ABSTRACT With the proliferation of cloud computing and the escalating demand for extensive data processing capabilities, an increasing number of enterprises are embracing hybrid cloud solutions. However, as more businesses move toward hybrid clouds, the need for effective solutions to privacy and security concerns becomes increasingly important. Although current scheduling approaches for cloud computing have addressed privacy protection to some extent, few have adequately considered the unique challenges posed by hybrid clouds. To address this gap, we propose a novel approach for scheduling jobs in hybrid clouds that prioritizes privacy protection. Our approach, called PH‐DRL, leverages Deep Reinforcement Learning (DRL) to intelligently allocate jobs to virtual machines, optimizing both privacy and Quality of Service (QoS), while minimizing response time. We present the detailed implementation of our approach and our experimental results demonstrate the superior performance of PH‐DRL in terms of privacy protection compared to existing methods.

Formal verification of a UML class diagram annotated with OCL constraints has been a long-standing challenge in Model-driven Engineering. In the past decades, many tools and techniques have been proposed to tackle this challenge. However, they do not scale well and are often unable to locate the conflicts when then number of OCL constraints significantly increases. In this paper, we present a new tool called QMaxUSE. This tool is designed for verifying UML class diagrams annotated with large number of OCL invariants. QMaxUSE is easy to install and deploy. It offers two distinct features. (1) A simple query language that allows users to choose parts of a UML class diagram to be verified. (2) A new procedure that is capable of performing concurrent verification.

Abstract Verifying whether a UML class diagram annotated with Object Constraint Language (OCL) constraints is consistent involves finding valid instances that provably meet its structural and OCL constraints. Recently, many tools and techniques have been proposed to find valid instances. However, they often do not scale well when the number of OCL constraints significantly increases. In this paper, we present a new tool called QMaxUSE that is capable of automatically verifying a large number of OCL invariants. QMaxUSE works by decomposing them into a set of different queries. It then uses an SMT solver to concurrently verify each query and pinpoints conflicting OCL invariants. Our evaluation results suggest that QMaxUSE can offer up to 30x efficiency improvement in verifying UML class diagrams with a large number of OCL invariants.

Abstract Checking the consistency of a metamodel involves finding a valid metamodel instance that provably meets the set of constraints that are defined over the metamodel. These constraints are often specified in Object Constraint Language. Often, a metamodel is inconsistent due to conflicts among the constraints. Existing approaches and tools are typically incapable of pinpointing the conflicting constraints, and this makes it difficult for users to debug and fix their metamodels. In this paper, we present a formal approach for locating conflicting constraints in inconsistent metamodels. Our approach has four distinct features: (1) users can rank individual metamodel features using their own domain-specific knowledge, (2) we transform these ranked features to a weighted maximum satisfiability modulo theories problem and solve it to compute the set of maximum achievable features, (3) we pinpoint the conflicting constraints by solving the set cover problem using a novel algorithm, and (4) we have implemented our approach into a fully automated tool called MaxUSE. Our evaluation results, using our assembled set of benchmarks, demonstrate the scalability of our work and that it is capable of efficiently finding conflicting constraints.

One of the challenges for using metamodels in Model Driven Engineering is to automatically generate metamodel instances. Each instance should satisfy many constraints defined by a metamodel. Such instances can then be used for verifying or validating metamodels. Recent studies have already shown that this can be tackled by using SAT/SMT solvers. However, such instance generation does not take coverage criteria into account, and instances satisfying specified coverage criteria could be useful for testing model transformation. In this paper, we present an approach consisting of two techniques for coverage oriented metamodel instance generation. The first technique realises the standard coverage criteria defined for UML class diagrams, while the second technique focuses on generating instances satisfying graph-based criteria. With our approach, both kinds of criteria are translated to SMT formulas which are then investigated by an SMT solver. Each successful assignment is then interpreted as a metamodel instance that provably satisfies a coverage criteria or a graph property. We have already integrated this approach into our existing tool to demonstrate the feasibility.

In this paper we present an approach to generating instances of metamodels using a Satisfiability Modulo Theories (SMT) solver as a back-end engine. Our goal is to automatically translate a metamodel and its invariants into SMT formulas which can be investigated for satisfiability by an external SMT solver, with each satisfying assignment for SMT formulas interpreted as an instance of the original metamodel. Our automated translation works by interpreting a metamodel as a bounded Attributed Type Graph with Inheritance (ATGI) and then deriving a finite universe of all bounded attribute graphs typed over this bounded ATGI. The graph acts as an intermediate representation which we then translate into SMT formulas. The full translation process, from metamodels to SMT formulas, and then from SMT instances back to metamodel instances, has been successfully automated in our tool, with the results showing the feasibility of this approach.