一对多协商协调策略研究

介绍了一种基于模糊逻辑的协调策略。协调策略考虑协商过程中的各种因素,包括时间、对手数目、对手的提议等,使用模糊规则和模糊推理,对多个相互影响的并发一对一协商进行协调。实验证明,该策略能够很好地适应信息不完全的环境。     

一对多协商协调策略

重点研究了一对多协商中对多个并发的一对一协商进行协调的协调策略。首先提出了相对效用理论,接着提出了基于相时效用理论的一对多协商协调策略,谊策略能够很好地解决当多个并发协商进程同时获得满足艘用评估的提议,特别是存在多个最大相同效用提议时的取舍问题,为协商更加有效、健壮、有序地执行提供了坚实的基础。     

A two-time-scale load balancing framework for minimizing electricity bills of Internet Data Centers

Internet Data Center (IDC) is one of important emerging cyber-physical systems. To guarantee the quality of service for their worldwide users, large Internet service providers usually operate multiple geographically distributed IDCs. The enormous power consumption of these data centers may lead to both huge electricity bills and considerable carbon emissions. To mitigate these problems, on-site renewable energy plants are emerging in recent years. Since the renewable energy is intermittent, greening geographical load balancing (GGLB for short) has been proposed to reduce both the electricity bills and carbon emissions by following the renewables. However, GGLB is not able to well deal with the wildly fluctuating wind power when applied into IDCs with on-site wind energy plants. It may either fail to minimize the total electricity bills or incur the costly frequent on–off switching of servers. In order to minimize the total electricity bills of geographically distributed IDCs with on-site wind energy plants, we formulate the total electricity bills minimization problem and propose a novel two-time-scale load balancing framework TLB to solve it. First, TLB models the runtime cooling efficiency for each IDC. Then it predicts the future fine-grained (e.g., 10-min) on-site wind power output at the beginning of each scheduling period (e.g., an hour). After that, TLB transforms the primal optimization problem into a typical mixed-integer linear programming problem and solves it to finally obtain the optimal scheduling policy including the open server number as well as the request routing policy. It is worth noting that the open server number of each IDC will remain the same during each scheduling period. As an application instance of TLB, we also design a two-time-scale load balancing algorithm TLB-ARMA for our experimental scenario. Evaluation results based on real-life traces show that TLB-ARMA is able to reduce the total electricity bills by as much as 12.58 % compared with the hourly executed GGLB without incurring the costly repeated on–off switching of servers.     

Negotiation in a multi-agent system for construction claims negotiation

Negotiation is an important approach for agents to co-operate and reach agreement in multiagent systems (MAS). Different negotiation theories and models have been deployed in a variety of applications. This paper is concerned with the applicability of these theories to the domain of agent-based construction claims negotiation. The peculiarities of this domain are highlighted and the approach adopted in the development of a multi-agent system for construction claims negotiation (MASCOT) described. Of particular interest is the integration of Zeuthen's bargaining model with a Bayesian learning mechanism, which addresses the characeristics of the construction claims negotiation. Examples are presented to demonstrate the impact of various negotiation approaches on the conduct and outcome of construction claims negotiations.     

A multi-agent system for distributed multi-project scheduling: An auction-based negotiation approach

Simultaneously running multiple projects are quite common in industries. These projects require local (always available to the concerned project) and global (shared among the projects) resources that are available in limited quantity. The limited availability of the global resources coupled with compelling schedule requirements at different projects leads to resource conflicts among projects. Effectively resolving these resource conflicts is a challenging task for practicing managers. This paper proposes a novel distributed multi-agent system using auctions based negotiation (DMAS/ABN) approach for resolving the resource conflicts and allocating multiple different types of shared resources amongst multiple competing projects. The existing multi-agent system (MAS) using auction makes use of exact methods (e.g. dynamic programming relaxation) for solving winner determination problem to resolve resource conflicts and allocation of single unit of only one type of shared resource. Consequently these methods fail to converge for some multi-project instances and unsuitable for real life large problems. In this paper the multi-unit combinatorial auction is proposed and winner determination problem is solved by efficient new heuristic.The proposed approach can solve complex large-sized multi-project instances without any limiting assumptions regarding the number of activities, shared resources or the number of projects. Additionally our approach further allows to random project release-time of projects which arrives dynamically over the planning horizon. The DMAS/ABN is tested on standard set of 140 problem instances. The results obtained are benchmarked against the three state-of-the-art decentralized algorithms and two existing centralized methods. For 82 of 140 instances DMAS/ABN found new best solutions with respect to average project delay (APD) and produced schedules on an average 16.79% (with maximum 57.09%) lower APD than all the five methods for solving the same class of problems.     

一种基于市场驱动的Multi-agent自动谈判策略

针对当前电子商务中基于Agent的谈判系统的谈判策略的静态性问题,提出基于市场驱动的谈判策略。Agent在谈判中能根据变化的市场情况做出可以调整比率的让步,帮助用户做出最优的交易决策,且自动地选择合适的策略。实验结果表明,采用基于市场驱动的策略比采用固定的策略的谈判结果更让用户感到满意。     

A decision support system for machine selection with load balancing

This paper presents a new approach towards the machine selection problem. This approach utilizes a decision support system (DSS) for advising the production manager which machine to select. The DSS is of special kind: its machine selection rules are modified to ensure equal load on the machines. The basic structure of the decision tree is defined by process experts and the rules threshold values are dynamically evaluated.

The implementation domain is the aluminum extrusion process, with the objective of aiding the manager in selecting the appropriate extrusion press machine from a set of similar machines. These machines are the most expensive equipment in the process, and are expected to be fully utilized.

The methodology presented in this paper was implemented in such a facility resulting in about 3% improvement on machine utilization.     

A case for on-machine load balancing

This paper diverges from the traditional load balancing, and introduces a new principle called the on-machine load balance rule. The on-machine load balance rule leads to resource allocations that are better in tolerating uncertainties in the processing times of the tasks allocated to the resources when compared to other resource allocations that are derived using the conventional “across-the-machines” load balancing rule. The on-machine load balance rule calls for the resource allocation algorithms to allocate similarly sized tasks on a machine (in addition to optimizing some primary performance measures such as estimated makespan and average response time). The on-machine load balance rule is very different from the usual across-the-machines load balance rule that strives to balance load across resources so that all resources have similar finishing times.We give a mathematical justification for the on-machine load balance rule requiring only liberal assumptions about task processing times. Then we validate with extensive simulations that the resource allocations derived using on-machine load balance rule are indeed more tolerant of uncertain task processing times.     

负载均衡系统设计方案

网络流量与日俱增,而对流量的需求会积聚在服务器端,如何提高服务器处理能力是一个现实的问题,采用负载均衡调度器来构造高效集群系统是一种有效的办法.通过网络发展需求和负载均衡技术分析比较,给出几种负载均衡调度器的设计方案和由此负载均衡调度器构成的集群体系结构,最后给出负载均衡调度器具体应用.     

A new fuzzy-decision based load balancing system for distributed object computing

Distributed object computing systems are widely envisioned to be the desired distributed software development paradigm due to the higher modularity and the capability of handling machine and operating system heterogeneity. Indeed, enabled by the tremendous advancements in processor and networking technologies, complex operations such as object serialization and data marshaling have become very efficient, and thus, distributed object systems are being built for many different applications. However, as the system scales up (e.g., with larger number of server and client objects, and more machines), a judicious load balancing system is required to efficiently distribute the workload (e.g., the queries, messages/objects passing) among the different servers in the system. Unfortunately, in existing distributed object middleware systems, such a load balancing facility does not exist. In this paper, we present the design and implementation of a new dynamic fuzzy-decision-based load balancing system incorporated in a distributed object computing environment. Our proposed approach works by using a fuzzy logic controller which informs a client object to use the most appropriate service such that load balancing among servers is achieved. We have chosen Jini to build our experimental middleware platform, on which our proposed approach as well as other related techniques are implemented and compared. Extensive experiments are conducted to investigate the effectiveness of our fuzzy-decision-based algorithm, which is found to be consistently better than other approaches.     

A protocol for multi-agent negotiation in a group-choice decision making process

Negotiation is one of the most important features of agent interactions found in multi-agent systems, because it provides the basis for managing the expectations of the individual negotiating agents, and it enables selecting solutions that satisfy all the agents as much as possible. In order for negotiation to take place between two or more agents there is need for a negotiation protocol that defines the rules of the game; consequently, a variety of agent negotiation protocols have been proposed in literature. However, most of them are inappropriate for Group-Choice Decision Making (GCDM) because they do not explicitly exploit tradeoff to achieve social optimality, and their main focus is solving two-agent negotiation problems such as buyer–seller negotiation. In this paper we present an agent negotiation protocol that facilitates the solving of GCDM problems. The protocol is based on a hybrid of analytic and artificial intelligence techniques. The analytic component of the protocol utilizes a Game Theory model of an n-person general-sum game with complete information to determine the agreement options, while the knowledge-based (artificial intelligence) component of the protocol is similar to the strategic negotiation protocol. Moreover, this paper presents a tradeoff algorithm based on Qualitative Reasoning, which the agents employ to determine the ‘amount’ of tradeoff associated with various agreement options. Finally, the paper presents simulation results that illustrate the operational effectiveness of our agent negotiation protocol.     

LTE系统中的分布式负载迁移协商均衡算法

为了提高长期演进计划(long term evolution,LTE)系统中业务处理资源的利用率,降低由于系统处理资源分配不均而导致的呼损率,给出了一种适用于LTE系统的分布式负载迁移协商均衡算法.分析了LTE系统通信的特点,指出可利用LTE系统eNodeB (evolved NodeB)间使用X2接口进行数据通信的特点,在组网的eNodeB问进行负载迁移.在此基础上建立了负载迁移协商均衡模型,指出了算法的适用场景,说明了算法均衡粒度的选取标准和性能评价指标.通过与传统的轮询算法进行比较,使用分布式事件驱动方法对该算法进行了性能分析,验证结果表明了该算法的有效性.     

多Agen t 协商行为的效用分析

给出多Agent协商行为的一种统计模型以及效用函数的表达式，从统计的角度分析了多Agent协商行为的行为效用，并给出了相关参数的定性分析，从而为更好地设计多Agent系统的协商组织规则和协商策略提供了效用依据．     

A load index and load balancing algorithm for heterogeneous clusters

This paper presents a load balancing algorithm specifically designed for heterogeneous clusters, composed of nodes with different computational capabilities. The method is based on a new index, which takes into consideration two levels of processors heterogeneity: the number of cores per node and the computational power of each core. The experimental results show that this index allows achieving balanced workload distributions even on those clusters where heterogeneity can not be neglected.     

多agent两阶段多属性协商模型

多议题模型中,在议题的评价机制上买卖双方大多采用多属性效用理论或其变形理论,如距离函数等。提出了多属性效用理论实际应用中的悖论问题,分析了造成理论应用偏差的原因和B2C领域电子商务活动交易的特点,进而提出了一个协商拍卖模型。该模型分两个阶段来进行,提高了协商效率,而且允许卖方体现商品的特色,差异化自己的商品。     

A multi-agent approach for planning activities in decentralized electricity markets

Despite the rigour and ability of game theory to cope with oligopolistic electric markets, it fails to model many existing behaviours in the real-world circumstances. The traditional models such as statistical extrapolation or econometrics are not capable to anticipate the changes in the pattern of the market prices due to the future structural changes. Furthermore, in such free and open markets, there is a more intense need for each participant to benefit from a certain level of autonomy, while keeping some capabilities to interact, communicate, collaborate and negotiate with other participants in an efficient way. As a result, there is a need for a novel framework of modelling that could include game theoretical assumptions as well as other more complex assumptions. Agent technologies in general and agent-based simulation in particular offer this possibility. This paper proposes, in a decision-making perspective, a new multi-agent architecture specifically designed to support planning activities in decentralized electricity markets, with a certain level of flexibility. In this model, synthetic agents are created allowing flexible representations of the multi-functional market players and possible mergers and coalitions in the electricity market.     

基于负载均衡的高吞吐量IPsec VPN系统

为了解决连接网络型IPsec VPN随着用户接入增多、网络流量增大,而引起的响应时间延长、吞吐量降低的问题,提出一种高吞吐量IPsec VPN解决方案。在网关处组建服务器集群,利用特定的负载均衡策略,实现了对IPsec数据流的均匀分配。模拟实验结果表明,随着网络流量的增加,负载均衡技术可以一定程度减少IPsec VPN系统响应时间,提高系统的吞吐量。     

A repartitioning hypergraph model for dynamic load balancing

In parallel adaptive applications, the computational structure of the applications changes over time, leading to load imbalances even though the initial load distributions were balanced. To restore balance and to keep communication volume low in further iterations of the applications, dynamic load balancing (repartitioning) of the changed computational structure is required. Repartitioning differs from static load balancing (partitioning) due to the additional requirement of minimizing migration cost to move data from an existing partition to a new partition. In this paper, we present a novel repartitioning hypergraph model for dynamic load balancing that accounts for both communication volume in the application and migration cost to move data, in order to minimize the overall cost. The use of a hypergraph-based model allows us to accurately model communication costs rather than approximate them with graph-based models. We show that the new model can be realized using hypergraph partitioning with fixed vertices and describe our parallel multilevel implementation within the Zoltan load balancing toolkit. To the best of our knowledge, this is the first implementation for dynamic load balancing based on hypergraph partitioning. To demonstrate the effectiveness of our approach, we conducted experiments on a Linux cluster with 1024 processors. The results show that, in terms of reducing total cost, our new model compares favorably to the graph-based dynamic load balancing approaches, and multilevel approaches improve the repartitioning quality significantly.     

A Dynamic Load Balancing Mechanism for Distributed Systems

It is desirable in a distributed system to have the system load balanced evenly among the nodes so that the mean job response time is minimized.In this paper,we present a dynamic load balancing mechanism(DLB).It adopts a cntralized approach and is network topology independent.The DLB mechanism employs a set of threscholds which are automatically adjusted as the system load changes.It also provides a simple mechanism for the system to switch between periodic and instantaneous load balancing policies with ease.The performance of the proposed algorithm is evaluated by intensive simulations for various parameters.Te simulation results show that the mean job response time in a system implementing DLB algorithm is significantly lower than the same system without load balancings.Furthermore,compared with a previously proposed algorithm,DLB algorithm demonstrates improved performance,especially when the system is heavily loaded and the load is unevenly distributed.