A dynamically self-configurable service process engine

The performance of a process engine is one of the key factors that contribute to the successful deployment of systems, based on a service-oriented architecture. A novel service process engine that can be self-configured dynamically is introduced in the paper. It is based on the Jini platform, and leverages of Jini services to provide key functionalities. It automatically maintains the global performance by performing load balancing and configuring the system structure dynamically. A heuristic algorithm is applied to indicate every client’s request with a workload tag after a service process model is designed. Based on workload tags of client requests and the status of available services in the engine, a controller allocates the requests to appropriate services and dynamically reconfigures the engine based on fuzzy control algorithms. Algorithms and the architecture for the engine are discussed in detail; in addition, performance experiments are performed to show the effectiveness and feasibility of the proposed approach.     

Modeling user context with applications to media retrieval

In this paper, we develop a theoretical understanding of multi-sensory knowledge and user context and their inter-relationships. This is used to develop a generic representation framework for multi-sensory knowledge and context. A representation framework for context can have a significant impact on media applications that dynamically adapt to user needs. There are three key contributions of this work: (a) theoretical analysis, (b) representation framework and (c) experimental validation. Knowledge is understood to be a dynamic set of multi-sensory facts with three key properties – multi-sensory, emergent and dynamic. Context is the dynamic subset of knowledge that affects the communication between entities. We develop a graph based, multi-relational representation framework for knowledge, and model its temporal dynamics using a linear dynamical system. Our approach results in a stable and convergent system. We applied our representation framework to a image retrieval system with a large collection of photographs from everyday events. Our experimental validation with the retrieval evaluated against two reference algorithms indicates that our context based approach provides significant gains in real-world usage scenarios.     

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.     

CPU + GPU scheduling with asymptotic profiling

Hybrid systems with CPU and GPU have become new standard in high performance computing. Workload can be split and distributed to CPU and GPU to utilize them for data-parallelism in hybrid systems. But it is challenging to manually split and distribute the workload between CPU and GPU since the performance of GPU is sensitive to the workload it received. Therefore, current dynamic schedulers balance workload between CPU and GPU periodically and dynamically. The periodical balance operation causes frequent synchronizations between CPU and GPU. It often degrades the overall performance because of the overhead of synchronizations. To solve the problem, we propose a Co-Scheduling Strategy Based on Asymptotic Profiling (CAP). CAP dynamically splits and distributes the workload to CPU and GPU with only a few synchronizations. It adopts the profiling technique to predict performance and partitions the workload according to the performance. It is also optimized for GPU’s performance characteristics. We examine our proof-of-concept system with six benchmarks and evaluation result shows that CAP produces up to 42.7% performance improvement on average compared with the state-of-the-art co-scheduling strategies.     

分布式数据库中一致性与可用性的关系

随着各类应用在数据量和业务量上的扩展,单机数据库系统越发难以应对现实需求。分布式数据库能够根据业务的需求动态地扩容,因此逐步开始受到应用的青睐。近年来,分布式数据库产品层出不穷,并在互联网应用中被大量投入使用。然而,分布式数据库的系统复杂度前所未有。为了让系统可用,设计者需要在多种属性中作合理选择和折中。这造成现有的数据库产品形态各异、优缺点对比分明。至今为止,尚未有人对分布式数据库的设计空间和折中方案进行过深入分析和整理。本文作者在对多个分布式数据库产品进行深入理解之后认识到：分布式数据库系统的设计方案可以通过三个属性进行基本刻画–操作一致性、事务一致性和系统可用性。虽然这三个属性并不新颖,但它们在数据库语境下的含义在文献中尚未得到充分澄清。本文对这三个属性进行澄清,并通过它们对典型数据库产品的格局进行概括、对现有的分布式数据库技术进行综述。此外,本文还对这三个属性之间的相互关系进行深入分析,以期帮助未来的开发者在分布式数据库的设计过程中作出合理选择。     

Performance evaluation of a new scheduling algorithm for distributed systems with security heterogeneity

High quality of security service is increasingly critical for applications running on heterogeneous distributed systems. However, existing scheduling algorithms for heterogeneous distributed systems disregard security requirements of applications. To address this issue, in this paper, we introduce security heterogeneity concept for our scheduling model in the context of distributed systems. Based on the concept, we propose a novel heuristic scheduling algorithm, or SATS, which strives to maximize the probability that all tasks are executed without any risk of being attacked. Extensive experimental studies using real-world traces indicate that the scheduling performance is affected by heterogeneities of security and computational power. Additionally, empirical results demonstrate that with respect to security and performance, the proposed scheduling algorithm outperforms existing approaches under a wide spectrum of workload conditions.     

基于网络的Context Aware智能控制系统

提出一种基于网络的Context Aware智能控制系统的体系结构、Context任务控制管理模型。系统将机器人作为Context Aware智能系统的智能接口。通过对Java JINI的服务查找机制进行修改,增强了JINI的服务查找、分发机制,使得系统可以通过综合分析服务提供者当前状态、服务申请者的状态、系统当前的性能以及Context感知信息,经模糊决策,通过”Pull”方式,或“Push”方式给服务申请者(或第三方用户)提供所需的服务。最后对系统性能进行分析。一个应用实例说明Context Aware智能控制系统的应用。     

The impact of workload variability on the energy efficiency of large-scale heterogeneous distributed systems

Previous studies have shown that the workload variability has a serious impact on the performance of large-scale distributed architectures, since it may cause significant fluctuations in service demands. Energy efficiency is one of the aspects of such platforms that are of paramount importance and therefore it is imperative to investigate how it may also be affected by this factor. Towards this direction, in this paper we investigate via simulation the impact of workload variability, in terms of computational volume and interarrival times, on the energy consumption of a large-scale heterogeneous distributed system. The workload consists of real-time bag-of-tasks jobs that arrive dynamically at the system. The execution rate and power consumption characteristics of the processors are modeled after real-world processors, according to the Standard Performance Evaluation Corporation (SPEC) Power benchmark. Four heuristics are employed for the scheduling of the workload, two commonly used baseline policies and two proposed energy-aware heuristics. The simulation results reveal that the workload variability has a significant impact on the energy consumption of the system and that the severity of the impact depends on the employed scheduling technique.     

Evaluate and control service and transaction dependability of complex IoT systems

Observing and controlling the dependability of service provision of complex IoT systems is challenging. In practice, many organizations struggle to derive consumer needs related to quality and to observe and quantify the service provision in the context of the dynamic behavior of a complex distributed system. In this paper, we present an approach to define and evaluate the dependability of complex IoT systems. Our approach is an adaptation of the ISO/IEC 25040, an international standard for the evaluation process for system and software quality, which is part of the systems and software quality requirements and evaluation (SQuaRE) series. Our approach was designed and evaluated with action research in an industrial study at Robert Bosch GmbH. Based on the framework of the SQuaRE series, we integrated different elements of site reliability engineering (SRE) and combined them with distributed tracing as a promising measurement method. Our approach introduces the IoT transaction concept to reduce modeling and observation efforts while increasing operationalization to measure performance against dependability targets. Our adaption was effectively applied, consumer-centricity along different system stakeholders were enhanced, and negative consequences of organizational silos were reduced. This has improved the dependability evaluation of service provision to enable fast feedback cycles for service performance control and improvement.

     

Architecture-level software performance abstractions for online performance prediction

Modern service-oriented enterprise systems have increasingly complex and dynamic loosely-coupled architectures that often exhibit poor performance and resource efficiency and have high operating costs. This is due to the inability to predict at run-time the effect of workload changes on performance-relevant application-level dependencies and adapt the system configuration accordingly. Architecture-level performance models provide a powerful tool for performance prediction, however, current approaches to modeling the context of software components are not suitable for use at run-time. In this paper, we analyze typical online performance prediction scenarios and propose a performance meta-model for (i) expressing and resolving parameter and context dependencies, (ii) modeling service abstractions at different levels of granularity and (iii) modeling the deployment of software components in complex resource landscapes. The presented meta-model is a subset of the Descartes Meta-Model (DMM) for online performance prediction, specifically designed for use in online scenarios. We motivate and validate our approach in the context of realistic and representative online performance prediction scenarios based on the SPECjEnterprise2010 standard benchmark.     

基于模糊原理的中间件自适应优化

J2EE中间件系统得到了广泛的使用。为了提高服务性能,需要配置系统参数。传统的手工配置法费时费力,而且在负载变化的环境中不能自适应改变,造成了性能随时间越来越低下。为此,我们提出了基于模糊控制J2EE应用服务器自适应调优系统。系统实时监控性能数据,并基于模糊决策器,动态改变参数,优化性能。通过测试表明,该系统能减少响应时间,提高系统性能。     

Multivariate point process models for response times in multiprogrammed systems

We consider the formulation of marked multivariate point process models for job response times in multiprogrammed computer systems. Complementing queueing network representation of the structure of the system to be modeled, the particularR-process (Response time process) model we propose permits representation of resource contention, facilitates the incorporation of realistic workload characteristics into system performance predictions, and can reproduce inhomogeneities observed in running systems. Specification of the structure of theR-process model is conditional on workload marks; this effectively separates the difficult problem of formal representation of workload characteristics from the overall problem of response time prediction. To illustrate these ideas, an application to database management systems is considered. Evidence of the predictive capability of theR-process model, based on statistical analysis of response time data from an IMS system, is also given.     

面向机会社会网络的服务广告分发机制

提出面向机会社会网络的服务广告分发机制,解决移动网络环境中由于节点移动性、拓扑动态性引起的集中式服务注册库失效的问题.首先分析机会社会网络中服务表现出的社会特征,提出服务社会上下文及其参数度量方法;然后提出基于社会上下文的服务广告分发机制,该机制根据服务行业相关度判断用户对服务的感兴趣程度,以确定广告目标节点,根据可靠度和活跃度计算节点效用,并根据行业时空共存关系预测节点和服务行业相遇概率,以动态选择服务广告代理;最后在社区移动模型下进行了仿真实验,验证了算法的正确性和有效性.     

Courier: Multi-dimensional QoS guarantees for the consolidated storage system

The increasing cost and complexity of data management is driving data centers to consolidate resource and provide storage service for multiplex applications. Therefore, storage systems must be able to guarantee multi-dimensional Quality of Service (QoS) for various applications. However, satisfying performance targets for each workload is challenging, because that the I/O characteristics of workloads usually varies widely and capability of storage system changes significantly. In this paper, we design and implement a novel QoS scheduler, Courier, to maintain satisfactory performance for applications even in this highly-volatile scenario. Courier dynamically alternates between a feedback-based latency controller and reward budget-based scheduling to achieve per-application performance requirement. The feedback-based controller is employed to estimate request service times and adjust scheduling strategy dynamically. Based on the estimation, it can identify time-critical requests from throughput-sensitive requests and schedule applications with time-critical requests preferentially to avoid latency violations. In addition, Courier rewards well-behavior application with more budget to maintain high storage utilization while providing performance guarantees. We evaluate the effectiveness of our approach using synthetic and real workloads, and the results show that Courier has good ability to achieve per-application performance targets.     

Ontological Semantics for Distributing Contextual Knowledge in Highly Distributed Autonomic Systems

Much recent research has focused on applying Autonomic Computing principles to achieve constrained self-management in adaptive systems, through self-monitoring and analysis, strategy planning, and self adjustment. However, in a highly distributed system, just monitoring current operation and context is a complex and largely unsolved problem domain. This difficulty is particularly evident in the areas of network management, pervasive computing, and autonomic communications. This paper presents a model for the filtered dissemination of semantically enriched knowledge over a large loosely coupled network of distributed heterogeneous autonomic agents, removing the need to bind explicitly to all of the potential sources of that knowledge. This paper presents an implementation of such a knowledge delivery service, which enables the efficient routing of distributed heterogeneous knowledge to, and only to, nodes that have expressed an interest in that knowledge. This gathered knowledge can then be used as the operational or context information needed to analyze to the system's behavior as part of an autonomic control loop. As a case study this paper focuses on contextual knowledge distribution for autonomic network management. A comparative evaluation of the performance of the knowledge delivery service is also provided. John Keeney holds a BAI degree in Computer Engineering and a PhD in Computer Science from Trinity College Dublin. His primary interests are in controlling autonomic adaptable systems, particularly when those systems are distributed. David Lewis graduated in Electronics Engineering from the University of Southampton and gained his PhD in Computer Science from University College London. His areas of interest include integrated network and service management, distributed system engineering, adaptive and autonomic systems, semantic services and pervasive computing. Declan O’Sullivan was awarded his primary degree, MSc and PhD in Computer Science from Trinity College Dublin. He has a particular interest in the issues of semantic interoperability and heterogeneous information querying within a range of areas, primarily network and service management, autonomic management, and pervasive computing.     

A geometric model for an effective rescheduling after reducing service in public transportation systems

Railway systems in metropolitan areas carry a high density of traffic daily, heterogeneously distributed, and exposed to the negative consequences derived from service disruptions. An interesting topic in the literature is to obtain performance protocols in the presence of contingencies which can affect the system operation, avoiding the propagation of perturbation and minimizing its consequences. The objective of this paper is to assess the decision of rescheduling the train service, reducing the current supply along one transportation line in order to reinforce the service of another line, exploited by the same public operator, which has suffered an incidence or emergency. A methodology, based on a geometric representation of solutions which allows the use of discrete optimization techniques, is developed in order to attend to the underlying demand with efficiency criteria in this context of unexpected incidents.     

Market‐based grid resource co‐allocation and reservation for applications with hard deadlines

Grid computing technology enables the creation of large‐scale IT infrastructures that are shared across organizational boundaries. In such shared infrastructures, conflicts between user requirements are common and originate from the selfish actions that users perform when formulating their service requests. The introduction of economic principles in grid resource management offers a promising way of dealing with these conflicts. We develop and analyze both a centralized and a decentralized algorithm for economic grid resource management in the context of compute bound applications with deadline‐based quality of service requirements and non‐migratable workloads. Through the use of reservations, we co‐allocate resources across multiple providers in order to ensure that applications finish within their deadline. An evaluation of both algorithms is presented and their performance in terms of realized user value is compared with an existing market‐based resource management algorithm. We establish that our algorithms, which operate under a more realistic workload model, can closely approximate the performance of this algorithm. We also quantify the effect of allowing local workload preemption and different scheduling heuristics on the realized user value. Copyright © 2009 John Wiley & Sons, Ltd.     

Economics‐inspired decentralized control approach for adaptive grid services and applications

Grid technologies facilitate innovative applications among dynamic virtual organizations, while the ability to deploy, manage, and properly remain functioning via traditional approaches has been exceeded by the complexity of the next generation of grid systems. An important method for addressing this challenge may require nature‐inspired computing paradigms. This technique will entail construction of a bottom‐up multiagent system; however, the appropriate implementation mechanism is under consideration in order for the autonomous and distributed agents to emerge as a controlled grid service or application. A credit card management service in economic interactions is considered in this article for a decentralized control approach. This consideration is based on a preliminarily developed ecological network‐based grid middleware that has features desired for the next generation grid systems. The control scheme, design, and implementation of the credit card management service are presented in detail. The simulation results show that (1) agents are accountable for their activities such as behavior invocation, service provision, and resource utilization and (2) generated services or applications adapt well to dynamically changing environments such as agent amounts as well as partial failure of agents. The approach presented herein is beneficial for building autonomous and adaptive grid applications and services. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 1269–1288, 2006.     

Client-access protocols for replicated services

Addresses the problem of replicated service provision in distributed systems. Existing systems that follow the state machine approach concentrate on the synchronization of the server replicas and do not consider the problem of client interaction with the server group. This paper analyzes client interaction and identifies a number of access protocols to meet a range of client requirements and system models. The paper demonstrates that protocols for the “open” group model-clients external to the group of servers-satisfy the requirements of the state machine approach, even when replication is transparent to the clients. Experimental performance results indicate that the “open” model is clearly desirable when the service is used by a large, dynamically changing set of clients, a situation which pertains to Internet service provision