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Secure Range Queries on Semantic Trajectories in Fog-based Cloud Computing

Secure Range Queries on Semantic Trajectories in Fog-based C...

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international Symposium on parallel and distributed processing with applications, ISPA

作者： Yiping Teng Shiqing Wang Miao Li Yuyao Tang Bingfeng Yu Chunlong Fan School of Computer Shenyang Aerospace University Shenyang

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

With the advances in positioning techniques, trajectories are emerged with semantic information such as location-based activities and sign-ins. Essential for applications such as trip recommendations, range queries on semantic trajectories are utilized to identify trajectories that satisfy specific spatio-temporal conditions as well as keyword-related criteria. To reduce the costs of query processing services, data owners often outsource the data services to public platforms, of which the fog-based cloud can offer enhanced storage and computing capabilities and support efficient access to user data. However, outsourcing data in plaintext may raise privacy concerns. To this end, we study the problem of secure range queries on semantic trajectories on the fog-based cloud platform. Initially, to effectively organize semantic trajectories, we design a secure index structure within fog-based cloud framework by distributing spatio-temporal data of trajectories across the fog servers based on semantic contents. Subsequently, we propose a secure range query scheme where keyword pruning prioritizes the fog servers containing query keywords, and then secure spatio-temporal pruning is performed in parallel to obtain the candidate trajectories satisfying spatio-temporal constraints. Finally, on cloud servers, secure timespan verification is applied to ensure that the final results include all query keywords within the specified time span. A comprehensive analysis of the proposed scheme is provided in terms of computational complexity and security guarantees. Leveraging the keyword-first-pruning strategy and parallel processing on fog servers, the query scheme is evaluated to show stable and efficient performance through extensive experiments on real datasets.

关键词： Cloud computing Query processing Semantics Stability criteria parallel processing Trajectory Servers Security Indexes Computational complexity

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Achieving multilevel elasticity for distributed stream processing systems in the cloud environment: A review and conceptual framework 2022

Achieving multilevel elasticity for distributed stream proce...

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14th international conference on Contemporary Computing, IC3 2022

作者： Thakkar, Riddhi Bhavsar, Madhuri Institute of Technology Nirma University Gujarat Ahmedabad India

ISBN: (纸本)9781450396752

Recent awareness and advances in technology have triggered excessive use of social media, IoT devices, remote sensing devices, mobile applications, web applications, and gaming more than ever before in time. Such platforms are hosting their applications on the cloud as it provides various services on a pay-per-use basis. A Cloud Service Provider (CSP) should deliver all its services very swiftly to process real-time applications on time. Real-time stream computations are characteristically long-lived and receive data in an unpredictable form, requiring a fair amount of resources for their processing in constrained time. Such a dynamic nature of applications demands resource elasticity at runtime. The cloud architecture is stacked with different types of resources, each having a discrete adaption process with distinct elasticity properties. Scaling the absolute amount of resources leads to performance boosting. Recent literature landscapes the elasticity at Virtual Machine (VM) level, describing various techniques for scaling VMs. Each technique targets a distinct aspect with specific assumptions. However, the literature lacks a comprehensive survey at the operator level, where actual processing takes place and has a higher impact on the performance of the system. Compared to other works in the literature, this work presents a detailed analysis of various approaches targeting elasticity at the operator level of cloud architecture for stream processing applications, along with the conceptual framework, scaling at the operator, VM, and server levels. We have also discussed the various elastic approaches for scaling the resources at multilevel: VM and operator-level concurrently, for distributed Stream processing (DSP) applications running on the cloud. Conceptually, with the proposed framework, we can attain maximum resource utilization at each layer. In future work, we will evaluate the proposed framework with real-world application. © 2022 ACM.

关键词： Big data

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Implementation of FIR Digital Filter Algorithm in C Language on DSP Platform

Implementation of FIR Digital Filter Algorithm in C Language...

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Information Science, parallel and distributed Systems (ISPDS), international conference on

作者： Guoyong Wang Huaihua Normal College Huaihua China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

This article provides a detailed overview of the process of implementing an FIR digital filter using the TI TMS320 series DSP platform with the C programming language. It covers topics such as the working principle of FIR filters, design methods, DSP development environment setup, C code implementation, simulation verification, and system testing. Through simulation verification, the frequency response characteristics of the FIR filter are confirmed, and the system testing results demonstrate that the FIR filter achieves effective signal filtering under appropriate conditions with accurate frequency response and high signal-to-noise ratio. This document comprehensively validates the excellent signal processing performance of FIR filters on the DSP platform, offering valuable insights for applications involving FIR filter design on DSP platforms.

关键词： Performance evaluation System testing Information science Codes Finite impulse response filters Signal processing algorithms Digital signal processing

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Flexible application-aware approximation for modern distributed graph processing frameworks 5

Flexible application-aware approximation for modern distribu...

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5th ACM SIGMOD Joint international Workshop on Graph Data Management Experiences and Systems and Network Data Analytics, GRADES-NDA 2022, co-located with ACM SIGMOD conference

作者： Schramm, Michael Bhowmik, Sukanya Rothermel, Kurt Institute for Parallel and Distributed Systems University of Stuttgart Germany

ISBN: (纸本)9781450393843

The interest in the ability of processing data that has an underlying graph structure has grown in the recent past. This has led to the development of many distributed graph processing systems. However, due to rapidly growing amount of data, e.g., web graphs and social graphs, even such distributed graph processing frameworks end up requiring several minutes or even several hours to execute popular graph algorithms. This leads to the question: do we always need to know the exact answer for a large graph? The aforementioned modern distributed graph processing frameworks execute graph algorithms by exchanging messages between vertices. This paper introduces a novel message-dropping approach for approximation in these frameworks. As dropping messages would result in degradation of quality of result, our objective is to drop messages that have least adverse impact on quality. More specifically, we propose an application-aware approach that dynamically drops messages at runtime. We evaluate the effects of our approach for the PageRank algorithm on several representative real-world web graphs and compare its performance to that of existing approximation techniques for modern frameworks.. © 2022 ACM.

关键词： Drops

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parallel Directives Evaluation in Porous Media Application: A Case Study

Parallel Directives Evaluation in Porous Media Application: ...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Natiele Lucca Claudio Schepke Gabriel Tremarin Laboratory of Advances Studies in Computation (LEA) Federal University of Pampa (UNIPAMPA) Alegrete Brazil

High-performance computing provides the acceleration of scientific applications through the use of parallelism. applications of this type usually demand a lot of computation time for a version with a single code execution stream. The adoption of different models of parallel programming enables the development of concurrent code. In this sense, this paper evaluates parallel interfaces and their programming models. Therefore, as a case study, we evaluate a porous media application that simulates grain drying using OpenMP (loop, sections, tasks, target, and teams approach) and OpenACC programming interfaces. The results show a reduction in processing time in all test cases. The total parallel simulation time for a multicore architecture using 16 physical cores was 5.61 times less using loops, 5.96 using targets, and 7.50 using teams. Task and section directives produce around 1.20 speedup due to the limitations of concurrent task executions of the application. The reduction using a single GPU was 7.54. We also contribute with some collected traces, identifying the parallel steps and synchronization time.

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Fault Tolerance and Error Handling techniques in Apache Kafka 5

Fault Tolerance and Error Handling Techniques in Apache Kafk...

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5th international conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Vyas, Shubham Tyagi, Rajesh Kumar Sahu, Shashank Amity Institute of Information Technology Haryana Gurgaon India Department of Computer Science and Engineering Amity School of Engineering and Technology Haryana Gurgaon India

ISBN: (纸本)9798400709418

Real Time streaming framework enables the availability of data for users in near real time. For achieving the high throughput, low latency and to avoid loss of any events at runtime, real time streaming frameworks has their own methods to accomplish this task of message handling, but there are scenarios when due to huge volume of data at the source or due to other environment or Infrastructure related issues, some of the critical data might get lost in the process. This article evaluates methods to handle data rejections in conjunction with a fault-tolerant error handling recommendations for real time data streaming. There are many frameworks available for streaming the events in real time, in this article Apache Kafka is used to examine most frequent situations when an error can occur. This article will evaluate the key performance factors in terms of capability of the framework to recover when downtime occurs. Apache Kafka [15] is a distributed real time data streaming platform;it was developed by Linked inn and currently used by many applications across the industry. It works in producer consumer fashion and with correct configuration and set up, high Throughput with low latency can be achieved. This article will explain the configurations provided with the framework that can be utilized to design and develop optimized event streaming applications. This article is explaining the different parameters that can be used to design and develop the stream processing applications and what are the available options to handle the errors during the streaming to avoid any negative impact to the application. © 2023 ACM.

关键词： Errors

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Accelerate Model parallel Deep Learning Training Using Effective Graph Traversal Order in Device Placement 22nd

Accelerate Model Parallel Deep Learning Training Using Effec...

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22nd IFIP WG 6.1 international conference on distributed applications and Interoperable Systems (DAIS) Held as Part of the 17th international Federated conference on distributed Computing techniques (DisCoTec)

作者： Wang, Tianze Payberah, Amir H. Hagos, Desta Haileselassie Vlassov, Vladimir KTH Royal Inst Technol Stockholm Sweden

ISBN: (纸本)9783031160929;9783031160912

Modern neural networks require long training to reach decent performance on massive datasets. One common approach to speed up training is model parallelization, where large neural networks are split across multiple devices. However, different device placements of the same neural network lead to different training times. Most of the existing device placement solutions treat the problem as sequential decisionmaking by traversing neural network graphs and assigning their neurons to different devices. This work studies the impact of neural network graph traversal orders on device placement. In particular, we empirically study how different graph traversal orders of neural networks lead to different device placements, which in turn affects the training time of the neural network. Our experiment results show that the best graph traversal order depends on the type of neural networks and their computation graphs features. In this work, we also provide recommendations on choosing effective graph traversal orders in device placement for various neural network families to improve the training time in model parallelization.

关键词： Device Placement Model parallelization Deep Learning Graph Traversal Order

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Improving parallel Exhaustive Subgroup Discovery with Early Search Space Pruning 31

Improving Parallel Exhaustive Subgroup Discovery with Early ...

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31st IEEE international conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2024

作者： Jyoti Litoriya, Ashutosh Kailasam, Sriram School of Computing and Elect. Engg. Indian Institute of Technology Mandi India Dept. of Computer Sci. and Engg. and IT Jaypee University of Info. Tech. Solan India Genpact India Pvt. Ltd Bangalore India Dept. of Computer Sci. and Engg. National Institute of Technology Warangal India

ISBN: (纸本)9798331509118

In this digital era, vast amounts of data are generated from various applications, necessitating robust data mining algorithms to extract valuable insights. Subgroup discovery (SD) [1] is a widely used supervised descriptive data mining method that aims to find interesting subgroups of individuals (samples, objects) with high associations to a target property of interest. However, SD faces two major challenges. The first is the high computational expense of exploring the combinatorial search space of subgroups. Second, when testing multiple subgroups to identify a few significant associations, ensuring that each subgroup meets a quality threshold is not enough to control the occurrence of false positives. The latter issue has been addressed recently with permutation testing while for the former, exploring the search space of closed subgroups instead of all possible subgroups reduces the search space without loss of information. Pellegrina et al. [2] incorporated both these approaches and provided a sequential implementation TopKWY for discovering the top-k significant subgroups while controlling false discoveries. It is considered state-of-the-art. However, its huge computational requirement of assessing patterns against many permutations raises scalability concerns, while the specific optimizations in its algorithm for computing p-values do not generalize to other quality functions. Jyoti et al. [3] addressed both these concerns in their proposed framework ParaDiS. The algorithm of ParaDiS is parallel and distributed, employs efficient communication mechanisms for load sharing and generalizes to other quality functions. However, ParaDiS uses exhaustive search and therefore, suffers from performance bottlenecks. The existing heuristic techniques in SD neither control false discoveries nor consider the search space of closed subgroups. Further, these heuristics can miss potentially interesting subgroups w.r.t. quality. © 2024 IEEE

关键词： Economic and social effects

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On-the-Fly Calculation of Model Factors for Multi-paradigm applications 28th

On-the-Fly Calculation of Model Factors for Multi-paradigm A...

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28th international conference on parallel and distributed Computing (Euro-Par)

作者： Protze, Joachim Orland, Fabian Haldar, Kingshuk Koritzius, Thore Terboven, Christian Rhein Westfal TH Aachen IT Ctr Aachen Germany Univ Stuttgart HLRS Stuttgart Germany

ISBN: (纸本)9783031125973;9783031125966

Model factors provide initial insight into fundamental issues of parallel applications. These metrics elaborate beyond conventional HPC metrics to indicate whether an application suffers from systemic or local load imbalances, or high cost for synchronization or data transfer. The metrics are also useful to compare the parallel characteristics of different versions of the same application. This work proposes a model of separating the impact factors of different layered parallelism paradigms. In contrast to previous work in this area, we successfully separate all factors and can prove all efficiency values to be between 0 and 1. While we use MPI OpenMP as an example in this work, the general concepts also apply to layering other parallel programming paradigms. As a proof of concept, we present a tool that collects the necessary performance data and determines different critical paths in the execution without measurable runtime overhead. We evaluate the methodology with synthetic source code examples but also with a real-world application and an application using the latest or future MPI and OpenMP features in order to evaluate the methodology on applications with an overlap of asynchronous computation and communication at the highest possible concurrency.

关键词： Data transfer

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A Framework for Decentralized parallel Complex Event processing on Heterogeneous Infrastructures

A Framework for Decentralized Parallel Complex Event Process...

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9th IEEE international conference on Big Data (IEEE BigData)

作者： Roeger, Henriette Bhowmik, Sukanya Linn, Tobias Univ Stuttgart Inst Parallel & Distributed Syst Stuttgart Germany

ISBN: (纸本)9781665439022

Low-latency stream processing applications (SPA) distribute the event streams on parallel instances of the application's operators to avoid queuing. Each operator has a set of processing nodes for its *** and fog infrastructures promise, among other things, low communication latency and are thus of interest for low-latency SPAs. Yet, they include heterogeneous, even volatile nodes which makes the parallel processing of SPAs challengingAvailable solutions for parallel SPAs often assume homogeneity and stable ***, we aim to balance the stream processing among heterogeneous, instable infrastructures using a decentralized control mechanism. We propose a decentralized, dynamic, and lightweight approach to re-balance SPA-load according to the nodes current performance, while minimizing latencies. In doing so, we introduce the Coda-EFC framework for window-based stream processing on heterogeneous distributed infrastructures. It dynamically adapts its load balancing to node performance without computing quickly outdated schedules and aims for latency-optimal load balancing. We introduce two adaptive load balancing strategiesone tailored for heterogeneous, dynamic infrastructures, the other an adaptation of a state-of-the art approach.

关键词： Stream processing Multi-provider Infrastructure Fog Computing

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