基于多Agent的云服务中间件的研究与应用

在Web应用日益普及的今天,Web服务器承担的任务越来越繁重,特别是进行大数据量的计算时,服务器的硬件资源成了计算效率的瓶颈。为了解决这一问题,结合云计算和中间件的技术特点,提出了一种基于多Agent的云服务中间件的体系架构,该框架分为用户接口层、SOA层和资源管理层3个层次,内部采用多Agent技术。节点间通过ACL消息进行通信,负载均衡采用静态计算能力与动态负载相结合,内置的日志服务和容错服务保障系统稳定运行。将此框架应用到实际Web应用中可以大大提高Web服务器的计算效率。     

基于分布式中间件ICE的应用架构研究

在分布式系统中,分布式平台之间的差异增加了开发的复杂性.基于一种轻量级的分布式中间件ICE,提出了在分布式异构环境中的一般架构模型.该模型可以降低模块之间的耦合度,解决服务器负载过大的问题.将该模型应用到网络安全管理系统中,解决了网络安全管理系统中平台差异问题和服务器负载均衡问题.     

A stochastic load balancing algorithm for i‐Computing

This paper presents a stochastic dynamic load balancing algorithm for Internet computing, which is a new type of distributed computing involving heterogeneous workstations from different organizations on the Internet. To realize the practical environment, we assume the system to be comprised of heterogeneous, untrusted and non‐dedicated workstations connected by a non‐dedicated network. Our algorithm uses the product of the average processing time and the queue length of system jobs as the load index. Dynamic communication delay is included in the execution cost calculation. The transfer policy and the location policy are combined in a stochastic algorithm. State information exchange is done via information feedback and mutual updating. Simulations demonstrate that our algorithm outperforms conventional approaches over a wide range of system parameters. These results are reconfirmed by empirical experiments after we have implemented the algorithms on the Distributed Java Machine global virtual machine. Copyright © 2003 John Wiley & Sons, Ltd.     

Zorilla: a peer‐to‐peer middleware for real‐world distributed systems

The inherent complex nature of current distributed computing architectures hinders the widespread adoption of these systems for mainstream use. In general, users have access to a highly heterogeneous set of compute resources, which may include clusters, grids, desktop grids, clouds, and other compute platforms. This heterogeneity is especially problematic when running parallel and distributed applications. Software is needed which easily combines as many resources as possible into one coherent computing platform. In this paper, we introduce Zorilla: peer‐to‐peer (P2P) middleware that creates a single distributed environment from any available set of compute resources. Zorilla imposes minimal requirements on the resource used, is platform independent, and does not rely on central components. In addition to providing functionality on bare resources, Zorilla can exploit locally available middleware. Zorilla explicitly supports distributed and parallel applications, and allows resources from multiple sites to cooperate in a single computation. Zorilla makes extensive use of both virtualization and P2P techniques. We will demonstrate how virtualization and P2P combine into a simple design, while enhancing functionality and ease of use. Together, these techniques bring our goal a step closer: transparent, easy use of resources, even on very heterogeneous distributed systems. Copyright © 2011 John Wiley & Sons, Ltd.     

云计算中负载均衡优化模型及算法研究*

虚拟化技术使云计算中形成了各种各样的资源池,这对于用户来说资源可以按需分配、动态扩展和配置,但对于管理员来说资源的管理和分配变得复杂和困难。因此如何实现资源合理分配和负载均衡成为研究的热点。为此提出了任务调度的负载均衡优化模型,并采用改进的遗传算法（IGA）来求解。最后通过与遗传算法（GA）和Min-Min算法进行仿真对比实验,结果证明了IGA算法在计算资源负载均衡方面表现较优。     

MidHDC: Advanced topics on middleware services for heterogeneous distributed computing. Part 2

Currently distributes systems support different computing paradigms like Cluster Computing, Grid Computing, Peer-to-Peer Computing, and Cloud Computing all involving elements of heterogeneity. These computing distributed systems are often characterized by a variety of resources that may or may not be coupled with specific platforms or environments. All these topics challenge today researchers, due to the strong dynamic behavior of the user communities and of resource collections they use.The second part of this special issue presents advances in allocation algorithms, service selection, VM consolidation and mobility policies, scheduling multiple virtual environments and scientific workflows, optimization in scheduling process, energy-aware scheduling models, failure Recovery in shared Big Data processing systems, distributed transaction processing middleware, data storage, trust evaluation, information diffusion, mobile systems, integration of robots in Cloud systems.     

Meteor: a middleware infrastructure for content‐based decoupled interactions in pervasive grid environments

Emerging pervasive information and computational environments require a content‐based middleware infrastructure that is scalable, self‐managing, and asynchronous. In this paper, we propose associative rendezvous (AR) as a paradigm for content‐based decoupled interactions for pervasive grid applications. We also present Meteor, a content‐based middleware infrastructure to support AR interactions. The design, implementation, and experimental evaluation of Meteor are presented. Evaluations include experiments using deployments on a local area network, the wireless ORBIT testbed at Rutgers University, and the PlanetLab wide‐area testbed, as well as simulations. Evaluation results demonstrate the scalability, effectiveness, and performance of Meteor to support pervasive grid applications. Copyright © 2007 John Wiley & Sons, Ltd.     

Join-Idle-Queue: A novel load balancing algorithm for dynamically scalable web services

The prevalence of dynamic-content web services, exemplified by search and online social networking, has motivated an increasingly wide web-facing front end. Horizontal scaling in the Cloud is favored for its elasticity, and distributed design of load balancers is highly desirable. Existing algorithms with a centralized design, such as Join-the-Shortest-Queue (JSQ), incur high communication overhead for distributed dispatchers.We propose a novel class of algorithms called Join-Idle-Queue (JIQ) for distributed load balancing in large systems. Unlike algorithms such as Power-of-Two, the JIQ algorithm incurs no communication overhead between the dispatchers and processors at job arrivals. We analyze the JIQ algorithm in the large system limit and find that it effectively results in a reduced system load, which produces 30-fold reduction in queueing overhead compared to Power-of-Two at medium to high load. An extension of the basic JIQ algorithm deals with very high loads using only local information of server load.     

Cloud based management and control system for smart communities: A practical case study

In this study, the implementation of cloud based smart community management and control system was undertaken. SmartComunity.in is a flexible platform to manage and control the affairs of a condominium or society with thorough participation, visibility and transparency. Our research is the first attempt to study one such real life system of cloud based control and management in a smart housing community in India. There is a dearth of exploratory studies that explain the diffusion and adoption of cloud computing in different contexts and from a multiple stakeholder perspective. So, the main contribution of our research is to understand the framework of cloud computing based smart community services in India and the emerging cloud computing ecosystems. This research has wide ranging implications on the future of Internet of Things, and can be extended to elderly health and support, energy efficient systems and smart cities.     

A mobile crowd sensing ecosystem enabled by CUPUS: Cloud-based publish/subscribe middleware for the Internet of Things

Mobile crowd sensing (MCS) is a novel class of mobile Internet of Things (IoT) applications for community sensing where sensors and mobile devices jointly collect and share data of interest to observe phenomena over a large geographic area. The inherent device mobility and high sensing frequency has the capacity to produce dense and rich spatiotemporal information about our environment, but also creates new challenges due to device dynamicity and energy constraints, as well as large volumes of generated raw sensor data which need to be processed and analyzed to extract useful information for end users. The paper presents an ecosystem for mobile crowd sensing which relies on the CloUd-based PUblish/Subscribe middleware (CUPUS) to acquire sensor data from mobile devices in a flexible and energy-efficient manner and to perform near real-time processing of Big Data streams. CUPUS has unique features compared to other MCS platforms: It enables management of mobile sensor resources within the cloud, supports filtering and aggregation of sensor data on mobile devices prior to its transmission into the cloud based on global data requirements, and can push information of interest from the cloud to user devices in near real-time. We present our experience with implementation and deployment of an MCS application for air quality monitoring built on top of the CUPUS middleware. Our experimental evaluation shows that CUPUS offers scalable processing performance, both on mobile devices and within the cloud, while its data propagation delay is mainly affected by transmission delay on wireless links.     

一种基于QoS的云负载均衡机制的研究

提出一种基于QoS的云负载均衡机制,即:构建QoS模型和云资源模型;建立资源度量与QoS属性之间的映射;对虚拟机实例负载状况和虚拟机集群资源利用状况进行量化评估;感知用户的QoS并对比所监控的云节点的资源度量情况,根据对比结果,通过任务调度算法和弹性伸缩算法分别实现任务的分发和虚拟机集群的弹性伸缩,最终达到优化的负载均衡的目的.通过模拟试验,结果表明本方法与Round robin算法相比,有更好的负载均衡效果.     

Mobile agent‐based computational steering for distributed applications

The mobile agent‐based computational steering (MACS) for distributed applications is presented in this article. In the MACS, a mobile agent platform, Mobile‐C, is embedded in a program through the Mobile‐C library to support C/C++ mobile agent code. Runtime replaceable algorithms of a program are represented as agent services in C/C++ source code and can be replaced with new ones through mobile agents. In the MACS, a mobile agent created and deployed by a user from the steering host migrates to computing hosts successively to replace algorithms of running programs that constitute a distributed application without the need of stopping the execution and recompiling the programs. The methodology of dynamic algorithm alteration in the MACS is described in detail with an example of matrix operation. The Mobile‐C library enables the integration of Mobile‐C into any C/C++ programs to carry out computational steering through mobile agents. The source code level execution of mobile agent code facilitates handling issues such as portability and secure execution of mobile agent code. In the MACS, the network load between the steering and computing hosts can be reduced, and the successive operations of a mobile agent on multiple computing hosts are not affected whether the steering host stays online or not. The employment of the middle‐level language C/C++ enables the MACS to accommodate the diversity of scientific and engineering fields to allow for runtime interaction and steering of distributed applications to match the dynamic requirements imposed by the user or the execution environment. An experiment is used to validate the feasibility of the MACS in real‐world mobile robot applications. The experiment replaces a mobile robot's behavioral algorithm with a mobile agent at runtime. Copyright © 2009 John Wiley & Sons, Ltd.     

LinuxDirector: A connection director for scalable internet services

LinuxDirector is a connection director that supports load balancing among multiple Internet servers,which can be used to build scalable Internet services based on clusters of servers.LinuxDirector extends the TCP/IP stack of Linux Kernel to support three IP load balancing techniques,VS/NAT,VS/TUN and VS/DR.Four scheduling algorithms have been implemented to assign connections to different servers.Scalability is achieved by transparently adding or removing a node in the cluster.High availability is provided by detecting node or daemon failure and reconfiguring the system appropriately.This paper describes the design and implementation of LinuxDirector and presents serveral of its features including scalability,high availability and connection affinity.     

Power‐aware provisioning of virtual machines for real‐time Cloud services

Reducing power consumption has been an essential requirement for Cloud resource providers not only to decrease operating costs, but also to improve the system reliability. As Cloud computing becomes emergent for the Anything as a Service (XaaS) paradigm, modern real‐time services also become available through Cloud computing. In this work, we investigate power‐aware provisioning of virtual machines for real‐time services. Our approach is (i) to model a real‐time service as a real‐time virtual machine request; and (ii) to provision virtual machines in Cloud data centers using dynamic voltage frequency scaling schemes. We propose several schemes to reduce power consumption by hard real‐time services and power‐aware profitable provisioning of soft real‐time services. Copyright © 2011 John Wiley & Sons, Ltd.     

云计算平台中面向车联网应用的能耗感知调度算法

针对面向车联网应用的云计算平台的高能耗问题,提出一种采用节能整合策略的能耗感知调度算法——任务集整合算法(Task Set Consolidation Algorithm)。该算法的主要思想是通过减少活跃物理服务器的数目,有效降低云平台的能量消耗。建立了云平台模型、车联网任务集模型和能耗模型,确定了云平台的节能目标函数和变量因子。仿真实验通过模拟多维资源多并发任务集的云平台环境,以物理服务器的活跃时间和活跃数目、云平台的能量消耗作为性能指标,将任务集整合算法与现有算法进行了比较。实验结果表明,TSC算法能够在避免任务集资源发生冲突的情况下,使面向车联网应用的云平台激活的物理服务器数量达到最少,能耗降到最低。     

Arcademis: a framework for object‐oriented communication middleware development

This paper presents Arcademis, a Java‐based framework for communication middleware development. Arcademis consists of a set of abstract classes, interfaces and concrete components that define the general architecture of middleware systems. Its main objective is to support the implementation of non‐monolithic and easily configurable middleware platforms. Arcademis can be used by middleware developers to deploy systems that meet the requirements of a particular network or technology. Instances of Arcademis can also be customized by distributed systems engineers to meet the requirements of a particular application. For example, new transport protocols, connection management policies, authentication algorithms or invocation semantics can be easily configured in middleware platforms derived from Arcademis. In order to illustrate the use of the framework, the paper describes the RME system, a middleware derived from Arcademis that adds a remote method invocation service to the CLDC configuration of Java 2 Micro Edition (J2ME). Copyright © 2006 John Wiley & Sons, Ltd.     

Fault Aware Dynamic Resource Manager for Fault Recognition and Avoidance in Cloud

Fault tolerance (FT) schemes are intended to work on a minimized and static amount of physical resources. When a host failure occurs, the conventional FT frequently proceeds with the execution on the accessible working hosts. This methodology saves the execution state and applications to complete without disruption. However, the dynamicity of open cloud assets is not seen when taking scheduling choices. Existing optimization techniques are intended in dealing with resource scheduling. This method will be utilized for distributing the approaching tasks to the VMs. However, the dynamic scheduling for this procedure doesn’t accomplish the objective of adaptation of internal failure. The scheme prefers jobs in the activity list with the most elevated execution time on resources that can execute in a shorter timeframe, but it suffers with higher makespan; poor resource usage and unbalance load concerns. To overcome the above mentioned issue, Fault Aware Dynamic Resource Manager (FADRM) is proposed that enhances the mechanism to Multi-stage Resilience Manager at an application-level FT arrangement. Proposed FADRM method gives FT a Multi-stage Resilience Manager (MRM) in the client and application layers, and simultaneously decreases the over-head and degradations. It additionally provides safety to the application execution considering the clients, application and framework necessities. Based on experimental evaluations, Proposed Fault Aware Dynamic Resource Manager (FADRM) method 157.5 MakeSpan (MS) time, 0.38 Fault Rate (FR), 0.25 Failure Delay (FD) and improves 5.5 Performance Improvement Ratio (PIR) for 25, 50, 75 and 100 tasks and 475 MakeSpan (MS) time, 0.40 Fault Rate (FR), 1.30 Failure Delay (FD) and improves 6.75 improves Performance Improvement Ratio (PER) for 100, 200, 300 and 500 Tasks compare than existing methodologies.