Network and Service Management Effects on Distributed Net-Centric Fusion Data Quality Ivan Kadar Interlink Systems Sciences, Inc. Lake Success, NY, USA
[email protected]
Erik Blasch Air Force Research Lab. WPAFB, OH, USA
[email protected]
Abstract – In real-world application of Net-Centric Service Oriented Architectures (SOA) for distributed tracking and fusion, the network parameters provided are assumed to be adequate to meet the systems performance and information quality requirements by the end users. The usual focus in SOA is to provide a negotiated Quality-of-Service (QoS) for enterprise services and not necessarily focus on fusion “application” data quality and on the associated management of the information flow in the transport layer. Recently, in prior papers, there has been some limited attention given to network service and connection management in the Joint Battlespace Infosphere (JBI) framework. However, the need to couple tracking and fusion systems design with an understanding and use of “general methods of network service connection and resource management” has not been addressed to achieve needed fusion data quality (FDQ). In this position paper we re-examine the above premise, discuss specific issues of communications, networking, scheduling and resource management architecture models and constraints that need to be addressed as these system components/parameters effect and interact with the design of distributed tracking and fusion algorithms to achieve the required FDQ in net-centric potentially ad-hoc dynamically configured networks environment. Keywords: Distributed Tracking and Fusion, Net-centric Service & Connection Resource Management, Modeling, Simulation, Performance Prediction and Assessment
1
Introduction
In net-centric service oriented architectures (SOA) framework, distributed estimation (distributed target tracking and fusion) relies on the SOA constructs and on the associated given (assumed) net-interconnectivity providing sufficient information rate transfer, while incurring minimum delays within the allocated Quality-ofService (QoS) for optimum performance e.g., [1-4]. Note that the “information transfer” was expressed as “rate” rather than bandwidth to focus on update rate needed for optimum system performance. This was exemplified in [5] (in the JBI [6] framework) by showing that the optimal update rate for a tracker is effected by the uncertainty in target dynamics.
Chun Yang Sigtem Technology, Inc. San Mateo, CA, USA
[email protected]
As highlighted in previous papers [1-4, 7] Connection Resource Management (CNRM) [1-4] is to provide seamless resource-allocation and sharing of the information products for spatially and geographically diverse, dynamically changing ad-hoc distributed networks [8, 10] in net centric environments, such as the Global Information Grid (GIG) [7, 9, 11], enabled by Network (connection) and Service (policy) Management (NSM) [1, 3, 12-13]. For optimum distributed information fusion performance [14, 15] CNRM/NSM systems must minimize communications delays and maximize message throughput in real-time, adaptively allocate routing and bandwidth resources, message rates/track updates, encode track data as needed, reduce or eliminate out-of-sequence (OOS) measurements and take into account data pedigree, its reliability and trustworthiness to both achieve fusion data quality, (FDQ) [16] and thereby optimum/near optimum distributed estimation of target state [14, 15]. However, channel allocation services provided by most NSM systems are static as negotiated between the server and the user (subscriber) based on anticipated need by Service Level Agreements (SLAs)/Service Level Specifications (SLSs)-based policies [5, 7 12]. The performance of the NSM system is measured in terms of QoS [5, 7, 12] and meeting QoS performance does not necessarily yield optimum distributed state estimates, i.e., “optimized FDQ.” The issues and challenges in designing FDQ-dependent adaptive assignment methods within the “Publish, Subscribe and Discover,” an essentially applications independent network architecture messaging paradigm, are explored, that has become the central mechanism for information dissemination and retrieval (smart push and pull) in net-centric systems such as the future GIG [7, 9] for distributed fusion. In addition, issues, methods, modeling of connection scheduling/assignment for tracking and fusion systems performance prediction/assessment in this environment are also discussed.
2 Issues and Challenges In order to assess the effects of CNRM/NSM on the performance of distributed fusion the following sections outline fusion needs, requirements and CNRM/NSM implementation issues and challenges.
2.1 Issues – Distributed Fusion: Needs and Perspectives Questions and issues which naturally arise include: (1) What information is exchanged between platforms, e.g., track data, tracklets, sufficient statistics,...? (2) How to minimize the number of bits needed to encode information with sufficient accuracy? (3) What is the best source coding scheme (with a fidelity criterion) for band limited channels, subject to a given best estimate error? (4) Can the best estimate information be updated at rate dependent on target state variables? That is, can message length and rate be adaptive? (5) What are the effects of time delay on best estimates? Such as OOS measurements [17], best estimates errors, double counting, missed and unreliable pedigree, …? (6) Which interacting measures-of-merits (MOMs) to use: delay, throughput, rate distortion and FDQ? 2.2 Issues on Data/Information Constraints in CNRM for Distributed Fusion – Potential Solution Options (1) Distributed fusion in networks with bandwidth/time constraints: solution options are source coding and/or adaptive connection resource allocation, i.e., combined NSM; and (2) Distributed state estimation methods subject to information rate/bandwidth constraints and/or unconstrained [1-4, 18]: • Optimum source coding schemes applied to the sensor track data, or sufficient statistic thereof, with respect to user defined measures-of-performance, such as rate distortion theory and associated Kullback-Leibler divergence based methods that minimize data rate and maximize information, but incur target state estimate errors [14, 15], • And bandwidth allocation and routing schemes ondemand basis using optimum channel allocation scheduling that maximizes throughput and minimizes delays by allocating bandwidth necessary among distributed sensors in the network and potentially reduce or eliminate the need for source coding and reduce target state estimate errors, over source coding methods [1, 18-20] and provide optimum on-demand data rate, message length and FDQ via NSM. Questions arise: (1) How to optimize in real time? (2) Are these metrics and approaches adequate and appropriate? (3) What distributed fusion scheme is best, e.g., with/without feedback? (4) Should relative sensors-to-target geometry be subject to CNM as well? While the above potential options are important at the transport layer, in subsequent sections we address NSM methods, which are focus of the balance of the paper.
2.3 NSM Perspectives: Issues and Challenges in Meeting FDQ 2.3.1 Publish and Subscribe Paradigm The main function is to “Push” information everywhere in ad hoc networks for anytime “smart pull” and discovery: (1) Each source message is labeled with a topic (PUBLISH) rather than sent to specific recipient; (2) The sending system sends messages to all users that have asked to receive messages on the topic (SUBSCRIBE) on a regular or event dependent bases (“smart Push”); (3) Thus “Publish” is a form of “Push technology” and “Subscribe” on an as needed basis can be “smart Pull”; (4) Smart Push makes data available with minimum delays as needed throughout the entire network [7, 12]. Potential Issues/Challenges: Messages are multicast over entire network allowing a dynamic network topology. Network overload needs to be monitored. • “Users receive the messages on a regular basis” implies constant update rate which can effect fusion performance, i.e., because receive rate is determined by “published” rate which may not yield the required FDQ [1, 4, 5]. • Question: How to adjust update (push) rate, message length and bandwidth as required by monitoring subscriber FDQ both for track to track fusion and track updates? This is addressed in Section 2.3.2. 2.3.2 Network Quality of Service (QoS) vs. Fusion Data Quality (FDQ) over the Network The network Quality of Service (QoS) [12] is usually defined by Service Level Agreements (SLAs) [12] and Service Level Specifications (SLSs) [12] to provide requested fixed communications parameters for the channel such as: packet size; requested packet rate; throughput and error rate (implies reliability); requested bandwidth and end-to-end delay and transit delay. However, given the agreed on “fixed parameters” system level issues arise. Potential Issues/Challenges: • QoS does not include message and applications dependent quality parameters. Postulate: for a given QoS, fusion systems either have to operate at the specified bandwidth and at constant message length/update rate, or use a selected bandwidth, i.e., “sub-channels” or on-demand bandwidth allocation request to provide the desired fusion data quality (FDQ). Section 2.3.3 below poses potential issues, questions and thereby postulating solution options. 2.3.3 Distributed Networking with Adaptive Bandwidth Allocation vs. Subscription-Demand-based Channel and Data Allocation: Issues, Challenges and Questions to Be Answered • It is well known that required track update rates are dependent on target state parameters for optimum tracking and FDQ. The question arises as to how to
allocate message length and data/update rate specified from an optimum state estimate (FDQ) requirements perspective via the network and service parameters control [12] in general. • How to implement an applications dependent dynamic SLA/SLS with a “Quality Parameter Request” (QPR) [13], wherein sub-channel QoSsc parameters are optimized and “reserved” to meet FDQ requirements? • How to model and augment the network traffic assignment (receive-transmit interconnect switch time slots) matrix {S i j} elements as “cost functions” of message packets routing and flow control based on expected traffic, subject to minimum delay with maximum throughput constraints with data quality requirements to meet best estimate FDQ? For adaptive QPR the elements of {Sij} should be weighted by FDQ parameters. In addition, the constraints for receive and transmit connectivity in the assignment matrix should be made variable based on needs. The assignments may be activated using a “bandwidth broker” service policy [13] aware of the latest network status, traffic load and scheduling/assignment constraints. Finally, analytical models of traffic and optimum assignment coupled with network simulation methods [2, 8, 21, 22] are needed in order to compute performance prediction/assessment parameters required for optimum net-centric distributed tracking and fusion systems designs.
3 Conclusions The potential issues, challenges and solution options to the distributed information fusion problem with bandwidth constraints were outlined. Both candidate adaptive NSM methods and distributed fusion performance issues related to and associated with the use of the Publish and Subscribe architecture paradigm were posed. These issues were examined in order to highlight potential methods to achieve the required FDQ via smart information push and pull, and simultaneously, adequate data quantity subject to QoS/SLS. Therefore, it is conjectured that in order to achieve overall distributed “network” effectiveness, these systems must be adaptive, and be able to retrieve distributed data dependent on FDQ on demand basis in real time. However, as noted, in using the Publish, Subscribe and Discover service paradigm, the performance requirements, i.e., the QoS, are usually negotiated via SLAs/SLSs between users and the network provider. Therefore, QoS parameters are fixed per SLA/SLS session and the selected parameters based on end-to-end channel performance may not necessary satisfy the FDQ requirements for optimum distributed fusion and bandwidth utilization [2-4, 14]. These observations were the motivating factors towards raising these issues, and adapting the use of the “Quality Parameter Request” (QPR) type channel reservation request, wherein sub-
channel QoS parameters are optimized and adjusted adaptively to meet tracking/fusion systems requirements. Furthermore, the importance of coupling tracking and fusion systems design with an understanding and use of “general methods of network service connection modeling and resource control” has been addressed to both achieve needed data fusion quality (DFQ) and allow the development of performance prediction and assessment methods. These requirements identify the need for close collaboration between “network” and “tracking/fusion” systems designers to achieve fruitful results in this important and current area of distributed fusion research.
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