Engage

Abstract

Many modern applications are built by combining independently developed packages and services that are distributed over many machines with complex inter-dependencies. The assembly, installation, and management of such applications is hard, and usually performed either manually or by writing customized scripts. We present Engage, a system for configuring, installing, and managing complex application stacks. Engage consists of three components: a domain-specific model to describe component metadata and intercomponent dependencies; a constraint-based algorithm that takes a partial installation specification and computes a full installation plan; and a runtime system that co-ordinates the deployment of the application across multiple machines and manages the deployed system. By explicitly modeling configuration metadata and intercomponent dependencies, Engage enables static checking of application configurations and automated, constraint-driven, generation of installation plans across multiple machines. This reduces the tedious manual process of application configuration, installation, and management.We have implemented Engage and we have used it to successfully host a number of applications. We describe our experiences in using Engage to manage a generic platform that hosts Django applications in the cloud or on premises.

Component-based Distributed Software Reconfiguration:A Verification-oriented Survey

“…In a distributed system, configuring a system includes deploying this system, i.e., placing each of the entities of the system at a location and configuring the hosting machine so that the software element can run properly. The placement of the entities is generally the solution of an optimization or a satisfiability problem [7,72,73], while the local configuration often consists in installing and configuring packages and modules, configuring the operating system, and sometimes running containers or virtual machines [54,59].…”

“…Consequently, many models also distinguish between active and inactive components [25,31]. Finally, other models, specific to the management aspect of component-based architectures (comparable to DevOps tools) offer either advanced fixed life-cycle definitions [59,60], or the definition of a programmable life-cycle with an arbitrary number of states [41,42,54]. Transitions between these states often have to be specified and mapped to actions, for example with a finite state machine [54,59].…”

Toward safe and efficient reconfiguration with Concerto

“…For example, a Fractal component can wrap an existing piece of software and add a membrane to control it (e.g., Jade and Tune approaches). SmartFrog [43], Engage [19], Aeolus [44] and the contribution detailed in [45,46,47] offer ways to program the lifecycle of any distributed software systems, including reconfiguration steps, by using finite state automata.…”

“…Engage [19], Aeolus [57,44], and the contribution of Brogi et al [45,46,47] are component-oriented reconfiguration models in which the lifecycle of each component is modeled as a fully customizable finite state machine. In addition to featuring programmable lifecycles, these solutions automatically coordinate the lifecycles of components through ports or dependencies.…”

“…Additionally, these tools usually lack well-documented semantics, making them unsuitable for static analysis, performance estimation or formal verification. Furthermore, academic models such as Aeolus [18] or Engage [19] lack the ability to describe independent reconfiguration tasks for a given component, which is needed to execute them in parallel. Other models may offer the right level of parallelism, but be unsuitable to represent reconfigurations.…”