Seeing Through Black Boxes : Tracking Transactions through Queues under Monitoring Resource Constraints

TL;DR

This paper addresses the challenge of optimally allocating limited monitoring resources in distributed queueing systems to improve transaction tracking accuracy, balancing between precise identifiers and imprecise timestamps.

Contribution

It introduces two heuristics for resource allocation based on predicting timestamp accuracy, with theoretical conditions for near-optimality and validation through simulations.

Findings

Heuristics perform close to optimal in simulations.

Optimal allocation depends on queueing dynamics and resource constraints.

Conditions for heuristic optimality are derived and validated.

Abstract

The problem of optimal allocation of monitoring resources for tracking transactions progressing through a distributed system, modeled as a queueing network, is considered. Two forms of monitoring information are considered, viz., locally unique transaction identifiers, and arrival and departure timestamps of transactions at each processing queue. The timestamps are assumed available at all the queues but in the absence of identifiers, only enable imprecise tracking since parallel processing can result in out-of-order departures. On the other hand, identifiers enable precise tracking but are not available without proper instrumentation. Given an instrumentation budget, only a subset of queues can be selected for production of identifiers, while the remaining queues have to resort to imprecise tracking using timestamps. The goal is then to optimally allocate the instrumentation budget to maximize the overall tracking accuracy. The challenge is that the optimal allocation strategy depends on accuracies of timestamp-based tracking at different queues, which has complex dependencies on the arrival and service processes, and the queueing discipline. We propose two simple heuristics for allocation by predicting the order of timestamp-based tracking accuracies of different queues. We derive sufficient conditions for these heuristics to achieve optimality through the notion of stochastic comparison of queues. Simulations show that our heuristics are close to optimality, even when the parameters deviate from these conditions.