Evaluation of intelligent agents in consumer-to-business e-Commerce

Electronic commerce has changed traditional business trading behaviors, since consumers can easily consume through the Internet e-commerce platform. The Internet provides numerous products and services, but consumers find it is hard to choose their favorite ones. The consumer-to-business (C2B) is popular in recent years and it has become one of the best choices for customers forming a group to buy products. Thus, more and more consumers participate in online group buying. However, consumers anticipate to different prices when buying products and demand service. Thus, applying an intelligent agent into negotiation can effectively decrease efforts spent on collecting buyer information, transaction costs, and negotiation with sellers.This study proposed a system that applies an intelligent agent into the C2B e-commerce process, and evaluates the system through an experiment. Additionally, a questionnaire is used to investigate the benefits of the proposed intelligent agent systems. Analytical results show that the proposed intelligent system can increase user satisfaction, reduce performance risk, and raise perceived fairness, but nothing help on perceived value. It implied that the system still needs efforts and time to promote in nowadays commerce. If people can understand its value from finding the information they need, it must grant the more perceived value. Additionally, this system is not only applicable to C2B, but it can extend to other e-commerce models, because the agent can help the negotiation between the sellers and buyers.     

Broker-based SLA-aware composite service provisioning

QoS-aware service composition aims to satisfy users’ quality of services (QoS) needs during service composition. Traditional methods simply attempt to maximize user satisfaction by provisioning the composite service instance with the best QoS. These “best-effort” methods fail to take into account that there also exist other consumers competing for the service resources and their decisions of service selection/composition can impact on QoS. Since user's QoS needs can be met once the demanded level is reached, in this paper, we propose an “on-demand” strategy for QoS-aware service composition to replace the traditional “best-effort” strategy. The service broker is introduced to facilitate implementation of this strategy: it first purchases a number of service instances for each component from providers and then provisions the composite services with different QoS classes to consumers. This paper focuses on how the broker follows the service level agreement (SLA) to provision composite services in the “on-demand” manner. This problem is formally expressed as the minimization of the QoS distance function between SLA and QoS of composite service instances, under a series of constraints. Heuristic approaches are proposed for the problem and experiments are conducted at last to verify their effectiveness and efficiency.     

面向云联网的云服务协商机制

随着云计算的快速发展,越来越多的用户开始使用云服务提供商提供的服务,而云联网作为云计算研究的新领域,可以实现跨云服务提供商的服务,当单个云服务提供商无法满足用户的服务需求时,云服务提供商之间以合作的方式为用户提供服务,以便更好地满足用户的服务需求。针对上述情况,提出了面向云联网的云服务协商机制,该机制利用云联网和改进的经典合同网模型来实现云服务提供商的交互协商。为了有效地选出合作伙伴以提高合作效率,还为每一个云服务提供商建立了一个熟人集。实验表明,本文设计的机制可以有效地提高云服务提供商之间的合作效率,并且可以更好地满足用户的服务需求。     

Mandi: a market exchange for trading utility and cloud computing services

The recent development in Cloud computing has enabled the realization of delivering computing as an utility. Many industries such as Amazon and Google have started offering Cloud services on a “pay as you go” basis. These advances have led to the evolution of the market infrastructure in the form of a Market Exchange (ME) that facilitates the trading between consumers and Cloud providers. Such market environment eases the trading process by aggregating IT services from a variety of sources, and allows consumers to easily select them. In this paper, we propose a light weight and platform independent ME framework called “Mandi”, which allows consumers and providers to trade computing resources according to their requirements. The novelty of Mandi is that it not only gives its users the flexibility in terms of negotiation protocol, but also allows the simultaneous coexistence of multiple trading negotiations. In this paper, we first present the requirements that motivated our design and discuss how these facilitate the trading of compute resources using multiple market models (also called negotiation protocols). Finally, we evaluate the performance of the first prototype of “Mandi” in terms of its scalability.     

云计算中基于SLA的服务可信协商与访问控制策略

针对当前云计算中因服务提供者(SP)的信任保障机制缺失而容易被不可信服务消费者(SC)滥用的现象,提出面向SC实体的服务可信协商及访问控制策略.该策略首先依据系统信任规则来表达服务实体的可信程度,然后通过求解SC实体的直接和间接信任推理空间建立信任证据的举证方法,同时采用服务级别协议(service level agreements,SLA)构建交互双方的协商机制,最后综合信任传递与迭代计算策略,确定服务交互的SLA等级,提供相应级别的服务,从而达到访问控制的目的.理论分析与实验结果表明,该方法虽少量增加了协商的次数,但能较好解决服务被滥用以及利用率不高的问题,为云计算环境下信任协商研究提供一种有效的新方法.     

A supplier offer modification approach based on fuzzy systems for automated negotiation in e-commerce

In E-commerce, numbers of transactions are increasing day by day in B2B and B2C trade. Online negotiation is possible because of automated negotiation. Autonomous entities such as agents could help in these situations. Providing an offer which has maximum utilities for both trading parties into possible shortest time is the main aim of this work. Proposed model applies issue trade-offs strategy in which multiple issues are traded-offs against one another to maximize participant satisfaction. To make trade-offs the model applies a fuzzy system approach. The automated negotiation model in this article has a process without offer generating and exchanging between buyer and supplier agents to explore time-consuming negotiation process in earlier researches. Mediator component searches an optimal offer that satisfies buyer and supplier requirements. The system will utilize fuzzy inference systems to automate negotiation process and considers two effective factors in the negotiation process: requirements and preferences. Requirements are qualitative or quantitative values which the participants assign to issues for negotiation. Preferences of the participants are priorities assigned to the issues. These values express an importance measure of the issues from a participant perspective. Analytic hierarchy process (AHP) is used to get preferences of the issues. Proposed model applies different Fuzzy Inference System (FIS) schemes for qualitative and quantitative negotiation issues to enhance the satisfaction level of buyers and suppliers. Experimental results show that how the model fulfills the main aim of our work.     

A negotiation-based scheme for service level pricing for wireless access

The Next Generation Network (NGN) will enable service providers to offer broadband services with differentiated quality to their subscribers. In this way, the providers will be able to satisfy and retain the critical segment of the top-demanding customers, who generate the largest share of the provider’s revenues. However, service pricing is as important as service quality. In order to offer competitive prices, bilateral per service negotiation between the customer and the supplier is an interesting practical solution that can provide better results than rigid offers based on fixed price-rate lists. In the application scenario of Broadband Wireless Access, the supplier can adapt its offer of wireless access bandwidth to the dynamics of customer-generated traffic, giving more bandwidth per session when traffic is low and less bandwidth per session when traffic is high. We present and study the performance of an algorithm for automatic service level pricing in which two agents negotiate over the service transmission rate and the price on behalf of the user and of the provider. The performance of the proposed negotiation scheme is evaluated with simulations. The paper also compares our algorithm to the fixed-price scheme and to an auction-based strategy based on the Vickrey scheme.     

Towards efficient and fair resource trading in community-based cloud computing

In this paper, we investigate the resource trading problem in a community-based cloud computing setting where multiple tenants communicate in a peer-to-peer (P2P) fashion. Enabling resource trading in a community cloud unleashes the untapped cloud resources, thus presents a flexible solution for managing resource allocation. However, finding an efficient and fair resource allocation is challenging mainly due to the heterogeneity of tenants. Our work first develops a market-oriented model to support resource negotiation and trading. Based on this model, we adopt a multiagent-based technique that allows a group of autonomous tenants to reach an efficient and fair resource allocation. Further, when budget constraint presents, we propose a directed hypergraph model to facilitate resource trading amongst heterogeneous tenants. We analyze the application of the directed hypergraph model to trading decision making, and design a series of heuristic-based resource trading protocols for both budget-unaware and budget-aware scenarios. The performances of the proposed protocols are validated through simulations. The results are in tune with the theoretical analysis and provide insights into practical application issues.     

An SLA-based cloud computing that facilitates resource allocation in the distributed data centers of a cloud provider

The number of cloud service users has increased worldwide, and cloud service providers have been deploying and operating data centers to serve the globally distributed cloud users. The resource capacity of a data center is limited, so distributing the load to global data centers will be effective in providing stable services. Another issue in cloud computing is the need for providers to guarantee the service level agreements (SLAs) established with consumers. Whereas various load balancing algorithms have been developed, it is necessary to avoid SLA violations (e.g., service response time) when a cloud provider allocates the load to data centers geographically distributed across the world. Considering load balancing and guaranteed SLA, therefore, this paper proposes an SLA-based cloud computing framework to facilitate resource allocation that takes into account the workload and geographical location of distributed data centers. The contributions of this paper include: (1) the design of a cloud computing framework that includes an automated SLA negotiation mechanism and a workload- and location-aware resource allocation scheme (WLARA), and (2) the implementation of an agent-based cloud testbed of the proposed framework. Using the testbed, experiments were conducted to compare the proposed schemes with related approaches. Empirical results show that the proposed WLARA performs better than other related approaches (e.g., round robin, greedy, and manual allocation) in terms of SLA violations and the provider’s profits. We also show that using the automated SLA negotiation mechanism supports providers in earning higher profits.     

Multi-attribute negotiation mechanism for manufacturing service allocation in smart manufacturing

Smart manufacturing is undergoing rapid development along with many disruptive technologies, such as Internet of Things, cyber-physical system and cloud computing. A myriad of heterogeneous manufacturing services can be dynamically perceived, connected and interoperated to satisfy various customized demands. In smart manufacturing, the market equilibrium is variable over time due to changes in demand and supply. Thus, efficient manufacturing service allocation (MSA) is critical to implementation of smart manufacturing. This paper considers the MSA problem under market dynamics with maximization of utility of customers and service providers. Many conventional methods generally allocate manufacturing services to the customers by multi-objective optimization without considering the impact of interactions between customers and service providers. This paper presents a multi-attribute negotiation mechanism to address the MSA problem under time constraints relying on autonomous agents. The proposed negotiation mechanism is composed of two models: an atomic manufacturing service negotiation model and a composite manufacturing service coordination. The former model is based on automated negotiation to seek an atomic manufacturing service over multiple attributes for an individual subtask. The latter model incorporates the global distribution and surplus redistribution to coordinate and control multiple atomic manufacturing service negotiations for the whole manufacturing task. Numerical studies are employed to verify the effectiveness of the multi-attribute negotiation mechanism in solving the MSA problem. The results show that the proposed negotiation mechanism can address the MSA problem and surplus redistribution can effectively improve the success rate of negotiations.     

Lifetime service level agreement management with autonomous agents for services provision

In the web services environment, service level agreements (SLA) refers to mutually agreed understandings and expectations between service consumers and providers on the service provision. Although management of SLA is critical to wide adoption of web services technologies in the real world, support for it is very limited nowadays, especially in web service composition scenarios. There lacks adequate frameworks and technologies supporting various SLA operations such as SLA formation, enforcement, and recovery. This paper presents a novel agent-based framework which utilises the agents’ ability of negotiation, interaction, and cooperation to facilitate autonomous SLA management in the context of service composition provision. Based on this framework, mechanisms for autonomous SLA operations are proposed and discussed. Results from simulations show that by integrating agents and web services the framework can address issues of SLA management drawn from sophisticated service composition scenarios.     

Automated traders in commodities markets: Case of producer–consumer institution

Automatizing commodities’ price negotiation was hard to achieve in practice, mainly because of logistical complications. The purpose of our work is to show that it is possible to automatize thoroughly commodities’ trading in the futures market by replacing human traders with artificial agents. As a starting step, we designed a market institution, called producer–consumer, where only an automated seller and an automated buyer can trade on behalf of the producer and consumer, respectively. The producer and consumer periodically feed their trading agents with supply and demand (S&D) forecasts. We suggested a parameterizable trading strategy, called bands and frequencies, for the agents. To measure the overall efficiency of this trading system in terms of price stability and liquidity, we made some hypotheses on the benchmark price curve and its linkages to S&D curves and other relevant market variables. Then we proposed analytical tools to measure strategy performance. Finally, we conducted some computer simulations to prove the workability of this approach.     

云环境下基于服务等级协议的信任评估模型

针对用户在交互过程中对云服务商缺乏信任的问题,提出了一个基于服务等级协议(SLA)的云计算信任模型。在该模型中,云服务商在向服务中心--可信的第三方平台注册时,首先提交自身的实力评估报告,说明其实力、运营、技术及提供的服务属性等,服务中心根据相关的评价标准对该云服务商进行评估,得到系统信任;其次把系统信任引入到传统的声誉机制中,把系统信任、直接信任和间接信任作为评估一个云服务提供商的三个重要因素,并计算出云服务商的综合信任度;最后用户根据云服务商提供的服务和综合信任度与其进行SLA协商,确定最终交互对象,屏蔽掉不诚实或信誉较低的云服务商。实验结果表明,在基于服务等级协议的信任模型中,由于引入了系统信任,云服务商综合信誉的获得更全面准确、有效地防止了云服务商的不诚信行为,提高了交互的成功率。     

支持中国墙策略的云组合服务信息流控制模型

由于所属服务提供商在商业上的竞争关系,云组合服务的组件服务之间可能存在利益冲突,必须对云组合服务的信息流进行控制以避免敏感信息在冲突组件服务间流动。针对云组合服务中的利益冲突问题,在构建云组合服务加权有向图模型的基础上,形式地描述了复杂组合结构下的信息流,定义了云服务的联盟关系与数据的依赖关系的概念,并将中国墙策略中的冲突关系扩展为组合冲突关系。在此基础上,提出了一种支持中国墙策略的云组合服务信息流控制模型（CW-CCSIFC）,给出了模型的形式化描述及相关定理的证明。分析表明,CW-CCSIFC模型可以防止有利益冲突的云服务间的非法信息流动,保护云组合服务的信息流安全。     

Service-level agreement–aware scheduling and load balancing of tasks in cloud

Cloud computing is an innovative computing paradigm designed to provide a flexible and low-cost way to deliver information technology services on demand over the Internet. Proper scheduling and load balancing of the resources are required for the efficient operations in the distributed cloud environment. Since cloud computing is growing rapidly and customers are demanding better performance and more services, scheduling and load balancing of the cloud resources have become very interesting and important area of research. As more and more consumers assign their tasks to cloud, service-level agreements (SLAs) between consumers and providers are emerging as an important aspect. The proposed prediction model is based on the past usage pattern and aims to provide optimal resource management without the violations of the agreed service-level conditions in cloud data centers. It considers SLA in both the initial scheduling stage and in the load balancing stage, and it looks into different objectives to achieve the minimum makespan, the minimum degree of imbalance, and the minimum number of SLA violations. The experimental results show the effectiveness of the proposed system compared with other state-of-the-art algorithms.     

Adaptive Probabilistic Behavioural Learning System for the effective behavioural decision in cloud trading negotiation market

In cloud e-commerce application, building an automated negotiation strategy by understanding the uncertain information of the opponent preferences, utilities, and tactics is highly challenging. The key issue is to analyse and predict the uncertain behaviour of the opponent tactics to suggest the appropriate counter tactics that can reach maximum consensus. To handle such uncertain information, negotiation strategies follow several tactics with and without learning ability. Strategies without learning ability are restricted to negotiate with the opponent having only deterministic behaviour. To overcome this problem most researchers exploited the negotiation strategies with fixed learning ability using Bayesian learning, neural network learning, and genetic tactics. These tactics can learn the opponent’s behaviour and cannot guarantee to generate suitable counter-offer for all offers submitted by the opponent cloud service provider. This limitation motivates to propose a novel Adaptive Probabilistic Behavioural Learning System for managing the opponent having unpredictable random behaviours. The proposed Adaptive Probabilistic Behavioural Learning System contains a Behavioural Inference Engine to analyse the sequence of negotiation offer received by the broker for effectively learning the opponent’s behaviour over several stages of negotiation process. It also formulates the multi-stage Markov decision problem to suggest the broker with appropriate counter-offer behavioural tactics generation based on the adaptive probabilistic decision taken over the corresponding negotiation stage. Therefore, this research work can outperform the existing fixed behavioural learning tactics and hence maximize the utility value and success rate of negotiating parties without any break-off.     

A brokerage system for solar data archives

Scientific research and practical applications of solar physics require data and computational services to be integrated seamlessly and efficiently. The European Grid for Solar Observations (EGSO) leverages Grid‐oriented concepts and technology to provide a high‐performance infrastructure for solar applications. In this paper, an architecture for a data brokerage service is proposed. Brokers interact with providers and consumers in order to build a profile of both parties. In particular, a broker interacts with providers in order to gather information on the data potentially available to consumers, and with the consumers in order to identify the set of providers that are most likely to satisfy specific data needs. The brokerage technique is based on a multi‐tier management of metadata. Copyright © 2006 John Wiley & Sons, Ltd.     

不确定感知的自适应云计算服务组合

云计算服务组合是从众多分布在不同云计算平台上的远程服务中选择合适的组件服务来构建可伸缩的松耦合的增值应用.传统的服务组合方法通常将服务选择与服务组合分阶段进行,由于云计算环境的动态性和服务自身演化的随机性,不能保证选择阶段性能最优的服务在组合服务执行阶段依然是最优的.考虑到云计算环境服务组合的动态性和随机性,建立基于部分可观测Markov决策过程(partially observable Markov decision process, POMDP)的服务组合模型SC_POMDP (service composition based on POMDP),并设计用于模型求解的Q学习算法.SC_POMDP模型在组合服务运行中动态地进行服务质量(quality of service, QoS)最优的组件服务选择,且认为组合服务运行的环境状态是不确定的,同时SC_POMDP考虑了组件服务间的兼容性,可保证服务组合对实际情境的适应性.仿真实验表明,所提出的方法能成功地解决不同规模的服务组合问题,在出现不同比率的服务失效时,SC_POMDP仍然能动态地选择可用的最优组件服务,保证服务组合能成功地执行.与已有方法相比,SC_POMDP方法所选的服务有更优的响应时间和吞吐量,表明SC_POMDP可有效地提高服务组合的自适应性.