EGALITARIAN NEGOTIATIONS IN AGENT SOCIETIES

Achieving fairness among the members of a society is an important issue in many resource allocation problems. The increasing focus of the computer sciences community is motivated by a large panel of applications where other welfare notions do not suit. Centralized approaches are usually used, but they suffer from important drawbacks. Because relationships among society members are not considered, the provided resource allocation may not be achievable in practice. This study seeks to provide an evenly distributed approach based on a multiagent system, which overcomes the main drawbacks of centralized approaches. We provide an adaptive, anytime, and scalable algorithm to hold efficient egalitarian negotiations. Negotiation processes lead to socially efficient allocations as an emergent phenomenon. We also evaluate the “price of the social graph” toward the negotiation efficiency, and we provide the agent behavior to implement in order to achieve fair allocations.     

Simple negotiation schemes for agents with simple preferences: sufficiency,necessity and maximality

We investigate the properties of an abstract negotiation framework where agents autonomously negotiate over allocations of indivisible resources. In this framework, reaching an allocation that is optimal may require very complex multilateral deals. Therefore, we are interested in identifying classes of valuation functions such that any negotiation conducted by means of deals involving only a single resource at a time is bound to converge to an optimal allocation whenever all agents model their preferences using these functions. In the case of negotiation with monetary side payments amongst self-interested but myopic agents, the class of modular valuation functions turns out to be such a class. That is, modularity is a sufficient condition for convergence in this framework. We also show that modularity is not a necessary condition. Indeed, there can be no condition on individual valuation functions that would be both necessary and sufficient in this sense. Evaluating conditions formulated with respect to the whole profile of valuation functions used by the agents in the system would be possible in theory, but turns out to be computationally intractable in practice. Our main result shows that the class of modular functions is maximal in the sense that no strictly larger class of valuation functions would still guarantee an optimal outcome of negotiation, even when we permit more general bilateral deals. We also establish similar results in the context of negotiation without side payments.     

Design and implementation of economics-based resource management system in ad hoc grid

The ad hoc grid is a spontaneous organization of cooperating heterogeneous nodes into a logical community without a fixed infrastructure and with only minimal administrative requirements. Resource management for ad hoc grids is challenging due to the participation of heterogeneous, dynamic, autonomous and ephemeral grid nodes. The paper proposes an ad hoc grid resource management system, the producers and consumers of ad hoc grid resource are modeled as the self-interested decision-makers described in microeconomic theory. All market participants in the ad hoc grid environment including grid resources and services can be represented as agents. We apply economic agents to build ad hoc grid resource management, where ad hoc grid resource consumers and providers can buy and sell ad hoc grid resource based on an underlying economic architecture. The main processes involved in ad hoc grid resource management are resource registration, discovery, and resource allocation. The experiments are conducted to compare ad hoc grid resource allocation algorithm with other ad hoc grid resource allocation algorithm. Simulation results show that our proposed algorithm is more efficient than compared allocation scheme.     

Combining Futures and Spot Markets: A Hybrid Market Approach to Economic Grid Resource Management

Economic forms of resource management in which users can express their valuations for service, offer new possibilities for optimizing resource allocations in Grids. If users are to correctly express these valuations, quality of service guarantees need to be given with respect to the turnaround time of their workloads. Market mechanisms that support bidding and allocations in future time are crucial for delivering such guarantees. To deal with the significant delays that these mechanisms introduce in the allocation process, we present a hybrid market approach in which a low-latency spot market coexists with a higher latency futures market. Based on simulated market scenarios, we show how this combination can significantly increase the total value realized by the Grid infrastructure. We also demonstrate how providers can react to price dynamics in such a hybrid market setting.     

A multiagent-based complex systems approach for dynamic negotiation mechanism in virtual enterprise

Nowadays a sophisticated match-making mechanism is necessary for appropriate collaborations in virtual enterprise (VE). Virtual market based match-making operation enables effective partner search in terms of products allocation by distributing the scheduled resources according to the market prices, which define common scale of value across the various products. We formulate the VE match-making model as discrete resource allocation problem, and propose a complex market-oriented programming framework based on the economics of complex systems. Three types of heterogeneous agents are defined in the complex virtual market. It is described that their interactions with micro behaviour emerge a macro order of the virtual market, and the clearing price dynamism can be analysed in economic terms. The applicability of the framework into resource allocation problem for VE is also discussed.     

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.     

Bargaining on an Internet Agent-based Market: Behavioral vs. Optimizing Agents

In this paper, we analyze an Internet agent-based market where non-cooperative agents using behavioral rules negotiate the price of a given product in a bilateral and sequential manner. In this setting, we study the optimal decision-making process of a buying agent that enters the market. Our approach is based on Negotiation Analysis (Raiffa, 1982; Sebenuis, 1992) and we consider that the optimizing buying agent maximizes her discounted expected utility using subjective probabilities. The optimal decision-making process of the buying agent is treated as a stochastic control problem that can be solved by dynamic programming. Three types of behavioral agents are studied, namely conceder agents, boulware agents and imitative agents. A set of simulations is undertaken in order to predict the average outcome in a negotiation process for different parameters of the optimizing buying agent and for the three possible selling agents' behaviors. Finally, we compare the performance of the optimizing agent with that of behavioral buying agents.     

基于风险策略的多单元连续双向拍卖的网格资源分配

针对计算网格资源的特点,提出一种基于风险策略的多单元连续双向拍卖的网格资源分配机制,实现对网格资源灵活有效的管理。首先,介绍了基于多单元连续双拍卖的网格资源分配框架。其次,针对计算网格资源的有限性,提出了RB2-MCDA机制。RB2-MCDA机制是在多单元连续双向拍卖中,代理采用Risk-Based2策略进行资源交易。Risk-Based2策略是一种基于风险行为的代理策略。实验结果表明,在不同规模的有限资源的计算网格中采用RB2-MCDA机制能够实现较高的资源分配效率,当资源需求量接近供给量时,分配效率超过99%。     

Strategic agents for multi-resource negotiation

In electronic commerce markets where selfish agents behave individually, agents often have to acquire multiple resources in order to accomplish a high level task with each resource acquisition requiring negotiations with multiple resource providers. Thus, it is crucial to efficiently coordinate these interrelated negotiations. This paper presents the design and implementation of agents that concurrently negotiate with other entities for acquiring multiple resources. Negotiation agents in this paper are designed to adjust (1) the number of tentative agreements for each resource and (2) the amount of concession they are willing to make in response to changing market conditions and negotiation situations. In our approach, agents utilize a time-dependent negotiation strategy in which the reserve price of each resource is dynamically determined by (1) the likelihood that negotiation will not be successfully completed (conflict probability), (2) the expected agreement price of the resource, and (3) the expected number of final agreements. The negotiation deadline of each resource is determined by its relative scarcity. Agents are permitted to decommit from agreements by paying a time-dependent penalty, and a buyer can make more than one tentative agreement for each resource. The maximum number of tentative agreements for each resource made by an agent is constrained by the market situation. Experimental results show that our negotiation strategy achieved significantly more utilities than simpler strategies.     

Online pricing for bandwidth provisioning in multi-class networks

We consider the problem of pricing for bandwidth provisioning over a single link, where users arrive according to a known stochastic traffic model. The network administrator controls the resource allocation by setting a price at every epoch, and each user’s response to the price is governed by a demand function. We formulate this problem as a partially observable Markov decision process (POMDP), and explore two novel pricing schemes––reactive pricing and spot pricing––and compare their performance to appropriately tuned flat pricing. We use a gradient-ascent approach in all the three pricing schemes. We provide methods for computing unbiased estimates of the gradient in an online (incremental) fashion. Our simulation results show that our novel schemes take advantage of the known underlying traffic model and significantly outperform the model-free pricing scheme of flat pricing.     

Emergent Properties of a Market-based Digital Library with Strategic Agents

The University of Michigan Digital Library (UMDL) is designed as an open system that allows third parties to build and integrate their own profit-seeking agents into the marketplace of information goods and services. The profit-seeking behavior of agents, however, risks inefficient allocation of goods and services, as agents take strategic stances that might backfire. While it would be good if we could impose mechanisms to remove incentives for strategic reasoning, this is not possible in the UMDL. Therefore, our approach has instead been to study whether encouraging the other extreme—making strategic reasoning ubiquitous—provides an answer.Toward this end, we have designed a strategy (called the p-strategy) that uses a stochastic model of the market to find the best offer price. We have then examined the collective behavior of p-strategy agents in the UMDL auction. Our experiments show that strategic thinking is not always beneficial and that the advantage of being strategic decreases with the arrival of equally strategic agents. Furthermore, a simpler strategy can be as effective when enough other agents use the p-strategy. Consequently, we expect the UMDL is likely to evolve to a point where some agents use simpler strategies and some use the p-strategy.     

Using argumentation to model agent decision making in economic experiments

In this paper we demonstrate how a qualitative framework for decision making can be used to model scenarios from experimental economic studies and we show how our approach explains the results that have been reported from such studies. Our framework is an argumentation-based one in which the social values promoted or demoted by alternative action options are explicitly represented. Our particular representation is used to model the Dictator Game and the Ultimatum Game, which are simple interactions in which it must be decided how a sum of money will be divided between the players in the games. Studies have been conducted into how humans act in such games and the results are not explained by a decision-model that assumes that the participants are purely self-interested utility-maximisers. Some studies further suggest that differences in choices made in different cultures may reflect their day to day behaviour, which can in turn be related to the values of the subjects, and how they order their values. In this paper we show how these interactions can be modelled in agent systems in a framework that makes explicit the reasons for the agents’ choices based upon their social values. Our framework is intended for use in situations where agents are required to be adaptable, for example, where agents may prefer different outcome states in transactions involving different types of counter-parties.     

A survey of approximability and inapproximability results for social welfare optimization in multiagent resource allocation

Multiagent resource allocation provides mechanisms to allocate bundles of resources to agents, where resources are assumed to be indivisible and nonshareable. A central goal is to maximize social welfare of such allocations, which can be measured in terms of the sum of utilities realized by the agents (utilitarian social welfare), in terms of their minimum (egalitarian social welfare), and in terms of their product (Nash product social welfare). Unfortunately, social welfare optimization is a computationally intractable task in many settings. We survey recent approximability and inapproximability results on social welfare optimization in multiagent resource allocation, focusing on the two most central representation forms for utility functions of agents, the bundle form and the k-additive form. In addition, we provide some new (in)approximability results on maximizing egalitarian social welfare and social welfare with respect to the Nash product when restricted to certain special cases.     

Maximizing stochastic robustness of static resource allocations in a periodic sensor driven cluster

This research investigates the problem of robust static resource allocation for distributed computing systems operating under imposed Quality of Service (QoS) constraints. Often, such systems are expected to function in an environment where uncertainty in system parameters is common. In such an environment, the amount of processing required to complete a task may fluctuate substantially. Determining a resource allocation that accounts for this uncertainty—in a way that can provide a probability that a given level of QoS is achieved—is an important area of research. We have designed novel techniques for maximizing the probability that a given level of QoS is achieved. These techniques feature a unique application of both path relinking and local search within a Genetic Algorithm. In addition, we define a new methodology for finding resource allocations that are guaranteed to have a non-zero probability of addressing the timing constraints of the system. We demonstrate the use of this methodology within two unique steady-state genetic algorithms designed to maximize the robustness of resource allocations. The performance results for our techniques are presented for a simulated environment that models a heterogeneous cluster-based radar data processing center.     

资源有限连续双向拍卖环境下的理性策略

网络系统的动态资源分配是未来IT系统必须解决的一个基本问题。针对agent资源的有限性,提出了连续双向拍卖环境下（Continuous Double Auction,CDA）agent具有理性行为的GD2策略。GD2策略是一种包含价格和数量的二维报价策略,agent通过建立信任函数和计算最大期望利润调整报价,实验表明GD2策略可以实现较高的动态资源分配效率,平均分配效率超过98%。     

基于网格环境的资源管理模型研究

针对目前网格环境下资源管理存在的问题和现状，提出了一种基于市场机制的资源管理模型。该模型以一般均衡理论为基础，计算出资源的市场价格，依靠市场机制，实现计算网格资源的优化分配。     

Distributed resource management with heterogeneous linear controls

Relative differentiation in distributed resource sharing can be implemented using heterogeneous linear controls with binary feedback and this method can provide efficient and weighted max–min fair resource allocations. We prove this using a discrete-time model of a single resource, shared among a number of users with heterogeneous Additive Increase Multiplicative Decrease (AIMD) controls. AIMD has been implemented in the congestion avoidance mechanism of Internet's Transmission Control Protocol (TCP) and beyond its simplicity it has been proved extremely efficient and robust. We show how AIMD can be parametrized in order to allow the scaling of user allocations according to a given set of weights. We also analyze the effects of different parameter choices on the performance and the oscillating behaviour of the system. Our analysis is supported by simulations and the results provide useful insights to the performance and the properties of distributed resource sharing.     

Achieving concurrency in cloud-orchestrated Internet of Things for resource sharing through multiple concurrent access

Cloud-orchestrated Internet of Things (IoT) facilitates proper utilization of network resources and placating user demands in smart communications. Multiple concurrent access (MCA) techniques designed for cloud-assisted communication helps to achieve better resource sharing features with fault tolerance ability. A multi-objective resource allocation and sharing (RAS) for balancing MCA in cloud-orchestrated IoT is presented in this article. The RAS constraints are modeled through linear programming (LP) as an optimization approach. The constraints are resolved using genetic representations (GR) for reducing the unserviced requests and failed resource allocations. Conventional genetic stages are inherited by the LP model to solve resource allocation and access issues reducing latency. The combined LP and GR jointly resolve resource allocation and MCA stagnation in cloud network. A fair outcome of LP-GR is estimation using the metrics response latency, resource utilization, request handled, and average latency.     

Multiagent resource allocation with sharable items

We study a particular multiagent resource allocation problem with indivisible, but sharable resources. In our model, the utility of an agent for using a bundle of resources is the difference between the value the agent would assign to that bundle in isolation and a congestion cost that depends on the number of agents she has to share each of the resources in her bundle with. The valuation function determining the value and the delay function determining the congestion cost can be agent-dependent. When the agents that share a resource also share control over that resource, then the current users of a resource will require some compensation when a new agent wants to join them using the resource. For this scenario of shared control, we study the existence of distributed negotiation protocols that lead to a social optimum. Depending on constraints on the valuation functions (mainly modularity), on the delay functions (such as convexity), and on the structural complexity of the deals between agents, we prove either the existence of a sequences of deals leading to a social optimum or the convergence of all sequences of deals to such an optimum. We also analyse the length of the path leading to such optimal allocations. For scenarios where the agents using a resource do not have shared control over that resource (i.e., where any agent can use any resource she wants), we study the existence of pure Nash equilibria, i.e., allocations in which no single agent has an incentive to add or drop any of the resources she is currently holding. We provide results for modular valuation functions, we analyse the length of the paths leading to a pure Nash equilibrium, and we relate our findings to results from the literature on congestion games.     

Congestion control in utility fair networks

This paper deals with a congestion control framework for elastic and real-time traffic, where the user’s application is associated with a utility function. We allow users to have concave as well as non-concave utility functions, and aim at allocating bandwidth such that utility values are shared fairly. To achieve this, we transform all utilities into strictly concave second order utilities and interpret the resource allocation problem as the global optimization problem of maximizing aggregate second order utility. We propose a new fairness criterion, utility proportional fairness, which is characterized by the unique solution to this problem. Our fairness criterion incorporates utility max–min fairness as a limiting case. Based on our analysis, we obtain congestion control laws at links and sources that (i) are linearly stable regardless of the network topology, provided that a bound on round-trip-times is known, (ii) provide a utility proportional fair resource allocation in equilibrium. We further investigate the efficiency of utility fair resource allocations. Our measure of efficiency is defined as the worst case ratio of the total utility of a utility proportional fair rate vector and the maximum possible total utility. We present a generic technique, which allows to obtain upper bounds on the efficiency loss. For special cases, such as linear and concave utility functions, and non-concave utility functions with bounded domain, we explicitly calculate such upper bounds. Then, we study utility fair resource allocations with respect to bandwidth fairness. We derive a fairness metric assessing the aggressiveness of utility functions. This allows us to design fair utility functions for various applications. Finally, we simulate the proposed algorithms using the NS2 simulator.