A distributed and multi‐tiered software architecture for assessing e‐Commerce recommendations

Recently, an ever increasing number of e‐Commerce tools has been made available that are able to help customers by generating purposed recommendations. Many of them are centralized so that they have to face problems related to efficiency and scalability. A few of them are distributed, but in this case, the complexity of the e‐Commerce process implies computation overhead on the client side, which is often unsuitable if mobile devices are used by customers. In this paper, we study how the software distribution in recommender systems affects their performances, depending on the characteristics of the e‐Commerce population. To this end, we present a distributed testbed architecture for e‐Commerce recommender systems using a multi‐tiered agent‐based approach to generate effective recommendations without requiring such an onerous amount of computation per single client. We use such a testbed to study the main advantages and limitations associated with the problem of distributing the computation of recommendations. Copyright © 2016 John Wiley & Sons, Ltd.     

Cooperative robust output regulation problem for discrete‐time linear time‐delay multi‐agent systems

In this paper, we study the cooperative robust output regulation problem for discrete‐time linear multi‐agent systems with both communication and input delays by a distributed internal model approach. We first introduce the distributed internal model for discrete‐time multi‐agent systems with both communication and input delays. Then, we define the so‐called auxiliary system and auxiliary augmented system. Finally, we solve our problem by showing, under some standard assumptions, that if a distributed state feedback control or a distributed output feedback control solves the robust output regulation problem of the auxiliary system, then the same control law solves the cooperative robust output regulation problem of the original multi‐agent systems.     

Distributed event‐triggered tracking control with a dynamic leader for multiple Euler‐Lagrange systems under directed networks

The distributed tracking control for multiple Euler‐Lagrange systems with a dynamic leader is investigated in this article via the event‐triggered approach. Only a portion of followers have access to the leader, and the communication topology among all agents is directed that contains a directed spanning tree rooted at the leader. The case that the leader's generalized velocity is constant is first considered, and a distributed event‐based control law is developed by using a velocity estimator. When the leader's generalized velocity is time‐varying, novel distributed continuous estimators are proposed to avoid the undesirable chattering effect while guaranteeing that the estimate errors converge to zeros. With the designed distributed estimators, another distributed event‐based control protocol is provided. Controller update frequency and resource consumption in our work can be reduced by applying the aforementioned two distributed control laws, and the tracking errors can converge to zeros. In addition, it is rigorously proved that no agent exhibits Zeno behavior. Finally, the effectiveness of the proposed distributed event‐based control laws is elucidated by a number of simulation examples.     

High performance computing for three-dimensional agent-based molecular models

Agent-based simulations are increasingly popular in exploring and understanding cellular systems, but the natural complexity of these systems and the desire to grasp different modelling levels demand cost-effective simulation strategies and tools.In this context, the present paper introduces novel sequential and distributed approaches for the three-dimensional agent-based simulation of individual molecules in cellular events. These approaches are able to describe the dimensions and position of the molecules with high accuracy and thus, study the critical effect of spatial distribution on cellular events. Moreover, two of the approaches allow multi-thread high performance simulations, distributing the three-dimensional model in a platform independent and computationally efficient way.Evaluation addressed the reproduction of molecular scenarios and different scalability aspects of agent creation and agent interaction. The three approaches simulate common biophysical and biochemical laws faithfully. The distributed approaches show improved performance when dealing with large agent populations while the sequential approach is better suited for small to medium size agent populations.Overall, the main new contribution of the approaches is the ability to simulate three-dimensional agent-based models at the molecular level with reduced implementation effort and moderate-level computational capacity. Since these approaches have a generic design, they have the major potential of being used in any event-driven agent-based tool.     

Distributed model predictive control for consensus of nonlinear second‐order multi‐agent systems

This paper proposes a distributed model predictive control algorithm for the consensus of nonlinear second‐order multi‐agent systems. At each update time, all the agents are permitted to optimize. A positively invariant terminal region and a corresponding auxiliary controller are developed for each agent. Furthermore, time‐varying compatibility constraint is presented to denote a degree of consistency between the assumed trajectories and the actual trajectories of each agent. Given the designed terminal ingredients (terminal region, auxiliary controller, and terminal cost) and compatibility constraints, the recursive feasibility and closed‐loop stability of the whole system are guaranteed. The simulation results are given to illustrate the effectiveness of the proposed approach. Copyright © 2016 John Wiley & Sons, Ltd.     

Distributing RePast agent‐based simulations with HLA

Large, experimental multi‐agent system (MAS) simulations are highly demanding tasks, both computationally and developmentally. Agent toolkits provide reliable templates for the design of even the largest MAS simulations, without offering a solution to computational limitations. Conversely, distributed simulation architectures offer performance benefits, but the introduction of parallel logic can complicate the design process significantly. The motivations of distribution are not limited to this question of processing power. True interoperation of sequential agent‐simulation platforms would allow agents designed using different toolkits to transparently interact in common abstract domains. This paper discusses the design and implementation of a system capable of harnessing the computational power of a distributed simulation infrastructure with the design efficiency of an agent toolkit. The system permits integration, through a higher‐level architecture (HLA) federation, of multiple instances of the Java‐based lightweight agent‐simulation toolkit RePast. This paper defines abstractly the engineering process necessary in creating such middleware, and reports on the experience in the specific case of the RePast toolkit. The paper also presents performance results that illustrate that significant speedup can be achieved through the integration of RePast with HLA. Copyright © 2008 John Wiley & Sons, Ltd.     

HPC‐GAP: engineering a 21st‐century high‐performance computer algebra system

Symbolic computation has underpinned a number of key advances in Mathematics and Computer Science. Applications are typically large and potentially highly parallel, making them good candidates for parallel execution at a variety of scales from multi‐core to high‐performance computing systems. However, much existing work on parallel computing is based around numeric rather than symbolic computations. In particular, symbolic computing presents particular problems in terms of varying granularity and irregular task sizes that do not match conventional approaches to parallelisation. It also presents problems in terms of the structure of the algorithms and data. This paper describes a new implementation of the free open‐source GAP computational algebra system that places parallelism at the heart of the design, dealing with the key scalability and cross‐platform portability problems. We provide three system layers that deal with the three most important classes of hardware: individual shared memory multi‐core nodes, mid‐scale distributed clusters of (multi‐core) nodes and full‐blown high‐performance computing systems, comprising large‐scale tightly connected networks of multi‐core nodes. This requires us to develop new cross‐layer programming abstractions in the form of new domain‐specific skeletons that allow us to seamlessly target different hardware levels. Our results show that, using our approach, we can achieve good scalability and speedups for two realistic exemplars, on high‐performance systems comprising up to 32000 cores, as well as on ubiquitous multi‐core systems and distributed clusters. The work reported here paves the way towards full‐scale exploitation of symbolic computation by high‐performance computing systems, and we demonstrate the potential with two major case studies. © 2016 The Authors. Concurrency and Computation: Practice and Experience Published by John Wiley & Sons Ltd.     

Event‐based distributed robust synchronization control for multiple Euler‐Lagrange systems without relative velocity measurements

This paper focuses on the event‐based distributed robust leaderless synchronization control for multiple Euler‐Lagrange systems with directed communication topology that contains a directed spanning tree. Update frequency of the system is reduced by taking advantages of the event‐triggered approach, which can help extend the service life of the controller. Robust control theory is employed to guarantee the synchronization stability of the networked Euler‐Lagrange systems when unmodeled dynamics occur. The cost on the distributed synchronization protocol design can be saved due to the relaxation of the requirement on relative velocity measurements. Furthermore, our results are more practical because unknown disturbance is taken into consideration. In addition, it can be rigorously analyzed that each agent can exclude the undesired Zeno behavior. Some simulation examples are provided in the end to demonstrate the effectiveness of the proposed event‐based distributed robust control algorithm.     

Improving scalability of event‐driven distributed objects architectures

The goal of this paper is to present practical techniques for improving the scalability of a distributed objects architecture built around an event‐driven paradigm and the evaluation of their effectiveness in the case of location systems. As a case study, the ABng location system constructed using a publisher/subscriber pattern over a CORBA compliant middleware is presented. The main contribution of this paper is the elaboration and evaluation of three main concepts that play a crucial role in improving the scalability of event‐driven architectures: events dissemination quality of service, notification tree based events‐routing, and transparent multicast event‐channel notification service. The applicability and efficiency of these techniques have been verified in practice by the ABng system implementation and performance testing. Copyright © 2000 John Wiley & Sons, Ltd.     

Helenos: A realistic benchmark for distributed transactional memory

Transactional memory (TM) is an approach to concurrency control that aims to make writing parallel programs both effective and simple. The approach has been initially proposed for nondistributed multiprocessor systems, but it is gaining popularity in distributed systems to synchronize tasks at large scales. Efficiency and scalability are often the key issues in TM research; thus, performance benchmarks are an important part of it. However, while standard TM benchmarks like the Stanford Transactional Applications for Multi‐Processing suite and STMBench7 are available and widely accepted, they do not translate well into distributed systems. Hence, the set of benchmarks usable with distributed TM systems is very limited, and must be padded with microbenchmarks, whose simplicity and artificial nature often makes them uninformative or misleading. Therefore, this paper introduces Helenos, a realistic, complex, and comprehensive distributed TM benchmark based on the problem of the Facebook inbox, an application of the Cassandra distributed store.     

Performance and effectiveness trade‐off for checkpointing in fault‐tolerant distributed systems

Checkpointing has a crucial impact on systems' performance and fault‐tolerance effectiveness: excessive checkpointing results in performance degradation, while deficient checkpointing incurs expensive recovery. In distributed systems with independent checkpoint activities there is no easy way to determine checkpoint frequencies optimizing response‐time and fault‐tolerance costs at the same time. The purpose of this paper is to investigate the potentialities of a statistical decision‐making procedure. We adopt a simulation‐based approach for obtaining performance metrics that are afterwards used for determining a trade‐off between checkpoint interval reductions and efficiency in performance. Statistical methodology including experimental design, regression analysis and optimization provides us with the framework for comparing configurations, which use possibly different fault‐tolerance mechanisms (replication‐based or message‐logging‐based). Systematic research also allows us to take into account additional design factors, such as load balancing. The method is described in terms of a standardized object replication model (OMG FT‐CORBA), but it could also be applied in other (e.g. process‐based) computational models. Copyright © 2006 John Wiley & Sons, Ltd.     

Adaptive iterative learning control for coordination of second‐order multi‐agent systems

In this paper, an efficient framework is proposed to the consensus and formation control of distributed multi‐agent systems with second‐order dynamics and unknown time‐varying parameters, by means of an adaptive iterative learning control approach. Under the assumption that the acceleration of the leader is unknown to any follower agents, a new adaptive auxiliary control and the distributed adaptive iterative learning protocols are designed. Then, all follower agents track the leader uniformly on [0,T] for consensus problem and keep the desired distance from the leader and achieve velocity consensus uniformly on [0,T] for the formation problem, respectively. The distributed multi‐agent coordinations performance is analyzed based on the Lyapunov stability theory. Finally, simulation examples are given to illustrate the effectiveness of the proposed protocols in this paper.Copyright © 2013 John Wiley & Sons, Ltd.     

Distributed event‐triggered feedback consensus control with state‐dependent threshold for general linear multi‐agent systems

This paper considers the distributed event‐triggered consensus problem for multi‐agent systems with general linear dynamics under undirected graphs. Based on state feedback, we propose a novel distributed event‐triggered consensus controller with state‐dependent threshold for each agent to achieve consensus, without continuous communication in either controller update or triggering condition monitoring. Each agent only needs to monitor its own state continuously to determine if the event is triggered. It is proved that there is no Zeno behavior under the proposed consensus control algorithm. To relax the requirement of the state measurement of each agent, we further propose a novel distributed observer‐based event‐triggered consensus controller to solve the consensus problem in the case with output feedback and prove that there is no Zeno behavior exhibited. Finally, simulation results are given to illustrate the theoretical results. Copyright © 2016 John Wiley & Sons, Ltd.     

Observer‐based consensus of second‐order multi‐agent system with fixed and stochastically switching topology via sampled data

This paper addresses the distributed observer‐based consensus problem of second‐order multi‐agent systems via sampled data. Firstly, for the case of fixed topology, a velocity‐independent distributed control law is proposed by designing a distributed observer to estimate the unavailable velocity, then a sufficient and necessary condition of consensus on design parameters and sampling period is obtained by using the matrix analysis method. Secondly, for the case of stochastically switching topology, a sufficient and necessary condition of mean square consensus is also proposed and proven, and an algorithm is provided to design the parameters in the consensus protocol. Two simulation examples are given to illustrate the effectiveness of the proposed consensus algorithms. Copyright © 2012 John Wiley & Sons, Ltd.     

Distributed Adaptive Event‐Triggered Control for Leader‐Following Consensus of Multi‐Agent Systems

This paper studies the leader‐following consensus problem for Lipschitz nonlinear multi‐agent systems using novel event‐triggered controllers. A distributed adaptive law is introduced for the event‐based control strategy design such that the proposed controllers are independent of system parameters and only use the relative states of neighboring agents, and hence are fully distributed. Due to the introduction of an event‐triggered control scheme, the controller of the agent is only triggered at it's own event times, and thus reduces the amount of communication between controller and actuator and lowers the frequency of controller updates in practice. Based on a quadratic Lyapunov function, the event condition which uses only neighbor information and local computation at trigger instants is established. Infinite triggers within a finite time are also verified to be impossible. The effectiveness of the theoretical results are illustrated through simulation examples.     

网络管理的多agent范型

网络管理系统是大规模的分布式软件系统 ,伸缩性和灵活性是今天的网络管理技术面临的两大难题 .软件 a-gent为这些难题提供了出色的解决方法 .本文首先指出当前网络管理中存在的问题 ;在此基础上 ,提出基于多 agent的网络管理结构 ;为了证明其技术可行性 ,专门建立了基于 agent的网络流量管理实验系统 .对该系统的评估证明了软件agent非常适合于开发分布式网络管理系统 ,具有实时性高、可扩充性好、自治能力强的特点 ,而且与传统系统相比 ,这种系统的建立、修改和升级更加经济     

Load‐balanced and locality‐aware scheduling for data‐intensive workloads at extreme scales

Data‐driven programming models such as many‐task computing (MTC) have been prevalent for running data‐intensive scientific applications. MTC applies over‐decomposition to enable distributed scheduling. To achieve extreme scalability, MTC proposes a fully distributed task scheduling architecture that employs as many schedulers as the compute nodes to make scheduling decisions. Achieving distributed load balancing and best exploiting data locality are two important goals for the best performance of distributed scheduling of data‐intensive applications. Our previous research proposed a data‐aware work‐stealing technique to optimize both load balancing and data locality by using both dedicated and shared task ready queues in each scheduler. Tasks were organized in queues based on the input data size and location. Distributed key‐value store was applied to manage task metadata. We implemented the technique in MATRIX, a distributed MTC task execution framework. In this work, we devise an analytical suboptimal upper bound of the proposed technique, compare MATRIX with other scheduling systems, and explore the scalability of the technique at extreme scales. Results show that the technique is not only scalable but can achieve performance within 15% of the suboptimal solution. Copyright © 2015 John Wiley & Sons, Ltd.     

Distributed output regulation of leader–follower multi‐agent systems

In this paper, distributed leader–follower control algorithms are presented for linear multi‐agent systems based on output regulation theory and internal model principle. By treating a leader to be followed as an exosystem, the proposed framework can be used to generalize existing multi‐agent coordination solutions to allow the identical agents to track an active leader with different dynamics and unmeasurable variables. Moreover, the obtained results for multi‐agent coordination control are an extension of previous work on centralized and decentralized output regulation to a distributed control context. Necessary and sufficient conditions for the distributed output regulation problem are given. Finally, distributed output regulation of some classes of multi‐agent systems with switching interconnection topologies are discussed via both static and dynamic feedback. Copyright © 2011 John Wiley & Sons, Ltd.     

Customer satisfaction‐aware scheduling for utility maximization on geo‐distributed data centers

With the increasingly growing amount of service requests from the world‐wide customers, the cloud systems are capable of providing services while meeting the customers' satisfaction. Recently, to achieve the better reliability and performance, the cloud systems have been largely depending on the geographically distributed data centers. Nevertheless, the dollar cost of service placement by service providers (SP) differ from the multiple regions. Accordingly, it is crucial to design a request dispatching and resource allocation algorithm to maximize net profit. The existing algorithms are either built upon energy‐efficient schemes alone, or multi‐type requests and customer satisfaction oblivious. They cannot be applied to multi‐type requests and customer satisfaction‐aware algorithm design with the objective of maximizing net profit. This paper proposes an ant‐colony optimization‐based algorithm for maximizing SP's net profit (AMP) on geographically distributed data centers with the consideration of customer satisfaction. First, using model of customer satisfaction, we formulate the utility (or net profit) maximization issue as an optimization problem under the constraints of customer satisfaction and data centers. Second, we analyze the complexity of the optimal requests dispatchment problem and rigidly prove that it is an NP‐complete problem. Third, to evaluate the proposed algorithm, we have conducted the comprehensive simulation and compared with the other state‐of‐the‐art algorithms. Also, we extend our work to consider the data center's power usage effectiveness. It has been shown that AMP maximizes SP net profit by dispatching service requests to the proper data centers and generating the appropriate amount of virtual machines to meet customer satisfaction. Moreover, we also demonstrate the effectiveness of our approach when it accommodates the impacts of dynamically arrived heavy workload, various evaporation rate and consideration of power usage effectiveness. Copyright © 2014 John Wiley & Sons, Ltd.