SRPT for Multi Server Systems Under Cellular Batching

In recent years, there has been a rapid growth of large-scale distributed deep learning (DDL) (this is a form of machine learning that lets computer programs learn patterns and adapt their performance) frameworks (Google's TensorFlow, MXNet, etc.), which exploit the massive parallelism of computing clusters to expedite the training and inference phases of deep learning systems. In a networked computing cluster that supports a large number of deep learning jobs, a key question is how to design efficient scheduling algorithms to allocate resources across different machines to minimize the overall job processing time (Essentially, we want to let computers process as many tasks as efficiently as possible). Toward this end, in this project, we propose to develop a suite of online scheduling algorithms that jointly optimize resource allocation and locality decisions for distributed deep learning training and inference phases. Our goal is to develop theoretically provable (near) delay-optimal scheduling and resource allocation optimization algorithms for RNN-based (recursive neural network) distributed deep learning based on cell-based batching in the inference phase

Multi-party Quantum Byzantine Agreement Without Entanglement

In this paper we propose a protocol of quantum communication to achieve Byzantine agreement among multiple parties. The striking feature of our proposal in comparison to the existing protocols is that we do not use entanglement to achieve the agreement. There are two stages in our protocol. In the first stage, a list of numbers that satisfies some special properties is distributed to every participant by a group of semi-honest list distributors via quantum secure communication. Then, in the second stage those participants exchange some information to reach agreement.Comment: 6 pages, 1 figur

자연어 생성 모델 추론 서비스의 효율적인 자원 스케일링 정책

학위논문(석사) -- 서울대학교대학원 : 공과대학 컴퓨터공학부, 2022. 8. 전병곤.Though number of different types of Deep Neural Network (DNN) models are increasing, language generation model is still the most in demand. There is also an increasing demand for serving the pre-trained model. However, managing computing resources in serving Natural Language Generation (NLG) model is not a trivial problem, because requests and responses of each query is different due to a variety of environment. Moreover, it is even more challenging to decide scaling policy, which minimizes both violation of service level objective (SLO) and GPU resource usage. In this paper, we discuss the problem of using efficient GPU resources in serving language generation model, and propose a design a serving framework which supports fast and accurate scaling policy. We implemented an deep learning inference serving framework with policy and validated our system on the serving request query workloads.다양한 유형의 심층 신경망 모델 (DNN)이 증가함에 따라 자연어 생성 모델에 대한 관심이 많아지고 있다. 또한 학습된 모델 이용한 추론 서비스에 대한 수요 또한 함께 증가하고 있다. 그러나 자연어 생성 모델 추론 서비스를 운용하는 데 있어서 컴퓨팅 자원을 효율적으로 사용하는 것은 단순한 문제가 아니다. 이는 추론 서비스에 들어오는 각 쿼리마다 추론 엔진에서 사용하는 컴퓨팅 자원이 다르기 때문이다. 그렇기에 추론 서비스에 대해 자원 스케일링 정책을 사용하는 것은 훨씬 더 어려운 일이다. 본 논문에서는 언어 생성 모델 추론 서비스에서 GPU 자원을 효율적으로 사용하는 문제에 대해 논의한다. 문제를 해결하기 위한 빠르고 정확한 자원 스케일링 정책을 제안하고, 요청 쿼리 워크로드에 대해서 해당 정책을 검증한다.1. Introduction 5 2. Background 8 2.1 Natural Language Generation Model 8 2.2 Scaling Inference Engine in Kubernetes Cluster 10 3. Related Work 12 3.1 Scaling in Machine Learning Inference Serving 12 3.2 Model-less Inference Serving 12 4. Observation 14 4.1 Various Input Queries Violates SLOs 14 5. Scaling Mechanism and Policy 19 5.1 Horizontal Pod Scaling Mechanism 19 5.2 Per-Token Latency Based Policy 20 6 System Design 21 6.1 System Architecture 21 6.2 Management Server API Design 23 6.3 Implementation 23 7. Evaluation 25 7.1 Evaluation Setup 25 7.1.1 Environment 25 7.1.2 Workloads 25 7.2 First Scaling Time 26 7.3 SLO Violations and Total Resource Usage 27 7.4 Appropriate Resource Usage 27 8. Conclusion 31석

Security Information Sharing in Smart Grids: Persisting Security Audits to the Blockchain

This article belongs to the Special Issue Advanced Cybersecurity Services DesignWith the transformation in smart grids, power grid companies are becoming increasingly dependent on data networks. Data networks are used to transport information and commands for optimizing power grid operations: Planning, generation, transportation, and distribution. Performing periodic security audits is one of the required tasks for securing networks, and we proposed in a previous work autoauditor, a system to achieve automatic auditing. It was designed according to the specific requirements of power grid companies, such as scaling with the huge number of heterogeneous equipment in power grid companies. Though pentesting and security audits are required for continuous monitoring, collaboration is of utmost importance to fight cyber threats. In this paper we work on the accountability of audit results and explore how the list of audit result records can be included in a blockchain, since blockchains are by design resistant to data modification. Moreover, blockchains endowed with smart contracts functionality boost the automation of both digital evidence gathering, audit, and controlled information exchange. To our knowledge, no such system exists. We perform throughput evaluation to assess the feasibility of the system and show that the system is viable for adaptation to the inventory systems of electrical companies.This work has been supported by National R&D Projects TEC2017-84197-C4-1-R, TIN2017-84844-C2-1-R, by the Comunidad de Madrid project CYNAMON P2018/TCS-4566 and co-financed by European Structural Funds (ESF and FEDER), and by the Consejo Superior de Investigaciones Científicas (CSIC) under the project LINKA20216 ("Advancing in cybersecurity technologies", i-LINK+ program)

Blockchain logging for process mining: a systematic review

Considerable progress was forcasted for collaborative business processes with the rise of blockchain programmable platforms. One of the saliant promises was auditable traces of business process execution, but practically that has posed challenges specially with regard to blockchain logs’ structure who turned out to be inadequate for process mining techniques. Approaches to answer this issue have started to emerge in the literature, some focusing on the creation process of event logs and others dealing with their retrieval from the blockchain. This work outlines the generic steps required to solve these challenges and analyzes findings in these approaches with a consideration for efficiency and future research directions