Satisfiability Of Constraints Research Articles

Dual-mutation mechanism-driven snake optimizer for scheduling multiple budget constrained workflows in the cloud

Scheduling workflows in cloud computing is to find the most appropriate mapping from a series of dependent tasks to a set of available virtual resources so as to minimize or maximize some user concerned objectives, which is very significant to a sustainable and high-efficient cloud data center. However, it still faces challenges since its NP-hardness and the diversified requirements of both the cloud service consumers and cloud service providers need to be satisfied at the same time, especially in addressing multiple applications requested simultaneously. This work proposes a dual-mutation mechanism-driven snake optimizer for scheduling multiple workflows in the cloud for minimizing the makespan of each workflow under user pre-defined budget constraints. Firstly, a continuous optimization algorithm, namely Snake Optimizer (SO) is adopted into discrete optimization, i.e., workflow scheduling. Secondly, a task execution order aware fitness function is designed to reduce the gaps or waiting time between parent and child tasks within a workflow and thus reduce the total execution time of the workflow. Besides, we analyze the existing snake optimizer and adjust the parameters corresponding to different evolutionary stages to adapt to our considered problem. Finally, a dual-mutation mechanism is developed by introducing a non-improvement iteration number for each snake and applying a standard bit mutation operation to prematurely converging snakes and the individuals randomly selected from the remaining snakes so that the population diversity can be enhanced and more potential solutions can be explored. Experimental results on a set of real-world scientific workflows show that our proposed algorithm is of great superiority in constraint satisfiability, meaning that compared with its peers, it is always the first to find feasible solutions.

Constraint guided gradient descent: Training with inequality constraints with applications in regression and semantic segmentation

Deep learning is typically performed by learning a neural network solely from data in the form of input–output pairs ignoring available domain knowledge. In this work, the Constraint Guided Gradient Descent (CGGD) framework is proposed that enables the injection of domain knowledge into the training procedure. The domain knowledge is assumed to be described as a conjunction of hard inequality constraints which appears to be a natural choice for several applications. Compared to other neuro-symbolic approaches, the proposed method converges to a point that makes an arbitrarily small error with respect to any inequality constraint on the training data and does not require to first transform the constraints into some ad-hoc term that is added to the learning (optimization) objective. It is empirically shown on four independent and small data sets that CGGD makes training less dependent on the initialization of the network, improves the constraint satisfiability on all data, improves the generalization of the model to unseen data, and relaxes the need for annotated data. Moreover, the method is tested for a regression and a semantic segmentation task.

Automatic generation of dominance breaking nogoods for a class of constraint optimization problems

Constraint Optimization Problems (COPs) ask for an assignment of values to variables in order to optimize an objective subject to constraints that restrict the value combinations in the assignment. They are usually solved by the classical Branch and Bound (B&B) search algorithm. Dominance breaking is an important technique in B&B to prune assignments that are subordinate to others concerning the objective value and/or the satisfiability of constraints. In practice, the addition of constraints for dominance breaking can drastically speed up the B&B search for solving many COPs. However, identification of suboptimal assignments in COPs and derivation of useful constraints for dominance breaking are usually problem-specific and require sophisticated human insights on the problem structure.This paper proposes the first theoretical and practical framework for automatic generation of dominance breaking constraints for a class of COPs consisting of efficiently checkable objectives and constraints. In particular, the framework focuses on generating nogood constraints representing incompatible value assignments and formulates nogood generation as solving auxiliary constraint satisfaction problems. The proposed method can generate nogoods of varying strengths for dominance breaking by controlling the number of involved variables. Experimentation on various benchmarks demonstrates the effectiveness of the proposal in both efficiency and ease of use. The superior performance is also supported by a theoretical analysis to compare the relative strength of automatically generated nogoods with manually derived dominance breaking constraints in the literature.

Enforcement of separation of duty constraints in attribute-based access control

While the verification of separation of duty (SoD) constraints on attribute based access control (ABAC) systems has been defined and examined in existing works, it still remains an open problem since the existing approaches are either inefficient or inadequate to check all SoD constraints; furthermore, the problem of solving SoD violations has not yet been addressed in the literature. In this paper, we present an integer linear programming (ILP) based approach to check the satisfiability of all SoD constraints. It can be applied to different SoDs. Hence, it is feasible in real-world scenarios. Moreover, we present an ILP based violation solution that can correct all SoD violations with a minimum number of restrictions regarding users’ normal operations on target objects. All SoD violations can be solved in a once-and-done way while simultaneously guaranteeing the satisfaction of all SoD constraints.

Gradient-Based Scheduler for Scientific Workflows in Cloud Computing

It is becoming increasingly attractive to execute workflows in the cloud, as the cloud environment enables scientific applications to utilize elastic computing resources on demand. However, despite being a key to efficiently managing application execution in the cloud, traditional workflow scheduling algorithms face significant challenges in the cloud environment. The gradient-based optimizer (GBO) is a newly proposed evolutionary algorithm with a search engine based on the Newton’s method. It employs a set of vectors to search in the solution space. This study designs a gradient-based scheduler by using GBO for workflow scheduling to minimize the usage costs of workflows under given deadline constraints. Extensive experiments are conducted on well-known scientific workflows of different sizes and types using WorkflowSim. The experimental results show that the proposed scheduling algorithm outperforms five other state-of-the-art algorithms in terms of both the constraint satisfiability and cost optimization, thereby verifying its advantages in addressing workflow scheduling problems.

Preprocessing succinct non-interactive arguments for rank-1 constraint satisfiability from holographic proofs

We design a new preprocessing succinct non-interactive argument (SNARG) for rank-1 constraint satisfiability (R1CS) from holographic Interactive Oracle Proofs (IOPs). The protocol is secure in the random oracle model (ROM). We achieve polylogarithm time verifier and polylogarithm proof size. Our construction consists of two parts: (1) a holographic IOP for R1CS with linear size proof and logarithmic query complexity. (2) A cryptography complier converts holographic IOPs to SNARGs. In the first part, univariate sumcheck and low degree testing play an important role in the holographic IOP. In the second part, we use Merkle tree scheme and Fiat-Shamir heuristic to achieve succinctness and non-interactivity.

Mutation and dynamic objective-based farmland fertility algorithm for workflow scheduling in the cloud

Nowadays, many scientific applications are deployed in the cloud to execute at a lower cost. However, the growing scale of workflows makes scheduling problems challenging. To minimize the workflow execution cost under deadline constraints, this article proposes a Mutation and Dynamic Objective-based Farmland Fertility (MDO-FF) algorithm for obtaining a near-optimal solution within a relatively shorter time. A Dynamic Objective Strategy (DOS) is introduced to accelerate the convergence speed, while a multi-swarm evolutionary approach and mutation strategies are incorporated to enhance the search diversity and help to escape from local optima. By seeking new potential solutions and searching in its corresponding neighborhoods, our proposed MDO-FF can make a good trade-off between exploration and exploitation. Extensive experiments are conducted on well-known scientific workflows with different types and sizes. The experimental results demonstrate that in most cases, our MDO-FF outperforms the existing algorithms in terms of constraint satisfiability and solution quality.

PSO+LOA: hybrid constrained optimization for scheduling scientific workflows in the cloud

Cloud computing provides a cost-effective deploying environment for hosting and executing workflows as its elasticity, scalability and pay-per-use model. Scientific applications are normally compute- or resource-intensive, and how to run them in the cloud while both meeting QoS of users and guaranteeing the benefits of cloud service providers (CSPs) is still a challenge and depends mainly on workflow scheduling. In this article, we propose a hybrid optimization approach, PSO+LOA, i.e., a combination of particle swarm optimization (PSO) and lion optimization algorithm (LOA) for scheduling workflows in the cloud to minimize the total execution time under budget constraints. The main contributions of our work are: (1) A Euclidean distance (ED) aware particle reposition strategy is defined for two close particles, so as to separate them away from each other, hence enhancing the capability of escaping from local optima. (2) To improve the search and convergence efficiency of original PSO, we modify the velocity update equation by introducing adaptive parameters. (3) Inspired by the multiple-swarm co-evolutionary mechanism of LOA, we integrate PSO with LOA to make a good balance between exploration and exploitation during the whole optimization process. Extensive experiments are conducted over well-known scientific workflows with different sizes and types through WorkflowSim. The experimental results demonstrate that in most cases, PSO+LOA outperforms the existing algorithms in the extent of budget constraint satisfiability, solution quality, i.e., it can generate much better solutions which meet the needs of different budget constraints, especially for large-scale applications, such as the average relative deviation index for PSO+LOA and genetic algorithm (GA) are 0.03% and 0.20%, respectively.

Finding Solutions to the Integer Case Constraint Satisfiability Problem Using Grover’s Algorithm

Constraint satisfiability problems, crucial to several applications, are solved on a quantum computer using Grover's search algorithm, leading to a quadratic improvement over the classical case. The solutions are obtained with high probability for several cases and are illustrated for the cases involving two variables for both 3- and 4-bit numbers. Methods are defined for inequality comparisons, and these are combined according to the form of the satisfiability formula, to form the oracle for the algorithm. The circuit is constructed using IBM Qiskit and is verified on an IBM simulator. It is further executed on one of the Noisy Intermediate-Scale Quantum (NISQ) processors from IBM on the cloud. Noise levels in the processor at present are found to be too high for successful execution. Running the algorithm on the simulator with a custom noise model lets us identify the noise threshold for successful execution.

Algorithms for Workflow Satisfiability Problem with User-independent Constraints

The Workflow Satisfiability Problem (WSP) is a problem of interest in access control of information security. In its simplest form, the problem coincides with the Constraint Satisfiability Problem, where the number of variables is usually much smaller than the number of values. Wang and Li (ACM Trans. Inf. Syst. Secur. 2010) were the first to study the WSP as a problem parameterized by the number of variables. Their paper initiated very fruitful research surveyed by Cohen, Crampton, Gutin and Wahlström (2017). In this paper, we overview more recent WSP algorithmic developments and discuss computational experiments performed on two new testbeds of WSP instances. These WSP instances are closer to real-world ones than those by Karapetyan et al. (JAIR 2019). One of the two testbeds is generated using a novel iterative approach for obtaining computationally hard WSP instances.

Flatten and conquer: a framework for efficient analysis of string constraints

We describe a uniform and efficient framework for checking the satisfiability of a large class of string constraints. The framework is based on the observation that both satisfiability and unsatisfiability of common constraints can be demonstrated through witnesses with simple patterns. These patterns are captured using flat automata each of which consists of a sequence of simple loops. We build a Counter-Example Guided Abstraction Refinement (CEGAR) framework which contains both an under- and an over-approximation module. The flow of information between the modules allows to increase the precision in an automatic manner. We have implemented the framework as a tool and performed extensive experimentation that demonstrates both the generality and efficiency of our method.

A sprouting graph-based approach to analysing timed workflow processes with shared resources

Recently, temporal constraint satisfiability is regarded as an important criterion in the field of business process management. Existing methods do not fully investigate the various modes of shared resources, cannot provide the solutions for temporal violations or cannot develop the prototype system to automatically verify them. In this paper, we propose a new sprouting graph based approach to analysing temporal constraints of workflow processes with shared resources. First of all, in order to deal with various modes of shared resources we propose the corresponding patterns to record the executing information of resources. Then, how to automatically construct sprouting graph is presented, and the procedure of solving temporal violations is proposed. Finally, we also develop a prototype system. Compared with existing works, our approach is more effective and practical in the real business environment.

Automated analysis of integrity constraints in multi-level models

Multi-level modelling is a technology for model-based development that enables the incremental refinement of models in successive meta-levels, which results in simpler and more intentional system descriptions in some scenarios. In this approach, integrity constraints can be placed at any meta-level, and need to indicate the meta-level below at which they should hold. This requires a careful design, as constraints defined at different meta-levels may interact in unexpected ways. Unfortunately, current techniques for the analysis of the satisfiability of constraints are designed for two meta-levels only. Hence, nowadays, the analysis of multi-level solutions is performed by hand, which is tedious and error-prone.In this paper, we define an automated procedure to check the satisfiability of integrity constraints in a multi-level setting, leveraging on “off-the-shelf” model finders. This procedure is supported by our multi-level modelling tool MetaDepth, which has been extended to reason on the satisfiability of constraints in multi-level models, and to perform automated model completion.

BiC2PAM: constraint-guided biclustering for biological data analysis with domain knowledge.

BackgroundBiclustering has been largely used in biological data analysis, enabling the discovery of putative functional modules from omic and network data. Despite the recognized importance of incorporating domain knowledge to guide biclustering and guarantee a focus on relevant and non-trivial biclusters, this possibility has not yet been comprehensively addressed. This results from the fact that the majority of existing algorithms are only able to deliver sub-optimal solutions with restrictive assumptions on the structure, coherency and quality of biclustering solutions, thus preventing the up-front satisfaction of knowledge-driven constraints. Interestingly, in recent years, a clearer understanding of the synergies between pattern mining and biclustering gave rise to a new class of algorithms, termed as pattern-based biclustering algorithms. These algorithms, able to efficiently discover flexible biclustering solutions with optimality guarantees, are thus positioned as good candidates for knowledge incorporation. In this context, this work aims to bridge the current lack of solid views on the use of background knowledge to guide (pattern-based) biclustering tasks.MethodsThis work extends (pattern-based) biclustering algorithms to guarantee the satisfiability of constraints derived from background knowledge and to effectively explore efficiency gains from their incorporation. In this context, we first show the relevance of constraints with succinct, (anti-)monotone and convertible properties for the analysis of expression data and biological networks. We further show how pattern-based biclustering algorithms can be adapted to effectively prune of the search space in the presence of such constraints, as well as be guided in the presence of biological annotations. Relying on these contributions, we propose BiClustering with Constraints using PAttern Mining (BiC2PAM), an extension of BicPAM and BicNET biclustering algorithms.ResultsExperimental results on biological data demonstrate the importance of incorporating knowledge within biclustering to foster efficiency and enable the discovery of non-trivial biclusters with heightened biological relevance.ConclusionsThis work provides the first comprehensive view and sound algorithm for biclustering biological data with constraints derived from user expectations, knowledge repositories and/or literature.

Hierarchical task network-based emergency task planning with incomplete information, concurrency and uncertain duration

This paper focuses on the emergency task planning problem with the following characteristics: incomplete initial environment information, concurrent execution and uncertain execution durations. To address this problem, the conditional temporal hierarchical task network (HTN) planning with uncertain durations should be investigated. However, the research work on conditional HTN planning mainly concentrates on the planning domain without temporal features and pays no attention to the handling of concurrency and uncertain durations. This motivates us to propose a planning paradigm based on the conditional HTN planning. The planning paradigm incorporates a temporal reasoning technique suitable for conditional HTN planning to achieve concurrency and a temporal management approach to confirm the satisfiability of temporal constraints efficiently. For temporal reasoning, rules for generating temporal constraints are designed to avoid three types of flaws due to interactions between durative actions; a mechanism for detecting invalid temporal constraints is also presented. Regarding temporal management, Conditional Simple Temporal Network with Uncertainty (CSTNU) is utilized to represent the temporal constraints involving uncontrollable intervals and observations, and an approach for eliminating redundant temporal constraints is proposed to reduce the temporal constraint to be represented in a CSTNU to promote efficiency. Finally, an experimental study of an earthquake rescue domain demonstrates the viability of the proposed planning paradigms.

Satisfiability of general intruder constraints with and without a set constructor

Many decision problems on security protocols can be reduced to solving deduction constraints expressing whether an instance of a given message pattern can be constructed by the intruder. Most constraint solving procedures for protocol security rely on two properties of constraint systems called monotonicity and variable-origination. In this work we relax these restrictions by giving a decision procedure for solving general intruder constraints (that do not have these properties) that stays in NP. The result is also valid modulo an associative, commutative and idempotent theory. The procedure can be applied to verify security protocols in presence of multiple intruders.

Parallelizing simplex within SMT solvers

The usual approach in parallelizing SAT and SMT solvers is either to explore different parts of the search space in parallel (divide-and-conquer approach) or to run multiple instances of the same solver with suitably altered parameters in parallel, possibly exchanging some information during the solving process (parallel portfolio approach). Quite a different approach is to parallelize the execution of time-consuming algorithms that check for satisfiability and propagations during the search space exploration. Since most of the execution time is spent in these procedures, their efficient parallelization might be a promising research direction. In this paper we present our experience in parallelizing the simplex algorithm which is typically used in the SMT context to check the satisfiability of linear arithmetic constraints. We provide a detailed description of this approach and present experimental results that evaluate the potential of the approach compared to the parallel portfolio approach. We also consider the combination of the two approaches.

An efficient SMT solver for string constraints

An increasing number of applications in verification and security rely on or could benefit from automatic solvers that can check the satisfiability of constraints over a diverse set of data types that includes character strings. Until recently, satisfiability solvers for strings were standalone tools that could reason only about fairly restricted fragments of the theory of strings and regular expressions (e.g., strings of bounded lengths). These solvers were based on reductions to satisfiability problems over other data types such as bit vectors or to automata decision problems. We present a set of algebraic techniques for solving constraints over a rich theory of unbounded strings natively, without reduction to other problems. These techniques can be used to integrate string reasoning into general, multi-theory SMT solvers based on the common DPLL(T) architecture. We have implemented them in our SMT solver cvc4, expanding its already large set of built-in theories to include a theory of strings with concatenation, length, and membership in regular languages. This implementation makes cvc4 the first solver able to accept a rich set of mixed constraints over strings, integers, reals, arrays and algebraic datatypes. Our initial experimental results show that, in addition, on pure string problems cvc4 is highly competitive with specialized string solvers accepting a comparable input language.

Scaling collapse at the jamming transition.

The jamming transition of particles with finite-range interactions is characterized by a variety of critical phenomena, including power-law distributions of marginal contacts. We numerically study a recently proposed simple model of jamming, which is conjectured to lie in the same universality class as the jamming of spheres in all dimensions. We extract numerical estimates of the critical exponents, θ=0.451±0.006 and γ=0.404±0.004, that match the exponents observed in sphere packing systems. We analyze finite-size scaling effects that manifest in a subcritical cutoff regime and size-independent but protocol-dependent scaling curves. Our results support the conjectured link with sphere jamming, provide more precise measurements of the critical exponents than previously reported, and shed light on the finite-size scaling behavior of continuous constraint satisfiability transitions.

Automated Verification Of Role-Based Access Control Policies Constraints Using Prover9

Access control policies are used to restrict access to sensitive records for authorized users only. One approach for specifying policies is using role based access control (RBAC) where authorization is given to roles instead of users. Users are assigned to roles such that each user can access all the records that are allowed to his/her role. RBAC has a great interest because of its flexibility. One issue in RBAC is dealing with constraints. Usually, policies should satisfy pre-defined constraints as for example separation of duty (SOD) which states that users are not allowed to play two conflicting roles. Verifying the satisfiability of constraints based on policies is time consuming and may lead to errors. Therefore, an automated verification is essential. In this paper, we propose a theory for specifying policies and constraints in first order logic. Furthermore, we present a comprehensive list of constraints. We identity constraints based on the relation between users and roles, between roles and permission on records, between users and permission on records, and between users, roles, and permission on records. Then, we use a general purpose theorem prover tool called Prover9 for proving the satisfaction of constraints.