Detecting and resolving deadlocks in mobile agent systems

Mobile agents environment is a new application paradigm with unique features such as mobility and autonomy. Traditional deadlock detection algorithms in distributed computing systems do not work well in mobile agent systems due to the unique feature property of the mobile agent. Existing deadlock detection and resolution algorithms in mobile agent systems have limitations such as performance inefficiency and duplicate detection/resolution when multiple mobile agents simultaneously detect/resolve the same deadlock. To address these problems, we propose an improved deadlock detection and resolution algorithm that adopts priority-based technique and lazy reaction strategy. The priority-based technique aims to ensure that there is only one instance of deadlock detection and resolution, and it also helps reduce mobile agent movement and data traffic together with the lazy reaction strategy. The liveness and safety properties of the proposed algorithm are proved in this paper. Theoretical analysis and experimental results show that the proposed algorithm provides better performance in terms of agent movement, data traffic, and execution time.     

The Performance of Alternative Strategies for Dealing with Deadlocks in Database Management Systems

There is growing evidence that, for a fairly wide variety of database workloads and system configurations, locking is the concurrency control strategy of choice. With locking, of course, comes the possibility of deadlocks. Although the database literature is full of algorithms for dealing with deadlocks, very little in the way of practical performance information is available to a database system designer faced with the decision of choosing a good deadlock resolution strategy. This paper is an attempt to bridge this gap in our understanding of the behavior and performance of alternative deadlock resolution strategies. We employ a simulation model of a database environment to study the relative performance of several strategies based on deadlock detection, several strategies based on deadlock prevention, and a strategy based on timeouts. We show that the choice of the best deadlock resolution strategy depends upon the level of data contention, the resource utilization levels, and the types of transactions. We provide guidelines for selecting a deadlock resolution strategy for different operating regions.     

移动Agent系统的一种分布式死锁检测方案

传统的分布式死锁解决方案不适合于实体在网络中自由移动的MAS系统.本文描述了一种移动Agent系统的分布式死锁算法,使用专职Agent从事死锁检测和解决.该方案的特点是地点参考、拓扑独立、容错、异步操作.文中建立了StochasticPetri Net模型,并使用仿真试验给出它和Diffusion Computation算法的性能比较.     

Mobile agent-enabled framework for structuring and building distributed systems on the internet

Mobile agent has shown its promise as a powerful means to complement and enhance existing technology in various application areas. In particular, existing work has demonstrated that MA can simplify the development and improve the performance of certain classes of distributed applications, especially for those running on a wide-area, heterogeneous, and dynamic networking environment like the Internet. In our previous work, we extended the application of MA to the design of distributed control functions, which require the maintenance of logical relationship among and/or coordination of proc- essing entities in a distributed system. A novel framework is presented for structuring and building distributed systems, which use cooperating mobile agents as an aid to carry out coordination and cooperation tasks in distributed systems. The framework has been used for designing various distributed control functions such as load balancing and mutual ex- clusion in our previous work. In this paper, we use the framework to propose a novel ap- proach to detecting deadlocks in distributed system by using mobile agents, which dem- onstrates the advantage of being adaptive and flexible of mobile agents. We first describe the MAEDD (Mobile Agent Enabled Deadlock Detection) scheme, in which mobile agents are dispatched to collect and analyze deadlock information distributed across the network sites and, based on the analysis, to detect and resolve deadlocks. Then the design of an adaptive hybrid algorithm derived from the framework is presented. The algorithm can dynamically adapt itself to the changes in system state by using different deadlock detec- tion strategies. The performance of the proposed algorithm has been evaluated using simulations. The results show that the algorithm can outperform existing algorithms that use a fixed deadlock detection strategy.     

Avoidance versus detection and recovery problem in buffer-spaceallocation of flexibly automated production systems

Motivated by recent developments in the semiconductor manufacturing industry, this paper undertakes an analytical investigation of the problem of selecting optimally the deadlock resolution strategy for buffer space allocation in flexibly automated production systems. In the process, it extends the behavioral models for the aforementioned systems currently considered in the literature, to account for probabilistic uncontrollable effects like the requirement for extra finishing steps and/or rework, and it introduces a new deadlock resolution scheme, characterized as randomized deadlock avoidance. The combination of these two extensions brings the considered system behavior(s) to the realm of probabilistic automata, an area of increasing academic interest. For the resulting model, and under the assumption of Markovian timings, it develops an analytical methodology for selecting the optimal deadlock resolution strategy that maximizes the steady-state system throughput, and it demonstrates its effectiveness through application to a "prototype" system configuration. The obtained results provide an interesting analytical expression of the need to assess the gains obtained by the increased concurrency supported by the deadlock detection and recovery strategy versus the productivity losses experienced under this approach due to increased system blocking, and/or additional material handling overheads. It turns out that, for the considered system configuration, the optimal selection scheme switches between detection and recovery and pure deadlock avoidance, every time that the time cost of deadlock recovery, tau(d), crosses a threshold Theta, which is a function of the remaining system behavioral and timing parameters. Beyond its own theoretical merit, this last result raises also the question of whether the policy randomization introduced in this work will ever enhance the performance of any configuration in the considered class of Resource Allocation Systems (RAS); this issue will be investigated in a sequel paper.     

Hybrid heuristic search approach for deadlock-free scheduling of flexible manufacturing systems using Petri nets

Based on the Petri net models of flexible manufacturing systems (FMSs), this paper focuses on deadlock-free scheduling problem with the objective of minimizing the makespan. Two hybrid heuristic search algorithms for solving such scheduling problems of FMSs are proposed. To avoid deadlocks, the deadlock control policy is embedded into heuristic search strategies. The proposed algorithms combine the heuristic best-first strategy with the controlled backtracking strategy based on the execution of the Petri nets. The scheduling problem is transformed into a heuristic search problem in the reachability graph of the Petri net, and a schedule is a transition sequence from the initial marking to the final marking in the reachability graph. By using the one-step look-ahead method in the deadlock control policy, the safety of a state in the reachability graph is checked, and hence, deadlock is avoided. Experimental results are provided and indicate the effectiveness of the proposed hybrid heuristic search algorithms in solving deadlock-free scheduling problems of FMSs. Especially, the comparison against previous work shows that both new algorithms are promising in terms of solution quality and computing times.     

一种基于正关系的多Agent协调算法

多Agent之间的协调(coordination)与协作(cooperation)已经成为多Agent系统(multiagent system,MAS)中的一个关键问题。这是因为MAS的主要研究目标之一就是使得多Agent的信念、意图、期望、行为达到协调甚至协作。在开放、动态的MAS环境下,具有不同目标的多个Agent必须对其资源的使用以及目标的实现进行协调[1,4]。例如,在出现资源冲突时,若没有很好的协调机制,就有可能出现死锁。而在另一种情况下,当单个Agent无法独立完成目标,需要其它Agent帮助时,则需要协作。本文提出了一种基于正关系的多Agent协调机制和协调算法。在该算法中,通过使用这种协调机制,Agent能委托或接受交互中的子计划,从而形成系统负载均衡和有效降低系统运行开销。     

Optimal Synchronization Control of Heterogeneous Asymmetric Input-Constrained Unknown Nonlinear MASs via Reinforcement Learning

The asymmetric input-constrained optimal synchronization problem of heterogeneous unknown nonlinear multiagent systems(MASs) is considered in the paper. Intuitively,a state-space transformation is performed such that satisfaction of symmetric input constraints for the transformed system guarantees satisfaction of asymmetric input constraints for the original system. Then, considering that the leader’s information is not available to every follower, a novel distributed observer is designed to est...     

A robust two-phase heuristic algorithm for the truck scheduling problem in a resource-constrained crossdock

This paper studies truck scheduling in a resource-constrained crossdock. The problem decides on the sequence of incoming and outgoing trucks at the dock doors of the crossdocking terminal, subject to the availability of crossdock resources including dock doors and material handling systems. The resources are assumed non-preemptive making it necessary to address the feasibility of the problem before its optimality as it might be entrapped in deadlock and no feasible solution is produced. The paper thus aims at developing an algorithmic approach capable of establishing solution feasibility for truck scheduling problem instances of various types and difficulty levels which at the same time can be readily implemented in an industrial setting. The proposed approach is a two-phase heuristic algorithm where in the first phase, a heuristic search is deployed to construct a feasible sequence of trucks for the assignment to dock doors and in the second, a rule-based heuristic is used to assign each sequenced truck to a proper dock door, subject to a limited number of forklifts, such that significant savings in the truck schedule length are achieved. Extensive experiments are conducted to evaluate the efficiency of the algorithm in terms of deadlock avoidance and solution quality. The evaluation is carried out against the solutions generated by the exact mathematical model of the problem and a constructive heuristic developed for a similar truck scheduling problem. Experimental results demonstrate that the proposed algorithm is robust in avoiding deadlock and generates feasible solutions for the instances where the other two approaches cannot. Furthermore, significant improvement in the solution quality is achieved by augmenting the algorithm to a re-starting heuristic.     

Evaluating spoken dialogue agents with PARADISE: Two case studies

This paper presents PARADISE (PARAdigm for DIalogue System Evaluation), a general framework for evaluating and comparing the performance of spoken dialogue agents. The framework decouples task requirements from an agent's dialogue behaviours, supports comparisons among dialogue strategies, enables the calculation of performance over subdialogues and whole dialogues, specifies the relative contribution of various factors to performance, and makes it possible to compare agents performing different taks by normalizing for task complexity. After presenting PARADISE, we illustrate its application to two different spoken dialogue agents. We show how to derive a performance function for each agent and how to generalize results across agents. We then show that once such a performance function has been derived, it can be used both for making predictions about future versions of an agent, and as feedback to the agent so that the agent can learn to optimize its behaviour based on its experiences with users over time.     

一种基于移动Agent技术的分布式死锁检测算法

死锁的处理长期以来一直是分布式系统的研究重点,已有许多成熟算法.随着网络技术的发展,越来越多的客户和资源可在网络中自由移动,这种可移动性使得传统算法面临了新的挑战.在这种新的应用背景下,本文结合移动Agent技术,提出了一种分布式系统死锁检测和解除算法：Agent Guard.该算法使用一个移动Agent,使其遵循一定的路线算法在各个站点间移动来收集资源请求和分配信息并进行分析,从而发现并解除死锁.模拟实验证明,A-gent Guard算法能取得较短的死锁持续时间,较小的伪死锁率,且网络的通信复杂度也有降低.     

Distributed conflict resolution among cooperating expert systems

Abstract: Cooperating experts approach attempts to integrate and coordinate the activities of multiple specialised problem solvers that come together to solve complex tasks such as design, medical diagnosis, business management and so on. Due to the different goals, knowledge and viewpoints of agents, conflicts may arise at any phase of the problem-solving process. Managing diverse expertise requires well-organised models of conflict resolution. In this paper, a model for cooperating experts is described which openly supports multi-agent conflict detection and resolution. The model is based on the idea that each agent has its own conflict knowledge which is separated from its domain level knowledge, and each agent has its own conflict resolution knowledge which is not accessible and known by others. Furthermore, there are no globally known conflict resolution strategies. Each agent involved in a conflict chooses a resolution scheme according to its self interest. The model is described by using an example in the domain of office design and it is compared with other systems.     

基于集束搜索和银行家算法的柔性制造系统无死锁调度

本文提出了一种基于启发式规则的无死锁调度算法,该算法基于集束搜索方法,局部评价函数和全局评价函数,在无缓冲区的情况下,采用单步前瞻的银行家算法来避免死锁。该算法可以迅速解决复杂制造系统的死锁和调度问题,折衷了计算时间的消耗和调度结果的质量。     

分布式自利agent任务分配算法

在有自利agent参与的任务分配情形中,由于agent的自利性,导致各agent不能有效合作,影响agent的个体收益和系统总收益.解决该问题的一个途径是对agent所得的收益进行合理分配.文中基于分布式自利agent联盟技能博弈模型,提出自利agent的任务分配算法.模型中提供技能的服务agent和管理任务的agent都是自利的,分别处于不同的地理位置,具有不同的视野范围.算法为任务agent设计效益分配策略,合理分配自己的收益给所需的技能,任务分配结果在保证个体自利性的前提下获得较高的系统收益.仿真结果验证文中算法的有效性,并考察自利agent的视野范围对自利agent的个体收益和系统总收益的影响.     

The role of information structures in game-theoretic multi-agent learning

Multi-agent learning (MAL) studies how agents learn to behave optimally and adaptively from their experience when interacting with other agents in dynamic environments. The outcome of a MAL process is jointly determined by all agents’ decision-making. Hence, each agent needs to think strategically about others’ sequential moves, when planning future actions. The strategic interactions among agents makes MAL go beyond the direct extension of single-agent learning to multiple agents. With the strategic thinking, each agent aims to build a subjective model of others decision-making using its observations. Such modeling is directly influenced by agents’ perception during the learning process, which is called the information structure of the agent’s learning. As it determines the input to MAL processes, information structures play a significant role in the learning mechanisms of the agents. This review creates a taxonomy of MAL and establishes a unified and systematic way to understand MAL from the perspective of information structures. We define three fundamental components of MAL: the information structure (i.e., what the agent can observe), the belief generation (i.e., how the agent forms a belief about others based on the observations), as well as the policy generation (i.e., how the agent generates its policy based on its belief). In addition, this taxonomy enables the classification of a wide range of state-of-the-art algorithms into four categories based on the belief-generation mechanisms of the opponents, including stationary, conjectured, calibrated, and sophisticated opponents. We introduce Value of Information (VoI) as a metric to quantify the impact of different information structures on MAL. Finally, we discuss the strengths and limitations of algorithms from different categories and point to promising avenues of future research.     

Multi-agent graphical games with input constraints: an online learning solution

This paper studies an online iterative algorithm for solving discrete-time multi-agent dynamic graphical games with input constraints. In order to obtain the optimal strategy of each agent, it is necessary to solve a set of coupled Hamilton-Jacobi-Bellman (HJB) equations. It is very difficult to solve HJB equations by the traditional method. The relevant game problem will become more complex if the control input of each agent in the dynamic graphical game is constrained. In this paper, an online iterative algorithm is proposed to find the online solution to dynamic graphical game without the need for drift dynamics of agents. Actually, this algorithm is to find the optimal solution of Bellman equations online. This solution employs a distributed policy iteration process, using only the local information available to each agent. It can be proved that under certain conditions, when each agent updates its own strategy simultaneously, the whole multi-agent system will reach Nash equilibrium. In the process of algorithm implementation, for each agent, two layers of neural networks are used to fit the value function and control strategy, respectively. Finally, a simulation example is given to show the effectiveness of our method.     

A cost-effective approach to deadlock handling in wormhole networks

Wormhole networks have traditionally used deadlock avoidance strategies. More recently, deadlock recovery strategies have begun to gain acceptance. In particular, progressive deadlock recovery techniques allocate a few dedicated resources to quickly deliver deadlocked packets. Deadlock recovery is based on the assumption that deadlocks are rare; otherwise, recovery techniques are not efficient. Measurements of deadlock occurrence frequency show that deadlocks are highly unlikely when enough routing freedom is provided. However, networks are more prone to deadlocks when the network is close to or beyond saturation, causing some network performance degradation. Similar performance degradation behavior at saturation was also observed in networks using deadlock avoidance strategies. In this paper, we take a different approach to handling deadlocks and performance degradation. We propose the use of an injection limitation mechanism that prevents performance degradation near the saturation point and, at the same time, reduces the probability of deadlock to negligible values. We also propose an improved deadlock detection mechanism that uses only local information, detects all deadlocks, and considerably reduces the probability of false deadlock detection over previous proposals. In the rare case when impending deadlock is detected, our proposal consists of using a simple recovery technique that absorbs the deadlocked message at the current node and later reinjects it for continued routing toward its destination. Performance evaluation results show that our new approach to handling deadlock is more efficient than previously proposed techniques     

Program restructing in segmented virtual memory

Program restructuring in a segmented virtual memory system is examined on the base of empirical data. The measure of connectivity is based on the decrease of the space-time integral caused by combining the two segments. A heuristic clustering algorithm, based on the use of effectively reduced memory reference strings, is presented. It turns out that performance problems in a segmented system are created by the large number of small segments. Restructuring is able to improve the performance of the system essentially; the optimal performance is reached with values of the memory control parameter which are one magnitude smaller than those applicable to original programs. The clusters formed do not correspond to program phases, but to smaller units of program execution; thus the clustering result supports a hierarchical model of program locality. The restructuring process is reasonably insensitive both to the memory control parameters and to the input data used.     

容错的分布式系统通用死锁模型检测解除算法

分布式系统技术为采用低成本购建高性能系统提供了有效的途径,但是由于资源的分配与需求可能产生冲突,造成系统中发生死锁,导致系统运行陷入停滞.在不可靠的分布式系统中,故障会干扰正常的死锁检测,但现有的死锁检测算法不具有容错功能.对失效形式进行了归类,提出一个容错的死锁检测解除算法.算法建立在通用的AND-OR模型基础上,采用扩散计算和集中规约方式,不仅能够检测到死锁,而且能给出死锁环的全部成员.若死锁拓扑处于静态且为环状,算法的消息复杂度的上限为e n-1,时间复杂度为d,其中e为死锁等待图中边的个数,n和d为构成死锁环的节点的个数,分析表明算法性能等于或优于同类算法.     

A performance study of deadlock prevention algorithms in a distributed file system

This paper describes an implementation and performance evaluation of different deadlock prevention algorithms. A deadlock prevention algorithm ensures that deadlock will never happen. The algorithms for deadlock prevention are proposed and implemented in a locally distributed system. A number of experiments were executed in a distributed system for various lengths of file operation and different numbers of files. The performance of the system and of each algorithm is evaluated and discussed. Some general results are derived for a single-host and a distributed system.