As SOA gains more traction through various implementations, building reliable service compositions remains one of the principal research concerns. Widely researched reliability assurance methods, often rely on applying redundancy or complex optimization strategies that can make them less applicable when it comes to designing service compositions on a larger scale. To address this issue, we propose a design time reliability improvement method that enables selective service composition improvements by focusing on the most reliability-critical workflow components, named weak points. With the aim of detecting most significant weak points, we introduce a method based on a suite of recommendation algorithms that leverage a belief network reliability model. The method is made scalable by using heuristic algorithms that achieve better computational performance at the cost of recommendation accuracy. Although less accurate heuristic algorithms on average require more improvement steps, they can achieve better overall performance in cases when the additional step-wise overhead of applying improvements is low. We confirm the soundness of the proposed solution by performing experiments on data sets of randomly generated service compositions.

While constructing QoS-aware composite work-flows based on service oriented systems, it is necessary to assess nonfunctional properties of potential service selection candidates. In this paper, we present CLUS, a model for reliability prediction of atomic web services that estimates the reliability for an ongoing service invocation based on the data assembled from previous invocations. With the aim to improve the accuracy of the current state-of-the-art prediction models, we incorporate user-service-, and environment-specific parameters of the invocation context. To reduce the scalability issues present in the state-of-the-art approaches, we aggregate the past invocation data using K-means clustering algorithm. In order to evaluate different quality aspects of our model, we conducted experiments on services deployed in different regions of the Amazon cloud. The evaluation results confirm that our model produces more scalable and accurate predictions when compared to the current state-of-the-art approaches.

The modern information systems on the Internet are often implemented as composite services built from multiple atomic services. These atomic services have their interfaces publicly available while their inner structure is unknown. The quality of the composite service is dependent on both the availability of each atomic service and their appropriate orchestration. In this paper, we present LUCS, a formal model for predicting the availability of atomic web services that enhances the current state-of-the-art models used in service recommendation systems. LUCS estimates the service availability for an ongoing request by considering its similarity to prior requests according to the following dimensions: the user's and service's geographic location, the service load, and the service's computational requirements. In order to evaluate our model, we conducted experiments on services deployed in different regions of the Amazon cloud. For each service, we varied the geographic origin of its incoming requests as well as the request frequency. The evaluation results suggest that our model significantly improves availability prediction when all of the LUCS input parameters are available, reducing the prediction error by 71 percent compared to the current state-of-the-art.

Contemporary web applications are often designed as composite services built by coordinating atomic services with the aim of providing the appropriate functionality. Although functional properties of each atomic service assure correct functionality of the entire application, nonfunctional properties such as availability, reliability, or security might significantly influence the user-perceived quality of the application. In this paper, we present CLUS, a model for reliability prediction of atomic web services that improves state-of-the-art approaches used in modern recommendation systems. CLUS predicts the reliability for the ongoing service invocation using the data collected from previous invocations. We improve the accuracy of the current state-of-the-art prediction models by considering user-, service- and environment-specific parameters of the invocation context. To address the computational performance related to scalability issues, we aggregate the available previous invocation data using K-means clustering algorithm. We evaluated our model by conducting experiments on services deployed in different regions of the Amazon cloud. The evaluation results suggest that our model improves both performance and accuracy of the prediction when compared to the current state-of-the-art models.

Small-scale multiprocessors are becoming increasingly economical and common, whereas larger multiprocessors continue to have higher per-node costs. The NUMAchine multiprocessor project seeks to make large-scale multiprocessors more economical while maintaining high performance by exploring architectural and hardware features for low-cost, modular multiprocessors. To demonstrate our approach, we have implemented a prototype system that is scalable to 128 processors. An efficient directory-based cache coherence protocol exploits our hierarchical ring-based interconnect and supports sequential consistency. This paper documents the design choices and the resulting performance of the system using both simulation results and measurements on the prototype hardware.

This paper describes the design and implementation of the NUMAchine multiprocessor. As the market for CC-NUMA multiprocessors expands, this research project provides a timely architectural design and cost-effective prototype. The key to the successful implementation of our 48-processor prototype is the use of off-the-shelf components and programmable logic devices. Since this machine will serve as a research vehicle for parallel software development, a number of hardware features to enhance experimentation have been included in the design.

In this paper, we introduce new analytical models for predicting the performance of parallel applications under various cache coherence protocol assumptions. The purpose of these models is to determine which protocols are to be used for which data blocks, and, in the case of dynamic protocols, also to determine when to change protocols. Although we focus on tightly-coupled multiprocessor systems, similar models can be derived for loosely-coupled distributed systems, such as networks of workstations. Our models are unique in that they lie between a large body of theoretical models that assume independence and a uniform distribution of memory accesses across processors, and a large body of address-trace oriented models that assume the availability of a precise characterization of interleaving behavior of memory accesses. The former are not very realistic, and the latter are not suitable for compile-time and run-time usage. In contrast, our models enable us to choose different input parameters depending on how the models will be used and depending on the needed accuracy in performance prediction. We present the models and show how the required parameters can be obtained. We assess the accuracy of our models on 15 parallel applications. For these applications, our most complete model predicts performance within a 10 percent margin when compared to a simulation of a sequentially consistent multiprocessor system. As part of this study, we also show the potential advantage of using dynamic hybrid protocols.

This paper provides a case study that shows how a demanding application stresses the capabilities of today's CAD tools, especially in the integration of products from multiple vendors.We relate our experiences in the design of a large, high-speed multiprocessor computer, using state of the art CAD tools.All logic circuitry is targeted to field-programmable devices (FPDs).This choice amplifies the difficulties associated with achieving a highspeed design, and places extra requirements on the CAD tools.Two main CAD systems are discussed in the paper: Cadence Logic Workbench (LWB) is employed for board-level design, and Altera MAX+plusII is used for implementation of logic circuits in FPDs.Each of these products is of great value for our project, but the integration of the two is less than satisfactory.The paper describes a custom procedure that we developed for integrating sub-designs realized in FPDs (via MAX+plusII) into our board-level designs in LWB.We also discuss experiences with Logic Modelling Smart Models, for simulation of FPDs and other types of chips.

A model for predicting relative performance in distributed shared memory systems is presented. The model is intended for use with systems that feature adaptive consistency schemes. Based on estimated read/write access patterns, the model determines the average steady-state communication costs in the system, which serve as a performance indicator. The model is applied to data items which may be either compact data objects or objects consisting of smaller data elements. It is shown that the best results are attained when protocol selection is done at the level of data elements.>

The proposed distributed shared memory model is based on a data replication scheme that provides an environment for a collection of processes that interact to solve a parallel programming problem. In the implementation of the scheme the authors suppose that the replicas of the shared data are present at each node and that an appropriate coherence protocol for maintaining the consistency among the replicas is applied. The performance of the distributed computation is very sensitive to the data-access behavior of the application and to the applied coherence protocol. Communication cost is regarded as an appropriate performance measure. Therefore, the authors first introduce a model characterizing the computation behavior with five workload parameters. Second, they formally describe the coherence protocols as cooperating state machines in order to evaluate their communication costs as functions of workload parameters.>