Multicore network processors have been playing an increasingly important role in computational processes, which emphasize on scalability and parallelism of the systems, in distributed environments especially in Intern...
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Multicore network processors have been playing an increasingly important role in computational processes, which emphasize on scalability and parallelism of the systems, in distributed environments especially in Internet-based delay-sensitive applications. It is an important but unsolved issue, however, to efficiently schedule tasks in network processors with multicore and multithread for improving the system throughput as much as possible. Profiling can gather runtime environment information and guide the compiler to optimize programs through scheduling tasks based on the runtime context. This paper proposes a profiling-based task scheduling approach, targeting on improving the throughput of multicore network processor (Intel IXP) systems in the balanced pipeline way. In this work, we investigate a profiling-based task scheduling framework, a task scheduling algorithm, and a set of performance models. Our task allocation scheme maps tasks onto the pipeline architecture and multiple threads of network processors in parallel, which incorporates the profiling context and global thread refinement. We evaluate our task scheduling algorithm by implementing representative network applications on the Intel IXP network processor. Experimental results demonstrate that our algorithm is able to schedule tasks in a balanced pipeline fashion and achieve the high throughput and data transmission rate. Copyright (c) 2012 John Wiley & Sons, Ltd.
The Raincore conceptual architecture and distributed services for network element clustering was described. Raincore enables developers to port their applications to run on top of cluster of networking elements. Web s...
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The Raincore conceptual architecture and distributed services for network element clustering was described. Raincore enables developers to port their applications to run on top of cluster of networking elements. Web server prototype and a commercial firewall cluster product were the two applications used. From RainWall's graphical user interface, a user can monitor the health of each firewall node and the virtual IP address assignment in the cluster. The core RainWall code is written in C/C++ and all state information is shared using RainCore protocols.
Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles (CAV), including perception, planning, and control. However, its reliance on vehicular data for model training presents significan...
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Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles (CAV), including perception, planning, and control. However, its reliance on vehicular data for model training presents significant challenges related to in-vehicle user privacy and communication overhead generated by massive data volumes. Federated learning (FL) is a decentralized ML approach that enables multiple vehicles to collaboratively develop models, broadening learning from various driving environments, enhancing overall performance, and simultaneously securing local vehicle data privacy and security. This survey paper presents a review of the advancements made in the application of FL for CAV (FL4CAV). First, centralized and decentralized frameworks of FL are analyzed, highlighting their key characteristics and methodologies. Second, diverse data sources, models, and data security techniques relevant to FL in CAVs are reviewed, emphasizing their significance in ensuring privacy and confidentiality. Third, specific applications of FL are explored, providing insight into the base models and datasets employed for each application. Finally, existing challenges for FL4CAV are listed and potential directions for future investigation to further enhance the effectiveness and efficiency of FL in the context of CAV are discussed.
Several recent research results describe how to design distributed Hash Tables (DHTs) that are robust to adversarial attack via Byzantine faults. Unfortunately, all of these results require a significant blowup in com...
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Several recent research results describe how to design distributed Hash Tables (DHTs) that are robust to adversarial attack via Byzantine faults. Unfortunately, all of these results require a significant blowup in communication costs over standard DHTs. For example, to perform a lookup operation, all such robust DHTs of which we are aware require sending O(log(3) n) messages while standard DHTs require sending only 0(log n), where n is the number of nodes in the network. In this paper, we describe protocols to reduce the communication costs of all such robust DHTs. In particular, we give a protocol to reduce the number of messages sent to perform a lookup operation from O(log(3) n) to O(log(2) n) in expectation. Moreover, we also give a protocol for sending a large (i.e. containing Omega(log(4) n) bits) message securely through a robust DHT that requires, in expectation, only a constant blowup in the total number of bits sent compared with performing the same operation in a standard DHT. This is an improvement over the O(log(2) n) bit blowup that is required to perform such an operation in all current robust DHTs. Both of our protocols are robust against an adaptive adversary. (c) 2007 Elsevier B.V. All rights reserved.
DIMSUM, an acronym for DIMension of a SUM of exponentials, is a highly automated expert system for fitting multiexponential models of increasing dimension to time series data. Up to now, a researcher has needed an ind...
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DIMSUM, an acronym for DIMension of a SUM of exponentials, is a highly automated expert system for fitting multiexponential models of increasing dimension to time series data. Up to now, a researcher has needed an individual copy of DIMSUM on his or her own computer as well as support to learn how to use it. W-3 DIMSUM, a new implementation of DIMSUM, is web-based, new territory for interactive biomodeling, allowing interactive multiexponential model building and model discrimination over the Internet. The algorithms used are numerically intensive, so we have implemented a distributed system, with numerical processing done on our server. Only the user interface is run on the client machine, but users can load and save data and results on their machines, facilitated by our use of Java WebStart. (c) 2005 Elsevier Ireland Ltd. All rights reserved.
The objective of the reported research is to develop a new design-model of distributed facilities, where production is integrated and;operates under computer supported collaboration. The main innovation has been the e...
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The objective of the reported research is to develop a new design-model of distributed facilities, where production is integrated and;operates under computer supported collaboration. The main innovation has been the exploitation of the analogy of the highly effective client-server computing environment for the benefit of production facilities design. As a variant of flexible manufacturing systems, the challenge is to manage the distributed material and information flow. The solution approach developed here is by protocols. The goal is to explain the design of the client-server production model and its coordination protocols, including functions of synchronization and resource allocation. An implementation of the model in an assembly and test facility and its application over the last three years serve to explain;the model and illustrate its-significance. Time-out and priority assignment protocols are defined and analysed in the context of the model to demonstrate its specific benefits.
A collaborative editing systems allows co-authors at different locations to edit a shared view of a single document simultaneously. A compound document binds various types of information to create a single seamless pr...
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A collaborative editing systems allows co-authors at different locations to edit a shared view of a single document simultaneously. A compound document binds various types of information to create a single seamless presentation. A collaborative compound document editing system is developed to combine both the systems described above. It supports distributed editing with replicated compound documents, and integrates notification mechanisms into concurrence control modules. The real-time conversational facilities and the mechanisms for tolerating the process faults are provided. The collaborative compound document editing system is endured with the coordinating and data exchanging capabilities. This paper also discusses design issues such as multi-user interfaces and the presentation of compound documents, and proposes our approaches.
Energy Harvesting Wireless Sensor Networks (EH-WSNs) main goal is to increase efficiency in settings where Energy Harvesting (EH) is restricted by environmental resources. To solve the drawbacks of conventional Wirele...
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Energy Harvesting Wireless Sensor Networks (EH-WSNs) main goal is to increase efficiency in settings where Energy Harvesting (EH) is restricted by environmental resources. To solve the drawbacks of conventional Wireless Sensor Networks (WSNs) routing methods that usually ignore EH, this work presents a multi-hop clustering and renewable energy-based routing protocol designed for EH-WSNs. The suggested method performs clustering both centralized and decentralized using energy circumstances and the quantity of captured energy. The protocol operates in three phases: cluster formation, data transmission, and centralized management. To evaluate the effectiveness of the proposed approach, we analyze three distinct scenarios with different settings. The findings show that the suggested approach greatly lowers the total network energy usage while allowing a higher number of nodes to stay operational. We find that our approach outperforms AEHAC, CRBS, HUCL, and EADUC in terms of average energy levels, overall efficiency, network stability, and number of live nodes during the simulation. The results taken together show that the suggested method continuously improves network efficiency and stability in all assessed situations.
Development of multi-agent systems is a complex process during which use of adequate development tools is essential. This paper focuses on debugging of multi-agent systems. A survey of existing multi-agent systems dev...
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Development of multi-agent systems is a complex process during which use of adequate development tools is essential. This paper focuses on debugging of multi-agent systems. A survey of existing multi-agent systems development tools is presented first, Subsequently a new tool is introduced, based on the innovative approach of the developer's conceptual models, These are multiple complementary abstractions of the multi-agent system concentrating on specific aspects of the system. In relation to these abstractions, several system views are defined that allow a graphical representation of the selected aspects of the system state and its dynamic behaviour, It is argued that these views support effectively understanding of system behaviour and thus efficient debugging. Examples of an implementation of this approach in the frame of the Esprit Project ARCHON are also discussed.
Semi-supervised learning (SSL) is widely-used to explore the vast amount of unlabeled data in the world. Over the decade, graph-based SSL becomes popular in automatic image annotation due to its power of learning glob...
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Semi-supervised learning (SSL) is widely-used to explore the vast amount of unlabeled data in the world. Over the decade, graph-based SSL becomes popular in automatic image annotation due to its power of learning globally based on local similarity. However, recent studies have shown that the emergence of large-scale datasets challenges traditional methods. On the other hand, most previous works have concentrated on single-label annotation, which may not describe image contents well. To remedy the deficiencies, this paper proposes a new graph-based SSL technique with multi-label propagation, leveraging the distributed computing power of the MapReduce programming model. For high learning performance, the paper further presents both a multi-layer learning structure and a tag refinement approach, where the former unifies both visual and textual information of image data during learning, while the latter simultaneously suppresses noisy tags and emphasizes the other tags after learning. Experimental results based on a medium-scale and a large-scale image datasets show the effectiveness of the proposed methods. (c) 2012 Elsevier Inc. All rights reserved.
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