Agent scheduling model for adaptive dynamic load balancing in agent-based distributed simulations

Agent-based distributed simulations are confronted with load imbalance problem, which significantly affects simulation performance. Dynamic load balancing can be effective in decreasing simulation execution time and improving simulation performance. The characteristics of multi-agent systems and time synchronization mechanisms make the traditional dynamic load balancing approaches not suitable for dynamic load balancing in agent-based distributed simulations. In this paper, an adaptive dynamic load balancing model in agent-based distributed simulations is proposed. Due to the complexity and huge time consuming for solving the model, a distributed approximate optimized scheduling algorithm with partial information (DAOSAPI) is proposed. It integrates the distributed mode, approximate optimization and agent set scheduling approach. Finally, experiments are conducted to verify the efficiency of the proposed algorithm and the simulation performance under dynamic agent scheduling. The experiments indicate that DAOSPI has the advantage of short execution time in large-scale agent scheduling, and the distributed simulation performance under this dynamic agent scheduling outperforms that under static random agent distribution.     

An agent-based solution for dynamic multi-node wavefront balancing in biological sequence comparison

Many parallel and distributed strategies were created to reduce the execution time of bioinformatics algorithms. One well-known bioinformatics algorithm is the Smith–Waterman, that may be parallelized using the wavefront method. When the wavefront is distributed across many heterogeneous nodes, it must be balanced to create a synchronous data flow. This is a very challenging problem if the nodes have variable computational power. This paper presents an agent-based solution for parallel biological sequence comparison applications that use the multi-node wavefront method. In our approach, autonomous agents are able to identify unbalanced computations and dynamically rebalance the load among the nodes. Two strategies were developed to the balancer agent in order to identify if the computations are balanced, one using global information and other using only local information. The global strategy demands a huge amount of data transfers, incurring in more communication, whereas the local strategy can decide about the balancing status using only local information. The results show that the balancing gains of strategies are very close. Thus, the local strategy is preferred, since it can be implemented in real wavefront balancers with almost the same benefits as the global strategy.     

A self‐organized load balancing mechanism for cloud computing

The growth in computer and networking technologies over the past decades established cloud computing as a new paradigm in information technology. The cloud computing promises to deliver cost‐effective services by running workloads in a large scale data center consisting of thousands of virtualized servers. The main challenge with a cloud platform is its unpredictable performance. A possible solution to this challenge could be load balancing mechanism that aims to distribute the workload across the servers of the data center effectively. In this paper, we present a distributed and scalable load balancing mechanism for cloud computing using game theory. The mechanism is self‐organized and depends only on the local information for the load balancing. We proved that our mechanism converges and its inefficiency is bounded. Simulation results show that the generated placement of workload on servers provides an efficient, scalable, and reliable load balancing scheme for the cloud data center. Copyright © 2016 John Wiley & Sons, Ltd.     

An enhanced load balancing mechanism based on deadline control on GridSim

A Grid is a network of computational resources that may potentially span many continents. Load balancing in a Grid is a hot research issue which affects every aspect of the Grid, including service selection and task execution. Thus, it is necessary and significant to solve the load balancing problem in a Grid. In this paper, we propose a dynamic, distributed load balancing scheme for a Grid which provides deadline control for tasks. In our scenario, first, resources check their state and make a request to the Grid Broker according to the change of load state. Then, the Grid Broker assigns Gridlets between resources and scheduling for load balancing under the deadline request. We apply our load balancing strategy into a popular Grid simulation platform GridSim. Experimental results prove that our proposed load balancing mechanism can (1) reduce the makespan, (2) improve the finished rate of the Gridlet, and (3) reduce the resubmitted time.     

A dynamic load balancing scheme with incentive mechanism in heterogeneous structured P2P networks

Dynamic load imbalance is a basic and inherent problem in structured P2P networks. Most existing research suffers from the problems of inefficiency in globally managing the nodes’ load information and consumption of network bandwidth. This paper describes the mechanisms for collecting and globally managing the dynamic load of each node, and based on which to present a load balancing strategy which transfers the load from overloaded to under loaded nodes so as to improve load balancing efficiency. In order to encourage the rational and selfish nodes to actively participate in the load balancing process, we also propose an incentive mechanism in dynamic load balancing, by which the differentiated services could be provided for the nodes according to their load balancing abilities. The simulation results indicate that our approach could tackle the load imbalance problem in structured P2P networks effectively and efficiently in terms of the load distribution and the transferred load volume.     

A GPU-assisted hybrid model for real-time crowd simulations

In this paper, we propose two new techniques for real-time crowd simulations; the first one is the clustering of agents on the GPU and the second one is incorporating the global cluster information into the existing microscopic navigation technique. The proposed model combines the agent-based models with macroscopic information (agent clusters) into a single framework. The global cluster information is determined on the GPU, and based on the agents' positions and velocities. Then, this information is used as input for the existing agent-based models (velocity obstacles, rule-based steering and social forces). The proposed hybrid model not only considers the nearby agents but also the distant agent configurations. Our test scenarios indicate that, in very dense circumstances, agents that use the proposed hybrid model navigate the environment with actual speeds closer to their intended speeds (less stuck) than the agents that are using only the agent-based models.     

A Direct Execution Approach to Simulating Mobile Agent Algorithms

Mobile agent technology has been applied to develop the solutions for various kinds of parallel and distributed computing problems. However, performance evaluation of mobile agent algorithms remains a difficult task, mainly due to the characteristics of mobile agents such as distributed and asynchronous execution, autonomy and mobility. This paper proposes a general approach based on direct execution simulation for evaluating the performance of mobile agent algorithms by collecting and analyzing the information about the agents during their execution. We describe the proposed generic simulation model, named MADES, the architecture of a software environment based on MADES, and a prototype implementation. A mobile agent-based distributed load balancing algorithm has been used for experiments with the prototype.     

Contextual Resource Negotiation-Based Task Allocation and Load Balancing in Complex Software Systems

In the complex software systems, software agents always need to negotiate with other agents within their physical and social contexts when they execute tasks. Obviously, the capacity of a software agent to execute tasks is determined by not only itself but also its contextual agents; thus, the number of tasks allocated on an agent should be directly proportional to its self-owned resources as well as its contextual agents' resources. This paper presents a novel task allocation model based on the contextual resource negotiation. In the presented task allocation model, while a task comes to the software system, it is first assigned to a principal agent that has high contextual enrichment factor for the required resources; then, the principal agent will negotiate with its contextual agents to execute the assigned task. However, while multiple tasks come to the software system, it is necessary to make load balancing to avoid overconvergence of tasks at certain agents that are rich of contextual resources. Thus, this paper also presents a novel load balancing method: if there are overlarge number of tasks queued for a certain agent, the capacities of both the agent itself and its contextual agents to accept new tasks will be reduced. Therefore, in this paper, the task allocation and load balancing are implemented according to the contextual resource distribution of agents, which can be well suited for the characteristics of complex software systems; and the presented model can reduce more communication costs between allocated agents than the previous methods based on self-owned resource distribution of agents.     

Autonomous mobile agent routing for efficient server resource allocation

Mobile agents are becoming increasingly important in the highly distributed applications frameworks seen today. Their routing/dispatching from node to node is a very important issue as we need to safeguard application efficiency, achieve better load balancing and resource utilization throughout the underlying network. Selecting the best target server for dispatching a mobile agent is, therefore, a multi-faceted problem that needs to be carefully tackled. In this paper we propose distributed, adaptive routing schemes (next node selection) for mobile agents. The proposed schemes overcome risks like load oscillations, i.e., agents simultaneously abandoning a congested node in search for other, less saturated node. We try to induce different routing decisions taken by agents to achieve load balancing and better utilization of network resources. We consider five different algorithms and evaluate them through simulations. Our findings are quite promising both from the user/application and the network/infrastructure perspective.     

Tasks for agent-based negotiation teams: Analysis,review, and challenges

An agent-based negotiation team is a group of interdependent agents that join together as a single negotiation party due to their shared interests in the negotiation at hand. The reasons to employ an agent-based negotiation team may vary: (i) more computation and parallelization capabilities; (ii) unite agents with different expertise and skills whose joint work makes it possible to tackle complex negotiation domains; (iii) the necessity to represent different stakeholders or different preferences in the same party (e.g., organizations, countries, and married couple). The topic of agent-based negotiation teams has been recently introduced in multi-agent research. Therefore, it is necessary to identify good practices, challenges, and related research that may help in advancing the state-of-the-art in agent-based negotiation teams. For that reason, in this article we review the tasks to be carried out by agent-based negotiation teams. Each task is analyzed and related with current advances in different research areas. The analysis aims to identify special challenges that may arise due to the particularities of agent-based negotiation teams.     

Algorithmic mechanism design for load balancing in distributed systems

Computational grids are promising next-generation computing platforms for large-scale problems in science and engineering. Grids are large-scale computing systems composed of geographically distributed resources (computers, storage etc.) owned by self interested agents or organizations. These agents may manipulate the resource allocation algorithm in their own benefit, and their selfish behavior may lead to severe performance degradation and poor efficiency. In this paper, we investigate the problem of designing protocols for resource allocation involving selfish agents. Solving this kind of problems is the object of mechanism design theory. Using this theory, we design a truthful mechanism for solving the static load balancing problem in heterogeneous distributed systems. We prove that using the optimal allocation algorithm the output function admits a truthful payment scheme satisfying voluntary participation. We derive a protocol that implements our mechanism and present experiments to show its effectiveness.     

使用Gini方法评测和优化集群节点结构

负载均衡机制是一种用于提高集群整体处理能力的方法,但是不合理的集群结构往往会影响负载均衡机制的效率,两者的不协调在一定程度上限制了系统性能的体现。通过建立负载均衡模拟系统和引入Gini方法,从节点性能分布、节点数量两个角度分析了集群节点结构与其负载均衡机制的相关性,进而提出了一个评测和优化系统能力的方法。最后用实验验证了这个方法的可行性,并给出了进一步的研究方向。     

Distributed average consensus with least-mean-square deviation

We consider a stochastic model for distributed average consensus, which arises in applications such as load balancing for parallel processors, distributed coordination of mobile autonomous agents, and network synchronization. In this model, each node updates its local variable with a weighted average of its neighbors’ values, and each new value is corrupted by an additive noise with zero mean. The quality of consensus can be measured by the total mean-square deviation of the individual variables from their average, which converges to a steady-state value. We consider the problem of finding the (symmetric) edge weights that result in the least mean-square deviation in steady state. We show that this problem can be cast as a convex optimization problem, so the global solution can be found efficiently. We describe some computational methods for solving this problem, and compare the weights and the mean-square deviations obtained by this method and several other weight design methods.     

An agent-based business automated system with self-adjusting visibility for reliability

This paper describes an agent-based model, called HAM, for business automation in large, distributed, and real-time systems. With resource bounds and time constraints, reliability and efficiency are difficult to achieve. We propose a self-restraining and self-stimulating control of agent interactions to meet the deadlines and to prevent agents from overloading system resources. The agent visibility and invisibility concepts are introduced and used to regulate the scopes of agent interactions and communications when it is needed. A mechanism, called DYVIREM, is designed to adjust agent visibility dynamically according to the deadlines and the resource limits. Through simulation experiments we also analyze the effects of agent visibility on the performance and on the quality of service of the proposed agent-based business model.     

An optimal scheduling algorithm for an agent-based multicast strategy on irregular networks

This paper describes an agent-based approach for scheduling multiple multicast on wormhole switch-based networks with irregular topologies. Multicast/broadcast is an important communication pattern, with applications in collective communication operations such as barrier synchronization and global combining. Our approach assigns an agent to each subtree of switches such that the agents can exchange information efficiently and independently. The entire multicast problem is then recursively solved with each agent sending message to those switches that it is responsible for. In this way, communication is localized by the assignment of agents to subtrees. This idea can be easily generalized to multiple multicast since the order of message passing among agents can be interleaved for different multicasts. The key to the performance of this agent-based approach is the message-passing scheduling between agents and the destination processors. We propose an optimal scheduling algorithm, called ForwardInSwitch to solve this problem. We conduct extensive experiments to demonstrate the efficiency of our approach by comparing our results with SPCCO, a highly efficient multicast algorithm reported in literature. We found that SPCCO suffers link contention when the number of simultaneous multiple multicast becomes large. On the other hand, our agent-based approach achieves better performance in large cases.     

流式处理系统的动态数据分配技术

流式数据处理中,数据倾斜等原因易导致计算节点的负载不均衡,降低系统处理能力。传统的负载均衡方法,比如算子分配、算子迁移和负载脱落等技术因为相对较高的性能代价,在流式处理系统中没有得到广泛的应用。针对流式处理系统的特点,提出一种新的负载均衡方法。在该方法中,计算单元的数据被划分为若干分区,并且数据分区可以在计算单元中动态分配和迁移,在较少干扰系统运行的情况下,通过动态调整各计算单元的分区,平衡各个计算单元的输入流和利用率,以此达到负载平衡的目的。在此基础上,设计并实现了流式处理系统的负载均衡算法和数据在线迁移技术。实验结果表明,该方法能够显著减少数据处理的平均延迟,提高系统吞吐量。     

The grid, the load and the gradient

An important concern for an efficient use of distributed computing is dealing with load balancing to ensure all available nodes and their shared resources are equally exploited. In large scale systems such as volunteer computing platforms and desktop grids, centralized solutions may introduce performance bottlenecks and single points of failure. Accordingly fully distributed alternatives have been considered, due to their inherent robustness and reliability. In extremely dynamic contexts, scheduling middlewares should adapt their job scheduling policies to the actual availability and overcome the volatility and heterogeneity typical of the underlying nodes. To deal with the dynamicity of a large pool of resources, self-organizing and adaptive solutions represent a promising research direction. Solutions based on bio-inspired methodologies are particularly suitable, as they inherently provide the desired features. In this paper we present a fully distributed load balancing mechanism, called ozmos, which aims at increasing the efficiency of distributed computing systems through peer-to-peer interaction between nodes. The proposed algorithm is based on a Chord overlay, and employs ant-like agents to spread information about the current load on each node, to reschedule tasks from overloaded systems to underloaded ones, and to relocate incompatible tasks on suitable resources in heterogeneous grids. By means of several evaluation scenarios we demonstrate the effectiveness of the proposed solution in achieving system-wide load balancing, both with homogeneous and heterogeneous resources. In particular we consider the load balancing performance of our approach, its scalability, as well as its communication efficiency.     

Agent-based load balancing routing for LEO satellite networks

To ensure an intelligent engineering of traffic over entire satellite networks, a distributed routing scheme for low-earth orbit (LEO) satellite networks, agent-based load balancing routing (ALBR), is presented. Two kinds of agents are used. Mobile agents migrate autonomously to explore the path connecting source and destination, to gather inter-satellite link (ISL) cost, identifier and latitude of visited satellites. Meanwhile, stationary agents employ exponential forgetting function to estimate ISL queueing delay, calculate ISL cost using the sum of propagation and queueing delays; evaluate path cost considering satellite geographical position as well as ISL cost, finally update routing items. Through simulations on a Courier-like system, the proposed scheme is shown to achieve better load balancing, and can especially decrease packet loss ratio efficiently, guarantee better throughput and end-to-end delay bound in case of high traffic load. Furthermore, results from the implementation complexity analysis demonstrate that with the aid of agent technology, ALBR has lower on-board computation, storage, signaling requirements than other on-board routing schemes.     

基于预测机制的分级负载均衡算法

为解决服务器集群负载分配不均的问题,根据用户访问的请求类型,综合考虑用户历史请求引起的负载增量和服务器节点性能,提出了基于预测机制的分级负载均衡算法。负载均衡节点根据用户访问的请求类型建立一次指数平滑预测模型,对相应请求类型引起的负载进行预测,并将预测负载划分为低负载、正常负载、重负载等三个负载等级,根据负载等级对用户请求进行调度,从而实现负载均衡。使用OPNET仿真软件进行测试,结果表明该算法能有效提高负载均衡效率,有较好的负载均衡效果。     

基于遗传算法的移动代理调度

移动代理能够从一台机器移动到网络上的另一台机器,这对平衡系统中的负载很有作用。然而在移动代理系统中缺乏调度机制,以指导代理的移动。论文基于遗传算法设计一个移动代理系统的调度框架,使代理能够移动到负载轻的机器,达到平衡负载的目的。方法的有效性通过实验得到了验证。