In　network　measurement,　data　plane　switches　measure　traffic　and　report　events　(e.g.,　heavy　hitters)　to　the　control　plane　via　control　channels.　The　control　plane　makes　decisions　to　process　events.　However,　current　network　measurement　suffers　from　two　problems.　First,　when　traffic　bursts　occur,　massive　events　are　reported　in　a　short　time　so　that　the　control　channels　may　be　overloaded　due　to　limited　bandwidth　capacity.　Second,　only　a　few　events　are　reported　in　normal　cases,　making　control　channels　underloaded　and　wasting　network　resources.　In　this　paper,　we　propose　Escala　to　provide　the　elastic　scaling　of　control　channels　at　runtime.　The　key　idea　is　to　dynamically　migrate　event　streams　among　control　channels　to　regulate　the　loads　of　these　channels.　Escala　offers　two　components,　including　an　Escala　monitor　that　detects　scaling　situations　based　on　realtime　network　statistics,　and　an　optimization　framework　that　makes　scaling　decisions　to　eliminate　overload　and　underload　situations.　We　have　implemented　a　prototype　of　Escala　on　Tofino-based　switches.　Extensive　experiments　show　that　Escala　achieves　timely　elastic　scaling　while　preserving　high　application-level　accuracy.