Using Objectives and Key Results (OKRs) and Slack: A Case Study of Coordination in Large-Scale Distributed Agile

Abstract

Today, many large-scale software projects have members working from home, which has changed the way teams coordinate work. To better understand coordination in this setting, we conducted a case study through which we examined two teams in a large-scale agile project by observing meetings and conducting 17 interviews. Through the lens of Relational Coordination Theory (RCT), we analyzed the use of the goal-setting framework Objectives and Key Results (OKRs) and the collaboration tool Slack. Slack was used for frequent, timely, and problem-solving communication and, and its use decreased the number of planned meetings. However, discussions often started on Slack and continued in virtual ad-hoc meetings. The use of OKRs facilitated knowledge sharing, helped the teams align their goals, and provided inter-team insights. The main implication of our research is that projects using OKRs need to support project members, especially in formulating the key results that align and motivate the teams to work toward the same mission.

Global Resource Scheduling for Distributed Edge Computing

“…The learning factor has the effect of evaluating itself and learning other particles. rand() is a pseudorandom number in the interval [0, 1]. In this paper, the parameters α,β of the traditional ACO are optimized by training.…”

“…Then, position x and speed v are converted into parameters {α, β}; The PSO is used to optimize these two parameters after each iteration of the ant colony algorithm as the initial parameters of the next iteration. The value range of α is set to [1,2], and the value range of β is set to [1,2]. So the lower bound of {α, β} is {1, 1}, and σ is the difference between the upper and lower bound.…”

“…The existence of different resource prices further complicates the challenge posed by the global resource scheduling problem. Several scholars have undertaken extensive research on large-scale distributed resource migration and dispatching, each based on different application scenarios [1][2][3]. For instance, Jia et al [4] proposed a two-layer distributed collaborative evolution (DCE) architecture with adaptive computing resources for large-scale optimization.…”