Graph-based root cause analysis for service-oriented and microservice architectures

The relations between technology, work organization, worker power, workers’ rights, and workers’ experience of work have long been central concerns of CSCW. European CSCW research, especially, has a tradition of close collaboration with workers and trade unionists in which researchers aim to develop technologies and work processes that increase workplace democracy. This paper contributes a practitioner perspective on this theme in a new context: the (sometimes global) labor markets enabled by digital labor platforms. Specifically, the paper describes a method for rating working conditions on digital labor platforms (e.g., Amazon Mechanical Turk, Uber) developed within a trade union setting. Preliminary results have been made public on a website that is referred to by workers, platform operators, journalists, researchers, and policy makers. This paper describes this technical project in the context of broader cross-sectoral efforts to safeguard worker rights and build worker power in digital labor platforms. Not a traditional research paper, this article instead takes the form of a case study documenting the process of incorporating a human-centered computing perspective into contemporary trade union activities and communicating a practitioner’s perspective on how CSCW research and computational artifacts can come to matter outside of the academy. The paper shows how practical applications can benefit from the work of CSCW researchers, while illustrating some practical constraints of the trade union context. The paper also offers some practical contributions for researchers studying digital platform workers’ experiences and rights: the artifacts and processes developed in the course of the work.

Rating Working Conditions on Digital Labor Platforms

Very recently, the unexpected combination of data structures and machine learning has led to the development of a new area of research, called learned data structures. Their distinguishing trait is the ability to reveal and exploit patterns and trends in the input data for achieving more efficiency in time and space, compared to previously known data structures. The goal of this chapter is to provide the first comprehensive survey of these results and to stimulate further research in this promising area.

Learned Data Structures

Currently, organizations face the need to create scalable applications in an agile way that impacts new forms of production and business organization. The traditional monolithic architecture no longer meets the needs of scalability and rapid development. The efficiency and optimization of human and technological resources prevail; this is why companies must adopt new technologies and business strategies. However, the implementation of microservices still encounters several challenges, such as the consumption of time and computational resources, scalability, orchestration, organization problems, and several further technical complications. Although there are procedures that facilitate the migration from a monolithic architecture to micro-services, none of them accurately quantifies performance differences. The current study aims primarily to analyze some related work that evaluated both architectures. Furthermore, we assess the performance and relationship between different variables of an application that runs in a monolithic structure compared to one of the micro-services. With this, the state-of-the-art review was initially conducted, which confirms the interest of the industry. Subsequently, two different scenarios were evaluated: the first one comprises a web application based on a monolithic architecture that operates on a virtual server with KVM, and the second one demonstrates the same web application based on a microservice architecture, but it runs in containers. Both situations were exposed to stress tests of similar characteristics and with the same hardware resources. For their validation, we applied the non-parametric regression mathematical model to explain the dependency relationship between the performance variables. The results provided a quantitative technical interpretation with precision and reliability, which can be applied to similar issues.

From Monolithic Systems to Microservices: A Comparative Study of Performance

This paper explores the intersection between microservices and Multi-Agent Systems (MAS), introducing the notion of a new approach to building MAS known as Multi-Agent MicroServices (MAMS). Our approach is illustrated through a worked example of a Vickrey Auction implemented as a microservice.

MAMS: Multi-Agent MicroServices✱

We believe our approach has enabled Netflix to quickly adopt and benefit from containers. Though the details may be Netflix-specific, the approach of providing low-friction container adoption by integrating with existing infrastructure and working with the right early adopters can be a successful strategy for any organization looking to adopt containers.

Titus: introducing containers to the Netflix cloud

https://dl.acm.org/doi/pdf/10.1145/3155112.3158370

Tim Bozarth

Papers

Titus: introducing containers to the Netflix cloud

Titus: Introducing Containers to the Netflix Cloud: Approaching container adoption in an already cloud-native infrastructure