Collaborative Problem Solving Agent for On-Board Real-Time Systems Shikha Jain ,Lonnie R. Welch, David M. Chelberg , Zhenyu Tan, David Fleeman, David Parrott, , Laboratory for Intelligent Real-Time Secure Systems (LIRTSS), School of EECS, Ohio University, Athens, Ohio – 45701. {shikha.jain | welch | chelberg | zhenyu.tan | david.fleeman | david.parrott |} @ohiou.edu
Abstract Breakthrough in Earth Science Observing will occur when constellations of Earth observing satellites are able to fully collaborate together and collectively monitor the conditions of our planet through a vast array of instruments. These satellites form a network that consists of distributed processes that need to respond to perceived scientific events, the spacecraft environment, spacecraft anomalies and user commands. The requests and responses exhibit dynamic behavior. In order to handle such dynamic environments, a method is needed to guarantee the real-time quality of service constraints. The DeSiDeRaTa resource management approach is being enhanced to characterize the dynamic aspects of intraconstellation topologies and to accommodate the concept of service levels and utility. This paper presents a design model of cooperative problem solving to show how the solution approach addresses the key challenges presented in the problem and specifies how the agent, resource manager and satellite constellations would operate correctly and interact in complex, dynamic and unpredictable environments. It extends the system model of DeSiDeRaTa to accommodate the concepts of utility, service levels and planning. The system model for the IPA is presented to show the proof of concept. Keywords: Real-time systems, agents, utility, satellite, distributed.
I. INTRODUCTION In the future, autonomous satellites will perform much of the event detection and response processing which ground-based stations presently perform. A sensor
Barbara Pfarr, Real-Time Software Engineering Branch, NASA Goddard Space Flight Center, Greenbelt, Maryland – 20771.
[email protected] Mei C. Liu, Chris Shuler Computer Sciences Corporation Lanham, Maryland - 20706 {mliu | cshuler}@csc.com
web ties these satellites constellations together. Sensor web consists of distributed processes that dynamically respond to the commands of users interested in measuring important terrestrial events (e.g., a volcanic event). In such a complex and dynamic environment, a method to adapt the system to handle such a scenario is needed. To address this problem, we propose a solution by unifying the agent based computing paradigm with the theory of adaptive resource management for dynamic real-time systems based on DeSiDeRaTa’s resource management (RM) approach. The goal of evolving the above approach is to enable the satellites to change the use of sensors at run-time and ultimately, optimize the use of computing and network resources in the sensor webs. One of the key challenges to overcome in the sensor web based technology is to handle the mission priorities when there are inadequate resources for all requests. The satellites in the sensor web need to collaborate amongst themselves to find out which satellite can best capture the event. These satellites will be capable of notifying other satellites, in the constellation, of any events, selecting the satellite that can best capture the event and schedule the observations. Performing the notification, selection and scheduling manually is a time consuming job that might not permit the real-time requirements of the system to be met. The satellites need to collaborate amongst themselves for rapid handling of requests. The commands given by the scientists based on the ground station must be delivered in the smallest possible time to the satellite that best matches the observation request. In order to achieve the dynamic configuration and planning of the sensor web and to meet the real-time requirements, there is a need for real-time control software systems. DeSiDeRaTa’s adaptive resource management strategy ensures the quality of service (QoS) objectives of the various essential processes by using a set of middleware mechanisms that provide efficient utilization of resources
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and continuous availability of the system. In addition, it guarantees a timely response to external events. This approach is being enhanced to accommodate the dynamic aspects of intra-constellation network topologies. The effectiveness of this approach will be demonstrated in a real-time system prototype called the IPA (an onboard image processing agent), developed by the Real-Time Software Engineering Branch at NASA’s Goddard Space Flight Center. The IPA defines a subset of the above problem. It uses the adaptive real-time agent concept to perform the event detection (on-board cloud detection) and response processing (decision-making) for the spacecraft: prioritizing data for downlink. Presently, the IPA and the DeSiDeRaTa approach are being enhanced to provide a test bed for the evaluation of the constellation management and measurement techniques. The next section explains the DeSiDeRaTa approach and the enhancements that are being made to accommodate agent based systems that have real-time constraints. Section 3 presents the discussion of how the solution approach can be used to address the key challenges presented in this paper. Section 4 presents a system model that describes the enhancements made to the DeSiDeRaTa approach. In section 5, a discussion of IPA illustrates the feasibility of our approach. The conclusions and future work are discussed in Section 6.
II. AN
APPROACH FOR RESOURCE MANAGEMENT OF ADAPTIVE REAL-TIME AGENTS
DeSiDeRaTa is an adaptive resource management technique [1] that provides middleware services for distributed dynamic real-time systems that cannot be
Initiator
characterized a priori. The DeSiDeRaTa system is based on the dynamic path paradigm that provides automatic QoS assessment and resource allocation. A dynamic path typically consists of sensors, actuators, and control software for filtering, evaluating and acting. Path-level QoS specification is used by the adaptive resource allocator to determine if the current configuration is achieving the desired QoS and to assist in selecting new configurations to improve the QoS. It uses specification language for specifying both static and dynamic attributes of an application. The resource manager is implemented using the unified software development process, which is use-case driven, architecture-centric, iterative and incremental. The use case model that depicts the relationships between the identified critical use cases and the actors of the system is shown in figure 1. As depicted in the figure, the Installer uses the “Start RM executables” use case to obtain and install RM on a selected configuration of hosts and start the middleware system. Once the RM is started, the realtime system developer uses the “Start an RT system in a feasible allocation” to cause RM to find a feasible allocation for a RT system and to start the applications of the RT system on the hosts indicated in the feasible allocation. As a next step, the dynamic real-time system initiates the “Maintain a feasible allocation” use case to inform RM of its real-time performance and of its resource needs. This causes RM to monitor the real-time performance as required by the real-time system developer. If the QoS is not met, then the RM performs a set of reallocation actions that will restore the required real-time performance. The logical architecture of the DeSiDeRaTa QoS management systems is shown in Figure 2. These subsystems are used to implement the various resource
Start an RT system in a feasible allocation
Real-Tim e System Developer
Host
M aintain a feasible allocation
Initiator
Operator
Real-Tim e System
Initiator
Start the RM executables Installer
Figure 1. Use Case Model – Critical Use Cases
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management functions. Each package is implemented as a set of classes whose functionalities are closely related and an interface is provided for each package for the subsystem interactions. The Real-Time System Management package contains classes that monitor and diagnose a Real-Time System. It gets the Real-Time System information from the Specification Management and Resource Instrumentation and Control packages. It then activates the Allocation Management to perform any changes needed in allocation. The Allocation Management package contains classes that handle all of the tasks relating to allocations. It finds feasible allocations using the information provided by Specification File Management and Resource Instrumentation and Control Management packages. Specification File Management
The DeSiDeRaTa network resource manager [6] is being developed to provide the capability of handling dynamic data retrieval and delivery. The current techniques employed in DeSiDeRaTa, though include features for monitoring, diagnosis and recovery of realtime applications, do not include support for the real-time communication links that are typically present in a distributed system. In such systems, QoS violations may also be caused by communication links that fail to transfer data in a real-time fashion. The network resource manager detects these failures and performs resource reallocations to provide real-time performance. To increase the applicability of agents for real-time space systems, a model of real-time agents is being developed. An agent can be defined [5] as anything that can be viewed as perceiving its environment through
User Management
Real -Time System Management Allocation Management
Instrumentation and Control Management
Meta-agent
Specification File Management
Figure 2. Architecture of DeSiDeRaTa
package contains classes that parse user specification files and build objects that are accessed by Allocation Management, RTS Management and User Management packages. The Resource Instrumentation and Control Management package contains two service packages: Resource Monitor and Application Control. Resource Monitor has objects that gather resource usage and availability on a particular host. Application Control has objects that start and stops applications. The User Management package contains classes that handle all user inputs. The meta-agent acts as the negotiator between the applications and the RM. It negotiates with RM to get the required resources to perform the acceptable actions. It uses the information provided by the Allocation Management package.
sensors and acting upon the environment through effectors. A real-time agent is one that is cognizant of when it must select an action. Its determination of what the world is like, and what it could be like given actions by its effectors, is bounded by timing requirements (e.g., when a response must be made to a condition in the environment in order to effective). Another aspect of which agents should be aware is computing resources. To determine if a set of analyses is feasible, it is necessary to know computing resource needs and computing resource availability. An agent that functions in dynamic environments (such as space environments) requires varying amounts of analyses, and thus its resource needs change. As the needs change, the agent can gracefully adapt by acquiring additional resources from a resource manager (RM). While it is desirable for agents to be cognizant of real-time requirements and computing
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resources, such issues can significantly complicate the development of on-board satellite agents. Thus a metaagent is being developed that manages the time and resources for the satellites. This meta-agent will be a part of the resource manager.
III.
APPLICATION OF ADAPTIVE RESOURCE MANAGEMENT TECHNIQUE TO SOLVE AUTONONOUS HOT-SPOT CONVERGENCE PROBLEM
The following scenario (from [3]) illustrates the operation of a constellation of earth observing (EO) satellites. Several sentinel satellites provide a line-of-sight view of all instrumented satellites in the constellation; each sentinel knows the precise location of all members in the constellation. The scenario involves the handling of a significant terrestrial event. A synthetic aperture radar satellite detects a volcanic event. The autonomous satellite brings the event into focus by rotating its instruments and altering its coverage area; on-board feature detectors analyze the data and assign priorities to
resolution; less important parts are sent back last; scientists are alerted and assume control of the spacecraft; they direct its instruments for specific follow-up measurements. To better equip the scientist with the capability of measuring significant terrestrial events with accuracy, the RM and meta-agent can be used. The satellites consult the meta-agent prior to performing analysis and state the utility of performing the analysis. Subsequently the metaagent determines the feasibility of performing the actions by considering the timing requirements and resource constraints. If adequate time and resources are not available, the meta-agent negotiates with the RM to find a satisfactory compromise. The system diagram that shows the integration of RM and meta-agent in such a scenario is shown in figure 3. The figure shows a single meta-agent, but we can also have multiple meta-agents. The meta-agents do not need to be centralized. They can be distributed across constellations in a way that there is a meta-agent for each constellation. These meta-agents will work asynchronously to manage the time and resource requirements of the constellations by interacting with the S11
S43 S42
C4
C1
S41 S32
Request S31
S21
S22
Success/ Failure
C3 S33
S12
Instrument Scheduler
Computing RM
S13
C2
Negotiate
S23 Meta Agent
Commands
Community of Earth scientists
C - Constellation S - Satellite
Figure 3. System Diagram
different parts of the image; data compression is employed; to communicate the data to the ground station, another member of the constellation must be used as a relay, since the ground station is out of view; more important portions of data are sent back first, at high
RM. These meta-agents form a hierarchical structure. The scientists will send their request to the metaagent specifying the location of the event, the type of sensor to be used to measure the event, the priority of the mission, a set of service levels at which the instrument
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can be operated and the deadline for that particular task. The sentinel satellites provide the precise location of all the satellites present in its constellation. RM provides the information regarding the resource availability on the satellites. The meta-agent uses the information provided in the request, the data from the sentinel satellites and the knowledge of available resources to plan a course of action to be taken to ensure consistent view of the hot spot. One of the situations that could arise in such a scenario is that the satellites chosen to provide the view do not meet the resource requirements due to catering to a more important process. Under such circumstances, RM can detect a violation and take corrective measures by either choosing a different satellite or by moving some of the less important processes to another satellite. Thus RM can maintain the QoS requirements of all the tasks. In Figure 3. satellites S12 , S22, S32 and S42 are the sentinel satellites in the four constellations. These four satellites behave as local agents for their constellation and the meta-agent manages the time and resources for all the satellites belonging to the constellations by using the information provided by the sentinel satellites. The instrument scheduler provides the observation schedule for the satellites. The meta-agent uses a utility-based model of the characteristics of the real-time system to plan the actions for the satellites. The resource manager is aware of the computing and network resources available on the constellations. In order for the meta-agent to determine the feasibility of the set of analyses, it needs to know the computing and network resource needs and computing and network resource availability. Since it works in a dynamic environment, it requires varying amount of analyses and thus its resource needs change. The RM is capable of handling such situations and serves the needs of the meta-agent. Thus, with the knowledge of the available resources and the observation schedule, the meta-agent plans the actions of earth observing satellites. If sufficient resources are not available, it negotiates with RM to acquire the additional resources by manipulating the service levels of less critical applications. In order for RM to control the real-time system, the characteristics of the real-time system need to be specified using the specification language provided by DeSiDeRaTa. Thus we need to make enhancements to the specification language and the system model described in [2] to accommodate the concept of service levels and utility. The next section presents a system model that characterizes the above concepts.
IV. SYSTEM MODEL In this section we present the system model capture the dynamic path paradigm with the notion utility. The complete specification for DeSiDeRaTa given in [2], where a software subsystem is specified
to of is to
include a set of applications, devices and paths. We extend the current model to include the utility associated with each application in the path, the utility associated with the path and the overall system utility. Each application has a set of acceptable service levels and the benefit provided at that level. The system model is stated in table 1. Symbol SS Pi ai,j ai,j.QU
Description Software Subsystem Path i Name of application j in path Pi A set of QoS levels, utility associated with each level and the resources used by the application at each level. QU = {(q1, r1, u1), (q2, r2, u2) …} QoS level at which application ai,j was running in cycle ‘c’ The utility value associated with path Pi The utility value associated with the system. The real-time deadline for the software system The real-time deadline for application ai,j to complete its task. The importance (priority) associated with the space mission
ai,j.q(c) Pi.Ui(p) SS.U(s) SS.D ai,j.D SM.I
Table 1: System Model
The parameter ‘q’ is defined as the service level associated with each application. It can be considered as an alternative to select when the application fails to meet its quality of service requirements. ‘q’ is specified as a discrete number. The application utility, 'u' is to be defined as the value accrued by the system when ai,j is allocated 'r' resources. We can define U as the utility function of ai,j. The utility function defines a surface along which the application can operate based on the resources allocated to it and other parameters that are used to calculate the utility. We define Pi.Ui(p) as the utility function associated with path Pi. It can be calculated as
Pi.Ui(p)= ∑a i, j .u , where, Σ denote the sum of benefit j
provided by each application constituting the path and ‘u’ is the benefit associated with application ai,j. In the similar fashion at a higher granularity, we define the overall system utility as SS.U(s). This can be computed as the sum of the utilities of paths constituting the system. Mathematically we can state it as
SS.U(s)= ∑Pi.Ui(p). i
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What resources are available?
RM
Camera Simulator High Low
Start image processor and determine algorithm Spec Image Processing Agent
Spec
Report results back to RM Cloud Display (Demonstrator)
Figure 4. High level conceptual diagram of IPA
The space operations can be modeled as space missions. For a space mission based system, the various requirements for any component need to be specified. A component can consist of a system, sub-system, paths, or applications. The missions need to be prioritized so that the real-time needs of the most critical mission are guaranteed. The priority for a mission is determined by the user and specified in the specification file. All these parameters can be considered as different dimensions of QoS and we can have a utility associated in each dimension. The IPA, camera simulator and the cloud cover display can be considered as the applications constituting a real-time system. The path for this real-time system can be modeled as:
System Path: Camera_simulator → IPA → cloud cover display For our specific example, the different quality of service levels will be the different algorithms that are used to process the image. Each algorithm has a benefit value associated with it given by the weight of the algorithm. The resource needs for each algorithm are also specified. For the camera simulator, the service levels will be defined by the camera resolution used to capture the image. Each of the applications in the real-time system needs to be profiled to characterize the amount of resources required by them. The real-time deadline associated with each application also needs to be specified.
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V. SUBSET OF THE PROBLEM INSTANCE – IPA To show the proof of concept, the resource management technique described in section II is being applied to IPA in the following steps: 1. Model the sensors, actuators, effectors and communication relationships within IPA. 2. Construct the model of dynamic real-time paths of IPA. 3. Insert probes (timestamps) into IPA for events and resource usage. 4. Obtain resource usage profiles for IPA applications and paths. 5. Specify the above properties in a specification file using DeSiDeRaTa’s QoS specification language [2]. 6. Deploy adaptive resource management for the system. The IPA software uses DeSiDeRaTa’s QoS management technology and the adaptive real-time agent concept to make data download decisions for the spacecraft. Currently the IPA uses simplified NOAA’s cloud cover detection algorithm to perform cloud coverage detection for the on-board images. The RM monitors the performance of the IPA. If the IPA performance does not meet the real-time constraints, the RM switches the cloud cover algorithm being used by IPA to satisfy the on-board sciences preprocessing performance requirements. Figure 4. shows the high level conceptual diagram of the IPA. The IPA can be automatically started and stopped by the RM. RM determines the algorithm that IPA must use to filter the data and detect cloud coverage based on the resources available and the algorithms specified in the specification file. Each algorithm is capable of running at different levels of fidelities depending on the resources available and the benefit provided. The IPA is instrumented to send timestamps to RM at the start and end of each processing cycle. RM monitors the QoS by comparing the observed and required real-time performance statistics. If it detects a QoS violation, it finds a feasible cloud cover detection algorithm to be used in the next cycle. The IPA generates a real-time report of each image chosen, cloud percentage, algorithm used to process the image, processing time and the size of the image. There are three alternatives for response processing post detection – data rejection, data compression and full download. IPA use a utility function based on algorithm weight, camera resolution and ground clear percentage to make post detection response decisions. The prototype is being built to show the proof of concept. The approach used in the image processing agent
prototype will constellations.
be
enhanced
to
handle
satellite
VI. CONCLUSIONS AND FUTURE WORK In the future, autonomous satellites will perform much of the event detection and response processing. These satellites behave as agents. This paper presents an adaptive resource management strategy for real-time agents, which is being evolved to guarantee the real-time performances. A solution approach is presented and illustrated by an example system. The future work includes extending the DeSiDeRaTa’s specification language and algorithms to accommodate the concept of fidelity and service levels based on a utility model. This system will be modified to handle more than one satellite concurrently and show the applicability of our approach for dynamically managing the information systems of satellites. Dynamic scenarios, challenging enough to force resource reallocations, will be run and the QoS performance in each case will be recorded. Several assessment metrics will be gathered to assess the quality of the adaptive resource management strategy that has been implemented. The improvements in the QoS per reallocation action will be measured. The meta-agent will be developed to handle dynamic planning. The approach used for managing the prototype system will be extended to work for satellites constellations. IPA will serve as a real-time test bed for the early evaluation of the methodology. REFERENCES [1] L. R. Welch, B. A. Shirazi, B. Ravindran and C. Bruggeman, “DeSiDeRaTa: QoS management technology for dynamic, scalable, dependable, real-time systems,” in Proceedings of The 15th IFAC Workshop on Distributed Computer Control Systems, pp. 7-12, September 1998. [2] L. R. Welch, B. Ravindran, B. A. Shirazi and C.Bruggeman, “Specification and modeling of dynamic, distributed real-time systems,” in Proceedings of The 19th IEEE Real-Time Systems Symposium, pp. 72-81, IEEE Computer Society Press, 1998. [3] G. E. Prescott, S. A. Smith and K. Moe, “Real-Time Information System Technology Challenges for NASA’s Earth Science Enterprise,” in Proceedings of the International Workshop on Real-Time Mission-Critical Systems, Dec. 1999. [4] S. Russell and P. Norvig, “Artificial Intelligence: A Modern Approach,” Prentice Hall, 1995. [5] Lonnie R. Welch and Scott Brandt, “The Value of Benefit in Real-Time Computing,” in The 9th Workshop on Parallel and Distributed Real-Time Systems, April 2001, Presenter.
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[6] Lu Tong, Carl Bruggeman, Brett Tjaden, Hong Chen, and Lonnie R. Welch, “Specification and Modeling of Network Resources in Dynamic, Distributed Real-time System,” in 14th International Conference on Parallel and Distributed Computing Systems (PDCS 2001), Dallas, Texas, August 8-10, 2001. [7] David Chelberg, Lonnie R. Welch, Arvind Lakshmikumar, Matthew Gillen, and Qiang Zhou, “ Ohio University's RoboCup Team: Combining Resource Management with Distributed Agents,” in The 33rd Southeastern Symposium on System Theory, March 2001.
[8] Binoy Ravindran, Lonnie R. Welch and Behrooz A. Shirazi,Management Middleware for Dynamic, Dependable Real-Time Systems,” in The Journal of Real-Time Systems, 20:183-196, Kluwer Academic Press, 2001. [9] Lonnie R. Welch, Carl Bruggeman and Purvi Shah, “A Novel Paradigm for Mission Critical Systems,” in The 2001 International Conference on Parallel and Distributed Processing Techniques and Applications, June 2001.
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