual suspicion” algorithm in [22] may be used to seamlessly tolerate crash failures of the SCC.
III.
F IRST C ASE : F RACTAL O RGANIZATION OF S EMANTIC S ERVICE M ATCHING
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 to guarantee the adoption of the faster in-memory method. A missed opportunity for improved performance derives from a technological limitation. In fact in its current implementation of federated queries Fuseki executes queries sent to remote services in sequence. As an example, in the federated query expressed in Fig. 5, the query expressed in Lines 9–21 is executed first while the query in Lines 23–36 is only executed after the first query is finished. On the contrary a concurrent execution of federated queries would enable activities to be propagated much faster through the FSO hierarchy. In other words constructing a virtual SPARQL endpoint to run federated queries does not allow the parallelism intrinsic in the FSO to be properly exploited.
Fig. 5.
oriented context changes may thus be associated to and managed in the lower layers while higher level, human-oriented situation identification may be appointed to the higher layers. This matches well with modern techniques for situation identification in pervasive computing [24] and—we conjecture— may be used to set up cost-effective services coupling quality-of-service and quality-of-experience design requirements. One such service is the subject of the following section.
Additional benefits from the introduction of the FSO may derive from the following two properties: 1)
2)
By dividing the nodes into a set of sub-communities representing physical entities the FSO allows domainspecific “priorities” to be introduced. In particular resources that are (physically or logically) “closer” to the service requester may be explored first. We conjecture this to result in a reduction of the costs of service delivery. As a consequence of introducing the FSO events and service requests are either sunk or propagated depending on their criticality and the resources available at each layer. The FSO allows nodes and corresponding roles to be decomposed according to the nature of the monitored events: low-level, machine-
Exemplary SPARQL federated query.
IV.
S ECOND C ASE : F RACTAL O RGANIZATION OF A T ELEMONITORING S ERVICE
The proposed concept of FSO will be applied in the design and implementation of the software components developed within 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 reach about 230,000 services.
iMinds and the Flemish Government Agency for Innovation by Science and Technology (IWT). The project aims to deliver a low-cost telemonitoring [25] solution for home care. As can be seen in Fig. 2, the system may be described as a multi-tier, distributed systems architecture, in which specially designed low-resolution sensors [26] and RFID readers are individually wrapped and exposed as manageable web services. These services are then structured within a hierarchical federation reflecting the architectural structure of the building in which they are deployed [27]. More specifically, the system maintains dedicated, manageable service groups for each room in the building, each of which contains references to the web service endpoint of the underlying sensors (as depicted in layers 0 and 1 in Fig. 2). These “room groups” are then aggregated into service groups representative of individual housing units. Finally, at the highest level of the federation, all units pertaining to a specific building are again exposed as a single resource (layer 3). All services and devices situated at layers 0– 3 are deployed and placed within the building and its housing units; all services are exposed as manageable web services and allow for remote reconfiguration. The system was designed to seamlessly integrate with external applications developed and offered by our industrial project partners (layer 4). Information between different web services in the architecture is exchanged by means of a standardised, asynchronous publish-and-subscribe mechanism [28]; subscriptions are automatically setup while the service group federation is initialised. Events are raised by the sensors (proxy software) at the lower tier, and can only “flow” upward. A dedicated software module is available within each resource to 1) accept events, 2) verify if actuation logic is available for the event to be dealt internally by some module contained within the resource logic, or 3) to propagate the event to the next level. Each event is annotated with a topic identifier when it is published, such that the system can decide on whether to trigger local actuation logic or propagate the event to the next tier [29]. In order to exemplify this approach, let us consider the application of this service-oriented architecture in the context of an elderly home. In this setting, one may reasonably expect permanent surveillance by mean of, e.g., a warden who interacts with the system by means of a user interface that connects to a back-end web service hosted at layer 3. If a fall is detected, the appurtenant software modules in the hub deployed in that room, fed with raw data from the underlying
sensor set, will raise an event. The corresponding fired activity calls for a warden to go and inspect the flat where the event originated. As no such role can be found neither in the room nor in the flat ambient, the event propagates to layer 3. Here the warden is notified and therefore he goes to the flat to provide the necessary assistance and get a first idea of the situation. An inter-layered social overlay network is set in motion for as long as it is necessary for it to deal with the fall. As the fired activity also calls for other higher level services, e.g., an ambulance and its driver, the event is also propagated upward until those assets are located. The driver in particular is instructed to expect a call from the warden within a certain time interval. The call may for instance inform the driver that 1) his/her service is indeed required; or 2) it is a case of a false alarm; or 3) extra roles are necessary (e.g., a specialist in certain treatments). In absence of a call the driver initiates his/her standard service procedure. We conjecture that the dynamic adaptation of the involved social overlay networks now exemplified will play a key role in facilitating the expression and the management of the quality vs. costs dynamic trade-offs mandated by Little Sister. V.
C ONCLUSIONS
The choice of the organizational structure is a key design factor as it determines the emergence of important design properties including, e.g., responsiveness to altered environmental conditions, timeliness, determinism, scalability, and performance—or the lack thereof. This paper focused on a case study—our Communities, socio-technical systems both characterized by a “flat” and centralized organization. Several shortcomings of these systems. were highlighted. After this we provided a high level description of the key elements of a second organization—the Fractal Social Organization. The FSO constitutes a natural evolution of our Communities in that it introduces a new, vertical “dimension”: Communities become the nodes of a distributed, hierarchical organization. As in sociocracy, said nodes are free to overcome the typical flaws of the hierarchic and centralized scheme by creating Social Overlay Networks that span across the hierarchy so as to provide reliable and cost-effective responses to the onset of change. Preliminary evidence of the effectiveness of FSO is reported through two ongoing experimentations. In the first case we argued that fractal organization may be beneficial in the framework for semantic description and
matching of our Communities. In particular we showed how dividing a big monolithic SPARQL endpoint for a flat community into a set of SPARQL endpoints responsible for a set of sub-communities avoids single points of failure and allows services to be queried with smaller target graphs. The reduced size of graphs enhances maintainability and allows services to be published through an in-memory graph store rather than on disk. We showed how this results in considerable improvement and conjectured that further enhancement shall be reached when technology will allow the intrinsic parallelism of the FSO to be exploited. A qualitative argument is put forward in the second case, which focuses on the design of a novel low-cost telemonitoring service that is being devised in the framework of Flemish ICON-program project “LittleSister”. A key requirement for this project is the definition of a service combining hard safety guarantees with low cost and low energy consumption. The fractal organization discussed in this paper matches well with those requirements in that it allows the monitoring and analysis processes to be partitioned according to the level of criticality and according to the complexity of the reflected information. Simple context changes may then be appointed to the comparably simpler lower layers of the FSO hierarchy while more and more complex and human-oriented situations may be assigned to the more advanced higher layers capable to enact complex high-order predictive behaviours as exemplified, e.g., in [30]. In turn—we conjecture—this may pave the way towards future effective architectures for the optimal self-adaptive reconfiguration of system resources [31]. ACKNOWLEDGMENT This work was partially supported by iMinds— Interdisciplinary institute for Technology, a research institute funded by the Flemish Government—as well as by the Flemish Government Agency for Innovation by Science and Technology (IWT). The iMinds LittleSister project is a project co-funded by iMinds with project support of IWT. Companies and organizations involved in the project are Universiteit Antwerpen, Universiteit Gent, Vrije Universiteit Brussel, Xetal, Christelijke Mutualiteit vzw, Niko Projects, JF Oceans BVBA, and SBD NV. R EFERENCES [1] H. Sun, V. De Florio, N. Gui, and C. Blondia, “Promises and challenges of ambient assisted living systems,” in Proc. of the 6th Int.l Conf. on Information Technology: New Generations (ITNG 2009), April 2009. [2] ——, “Participant: A new concept for optimally assisting the elder people,” in Proc. of the 20th IEEE Int.l Symp. on Comp.-Based Medical Sys. (CBMS-2007). Maribor (SI): IEEE Comp. Soc., June 2007. [3] ——, “The missing ones: Key ingredients towards effective ambient assisted living systems,” Journal of Ambient Intelligence and Smart Environments, vol. 2, no. 2, April 2010. [4] V. De Florio and C. Blondia, “Service-oriented communities: Visions and contributions towards social organizations,” in On the Move to Meaningful Internet Systems: OTM 2010 Workshops, ser. LNCS, Springer, 2010, vol. 6428, pp. 319–328. [5] D. C. Stark, The Biology of Business: Decoding the Natural Laws of Enterprise. Jossey-Bass, 1999, ch. Heterarchy: Distributing Authorithy and Organizing Diversity, pp. 153–179. [6] H. Sun, V. De Florio, and C. Blondia, “Implementing a role based mutual assistance community with semantic service description and matching,” in Proc. of the Int.l Conf. on Management of Emergent Digital EcoSystems (MEDES), Oct. 2013.
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