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Feedback-Directed Cross-Layer Optimization of Cloud-Based Functional Actor Applications 35

Feedback-Directed Cross-Layer Optimization of Cloud-Based Fu...

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35th IEEE international symposium on Software Reliability Engineering, ISSRE 2024

作者： Cappelletti, Andrea Grechanik, Mark University of Illinois Chicago ChicagoIL United States

ISBN: (纸本)9798350353884

Even though actor-based frameworks such as Akka have been used by thousands of companies for over a decade, it is very difficult to configure cloud-based functional actor applications in a way to minimize their response time and to utilize provisioned resources with the maximal efficacy at the computing distributed cloud cluster nodes where these applications are deployed. As a result, the performance-Tuning process is ad-hoc, laborious, manual, tedious and very *** developed a novel low-code approach for cross-layer optimization of actor-based distributed applications. In our method, runtime information is automatically collected and analyzed in parallel to the executing application using search-based genetic algorithms. These algorithms generate changes to the application design, which are automatically applied to regenerate the application while it continues to service clients' requests. We applied our approach to automatically created distributed Akka applications demonstrating its effectiveness in finding an optimal configuration. © 2024 IEEE.

关键词： distributed cloud

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parallel Integrity Authentication Data Structure Construction for Encrypted Range Queries 21

Parallel Integrity Authentication Data Structure Constructio...

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21st IEEE international symposium on parallel and distributed Processing with Applications, 13th IEEE international Conference on Big Data and Cloud computing, 16th IEEE international Conference on Social computing and Networking and 13th international Conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Wang, Zhaokang Pan, Jiahui Zhou, Lu Zhang, Zhonghui Ji, Caocong Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China

ISBN: (纸本)9798350329223

With the rapid growth of cloud computing, outsourcing databases to cloud servers is becoming increasingly popular. Query integrity authentication is an effective technique to obtain reliable query results from untrusted clouds. ServeDB (Wu et al., ICDE 2019) is a state-of-the-art system that provides integrity authentication for encrypted range queries. However, ServeDB is a serial system. The high construction cost of its authentication data structure SVETree is the main performance bottleneck that limits its scalability to large datasets. In this study, we overcome the scalability limitation by parallelizing the SVETree construction workflow using the MapReduce framework. We propose the link-free storage layout to store the tree-based SVETree structure in a distributed key-value storage. The parallel SVETree construction algorithm reduces the communication latency of the key-value storage with the batch put/get optimization. The algorithm balances the workload of different tree nodes with the record-centric parallelization optimization. Furthermore, it avoids triggering out-of-core shuffles with the multi-round in-memory shuffle technique. The experimental results show that the parallel algorithm running on 128 cores achieves a 52.7 × speedup over the original serial algorithm. The parallel algorithm exhibits near-linear data and machine scalability. © 2023 IEEE.

关键词： MapReduce

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The Universal Gossip Fighter 36

The Universal Gossip Fighter

引用

36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Gorbunova, Anastasiia Guerraoui, Rachid Kermarrec, Anne-Marie Kucherenko, Anastasiia Pinot, Rafael Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (纸本)9781665481069

The notion of adversary is a staple of distributed computing. An adversary typically models "hostile" assumptions about the underlying distributed environment, e.g., a network that can drop messages, an operating system that can delay processes or an attacker that can hack machines. So far, the goal of distributed computing researchers has mainly been to develop a distributed algorithm that can face a given adversary, the abstraction characterizing worst-case scenarios. This paper initiates the study of the somehow opposite approach. Given a distributed algorithm, the adversary is the abstraction we seek to implement. More specifically, we consider the problem of controlling the spread of messages in a large-scale system, conveying the practical motivation of limiting the dissemination of fake news or viruses. Essentially, we assume a general class of gossip protocols, called all-to-all gossip protocols, and devise a practical method to hinder the dissemination. We present the Universal Gossip Fighter (UGF). Just like classical adversaries in distributed computing, UGF can observe the status of a dissemination and decide to stop some processes or delay some messages. The originality of UGF lies in the fact that it is universal, i.e., it applies to any all-to-all gossip protocol. We show that any gossip protocol attacked by UGF ends up exhibiting a quadratic message complexity (in the total number of processes) if it achieves sublinear time of dissemination. We also show that if a gossip protocol aims to achieve a message complexity ff times smaller than quadratic, then the time complexity rises exponentially in relation to ff. We convey the practical relevance of our theoretical findings by implementing UGF and conducting a set of empirical experiments that confirm some of our results.

关键词： distributed computing Gossip Protocols Adaptive Adversaries

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parallel quantum circuit extraction from MBQC-patterns

Parallel quantum circuit extraction from MBQC-patterns

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1st international Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Quanz, Marcel Staudacher, Korbinian Fuerlinger, Karl Ludwig Maximilians Univ Munchen Munich Germany Ludwig Maximilians Univ Munchen MNM Team Munich Germany

ISBN: (纸本)9798350364613;9798350364606

In the model of measurement-based quantum computing (MBQC), computations are performed via sequential measurements on a highly entangled graph state. MBQC is a natural model for photonic quantum computing and has been shown to be useful for tasks like optimization and verification of general quantum computations. Therefore, it is often necessary to translate between MBQC and the predominantly used quantum circuit model in a fast and reliable way. While there are algorithms with linear complexity to extract quantum circuits from measurement patterns using additional ancilla qubits, efficient ancilla-free extraction has shown to be more costly. We develop strategies to parallelize an existing extraction algorithm based on ZX-calculus by exploiting the graph structure of measurement patterns and evaluate the performance on patterns obtained from a benchmark set of quantum circuits. Our results suggest that possible parallelization speedups are closely related to the graph structure of a pattern.

关键词： MBQC ZX-calculus gflow

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Qualitative QoS-aware Scheduling of Moldable parallel Jobs on HPC Clusters 22

Qualitative QoS-aware Scheduling of Moldable Parallel Jobs o...

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22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Hou, Zhengxiong Liu, Yubing Shen, Hong Liu, Jiyuan Gu, Jianhua Northwestern Polytechnical University School of Computer Science Xi'an China

ISBN: (纸本)9798331509712

In service oriented high-performance computing (HPC) clusters, end users have various Quality of Service (QoS) requirements. Most of the existing research work focuses on quantitative QoS requirements, such as deadlines, for rigid jobs. While, in many cases, it is more convenient for users to qualitatively state QoS requirements (such as performance-sensitive) at the submission of their jobs. Almost all kinds of QoS requirements will be greatly impacted by job scheduling, which determine the degree of job parallelism, execution time and waiting time, etc. Most modern parallel applications are moldable in the sense that they can choose a resource allocation before execution. Traditional sequential job scheduling mechanism with fixed resource allocation appears to be an obstacle to improve QoS for end users. To address this issue, we propose a novel qualitative QoS-aware sub-queue simultaneous scheduling method for moldable parallel jobs (with variable resource allocation) on HPC Clusters. We first define the qualitative QoS models for end users, then present our sub-queue simultaneous scheduling method, including job sequencing and resource allocation algorithms for a set of moldable parallel jobs on multi-core clusters. Our method can efficiently sequence queuing jobs and allocate appropriate resources for simultaneously running some performance-sensitive jobs in a sub-queue rather than running them one by one. Experimental results demonstrate the effectiveness of our method to improving QoS for end users. © 2024 IEEE.

关键词： Quality of service

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Spatial-Frequency Integration Network with Dual Prompt Learning for Few-shot Image Classification 22

Spatial-Frequency Integration Network with Dual Prompt Learn...

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22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Liu, Yi He, Jie Xu, Shoukun Jiang, Guangqi Changzhou University Academy of Computing and Artificial Intelligence Jiangsu Changzhou213164 China

ISBN: (纸本)9798331509712

Few-shot image classification is a challenging task that aims to recognize image classes based on only a few training images. However, existing methods face the following two main challenges: (1) Ignoring the frequency domain information during image feature extraction. (2) It does not take the semantic gap between multiple modalities into consideration, which limits the classification performance. To overcome these limitations, we propose a novel method named Spatial-Frequency Integration Network with Dual Prompt Learning for few-shot image classification. Firstly, we introduce a spatial-frequency integration module that combines spatial domain and low-frequency information to extract discriminative image features from the image modality. Secondly, we design a dual prompting module, which integrates learnable prompts and hand-crafted prompts to improve the generalization of applications to new classes. Thirdly, we propose an image-text interaction module to enhance inter-modal complementary and consistency. Both theoretical and experimental validations confirm the effectiveness of the proposed method in few-shot image classification. © 2024 IEEE.

关键词： Zero-shot learning

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DistrEdge: Speeding up Convolutional Neural Network Inference on distributed Edge Devices 36

DistrEdge: Speeding up Convolutional Neural Network Inferenc...

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36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Hou, Xueyu Guan, Yongjie Han, Tao Zhang, Ning New Jersey Inst Technol Newark NJ 07102 USA Windsor Univ Windsor ON Canada

ISBN: (纸本)9781665481069

As the number of edge devices with computing resources (e.g., embedded GPUs, mobile phones, and laptops) increases, recent studies demonstrate that it can be beneficial to collaboratively run convolutional neural network (CNN) inference on more than one edge device. However, these studies make strong assumptions on the devices' conditions, and their application is far from practical. In this work, we propose a general method, called DistrEdge, to provide CNN inference distribution strategies in environments with multiple IoT edge devices. By addressing heterogeneity in devices, network conditions, and nonlinear characters of CNN computation, DistrEdge is adaptive to a wide range of cases (e.g., with different network conditions, various device types) using deep reinforcement learning technology. We utilize the latest embedded AI computing devices (e.g., NVIDIA Jetson products) to construct cases of heterogeneous devices' types in the experiment. Based on our evaluations, DistrEdge can properly adjust the distribution strategy according to the devices' computing characters and the network conditions. It achieves 1.1 to 3x speedup compared to state-of-the-art methods.

关键词： distributed computing convolutional neural network edge computing deep reinforcement learning

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Enhancing Edge Caching with User Preferences and Access Patterns in Wireless Networks 22

Enhancing Edge Caching with User Preferences and Access Patt...

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22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Wen, Hao Zhang, Lei Capital Normal University Information Engineering College Beijing China

ISBN: (纸本)9798331509712

Emerging edge caching solutions, which deploy edge servers closer to users to provide rapid response services while alleviating redundant traffic pressure in the core network, have garnered significant attention as a research topic. However, with dynamically changing user requests and limited server caching resources, creating effective edge caching strategies is an urgent problem to solve. To address these challenges, we proposed UPR-AECS, an active edge caching strategy that leverages the user preference representation model for collaborative edge caching. Building user preference models captures dynamic content request patterns, guiding caching decisions to optimize limited storage resources for ideal caching *** this method, the proactive edge caching strategy pre-caches expected future content requests to enhance response times. The experimental results show that UPR-AECS outperforms the baseline methods in cache hit rate and reduction in user content retrieval latency. © 2024 IEEE.

关键词： Mobile edge computing

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Duo: Improving Data Sharing of Stateful Serverless Applications by Efficiently Caching Multi-Read Data 37

Duo: Improving Data Sharing of Stateful Serverless Applicati...

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37th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Huang, Zhuo Fan, Hao Cheng, Chaoyi Wu, Song Jin, Hai Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Serv Comp Technol & Syst Lab Cluster & Grid Comp LabSch Comp Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9798350337662

A growing number of applications are moving to serverless architectures for high elasticity and fine-grained billing. For stateful applications, however, the use of serverless architectures is likely to lead to significant performance degradation, as frequent data sharing between different execution stages involves time-consuming remote storage access. Current platforms leverage memory cache to speed up remote access. However, conventional caching strategies show limited performance improvement. We experimentally find that the reason is that current strategies overlook the stage-dependent access patterns of stateful serverless applications, i.e., data that are read multiple times across stages (denoted as multi-read data) are wrongly evicted by data that are read only once (denoted as read-once data), causing a high cache miss ratio. Accordingly, we propose a new caching strategy, Duo, whose design principle is to cache multi-read data as long as possible. Specifically, Duo contains a large cache list and a small cache list, which act as Leader list and Wingman list, respectively. Leader list ignores the data that is read for the first time to prevent itself from being polluted by massive read-once data at each stage. Wingman list inspects the data that are ignored or evicted by Leader list, and pre-fetches the data that will probably be read again based on the observation that multi-read data usually appear periodically in groups. Compared to the state-of-the-art works, Duo improves hit ratio by 1.1x-2.1x and reduces the data sharing overhead by 25%-62%.

关键词： serverless computing data sharing remote storage cache

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FIRST: Exploiting the Multi-Dimensional Attributes of Functions for Power-Aware Serverless computing 37

FIRST: Exploiting the Multi-Dimensional Attributes of Functi...

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37th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Zhang, Lu Li, Chao Wang, Xinkai Feng, Weiqi Yu, Zheng Chen, Quan Leng, Jingwen Guo, Minyi Yang, Pu Yue, Shang Shanghai Jiao Tong Univ Dept Comp Sci Shanghai Peoples R China Tencent Shenzhen Guangdong Peoples R China

ISBN: (纸本)9798350337662

Emerging cloud-native development models raise new challenges for managing server performance and power at microsecond scale. Compared with traditional cloud workloads, serverless functions exhibit unprecedented heterogeneity, variability, and dynamicity. Designing cloud-native power management schemes for serverless functions requires significant engineering effort. Current solutions remain sub-optimal since their orchestration process is often one-sided, lacking a systematic view. A key obstacle to truly efficient function deployment is the fundamental wide abstraction gap between the upper-layer request scheduling and the low-level hardware execution. In this work, we show that the optimal operating point (OOP) for energy efficiency cannot be attained without synthesizing the multi-dimensional attributes of functions. We present FIRST, a novel mechanism that enables servers to better orchestrate serverless functions. The key feature of FIRST is that it leverages a lightweight Internal Representation and meta-Scheduling (IRS) layer for collecting the maximum potential revenue from the servers. Specifically, FIRST follows a pipeline-style workflow. Its frontend components aim to analyze functions from different angles and expose their key features to the system. Meanwhile, its backend components are able to make informed function assignment decisions to avoid OOP divergence. We further demonstrate the way to create extensions based on FIRST to enable versatile cloud-native power management. In total, our design constitutes a flexible management layer that supports power-aware function deployment. We show that FIRST could allow 94% functions to be processed under the OOP, which brings up to 24% energy efficiency improvements.

关键词： FaaS power management multicore

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