Nowadays, numerous programming languages and frameworks offer concurrency based on the actor model. Among the actor libraries for the Java Virtual Machine, Akka is the most used one, as it is employed in various parallel and distributed applications and frameworks. Unfortunately, despite the spread of actors libraries, Akka in particular, existing profiling tools are not very effective at identifying performance drawbacks in applications using actors. In this tool paper, we aim at filling this gap by presenting AkkaProf, a profiling tool for Akka actors. AkkaProf provides detailed metrics on actor utilization and on the communication between them, two fundamental aspects of actor-based applications that are overlooked by other profilers. AkkaProf aids performance analysis in several applications and frameworks in both parallel and distributed environments.@en

Big-data applications are becoming the core of today's business operations, featuring complex data structures and high task fan-out. According to the publicly available Google trace, more than 40% of big-data jobs do not reach successful completion. Interestingly, a significant portion of tasks of such failed jobs undergo multiple types of repetitive failed executions and consume a non-negligible amount of resources. To conserve resources for big-data clusters, it is imperative to capture such failed tasks of failed jobs, a very challenging problem due to multiple types of failures associated with tasks and highly uneven tasks distribution. In this paper, we develop an on-line two-level Neural Network (NN) model which can accurately untangle the complex dependencies among tasks and jobs, and predict their execution classes in an extremely dynamic and heterogeneous system. Our proposed NN model predicts first the job class, and secondly three classes of failed tasks of failed jobs, based on a sliding learning window. Furthermore, we develop resource conservation policies that terminate failed tasks of failed jobs after a grace period that is derived from prediction confidences and task execution times. Overall, evaluating our results on a Google cluster trace, we are able to accurately capture failures of failures at big-data clusters, mitigate false negative tasks to 1%, and efficiently save system resources, achieving significant reductions of CPU, memory and disk consumption - as high as 49%.@en

Message exchange is a central activity in distributed computing frameworks. Nevertheless, past research has paid little attention on profiling techniques and tools for endpoint communication. In this paper, we fill this gap by introducing a new fine-grained profiler for endpoints and communication between them in distributed systems. Our tool aids efficient analysis, optimization, and tuning of endpoint communication in distributed frameworks. The paper presents an overview of our profiler, discusses employed profiling techniques and collected metrics, and shows preliminary results in profiling a well-known distributed computing framework.@en