206456124
Distributed Hardwired Barrier Synchronization for Scalable Multiprocessor Clusters
Conventional multiprocessors mostly use centralized, memory-based barriers to synchronize concurrent processes created in multiple processors. These centralized barriers often become the bottleneck or hot spots in the shared memory. In this paper, we overcome the difficulty by presenting a distributed and hardwired barrier architecture, that is hierarchically constructed for fast synchronization in cluster-structured multiprocessors. The hierarchical architecture enables the scalability of cluster-structured multiprocessors. A special set of synchronization primitives is developed for explicit use of distributed barriers dynamically. To show the application of the hardwired barriers, we demonstrate how to synchronize Doall and Doacross loops using a limited number of hardwired barriers. Timing analysis shows an O(10/sup 2/) to O(10/sup 5/) reduction in synchronization overhead, compared with the use of software-controlled barriers implemented in a shared memory. The hardwired architecture is effective in implementing any partially ordered set of barriers or fuzzy barriers with extended synchronization regions. The versatility, scalability, programmability, and low overhead make the distributed barrier architecture attractive in constructing fine-grain, massively parallel MIMD systems using multiprocessor clusters with distributed shared memory. >
7294278
Selfish Grids: Game-Theoretic Modeling and NAS/PSA Benchmark Evaluation
Selfish behaviors of individual machines in a grid can potentially damage the performance of the system as a whole. However, scrutinizing the grid by taking into account the noncooperativeness of machines is a largely unexplored research problem. In this paper, we first present a new hierarchical game-theoretic model of the grid that matches well with the physical administrative structure in real-life situations. We then focus on the impact of selfishness in intrasite job execution mechanisms. Based on our novel utility functions, we analytically derive the Nash equilibrium and optimal strategies for the general case. To study the effects of different strategies, we have also performed extensive simulations by using a well-known practical scheduling algorithm over the NAS (numerical aerodynamic simulation) and the PSA (parameter sweep application) workloads. We have studied the overall job execution performance of the grid system under a wide range of parameters. Specifically, we find that the optimal selfish strategy significantly outperforms the Nash selfish strategy. Our performance evaluation results can serve as a valuable reference for designing appropriate strategies in a practical grid
9852180
Risk-resilient heuristics and genetic algorithms for security-assured grid job scheduling
In scheduling a large number of user jobs for parallel execution on an open-resource grid system, the jobs are subject to system failures or delays caused by infected hardware, software vulnerability, and distrusted security policy. This paper models the risk and insecure conditions in grid job scheduling. Three risk-resilient strategies, preemptive, replication, and delay-tolerant, are developed to provide security assurance. We propose six risk-resilient scheduling algorithms to assure secure grid job execution under different risky conditions. We report the simulated grid performances of these new grid job scheduling algorithms under the NAS and PSA workloads. The relative performance is measured by the total job makespan, grid resource utilization, job failure rate, slowdown ratio, replication overhead, etc. In addition to extending from known scheduling heuristics, we developed a new space-time genetic algorithm (STGA) based on faster searching and protected chromosome formation. Our simulation results suggest that, in a wide-area grid environment, it is more resilient for the global job scheduler to tolerate some job delays instead of resorting to preemption or replication or taking a risk on unreliable resources allocated. We find that delay-tolerant min-min and STGA job scheduling have 13-23 percent higher performance than using risky or preemptive or replicated algorithms. The resource overheads for replicated job scheduling are kept at a low 15 percent. The delayed job execution is optimized with a delay factor, which is 20 percent of the total makespan. A Kiviat graph is proposed for demonstrating the quality of grid computing services. These risk-resilient job scheduling schemes can upgrade grid performance significantly at only a moderate increase in extra resources or scheduling delays in a risky grid computing environment
5802684
Adaptive Parallel Rendering on Multiprocessors and Workstation Clusters
This paper presents the design and performance of a new parallel graphics renderer for 3D images. This renderer is based on an adaptive supersampling approach that works for time/space-efficient execution on two classes of parallel computers. Our rendering scheme takes subpixel supersamples only along polygon edges. This leads to a significant reduction in rendering time and in buffer memory requirements. Furthermore, we offer a balanced rasterization of all transformed polygons. Experimental results prove these advantages on both a shared-memory SGI multiprocessor server and a Unix cluster of Sun workstations. We reveal performance effects of the new rendering scheme on subpixel resolution, polygon number, scene complexity, and memory requirements. The balanced parallel renderer demonstrates scalable performance with respect to increase in graphic complexity and in machine size. Our parallel renderer outperforms Crow's scheme in benchmark experiments performed. The improvements are made in three fronts: (1) reduction in rendering time, (2) higher efficiency with balanced workload,: and (3) adaptive to available buffer memory size. The balanced renderer can be more cost-effectively embedded within many 3D graphics algorithms, such as those for edge smoothing and 3D visualization. Our parallel renderer is MPI-coded, offering high portability and cross-platform performance. These advantages can greatly improve the QoS in 3D imaging and in real-time interactive graphics.
3123188
Trusted P2P transactions with fuzzy reputation aggregation
Internet commerce and online commodity exchanges suffer from distrust among sellers and buyers, who are often strangers to each other. The authors present a new P2P reputation system based on fuzzy logic inferences, which can better handle uncertainty, fuzziness, and incomplete information in peer trust reports. This system aggregates peer reputations with affordable message overhead. By testing the system using eBay transaction data in the public domain, the authors seek to demonstrate the efficacy and robustness of two P2P reputation systems - fuzzytrust and eigentrust - at establishing trust among the peers in P2P applications.
1711878
Trusted Grid Computing with Security Binding and Trust Integration
Trusted Grid computing demands robust resource allocation with security assurance at all resource sites. Large-scale Grid applications are being hindered by lack of security assurance from remote resource sites. We developed a security-binding scheme through site reputation assessment and trust integration across Grid sites. We do not treat the trust factor deterministically. Instead, we apply fuzzy theory to handle the fuzziness or uncertainties behind all trust attributes. The binding is achieved by periodic exchange of site security information and matchmaking to satisfy user job demands.PKI-based trust model supports Grids in multi-site authentication and single sign-on operations. However, cross certificates are inadequate to assess local security conditions at Grid sites. We propose a new fuzzy-logic trust model for distributed trust aggregation through fuzzification and integration of security attributes. We introduce the trust index of a Grid site, which is determined by site reputation from its track record and self-defense capability attributed to the risk conditions and hardware and software defenses deployed at a Grid site.A Secure Grid Outsourcing (SeGO) system is designed for secure scheduling a large number of autonomous and indivisible jobs to Grid sites. Significant performance gains are observed after trust aggregation, which is evaluated by running scalable NAS and PSA workloads over simulated Grids. Our security-binding scheme scales well with increasing user jobs and Grid sites. The new scheme can guide the security upgrade of Grid sites and predict the Grid performance of large workloads under risky conditions.
6947155
Interactive Learning of Scene Context Extractor Using Combination of Bayesian Network and Logic Network
The vision-based scene understanding technique that infers scene-interpreting contexts from real-world vision data has to not only deal with various uncertain environments but also reflect user's requests. Especially, learnability is a hot issue for the system. In this paper, we adopt a probabilistic approach to overcome the uncertainty, and propose an interactive learning method using combination of Bayesian network and logic network to reflect user's requirements in real-time. The logic network works for supporting logical inference of Bayesian network. In the result of some learning experiments using interactive data, we have confirmed that the proposed interactive learning method is useful for scene context reasoning.
14072422
Modular Bayesian Networks for Inferring Landmarks on Mobile Daily Life
Mobile devices get to handle much information thanks to the convergence of diverse functionalities. Their environment has great potential of supporting customized services to the users because it can observe the meaningful and private information continually for a long time. However, most of the information has been generally ignored because of the limitations of mobile devices. In this paper, we propose a novel method that infers landmarks efficiently in order to overcome the problems. It uses an effective probabilistic model of Bayesian networks for analyzing various log data on the mobile environment, which is modularized to decrease the complexity. The proposed methods are evaluated with synthetic mobile log data generated.
18907610
Generating Cartoon-Style Summary of Daily Life with Multimedia Mobile Devices
Mobile devices are treasure boxes of personal information containing user's context, personal schedule, diary, short messages, photos, and videos. Also, user's usage information on Smartphone can be recorded on the device and they can be used as useful sources of high-level inference. Furthermore, stored multimedia contents can be also regarded as relevant evidences for inferring user's daily life. Without user's consciousness, the device continuously collects information and it can be used as an extended memory of human users. However, the amount of information collected is extremely huge and it is difficult to extract useful information manually from the raw data. In this paper, AniDiary (Anywhere Diary) is proposed to summarize user's daily life in a form of cartoonstyle diary. Because it is not efficient to show all events in a day, selected landmark events (memorable events) are automatically converted to the cartoon images. The identification of landmark events is done by modeling causal-effect relationships among various events with a number of Bayesian networks. Experimental results on synthetic data showed that the proposed system provides an efficient and user-friendly way to summarize user's daily life.