Enhancing decentralized service discovery in open service-oriented multi-agent systems

Service-oriented multi-agent systems are dynamic systems that are populated by heterogeneous agents. These agents model their functionality as services in order to allow heterogeneous agents or other entities to interact with each other in a standardized way. Furthermore, due to the large-scale and adaptative needs of the system, traditional directory facilitators or middle-agents are not suitable for the management of agent services. This article proposes the introduction of homophily in service-oriented multi-agent systems to create efficient decentralized and self-organized structures where agents have a greater probability of establishing links with similar agents than with dissimilar ones. This similarity is based on two social dimensions: the set of services that an agent provides and the organizational roles that it plays. A second contribution is an algorithm for service discovery that it is carried out taking into account the local information that is related to the homophily between agents. The experiments compare our proposal with other proposals in distributed environments. The results show that the proposed structure and algorithm offer desirable features for service discovery in decentralized environments. Specifically, these features provide short paths and a high success rate in the service discovery process and resilience under deliberate failures.     

Agent-based game-theoretic model for collaborative web services: Decision making analysis

In agent and (web) service computing, collaboration takes place when distributed entities have limited knowledge and capabilities, so they cannot perform required tasks without interacting and helping each other. For instance, web services, which are loosely-coupled business applications, are called to cooperate in distributed settings for the sake of efficiency. In this context, agents that abstract and act on behalf of web services could act in cooperative groups that gather a number of agents sharing some common goals. Enabling those agent-based web services to decide about their strategies in terms of joining and acting within groups, inviting other agents to join, and leaving a group to act alone is an open issue that we address in this work. In this paper, we propose a framework where agent-based web services select strategies that maximize their outcomes. These strategies could be categorized into cooperative strategies involving other agents and strategies that highlight the single operative attitude. Although cooperation seems to bring better utility to cooperative agents, we highlight that web services in some environments obtain better outcome while they act individually (i.e., outside the group). This means that the cost of cooperation (in some particular cases) might negatively influence the outcome and obtained utility. As solution, we propose in this paper (1) an agent-based model that formalizes web services decision making where different parameters are considered; and (2) a game-theoretic framework that analyzes the web services strategies allowing them to maximize their acting performance where non-zero-sum games are being used. The paper presents theoretical results, which are also confirmed through extensive simulations.     

Applying intelligent agents and semantic web services in eGovernment environments

The increasing volume of eGovernment‐related services is demanding new approaches for service integration and interoperability in this domain. Semantic web (SW) technologies and applications can leverage the potential of eGovernment service integration and discovery, thus tackling the problems of semantic heterogeneity characterizing eGovernment information sources and the different levels of interoperability. eGovernment services will therefore be semantically described in the foreseeable future. In an environment with semantically annotated services, software agents are essential as the entities responsible for exploiting the semantic content in order to automate some tasks, and so enhance the user's experience. In this paper, we present a framework that provides a seamless integration of semantic web services and intelligent agents technologies by making use of ontologies to facilitate their interoperation. The proposed framework can assist in the development of powerful and flexible distributed systems in complex, dynamic, heterogeneous, unpredictable and open environments. Our approach is backed up by a proof‐of‐concept implementation, where the breakthrough of integrating disparate eGovernment services has been tested.     

Decentralized service discovery and selection in Internet of Things applications based on artificial potential fields

The Internet of Things (IoT) vision involves a future Internet integrated with real-world objects that can commonly offer their functionality trough services. In such pervasive environments of IoT networks, locating and invoking suitable services is quite challenging and traditional service discovery and selection approaches have been proven inadequate. In this paper, taking inspiration from natural metaphors, a decentralized service discovery and selection model is proposed. The model is based on artificial potential fields (APFs) which are formed upon each user service request and become active at points where services can be provided. Such points are termed as service provision nodes (SPNs). The strength of each APF depends on the percentage of requested services that can be provided by the respective SPN, as well as on SPN service load and availability with the aim to balance service load among SPNs. Service discovery and selection is then driven by artificial forces applied among user service requests and SPNs. Simulation results indicate that the proposed approach maintains satisfactory performance and scalability as the number of SPNs in an IoT network increase and efficient load balancing of the requested services among the SPNs in comparison with other approaches.     

Semantic-supported and agent-based decentralized grid resource discovery

One of the open issues in grid computing is efficient resource discovery. In this paper, we propose a novel semantic-supported and agent-based decentralized grid resource discovery mechanism. Without overhead of negotiation, the algorithm allows individual resource agents to semantically interact with neighbour agents based on local knowledge and to dynamically form a resource service chain to complete a task. The algorithm ensures the resource agent’s ability to cooperate and coordinate on neighbour knowledge requisition for flexible problem solving. The developed algorithm is evaluated by investigating the relationship between the success probability of resource discovery and semantic similarity under different factors. The experiments show the algorithm could flexibly and dynamically discover resources and therefore provide a valuable addition to the field.     

Decentralized event-triggered consensus with general linear dynamics

The consensus problem with general linear dynamics and undirected graphs is studied in this paper by means of event-triggered control strategies. A novel consensus protocol is proposed, where each agent implements a model of the decoupled dynamics of its neighbors. Under this control strategy, transmission of information does not occur continuously but only at discrete points in time. The approach presented in this paper provides both a decentralized control law and a decentralized communication policy. We are able to design thresholds that only depend on local information and guarantee asymptotic consensus. Positive inter-event times are guaranteed for particular cases of the linear dynamics. In an extension, a positive constant is added to the thresholds in order to exclude Zeno behavior for general linear dynamics. The difference between agents trajectories can be bounded in this case and bounds on the state disagreement are derived.     

Self-organizing network services with evolutionary adaptation

This paper proposes a novel framework for developing adaptive and scalable network services. In the proposed framework, a network service is implemented as a group of autonomous agents that interact in the network environment. Agents in the proposed framework are autonomous and capable of simple behaviors (e.g., replication, migration, and death). In this paper, an evolutionary adaptation mechanism is designed using genetic algorithms (GAs) for agents to evolve their behaviors and improve their fitness values (e.g., response time to a service request) to the environment. The proposed framework is evaluated through simulations, and the simulation results demonstrate the ability of autonomous agents to adapt to the network environment. The proposed framework may be suitable for disseminating network services in dynamic and large-scale networks where a large number of data and services need to be replicated, moved, and deleted in a decentralized manner.     

A SUPER‐AGENT‐BASED FRAMEWORK FOR REPUTATION MANAGEMENT AND COMMUNITY FORMATION IN DECENTRALIZED SYSTEMS

In this article, we propose a novel super‐agent‐based framework for reputation management and community formation in decentralized systems. We describe this framework in the context of Web service selection where agents with more capabilities act as super‐agents. These super‐agents serve as reputation managers to maintain reputation information of services and share the information with other consumer agents that have fewer capabilities than the super‐agents. In addition, super‐agents can maintain communities and build community‐based reputation for a service based on the opinions from all community members that have similar interests and judgement criteria as the super‐agents or the other community members. A practical reward mechanism is also introduced to create incentives for super‐agents to contribute their resources (to maintain reputation and form communities) and provide truthful reputation information. Experimental results obtained through simulation confirm that our approach achieves better effectiveness and scalability compared to the systems that do not use super‐agents and that do not form communities.     

Endogenous Neighborhood Selection and the Attainment of Cooperation in a Spatial Prisoner’s Dilemma Game

There is a large literature in economics and elsewhere on the emergence and evolution of cooperation in the repeated Prisoner’s Dilemma. Recently this literature has expanded to include games in a setting where agents play only with local neighbors in a specified geography. In this paper we explore how the ability of agents to move and choose new locations and new neighbors influences the emergence of cooperation. First, we explore the dynamics of cooperation by investigating agent strategies that yield Markov transition probabilities. We show how different agent strategies yield different Markov chains which generate different asymptotic behaviors in regard to the attainment of cooperation. Second, we investigate how agent movement affects the attainment of cooperation in various networks using agent-based simulations. We show how network structure and density can affect cooperation with and without agent movement.     

Dynamic service composition enabled by introspective agent coordination

Service composition has received much interest from many research communities. The major research efforts published to date propose the use of service orchestration to model this problem. However, the designed orchestration approaches are static since they follow a predefined plan specifying the services to be composed and their data flow, and most of them are centralized around a composition engine. Further, task decomposition is made prior to service composition, whereas it should be performed according to the available competencies. In order to overcome these limitations, we propose to model a dynamic approach for service composition. The studied approach relies on the decentralized and autonomous collaboration of a set of services whose aim is to achieve a specific goal. In our work, this goal is to satisfy requirements in software services that are freely expressed by human users (i.e. not predefined through a composition plan). We propose to enable the service collaborations through a multi-agent coordination protocol. In our model, agents offer services and are endowed with introspective capabilities, that is, they can access and reason on their state and actions at runtime. Thereby, the agents are capable of decomposing a monolithic task according to their service skills, and dynamically coordinating with their acquaintances to cover the whole task achievement. This paper presents the adaptive agent-based approach we propose for dynamic service composition, describes its architecture, specifies the underlying coordination protocol, called omposer, verifies the protocol’s main properties, and validates it by unfolding an implemented scenario.     

基于信誉的语义Web服务发现

服务发现在语义Web领域是一个挑战性问题.一个关键问题是发现满足用户功能需求的最佳服务.同样重要的是,电子商务应用还要求发现满足QoS需求的最佳服务.由此提出一个基于信誉管理的分布式服务选择框架.它主要有以下特点：用一个结构化的覆盖网络作为分布式服务存储系统来提高系统的有效性和可扩展性; 提出一个QoS感知的语义Web服务发现方法,并给出基于用户反馈的分布式信誉度评估算法.最后用一个例子示范算法如何解决实际问题.     

Secure mobile agents with controlled resources

Mobile agents often travel in a hostile environment where their security and privacy could be compromised by any party including remote hosts in which agents visit and get services. It is believed that the host visited by an agent should jointly sign a service agreement with the agent's owner; hence a proxy‐signing model was proposed in the literature, allowing every host in the agent system to sign a service agreement. We observe that this actually poses a serious problem whereby a host that should be excluded from an underlying agent network could also send a signed service agreement. In order to solve this problem, we propose two schemes achieving host authentication with controlled resources, where only selected hosts can be included in the agent network. We provide two schemes in this paper. The second scheme offers a smaller data size. We also define security models and provide rigorous security proofs to our schemes. Copyright © 2010 John Wiley & Sons, Ltd.     

A scalable multi-attribute hybrid overlay for range queries on the cloud

Cloud computing has become a promising paradigm as next generation computing model, by providing computation, software, data access, and storage services that do not need to know the location of physical resources interconnected across the globe providing such services. In such an environment, important issues as information sharing and resource/service discovery arise. In order to overcome critical limitations in centralized approaches for information sharing and resource/service discovery, this paper proposes a framework of a scalable multi-attribute hybrid overlay featured with decentralized information sharing, flexible resource/service discovery, fault tolerance and load balancing. Additionally, the proposed hybrid overlay integrates a structured P2P system with an unstructured one to support complex queries. Mechanisms such as load balancing and fault tolerance implemented in our proposed system to improve the overall system performance are also discussed. Experimental results show that the performance of the proposed approach is feasible and stable, as the proposed hybrid overlay improves system performance by reducing the number of routing hops and balancing the load by migrating requests.     

基于意图的物联网服务描述与发现

在物联网服务发现过程中,用户通常用自己的意图表达需求,而服务描述是对服务功能的说明,因此两者的不匹配会影响服务发现的准确率。同时随着服务的种类不断增多,服务发现的准确率不断降低。为了解决上述问题,提出在物联网服务描述中引入意图服务本体的方法,并在意图服务本体中扩展服务上下文和QoS。扩展后的意图服务本体存储在语义化服务描述OWL-S文件中,能够以意图的方式表达服务功能,丰富物联网服务描述的语义,提高服务发现的准确率。仿真结果表明,所提服务描述方法以及相应的服务发现算法相对于传统服务发现方法能提升6.7%的准确率。     

一种基于多Agent系统的云服务自组织管理方法

在开放动态的互联网环境下的云服务呈现出发散、动态演化、异构等特征,为了适应多样的应用需求以及持续变化的云环境,云需要以灵活、适应的方法来有效地管理和提供云服务.针对这一需求,提出了一种云服务自组织管理方法,该方法利用agent的环境感知和自主行为决策的能力,依据它的职责实现对云服务的自主管理,并通过agent间的交互以自组织的方式实现对服务资源的有效管理,进而适应云环境和应用需求的变化;提出了支持上述方法和机制的实现技术,包括云服务自组织管理的核心机制和实现框架、云服务汇聚和提供的运行机制及相关实现算法.利用支持语义Web服务发现匹配的公共测试数据设计并完成了两组验证实验,实验结果表明:所提出的方法可以在持续变化的云环境下有效地管理和提供云服务,进而满足动态多样化的应用需求.     

Service discovery in mobile ad hoc networks: A field theoretic approach

Service discovery in mobile ad hoc networks is challenging because of the absence of any central intelligence in the network. Traditional solutions as used in the Internet are hence not well suited for mobile ad hoc networks. In this paper, we present a novel decentralized service discovery mechanism for ad hoc networks. The basic idea is to distribute information about available services to the network neighborhood. We achieve this by using the analogy of an electrostatic field: A service is modelled by a (positive) point charge, and service request packets are seen as (negative) test charges which are attracted by the service instances. In our approach, we map the physical model to a mobile ad hoc network in a way where each network element calculates a potential value and routes service requests towards the neighbor with the highest potential, hence towards a service instance. Our approach allows for differentiation of service instances based on their capacity. We define the required protocols and methods which we implemented in a network simulator. Using extensive simulations, we evaluate the performance and robustness of the mechanisms. The results indicate good performance and convergence even in highly mobile environments. We believe that this technique can and should be further exploited, e.g., as a routing protocol in mobile ad hoc networks.     

A multi-agent-based framework for cloud service discovery and selection using ontology

As cloud computing is getting matured day by day, there has been overwhelming interest among the users to avail a plethora of cloud services. Often, these services appear identical in terms of their functionality though they differ in pricing models, computational power, storage policies and Quality-of-Service parameters making the process of service discovery and selection an intricate task. In the absence of any standard specifications, cloud service providers continue to use their own vocabulary and this further complicates the selection process. Even popular search engines like Google and MSN are not efficient enough to properly identify the most appropriate cloud service that can meet customer requirements. Thus, in the presence of multiple selection parameters and constraints, selecting a required cloud service is a daunting task. In order to address this issue, we work toward developing a reasoning mechanism to optimally resolve the similarities across cloud services by using cloud ontology. A multi-agent-based framework has been proposed for effective cloud service discovery and selection with the help of a standardized service registry and by employing semantically guided searching process.     

Decentralized adaptive consensus control for discrete‐time heterogeneous semiparametric multiagent systems

Different from the consensus control of traditional multiagent systems, this paper studies the decentralized adaptive consensus control for discrete‐time heterogeneous hidden leader‐following semiparametric multiagent system, in which the dynamic equation of each agent has both parametric uncertainties and nonparametric uncertainties. In the considered system, there is a hidden leader agent who can receive the reference signal, but it can only affect the states of those agents who are in its neighborhood. For other following agents, they do not know the leader's existence or the reference signal, and they can only receive information from their neighbors. Our goal is to design decentralized adaptive controllers to make sure that all agents can track the reference signal, and the closed‐loop system achieves consensus in the presence of mutual coupling relations. Due to the existence of both parametric and nonparametric uncertainties in the system, we need to estimate them separately. For the parametric part, we propose a novel dead zone with threshold converging to zero to modify the traditional gradient update law, while for the nonparametric part, we introduce an auxiliary variable including both two uncertainties to facilitate the nonparametric uncertainties compensation. Based on the certainty equivalence principle in adaptive control theory, the decentralized adaptive controller is designed for each agent to make sure that all of them can track the reference signal. Finally, under the proposed control protocol, strict mathematical proofs are given by using Lyapunov theory; then, simulation results are provided to demonstrate the effectiveness of proposed decentralized adaptive controllers.     

P2Care: A dynamic peer-to-peer network for collaboration in personalized healthcare service delivery

In the healthcare domain, the quality of personalized service delivery is strongly tied with collaboration among multiple stakeholders for accurate and reliable access to a variety of distributed and heterogeneous information and services. Existing healthcare information systems lack collaboration since they offer limited accessibility to resources and pose many restrictions in terms of scalability and flexibility. In this work, we propose P2Care, a decentralized, dynamic, scalable and self-organized network that exploits the functionalities of the structured peer-to-peer networks in order to provide collaboration. P2Care provides efficient ubiquitous access to medical information by organizing participating entities into groups according to common characteristics and interests. It incorporates enhanced lookup mechanisms with low response time and signaling overhead. The performance of the P2Care system has been evaluated through simulations. A prototype application demonstrates the effectiveness of the proposed system for cooperation and collaboration in order to facilitate personalized service delivery.