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Survey of Real Time Databases

Survey of Real Time Databases. Telvis Calhoun CSc 6710. Introduction. Data repository for a real-time systems Real-Time systems include: Automotive control systems Telecommunications Industrial Process Control Real-Time Systems impose temporal consistency constraints

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Survey of Real Time Databases

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  1. Survey of Real Time Databases Telvis Calhoun CSc 6710

  2. Introduction • Data repository for a real-time systems • Real-Time systems include: • Automotive control systems • Telecommunications • Industrial Process Control • Real-Time Systems impose temporal consistency constraints • Database must “closely” represent the real-time system in real time. • Value of data decreases with time.

  3. RTDBS Characteristics • Primary metric is number transactions that missed their deadlines. • Provide predictable response time. • Guarantee completion of time critical transactions • Usually designed as “in-memory” databases.

  4. Hard Real Time vs. Soft Real Time • Critical real-time systems such as nuclear power plants or fly-by-wire airplanes are Hard Real Time • Non-critical real time systems where missed transactions only degrade system quality are Soft Real Time • RTDB design depends on real time system characteristics • This presentation shows algorithms for soft-real time systems only.

  5. Dynamic On-Demand Scheduling • Goal: Minimize computational workload by initiating transactions “on-demand”. • Target System: Embedded systems with limited resources

  6. On-Demand: Definitions • Definitions • Base Data - Data continuously added to the database by sensors • Derived Data – Data calculated using base data or other derived data items • Read Set – All data items needed to calculate a derived data item • Similarity – Updates are not required for minor changes in base items even if the data is old

  7. On-Demand: Relationship Between Data Items • The relationship between base and derived items can be represented using a directed acyclic graph • Read set retrieved using On-Demand Depth-First Traversal (ODDFT)

  8. On-Demand: Basic Algorithm • When a base item (b) is updated, “flag” derived items that include the (b) in their read set. • When a transaction occurs for (d), traverse graph backwards from (d) to locate stale items . • Each stale item is prioritized and an update schedule is created and executed. • Execute updates until the transaction deadline expires.

  9. Quality of Service Scheduling • Goal: Maintain temporal consistency during transient overload periods. • Target System: Real time services systems with unpredictable workloads

  10. QoS Metrics • Two groups of transactions • Mandatory transactions must be computed before the transaction deadline. • Optional Transactions are executed if there is time available before the transaction deadline. • Quality of Data (QoD) • Maximum Data Error (MDE) - Defines the maximum deviation between a data item and its real world value • Quality of Transactions (QoT) • Mandatory miss percentage (MM)– Percentage of Mandatory Transactions that missed their deadline • Optional miss percentage (MO) – Percentage of optional transactions that missed their deadline.

  11. QoS Metrics cont. • Quality of Service (QoS) • Overshoot - Worst-case system performance in terms transaction miss percentages • Settling Time – Time to transition from overshoot to steady state. • Utilization – Computing resources used • QoT vs. QoD Trade-off • Increase the MDE (degrade data) to decrease optional transactions during transient overloads.

  12. QoS: Feedback Scheduler • Feedback control scheduler adapts QoT vs. QoD Trade-off

  13. QoS: Basic Algorithm • Define QoS and Transaction metrics: U, MM, MO • Monitor mandatory and optional miss percentages • During transient overload periods decrease optional updates by increasing MDE. • Feedback control scheduling is used to adapt the MDE in order to satisfy pre-determined QoS specifications. • Decrease MDE as workload decreases (i.e. increase triggered optional transactions).

  14. Deferrable Scheduling for Fixed Priority Systems • Goal: Actively schedule the maximum time between periodic sensor updates to minimize energy consumption. • Target System: Process control systems that require continuous sensor updates

  15. DS: Definitions • Validity interval: Time that a data item is considered fresh after an update transaction. • Response Time: Time required to retrieve data from a sensor. • Transaction Deadline: Time when a transaction must be complete

  16. DS: Basic Algorithm • Set the update transaction deadline (d) to be the end of the validity interval. • The transaction start time is derived backward from the deadline using the known response time (r).

  17. DS: Basic Algorithm cont. • Adjust the schedule for high priority preemption. • Construct a hyper-period that executes the schedule repeatedly to decrease scheduling overhead.

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