To illustrate the type of tasks considered, Figure 1 depicts measurement of latency, expressed through the round trip time. The abrupt increase in latency measurements, termed network volatility, can indicate noise in the network measurements, or, alternatively, network congestion. Here one sub-task can be “identify and remove the network measurement noise”, while the other sub-task is “detect network congestion”. Figure 1 also contains labeling thresholds for classifying noise and congestion, respectively, and they are generated by the proposed labeling functions. In the proposed framework, these labels are refined through a multi-task sharing stage and, finally, a stage in which the network operator can apply its domain expertise to increase the confidence of classification and provide reasoning behind the result.

JSAC: What do you think could be a major weakness of ARISE compared to single-task learning and in which scenarios ARISE can appear to be more challenged than the other approaches from the state-of-the-art?

Authors: In designing ARISE, we were motivated by two key observations. First, many networking problems of practical interest are multi-task in nature; that is, solving such a problem requires performing a number of different sub-tasks. Second, while these sub-tasks typically differ in terms of objective and feature engineering, there is often a significant overlap among the features that get selected for the various sub-tasks; we refer to such overlapping features as “composite characteristics.” An illustrative example is the long-standing problem of detecting amplification-style DDoS attacks. This problem is inherently multi-task---one sub-task per known protocol (e.g., DNS, NTP, ICMP) that can be exploited to generate a high volume of traffic for the purpose of overwhelming a target (e.g., web server, end host). At the same time, the different sub-tasks exhibit readily identifiable composite characteristics, such as certain aggregate traffic statistics (e.g., number of packets, bytes or flows) or the number of distinct source IPs.

ARISE is ill-suited for networking problems for which these two key observations do not hold. An example of such a problem is being asked to perform the basic task of removing noise in a given measurement dataset. This problem is not amenable to multi-task learning and also contains no composite characteristics. As a result, applying single-task learning is a more appropriate approach to tackling this problem. More generally, in cases where both ARISE and state-of-the-art alternatives are applicable, the challenge in using ARISE is that generating suitable multi-task learning models typically requires more domain knowledge than developing the type of black-box learning models that are at the center of most of the currently considered alternative solution approaches. However, instead of being problematic, we view this challenge as an opportunity to overcome the existing distrust that operators have in handing over critical decision making to black-box models they do not understand.

JSAC: What was criticized by the reviewers? Was there a criticism from the reviewers that led you to rethink part of your work?

Authors: One astute reviewer pointed out that ARISE ought to be compared to alternative advanced multi-task learning approaches such as meta-learning. Thanks to this comment, we were able to better position our work within the existing literature on modern meta-learning. For one, we were able to show that ARISE already shares several aspects with meta-learning (e.g., quickly adapting existing models to new tasks based on common representations of underlying information). Importantly, we demonstrate empirically that ARISE advances modern meta-learning by solving some inherent challenges in existing meta-learning algorithms. In particular, we show that ARISE does not require large labeled datasets, is effective (i.e., achieves high accuracy) and computationally efficient, and represents a first attempt at leveraging meta-learning to enhance the application of multi-task learning for solving networking problems in practice.