Synthesis of Configuration Change Procedure Using Model Finder

Abstract

SUMMARYManaging the configurations of complex systems consisting of various components requires the combined efforts by multiple domain experts. These experts have extensive knowledge about different components in the system they need to manage but little understanding of the issues outside their individual areas of expertise. As a result, the configuration constraints, changes, and procedures specified by those involved in the management of a complex system are often interrelated with one another without being noticed, and their integration into a coherent procedure for configuration represents a major challenge. The method of synthesizing the configuration procedure introduced in this paper addresses this challenge using a combination of formal specification and model finding techniques. We express the knowledge on system management with this method, which is provided by domain experts as first-order logic formulas in the Alloy specification language, and combine it with system-configuration information and the resulting specification. We then employ the Alloy Analyzer to find a system model that satisfies all the formulas in this specification. The model obtained corresponds to a procedure for system configurations that satisfies all expert-specified constraints. In order to reduce the resources needed in the procedure synthesis, we reduce the length of procedures to be synthesized by defining and using intermediate goal states to divide operation procedures into shorter steps. Finally, we evaluate our method through a case study on a procedure to consolidate virtual machines.

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

“…Instead of verifying a given reconfiguration program, automated techniques can be used to generate programs that satisfy a given specification, yielding correct-by-design reconfigurations. Kikuchi et al leverage the Alloy Analyzer for this purpose [84], using it to solve a constraint satisfaction problem and extracting a reconfiguration from the solution. No reference model is used, but a very simple component model is assumed, and the user is expected to specify constraints that model components and the semantics of reconfiguration operations.…”

SMT-Based Planning Synthesis for Distributed System Reconfigurations

“…Comparatively fewer works study the problem of reconfiguration planning in models with programmable component life cycles, such as Concerto. Kikuchi et al [20] synthesize reconfiguration plans with a model finder. Unlike us, they assume that all available reconfiguration operations are given in the input of the scheduling problem, which may limit scalability.…”

“…of componentbased systems, i.e., to generate programs that coordinate the non-functional operations required to perform such reconfigurations. There have been some attempts to synthesize reconfiguration programs for component-based systems (some of them relying on an SMT solver), but they either target ad hoc, nonexecutable models [20], or are limited to specific cases such as deployment [22], where the problem of executing parallel tasks is reduced to finding a precedence order. In contrast, our work targets the full scope of the component-based reconfiguration model Concerto [9], which provides a formally-defined execution model with expressive constraints on parallelism, as well as a concrete execution engine, making it suitable for formal analysis and experimental work.…”

A Pathfinding Problem for Fork-Join Directed Acyclic Graphs with Unknown Edge Length

“…In a previous paper by the author [1], a pathfinding problem for directed trees is studied under the following situation: each edge has a nonnegative integer length, but the length is unknown in advance and should be found by a procedure whose computational cost becomes exponentially larger as the length increases. Such a situation arises in an operation synthesis problem for reconfigurable cloud computing systems [2,3]. This problem is described as follows.…”

“…This is the ratio between the performance of an online algorithm and that of an offline algorithm. From the motivated examples shown in [2,3], we assume that the computational cost of the procedure for finding the length of each edge is dominant in the total computational cost. Therefore, it suffices to consider the number of procedure calls for the evaluation.…”