Edge computing is attracting more and more attention in recent years to fulfill the requirements of latency-critical and computation-intensive applications. By using the coding redundancy, coded edge computing has eme...
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ISBN:
(纸本)9781728125831
Edge computing is attracting more and more attention in recent years to fulfill the requirements of latency-critical and computation-intensive applications. By using the coding redundancy, coded edge computing has emerged to optimize the total computation latency. Compared withthe servers in cloud computing, edge devices located at the edge of network may not be reliable and trustworthy. In coded edge computing, even one incorrect intermediate result will lead to the incorrect final result. therefore, considering the low computation capabilities of edge devices and low latency requirements of user, we study the result verification problem for coded edge computing. Specifically, we propose an efficient Orthogonal Mark (OM) verification scheme by the properties of linear space. We also conduct solid theoretical analysis to show the successful verification probabilities under two kinds of attack models, respectively. Finally, we conduct extensive simulations to show the effectiveness of the proposed OM verification scheme when comparing with basic coded edge computing scheme and Decoding Comparison (DC) scheme.
P systems are distributed and parallelcomputing models. In this paper, we proposed an improved Quicksort algorithm, called ECTPP-Quicksort, which is based on evolution-communication tissuelike P systems with promoter...
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Serverless computing promises cost-efficiency and elasticity for high-productive software development. To achieve this, the serverless sandbox system must address two challenges: strong isolation between function inst...
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ISBN:
(纸本)9781450371025
Serverless computing promises cost-efficiency and elasticity for high-productive software development. To achieve this, the serverless sandbox system must address two challenges: strong isolation between function instances, and low startup latency to ensure user experience. While strong isolation can be provided by virtualization-based sandboxes, the initialization of sandbox and application causes non-negligible startup overhead. Conventional sandbox systems fall short in low-latency startup due to their application-agnostic nature: they can only reduce the latency of sandbox initialization through hypervisor and guest kernel customization, which is inadequate and does not mitigate the majority of startup overhead. this paper proposes Catalyzer, a serverless sandbox system design providing both strong isolation and extremely fast function startup. Instead of booting from scratch, Catalyzer restores a virtualization-based function instance from a well-formed checkpoint image and thereby skips the initialization on the critical path (init-less). Catalyzer boosts the restore performance by on-demand recovering both user-level memory state and system state. We also propose a new OS primitive, sfork (sandbox fork), to further reduce the startup latency by directly reusing the state of a running sandbox instance. Fundamentally, Catalyzer removes the initialization cost by reusing state, which enables general optimizations for diverse serverless functions. the evaluation shows that Catalyzer reduces startup latency by orders of magnitude, achieves <1ms latency in the best case, and significantly reduces the end-to-end latency for real-world workloads. Catalyzer has been adopted by Ant Financial, and we also present lessons learned from industrial development.
Nowadays computing platforms expose a significant number of heterogeneous processing units such as multicore processors and accelerators. the task-based programming model has been a de facto standard model for such ar...
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ISBN:
(纸本)9781728116440
Nowadays computing platforms expose a significant number of heterogeneous processing units such as multicore processors and accelerators. the task-based programming model has been a de facto standard model for such architectures since its model simplifies programming by unfolding parallelism at runtime based on data-flow dependencies between tasks. Many studies have proposed parallel strategies over heterogeneous platforms with accelerators. However, to the best of our knowledge, no dynamic task-based strategy of the Lattice-Boltzmann Method (LBM) has been proposed to exploit CPU+GPU computing nodes. In this paper, we present a dynamic task-based D3Q19 LBM implementation using three runtime systems for heterogeneous architectures: OmpSs, StarPU, and XKaapi. We detail our implementations and compare performance over two heterogeneous platforms. Experimental results demonstrate that our task-based approach attained up to 8.8 of speedup over an OpenMP parallel loop version.
Given a trace of a distributed computation and a desired predicate, the predicate detection problem is to find a consistent global state that satisfies the given predicate. the predicate detection problem has many app...
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ISBN:
(纸本)9781450360944
Given a trace of a distributed computation and a desired predicate, the predicate detection problem is to find a consistent global state that satisfies the given predicate. the predicate detection problem has many applications in the testing and runtime verification of parallel and distributedsystems. We show that many problems related to predicate detection are in the parallel complexity class NC, the set of decision problems decidable in polylogarithmic time on a parallel computer with a polynomial number of processors. Given a computation on n processes with at most m local states per process, our parallel algorithm to detect a given conjunctive predicate takes O(log mn) time and O(m(3)n(3) log mn) work. the sequential algorithm takes O(mn(2)) time. For data race detection, we give a parallel algorithm that takes O(logmn log n) time, also placing that problem in NC. this is the first work, to the best of our knowledge, that places the parallel complexity of such predicate detection problems in the class NC.
Withthe development of computer vision, Structure from Motion (SFM) which recovers sparse point clouds from image sequences has achieved great success. Large-scale scenes cannot be reconstructed with a single compute...
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the system of systems is the perspective of multiple systems as part of a larger, more complex system. A system of systems usually includes highly interacting, interrelated and interdependent sub-systemsthat form a c...
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ISBN:
(纸本)9783030227449;9783030227432
the system of systems is the perspective of multiple systems as part of a larger, more complex system. A system of systems usually includes highly interacting, interrelated and interdependent sub-systemsthat form a complex and unified system. Maintaining the health of such a system of systems requires constant collection and analysis of the big data from sensors installed in the sub-systems. the statistical significance for machine learning (ML) and artificial intelligence (AI) applications improves purely due to the increasing big data size. this positive impact can be a great advantage. However, other challenges arise for processing and learning from big data. Traditional data sciences, ML and AI used in small- or moderate-sized analysis typically require tight coupling of the computations, where such an algorithm often executes in a single machine or job and reads all the data at once. Making a generic case of parallel and distributedcomputing for a ML/AI algorithm using big data proves a difficult task. In this paper, we described a novel infrastructure, namely collaborative learning agents (CLA) and the application in an operational environment, namely swarm intelligence, where a swarm agent is implemented using a CLA. this infrastructure enables a collection of swarms working together for fusing heterogeneous big data sources in a parallel and distributed fashion as if they are as in a single agent. the infrastructure is especially feasible for analyzing data from internet of things (IoT) or broadly defined system of systems to maintain its well-being or health. As a use case, we described a data set from the Hack the Machine event, where data sciences and ML/AI work together to better understand Navy's engines, ships and system of systems. the sensors installed in a distributed environment collect heterogeneous big data. We show how CLA and swarm intelligence used to analyze data from system of systems and quickly examine the health and maintenance issues a
this article describes an approach to parallelizing of data mining algorithms in logical programming framework, for distributed data processing in cluster. As an example Naive Bayes algorithm implementation in Prolog ...
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ISBN:
(纸本)9783030308599;9783030308582
this article describes an approach to parallelizing of data mining algorithms in logical programming framework, for distributed data processing in cluster. As an example Naive Bayes algorithm implementation in Prolog framework, its conversion into parallel type and execution on cluster with MPI system are described.
the proceedings contain 136 papers. the special focus in this conference is on Computational Science. the topics include: DunDi: Improving Robustness of Neural Networks Using Distance Metric Learning;autism Screening ...
ISBN:
(纸本)9783030227401
the proceedings contain 136 papers. the special focus in this conference is on Computational Science. the topics include: DunDi: Improving Robustness of Neural Networks Using Distance Metric Learning;autism Screening Using Deep Embedding Representation;function and Pattern Extrapolation with Product-Unit Networks;fast and Scalable Outlier Detection with Metric Access Methods;Deep Learning Based LSTM and SeqToSeq Models to Detect Monsoon Spells of India;data Analysis for Atomic Shapes in Nuclear Science;a Novel Partition Method for Busy Urban Area Based on Spatial-Temporal Information;mention Recommendation with Context-Aware Probabilistic Matrix Factorization;synchronization Under Control in Complex Networks for a Panic Model;application of Continuous Time Quantum Walks to Image Segmentation;personalized Ranking in Dynamic Graphs Using Nonbacktracking Walks;an Agent-Based Model for Emergent Opponent Behavior;fine-Grained Label Learning via Siamese Network for Cross-modal Information Retrieval;MeshTrust: A CDN-Centric Trust Model for Reputation Management on Video Traffic;optimizing Spatial Accessibility of Company Branches Network with Constraints;a Fast 3D Finite-Element Solver for Large-Scale Seismic Soil Liquefaction Analysis;Performance Evaluation of Tsunami Inundation Simulation on SX-Aurora TSUBASA;parallelcomputing for Module-Based Computational Experiment;Heuristic Optimization with CPU-GPU Heterogeneous Wave computing for Estimating three-Dimensional Inner Structure;a Generic Interface for Godunov-Type Finite Volume Methods on Adaptive Triangular Meshes;synchronized Detection and Recovery of Steganographic Messages with Adversarial Learning;distributed Memory parallel Implementation of Agent-Based Economic Models;augmenting Multi-agent Negotiation in Interconnected Freight Transport Using Complex Networks Analysis;security-Aware distributed Job Scheduling in Cloud computingsystems: A Game-theoretic Cellular Automata-Based Approach;multi-source Manifold Ou
this paper presents solution to problem of edge coloring of sizable set of cubic graphs and examination of relations between these graphs. We solved this problem on various computingsystems and for various sizes of t...
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ISBN:
(纸本)9781728131795
this paper presents solution to problem of edge coloring of sizable set of cubic graphs and examination of relations between these graphs. We solved this problem on various computingsystems and for various sizes of the problem (various number of graphs). For the computations we used High-Performance computing Cluster and Amazon Web Services cloud environment. We measured and analyzed time of computation of edge coloring and other properties. Largest set we worked with contained almost 10 million graphs. We created new methodology, which can be used to finding order of the edges which optimizes time of computation of edge coloring for certain subset of graphs. On the basis of this methodology, we implemented algorithm for parallel edge coloring of set of graphs. For testing of the methodology, we designed 8 experiments. Results showed, that worst time of edge coloring of graph from set of 19 935 graphs before use of the methodology was 1260 ms. After application of our methodology, we found same order of edge coloring for whole group of 19 935 graphs and the highest time of coloring was 10 ms.
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