class TraceGenerator extends Serializable
This class is responsible to generate random traces of random event types. All the trace will have length randomly picked (using uniform distribution) between minTraceSize and maxTraceSize It can be used to evaluate the performance of SIESTA in throughput and correctness.
- Alphabetic
- By Inheritance
- TraceGenerator
- Serializable
- Serializable
- AnyRef
- Any
- Hide All
- Show All
- Public
- All
Instance Constructors
-
new
TraceGenerator(numberOfTraces: Int, numberOfDifferentActivities: Int, minTraceSize: Int, maxTraceSize: Int)
- numberOfTraces
The number of traces to be produced
- numberOfDifferentActivities
The number of different event types
- minTraceSize
The minimum trace length
- maxTraceSize
The maximum trace length
Value Members
-
final
def
!=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
##(): Int
- Definition Classes
- AnyRef → Any
-
final
def
==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
-
def
clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native() @IntrinsicCandidate()
-
final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
- Annotations
- @native() @IntrinsicCandidate()
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native() @IntrinsicCandidate()
-
final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
- val maxTraceSize: Int
- val minTraceSize: Int
-
final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
final
def
notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native() @IntrinsicCandidate()
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native() @IntrinsicCandidate()
- val numberOfDifferentActivities: Int
- val numberOfTraces: Int
-
def
produce(t: List[Int]): RDD[Sequence]
Utilizes the createSequence function (private) to generate random traces and then use spark to parallelize distribute them.
Utilizes the createSequence function (private) to generate random traces and then use spark to parallelize distribute them. The difference with the produce() function, is that this one generate traces with ids that are passed as parameters
- t
A list with the trace ids
- returns
An RDD that contains the randomly generated traces
-
def
produce(t: Iterable[Long]): RDD[Sequence]
Utilizes the createSequence function (private) to generate random traces and then use spark to parallelize distribute them.
Utilizes the createSequence function (private) to generate random traces and then use spark to parallelize distribute them. The difference with the produce() function, is that this one generate traces with ids that are passed as parameters
- t
A list with the trace ids
- returns
An RDD that contains the randomly generated traces
-
def
produce(): RDD[Sequence]
Utilizes the createSequence function (private) to generate random traces and then use spark to parallelize distribute them
Utilizes the createSequence function (private) to generate random traces and then use spark to parallelize distribute them
- returns
An RDD that contains the randomly generated traces
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
toString(): String
- Definition Classes
- AnyRef → Any
-
final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native()
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
Deprecated Value Members
-
def
finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( classOf[java.lang.Throwable] ) @Deprecated
- Deprecated