A Unified FPGA Virtualization Framework for General-Purpose Deep Neural Networks in the Cloud

INFerence-as-a-Service (INFaaS) has become a primary workload in the cloud. However, existing FPGA-based Deep Neural Network (DNN) accelerators are mainly optimized for the fastest speed of a single task, while the multi-tenancy of INFaaS has not been explored yet. As the demand for INFaaS keeps growing, simply increasing the number of FPGA-based DNN accelerators is not cost-effective, while merely sharing these single-task optimized DNN accelerators in a time-division multiplexing way could lead to poor isolation and high-performance loss for INFaaS. On the other hand, current cloud-based DNN accelerators have excessive compilation overhead, especially when scaling out to multi-FPGA systems for multi-tenant sharing, leading to unacceptable compilation costs for both offline deployment and online reconfiguration. Therefore, it is far from providing efficient and flexible FPGA virtualization for public and private cloud scenarios. Aiming to solve these problems, we propose a unified virtualization framework for general-purpose deep neural networks in the cloud, enabling multi-tenant sharing for both the Convolution Neural Network (CNN), and the Recurrent Neural Network (RNN) accelerators on a single FPGA. The isolation is enabled by introducing a two-level instruction dispatch module and a multi-core based hardware resources pool. Such designs provide isolated and runtime-programmable hardware resources, which further leads to performance isolation for multi-tenant sharing. On the other hand, to overcome the heavy re-compilation overheads, a tiling-based instruction frame package design and a two-stage static-dynamic compilation, are proposed. Only the lightweight runtime information is re-compiled with ∼1 ms overhead, thus guaranteeing the private cloud’s performance. Finally, the extensive experimental results show that the proposed virtualized solutions achieve up to 3.12× and 6.18× higher throughput in the private cloud compared with the static CNN and RNN baseline designs, respectively.

DESIGN OF PRIVATE CLOUD STORAGE USING SECURITY METHODS IDS AND IPS

Cloud computing is a form of technological progress that has developed along with the times, this has spurred the increasing use of the internet. By usingtechnology internet that is able to implement server a virtual, which has the aim of building a cloud computing server at the District Communications and Information Office. Padang Pariaman uses the Operating System (OS) Proxmox VE (Virtual Environment) 6.4. Cloud computing is able to provide storage services that can be used simultaneously. The results of this study produce a cloud computing server that implements a security system with themethods ids (intrusion detection system) and ips (intrusion prevention system)that are able to process data(storagestorage), use software simultaneously in the network, and use infrastructure within the scope of this research.network cloud computing at the District Communications and Information Office. Padang Pariaman using aservice model private cloud

UJI KECEPATAN RASPBERRY PI SEBAGAI PRIVATE CLOUD STORAGE UNTUK SMALL OFFICE HOME OFFICE: DENGAN STUDI KASUS DI UPT TIK

Small Office Home office atau SOHO adalah sebutan bagi sebuah entitas bisnis berukuran kecil dengan jumlah karyawan tidak lebih dari sepuluh orang. Sebuah soho umumnya merupakan berupa bisnis rintisan yang memiliki keterbatasan dalam pendanaan untuk pengadaan media penyimpanan terpusat. Untuk mengatasi permasalahan tersebut SOHO biasanya menggunakan layanan komputasi awan yaitu public cloud storage yang dimana akan digunakan sebuah server virtual dari penyedia layanan sebagai tempat pencadangan data kantor. Namun pada layanan public cloud storage pemilik bisnis tidak mendapat jaminan secara konkret jika data yang tersimpan pada server penyedia layanan tidak dapat dilihat oleh pihak dari luar kantor. Oleh karena itu peneliti berencana membuat sebuah private cloud storage menggunakan perangkat Raspberry PI 3B+ yang berukuran ringkas sehingga mudah disimpan, dan bersifat eksklusif hanya untuk individu di dalam entitas bisnis. Purwarupa ini diharapkan dapat memenuhi kebutuhan sebuah Small Office Home Office akan penyimpanan terpusat yang handal, aman, dan praktis dalam penerapannya. Pengembangan perangkat menggunakan metode purwarupa, spesifikasi Raspberry Pi yang digunakan masuk dalam golongan low-budget server. Penelitian diawali dengan menganalisa kebutuhan SOHO, lalu dilanjutkan dengan mendesain purwarupa seperti penggunaan kustomisasi yang diperlukan perangkat dan penerapan pada kondisi riil untuk diujikan kembali. Pengujian dilakukan untuk mengukur kecepatan dan membandingkan kecepatan transfer hasil pengujian dengan estimasi kecepatan yang dibutuhkan untuk layanan cloud computing pada SOHO. Private cloud storage pada penelitian ini menunjukan hasil bahwa transfer data belum optimal memenuhi kebutuhan penyimpanan terpusat dengan 10 user dan 2 akses ISP.

Block chain based Malware Detection using Machine Learning Algorithms for IoT enabled E-Health Applications

Due to increasing digitalization and the development of new technologies such as the IoT, the application of machine learning (ML) algorithms is rapidly expanding (IoT). ML algorithms are being used in healthcare, IoT, engineering, finance, and other fields in today's digital age. However, in order to predict/solve a specific issue, all of these algorithms must be taught. There's a good chance that the training datasets have been tampered with, resulting in skewed findings. As a result, we have suggested a blockchain-based approach to protect datasets produced by IoT devices for E-Health applications in this paper. To address the aforementioned problem, the suggested blockchain-based system makes use of a private cloud. For assessment, we created a mechanism that dataset owners may use to protect their data.

MoHRiPA—An Architecture for Hybrid Resources Management of Private Cloud Environments

The high demand for data processing in web applications has grown in recent years due to the increased computing infrastructure supply as a service in a cloud computing ecosystem. This ecosystem offers benefits such as broad network access, elasticity, and resource sharing, among others. However, properly exploiting these benefits requires optimized provisioning of computational resources in the target infrastructure. Several studies in the literature improve the quality of this management, which involves enhancing the scalability of the infrastructure, either through cost management policies or strategies aimed at resource scaling. However, few studies adequately explore performance evaluation mechanisms. In this context, we present the MoHRiPA—Management of Hybrid Resources in Private cloud Architecture. MoHRiPA has a modular design encompassing scheduling algorithms, virtualization tools, and monitoring tools. The proposed architecture solution allows assessing the overall system’s performance by using complete factorial planning to identify the general behavior of architecture under high demand of requests. It also evaluates workload behavior, the number of virtualized resources, and provides an elastic resource manager. A composite metric is also proposed and adopted as a criterion for resource scaling. This work presents a performance evaluation by using formal techniques, which analyses the scheduling algorithms of architecture and the experiment bottlenecks analysis, average response time, and latency. In summary, the proposed MoHRiPA mapping resources algorithm (HashRefresh) showed significant improvement results than the analyzed competitor, decreasing about 7% percent in the uniform average compared to ListSheduling (LS).

Integrated Work Flow Aids Data Digitization, Management for Offshore Drilling

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 202290, “Digital Documentation and Data Management for Offshore Drilling,” by Zhong Cheng, SPE, Xi’an Shiyou University and CNOOC, and Rongqiang Xu and Xiaolong Yu, CNOOC, et al., prepared for the 2020 SPE Asia Pacific Oil and Gas Conference and Exhibition, originally scheduled to be held in Perth, Australia, 20–22 October. The paper has not been peer reviewed. The industry is expending significant effort into using instrumentation and software to optimize operations in all domains for exploration and production to move toward the digital oil field. The complete paper describes an integrated geological-engineering data-management project covering all aspects of well-engineering work flows, with the objective of providing a continuous improvement platform to users. Introduction CNOOC has spent more than 20 years on the progression of information construction. A private cloud platform was completed in 2018, and the characteristics of oil and gas data and critical storage-management technologies were studied systematically. At the same time, nearly 20 kinds of drilling- operation analysis software have been developed independently. From the perspective of engineering technology, these provide real-time monitoring, remote decision-making, technical training, and other information resource services and support for offshore drilling operations. However, the following problems restrict the efficient operation of such projects: - Because of the lack of a unified data-integration-application platform, data sharing has not yet been realized. - In the process of real-time monitoring and remote decision-making, more engineering information based on drilling operations lacks the support of geomechanical data. - The knowledge base and case library to guide the prevention and handling of drilling-operation accidents have not been established. System-Target Analysis The design goals of the platform are embodied in three aspects: function, safety, and operability, while system performance requirements are summarized as adaptability, response speed, scalability, maintainability, and the effective-ness of failure-handling mechanisms. According to the functional requirements of different users for offshore-drilling cloud technical services, users generally are divided into three categories: headquarters decision-making managers, drilling-operation project teams, and system-operation and maintenance-service providers. System Construction Goals and Architecture Construction Goals - Chief among these was to build a geological-engineering integrated data-management platform. Another important goal was to build a case-management platform. An intelligent search engine is established to retrieve the corresponding disposal knowledge through a comprehensive information model. A knowledge-management subsystem is established, and users are linked with internal knowledge-management processes with the help of the cloud. The specific operation process is carried out in the private cloud, and the results are fed back to the user through the human/computer interface.