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Adaptive Resource Management Architecture for DRE Systems

This paper presents a dynamic resource management framework, Resource Allocation and Control Engine (RACE), for Distributed Real-time & Embedded Systems operating in uncertain environments with limited resources. RACE adapts at runtime to varied mission goals, ensuring end-to-end Quality of Service (QoS) requirements are met. The architecture can adjust to new missions, component reallocation, and finer resource variations, enhancing system resilience and efficiency.

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Adaptive Resource Management Architecture for DRE Systems

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  1. Adaptive Resource Management Architecture for DRE Systems Nishanth Shankaran nshankar@dre.vanderbilt.edu

  2. Motivation: Distributed Real-time & Embedded Systems System Characteristics • Operate under limited resources • Tight real-time performance QoS constraints • Dynamic & uncertain environments • Loss or degradation of hardware with time • Distribution of computation • Multiple nodes & data centers • Task distribution among hosts/data centers • Integration of information • Data collection – Radar • Compute counter measure(s) • Execute counter measure(s) • Coordinated operation • E.g., NASA Earth Science Mission & Total Ship Computing Environment Problem • Need to operate in open & unpredictable environments • No accurate apriori knowledge of operating conditions, resource availability, and input workload • Effective utilization of multiple resources – computational power and network bandwidth Solution Adaptive Resource Management Architecture – Resource Allocation and Control Engine (RACE)

  3. Resource Allocation and Control Engine • Dynamic resource management framework atop CORBA Component Model (CCM) middleware (CIAO/DAnCE) • Allocates components to available resources • Configure components to satisfy QoS requirements based on dynamic mission goals • Perform run-time adaptation • Coarse-grained mechanisms • React to new missions, drastic changes in mission goals, or unexpected circumstances such as loss of resources • e.g., component re-allocation or migration • Fine-grained mechanisms • Compensate for drift & smaller variations in resource usage • e.g., adjustment of application parameters, such as QoS settings

  4. DRE System Model QoS Setting at the Application Layer QoS Setting at the Middleware Layer QoS Setting at the OS Layer QoS Setting at the N/W Layer QoS parameters are all layers need to be configured/managed to met end-to-end QoS requirements

  5. System Model of a CCM Based DRE System Container provides an encapsulation for the application QoS settings are specified at the container level These settings are then used to configure the middleware RACE currently manages OS QoS parameters/knobs to meet e-2-e QoS requirements Bandwidth Broker determines Network QoS settings

  6. System Model of a DDS Based DRE System RACE can manage OS & N/W QoS settings even for DDS based systems

  7. Concluding Remarks and Future Work • Architecturally, both distribution middleware are similar • Resource/QoS management architecture developed for one can be applied for the other with minor modifications • Currently, we have applied RACE for CCM based DRE systems • In the future, we plan to apply RACE for DDS bases DRE systems

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