Distance-Based Trace Diagnosis for Multimedia Applications: Help Me TED!

Christiane Kamdem Kengne (1) , Noha Ibrahim (2) , Marie-Christine Rousset (2) , Maurice Tchuente (3)

1 LIG - Laboratoire d'Informatique de Grenoble
2 SLIDE - ScaLable Information Discovery and Exploitation [Saint Martin d’Hères]
3 LIRIMA - Laboratoire International de Recherche en Informatique et Mathématiques Appliquées

Execution traces have become essential resources that many developers analyze to debug their applications. Ideally, a developer wants to quickly detect whether there are anomalies on his application or not. However, in practice, size of multimedia applications trace can reach gigabytes, which makes their exploitation very complex. Usually, developers use visualization tools before stating a hypothesis. In this paper, we argue that this solution is not satisfactory and propose to automatically provide a diagnosis by comparing execution traces. We use distance based models and conduct a user case to show how TED, our automatic trace diagnosis tool, provides semantic added-value information to the developer. Performance evaluation over real world data shows that our approach is scalable.

Mots clés: Index Terms—Execution traces; Diagnosis; Audio/Video decod- ing; Multimedia applications

Domaines: Calcul parallèle, distribué et partagé [cs.DC]

Christiane Kamdem Kengne, Noha Ibrahim, Marie-Christine Rousset, Maurice Tchuente. Distance-Based Trace Diagnosis for Multimedia Applications: Help Me TED!. [Research Report] RR-LIG-045, LIG. 2013. ⟨hal-00923547v2⟩