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 Database must “closely” represent the real-time system in real time. Value of data decreases with time.

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.

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.

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

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

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)

On-Demand: Basic Algorithm 1. When a base item (b) is updated, “flag” derived items that include the (b) in their read set. 2. When a transaction occurs for (d), traverse graph backwards from (d) to locate stale items. 3. Each stale item is prioritized and an update schedule is created and executed. 4. Execute updates until the transaction deadline expires.

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

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 (M M ) – Percentage of Mandatory Transactions that missed their deadline Optional miss percentage (M O ) – Percentage of optional transactions that missed their deadline.

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.

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

QoS: Basic Algorithm 1. Define QoS and Transaction metrics: U, M M, M O 2. Monitor mandatory and optional miss percentages 3. During transient overload periods decrease optional updates by increasing MDE. 4. Feedback control scheduling is used to adapt the MDE in order to satisfy pre-determined QoS specifications. 5. Decrease MDE as workload decreases (i.e. increase triggered optional transactions).

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

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

DS: Basic Algorithm 1. Set the update transaction deadline (d) to be the end of the validity interval. 2. The transaction start time is derived backward from the deadline using the known response time (r).

DS: Basic Algorithm cont. 3. Adjust the schedule for high priority preemption. 4. Construct a hyper-period that executes the schedule repeatedly to decrease scheduling overhead.