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检索条件"任意字段=International Conference on Advances in Parallel and Distributed Computing"

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Efficient Edge Service Migration in Mobile Edge computing

Efficient Edge Service Migration in Mobile Edge Computing

引用

international conference on parallel and distributed Systems (ICPADS)

作者： Zeng Zeng Shihao Li Weiwei Miao Lei Wei Chengling Jiang Chuanjun Wang Mingxuan Zhang Information & Telecommunication Branch State Grid Jiangsu Electric Power CO. LTD. P.R. China State Grid Jiangsu Electric Power CO. LTD. P.R. China

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

Edge computing is one of the emerging technologies aiming to enable timely computation at the network edge. With virtualization technologies, the role of the traditional edge providers is separated into two: edge infrastructure providers (EIPs), who manage the physical edge infrastructure, and edge service providers (ESPs), who purchase slices of physical resources (e.g., CPU, bandwidth, memory space, disk storage) from EIPs and then cache service entities to offer their own value-added services to end users. These value-added services are also called virtual network function or VNF. As we know, edge computing environments are dynamic, and the requirements of edge service for computing resources usually fluctuate over time. Thus, when the demand of a VNF cannot be satisfied, we need to design the strategies for migrating the VNF so as to meet its demand and retain the network performance. In this paper, we concentrate on migrating VNFs efficiently (MV), such that the migration can meet the bandwidth requirement for data transmission. We prove that MV is NP-complete. We present several exact and heuristic solutions to tackle it. Extensive simulations demonstrate that the proposed heuristics are efficient and effective.

关键词： Computational modeling Heuristic algorithms Bandwidth Dynamic scheduling Data communication Virtualization Edge computing

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SrSpark: Skew-resilient Spark based on Adaptive parallel Processing

SrSpark: Skew-resilient Spark based on Adaptive Parallel Pro...

引用

international conference on parallel and distributed Systems (ICPADS)

作者： Yijie Shen Jin Xiong Dejun Jiang University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

MapReduce-based SQL processing systems, e.g., Hive and Spark SQL, are widely used for big data analytic applications due to automatic parallel processing on large-scale machines. They provide high processing performance when loads are balanced across the machines. However, skew loads are not rare in real applications. Although many efforts have been made to address the skew issue in MapReduce-based systems, they can neither fully exploit all available computing resources nor handle skews in SQL processing. Moreover, none of them can expedite the processing of skew partitions in case of failures. In this paper, we present SrSpark, a MapReduce-based SQL processing system that can make full use of all computing resources for both non-skew loads and skew loads. To achieve this goal, SrSpark introduces fine-grained processing and work-stealing into the MapReduce framework. More specifically, SrSpark is implemented based on Spark SQL. In SrSpark, partitions are further divided into sub-partitions and processed in sub-partition granularity. Moreover, SrSpark adaptively uses both intra-node and inter-node parallel processing for skew loads according to available computing resources in realtime. Such adaptive parallel processing increases the degree of parallelism and reduces the interaction overheads among the cooperative worker threads. In addition, SrSpark checkpoints sub-partition's processing results periodically to ensure fast recovery from failures during skew partition processing. Our experiment results show that for skew loads, SrSpark outperforms Spark SQL by up to 3.5x, and 2.2x on average, while the performance overhead is only about 4% under non-skew loads.

关键词： Adaptive systems Instruction sets conferences parallel processing Data warehouses Big Data Sparks

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parallel High Average-Utility Itemset Mining Using Better Search Space Division Approach 15th

Parallel High Average-Utility Itemset Mining Using Better Se...

引用

15th international conference on distributed computing and Internet Technology (ICDCIT)

作者： Sethi, Krishan Kumar Ramesh, Dharavath Sreenu, M. Indian Inst Technol ISM Dhanbad Dhanbad 826004 Jharkhand India Ashoka Inst Engn & Technol Hyderabad 500082 Telangana India

ISBN: (纸本)9783030053666;9783030053659

Since the last decade, High Utility Itemset (HUI) mining has emerged as a popular pattern mining approach. HUI mining discovers a set of itemset with their profit more than a user defined profit threshold. High Average-Utility Itemset (HAUI) mining is an improvement over HUI mining that involves the length of items to refine the patterns and keep a fair mining process. In the era of big data, traditional HAUI mining algorithms are not suitable to process large transaction dataset on standalone system due to limitation of processing resources. Therefore, several distributed frameworks have been developed to process big data on cluster of commodity hardwares. This paper presents a parallel version of the traditional HAUI-Miner algorithm and names it as parallel High-Average Utility Itemset Miner (PHAUIM). PHAUIM is a Spark-based distributed algorithm which splits the dataset into multiple chunks and distributes on cluster nodes to process each data chunk in parallel. In addition, an improved approach for search space division is developed. Proposed search space division technique fairly assigns the workload to each node and upgrades the performance. Comprehensive experiments have been performed to measure the performance of PHAUIM in terms of speedup and data scalability. PHAUIM is also compared with traditional HAUIM.

关键词： High average-utility itemset mining Big data Apache-Spark Search space

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A Tree-Based Model of Energy-Efficient Fog computing Systems in IoT 12th

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12th international conference on Complex, Intelligent, and Software Intensive Systems (CISIS)

作者： Oma, Ryuji Nakamura, Shigenari Enokido, Tomoya Takizawa, Makoto Hosei Univ Tokyo Japan Rissho Univ Tokyo Japan

ISBN: (纸本)9783319936598;9783319936581

A huge number and various types of devices like sensors and actuators are interconnected with clouds of servers in the IoT (Internet of Things). Here, a large volume of data created by sensors have to be efficiently transmitted and processed and actions have to be efficiently delivered to actuators at an opportune time. In order to reduce the delay time and increase the performance, data and processing are distributed to not only servers but also fog nodes in fog computing systems. On the other hand, the total electric energy consumed by fog nodes increases since a huge number of fog nodes are interconnected. In this paper, we newly propose a tree-based fog computing model to deploy processes and data to fog nodes so that the total electric energy consumption of nodes can be reduced in the IoT. In the evaluation, we show the total electric energy consumption of nodes in the tree-based model is smaller than the cloud model where processes and data are centralized.

关键词： Fog computing IoT (Internet of Things) Energy-efficient IoT Tree-based model of fog computing

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Research on CNN parallel computing and Learning Architecture Based on Real-Time Streaming Architecture 15th

Research on CNN Parallel Computing and Learning Architecture...

引用

15th international conference on distributed computing and Internet Technology (ICDCIT)

作者： Zhu, Yuting Qian, Liang Wang, Chuyan Ding, Lianghui Yang, Feng Wang, Hao Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China Air Force Mil Representat Off Shanghai Nanjing Nanjing Peoples R China

ISBN: (纸本)9783030053666;9783030053659

Convolutional neural network (CNN) is a deep feed-forward artificial neural network, which is widely used in image recognition. However, this mode highlights the problems that the training time is too long and memory is insufficient. Traditional acceleration methods are mainly limited to optimizing for an algorithm. In this paper, we propose a method, namely CNN-S, to improve training efficiency and cost based on Storm and is suitable for every algorithm. This model divides data into several sub sets and processes data on several machine in parallel flexibly. The experimental results show that in the case of achieving a recognition accuracy rate of 95%, the training time of single serial model is around 913 s, and in CNN-S model only needs 248 s. The acceleration ratio can reach 3.681. This shows that the CNN-S parallel model has better performance than single serial mode on training efficiency and cost of system resource.

关键词： CNN parallel computing Apache storm Real time

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Proceedings of ScalA 2019: 10th Workshop on Latest advances in Scalable Algorithms for Large-Scale Systems - Held in conjunction with SC 2019: The international conference for High Performance computing, Networking, Storage and Analysis

Proceedings of ScalA 2019: 10th Workshop on Latest Advances ...

引用

10th IEEE/ACM Workshop on Latest advances in Scalable Algorithms for Large-Scale Systems, ScalA 2019

ISBN: (纸本)9781728159898

The proceedings contain 9 papers. The topics discussed include: GPU acceleration of communication avoiding chebyshev basis conjugate gradient solver for multiphase CFD simulations;optimization of a solver for computational materials and structures problems on NVIDIA Volta and AMD instinct GPUs;toward half-precision computation for complex matrices: a case study for mixed precision solvers on GPUs;parallel multigrid methods on manycore clusters with IHK/mckernel;making speculative scheduling robust to incomplete data;and parallel SFC-based mesh partitioning and load balancing.

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Task Scheduling for Heterogeneous computing using a Predict Cost Matrix 19

Task Scheduling for Heterogeneous Computing using a Predict ...

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48th international conference on parallel Processing (ICPP)

作者： Djigal, Hamza Feng, Jun Lu, Jiamin Coll Comp & Informat Nanjing Peoples R China

ISBN: (纸本)9781450371964

This paper presents a list-based scheduling algorithm called Predict Priority Task Scheduling (PPTS) for heterogeneous computing. The main goal is to minimize the scheduling length by introducing a lookahead feature in the two phases of the PPTS algorithm, namely the task prioritizing phase and the processor selection phase. Existing list scheduling algorithms, such as PEFT and Lookahead have introduced this feature only in the processor selection phase. The novelty of the PPTS algorithm is its ability to look ahead not only in the processor selection phase but also in the task prioritizing phase, without increasing the time complexity. This is achieved based on a predict cost matrix (PCM), which determines the two phases of the proposed algorithm while minimizing the scheduling length and maintaining the same complexity of the existing related algorithms. The experiments based on real applications show that PPTS algorithm outperforms the existing related algorithms in terms of scheduling length ratio.

关键词： distributed computing Application Scheduling Task Graph List-Scheduling

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A Performance Study of Static Task Scheduling Heuristics on Cloud-Scale Acceleration Architecture 19

A Performance Study of Static Task Scheduling Heuristics on ...

引用

5th international conference on computing and Data Engineering (ICCDE)

作者： Shi, Yang Chen, Zhaoyun Quan, Wei Wen, Mei Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc Changsha Peoples R China

ISBN: (纸本)9781450361248

Accelerator based computing platforms have been frequently employed to meet the ever-increasing performance requirements of cloud computing. However, due to their complexity, the efficiency of these platforms is still an open research question. As a major solution for improving the efficiency of a computing platform, task scheduling, which has been studied extensively in recent decades, should be investigated in the context of accelerator-based cloud computing platforms. For this purpose, this paper compares 10 typical task scheduling algorithms on a classic cloud computing platform proposed by Microsoft, using both random and real application task sets. These 10 task scheduling algorithms can be divided into three categories: the Round Robin algorithm, list-based heuristics and computing-intensive searching algorithms. Experimental results show that searching algorithms outperform the other categories on small task sets;however when the scale of the scheduling problem increases towards that of real situations, list-based heuristics yield better results. Of these, Min-min and CROP are the most prominent.

关键词： Task scheduling static heuristics cloud computing dynamic task graph acceleration architecture

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A holistic stream partitioning algorithm for distributed stream processing systems 20

A holistic stream partitioning algorithm for distributed str...

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20th international conference on parallel and distributed computing, Applications and Technologies, PDCAT 2019

作者： Li, Kejian Liu, Gang Lu, Minhua Guangdong Province Key Laboratory of Popular High Performance Computers Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Biomedical Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9781728126166

The performances of modern distributed stream processing systems are critically affected by the distribution of the load across workers. Skewed data streams in real world are very common and pose a great challenge to these systems, especially for stateful applications. Key splitting, which allows a single key to be routed to multiple workers, is a great idea to achieve good balance of load in the cluster. However, it comes with the cost of increased memory consumption and computation overhead as well as network communication. In this paper, we present a new definition of metric to model the cost of key splitting for intra-operator parallelism in stream processing systems and provide a novel perspective to reduce replication factor while keeping both overall load imbalance and processing latency low. Similar to previous work, our approach treats the head and the tail of the distribution differently in order to reduce memory requirements. For the head, it uses our proposed notion of regional load imbalance to decide dynamically whether to make one more worker responsible for the heavy hitter or not. For the tail, it simply uses hash partitioning to keep the size of the routing table for the head as small as possible. Extensive experimental evaluation demonstrates that our approach provides superior performance compared to the state-of-the-art partitioning algorithms in terms of load imbalance, replication factor and latency over different levels of skewed stream distributions. © 2019 IEEE.

关键词： distributed parameter control systems

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parallel algorithms optimization using actor model 12

Parallel algorithms optimization using actor model

引用

12th international Scientific and Practical conference of Programming, UkrPROG 2020

作者： Doroshenko, Anatoliy Tulika, Eugene

Algorithms of parallelization of matrix operations are important components in accelerating scientific calculations. Therefore, optimization of algorithms by execution time, number of operations or volume of transmitted data can significantly reduce the budget of calculations, reduce the time to get results and allow to solve larger tasks. Classic implementations of block-recursive matrix algorithms are limited by rigid placement topology actors and are characterized by a predetermined procedure for performing distributed operations. This approach does not consider the possible heterogeneity of the computing cluster, where the calculations on each cluster node can occur at its own speed, slowing down the calculation to the slowest node. The evolution of such approaches is to break the algorithm into separate tasks and creation of a directed orderly graph of tasks, which will be used by the central orchestration component to manage the progress of the process. In this paper, we offer a new perspective on this approach - the use of a choreography component in which the central coordinating element is absent and instead each component is responsible for coordinating its work with related components. This provides advantages in better adaptability to changing conditions and improves system fault tolerance. The paper formalizes the model of distribution and coordination of tasks in the computational cluster in the form of asynchronous reactive processes with messaging presented by model actors and choreography of the actors, and proposes tools for practical steps in working with block-recursive algorithms by means of the model of actors, namely: 1) creation of an adaptive scheme to place matrix blocks between cluster nodes to enhance effectiveness within a particular cluster and adapting to the heterogeneity of the hardware cluster in which the calculation takes place;2) auto-tuning the schemes of the location of actors in the cluster, which takes into account the statis

关键词： Location

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