Sumo Logic Brings Continuous Intelligence to Amazon Web Services
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Blockchain-Based Local Energy Markets
Abstract CHRISTIDIS, KONSTANTINOS. Blockchain-Based Local Energy Markets. (Under the direction of Michael Devetsikiotis and Srdjan Lukic.) A growing customer base for solar-plus-storage at the grid edge has resulted in stronger interest at the regulatory level towards energy markets at the distribution level. Blockchains — systems that have been popularized recently by technologies such as Bitcoin and Ethereum — allow us to establish transparent marketplaces without the need for a central authority. This thesis investigates the feasibility of local energy markets (LEMs) running on blockchains, and also introduces a canonical framework for designing and evaluating blockchain-based LEMs — a first in this space. We begin by examining whether existing blockchain implementations are capable of supporting such markets. We dissect blockchains into their core components, perform an analytical survey on the space, and introduce a taxonomy for blockchain systems. Our findings suggest an impedance mismatch for our use case; we identify a number of integration issues for IoT applications, and offer workarounds where possible. Shifting back into the original goal of designing a realistic blockchain-based LEM, a common theme we find across all relevant literature is the treatment of the blockchain component as a black box. Armed with a proper understanding of the blockchain space from our earlier analysis, we make the case that this approach is flawed because the choices in this layer affect the market’s performance significantly. First, we explicitly identify the design space that the blockchain layer introduces, and analyze how the design choices made therein affect the performance, governance, and degree of decentralization of these markets. -
Cloud Computing
JYOTI NIVAS COLLEGE PG CENTER DEPARTMENT OF MCA III YEAR TECH-ON-TAP E-JOURNAL ON CLOUD COMPUTING Issue 1, August 2020 Sl.no Title Page.no 1 Alibaba Cloud 1 2 Workday and Softchoice 2 3 Citrix Cloud Services and Nippon Data 4 4 Softlayer and Bluelock 6 5 IBM Cloud 7 6 Oracle Cloud 9 7 BackBlaze and Jira Cloud 10 8 SnowFlake 11 9 AWS Kiinesis and Verizon Cloud 12 10 Amazon Elastic Compute Cloud (EC2) 13 11 Google Cloud and Navtech Platform 14 12 Tresorit 16 13 Massive Grid and Egnyte 17 14 Microsoft Azure and Cloud-myNav 19 15 Box and OpenStack 20 16 Kamatera and SAP-S/4 Hana Cloud 22 17 Cloud Ways and Adobe Service 24 18 Cloud Sigma and Prolifics 25 19 MessageOPS - Cloud Service Provider 27 20 pCloud Service Provider 28 21 CtrlS and Zendesk 29 22 Century link and Service Now 31 23 Milesweb 33 24 Zoom Cloud 35 25 Collibra 36 26 GoDaddy and Amazon Web Services 37 27 Ubiquity Hosting and Mage Cloud 39 CLOUD SERVICE PROVIDERS Kushmetha k. A(18MCA08) Brunda S(18MCA05) Cloud Service Providers are the companies that offer network services, infrastructure or the business applications in the cloud. These cloud services are hosted in a data center using network connectivity that can be accessed by companies or individuals. Few different forms of services that can be used in the cloud by the CSPs include Software as a Service(SaaS), Platform as a Service(PaaS) and Infrastructure as a Service(IaaS). Alibaba Cloud: It is a chinese cloud computing company founded in 2009 by Jack Ma and Simon Hu. -
Industry Trends in Cloud Computing
Industry Trends in Cloud Computing David Dempsey • Felicity Kelliher Industry Trends in Cloud Computing Alternative Business-to-Business Revenue Models David Dempsey Felicity Kelliher Salesforce School of Business Dublin, Ireland Waterford Institute of Technology Waterford, Ireland ISBN 978-3-319-63993-2 ISBN 978-3-319-63994-9 (eBook) https://doi.org/10.1007/978-3-319-63994-9 Library of Congress Control Number: 2017955977 © The Editor(s) (if applicable) and The Author(s) 2018 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and trans- mission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. -
State of 2017
STATE OF MODERN APPLICATIONS IN THE CLOUD 2017 Introduction — The Rise of Modern Applications What is the Modern Application? Today’s leading enterprises are striving to deliver high performance, highly scalable and always-on digital services. These services are built on custom “modern architectures” – an application stack with new tiers, new technologies, microservices and typically running on cloud platforms like Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform, etc. What does this report provide? The goal of this report is to provide data-driven insights, best practices and trends by analyzing technology adoption among Sumo Logic customers who run massive mission-critical modern applications on cloud platforms like AWS, Azure, and Google Cloud. Who should read this report? Cloud architects, Operations, DevOps and Security leaders and practitioners should leverage the learnings from this report to build, operate and secure modern applications effectively. Data Methodology and Assumptions • This data is derived from 1500+ Sumo Logic customers running applications on cloud platforms like AWS, Azure, Google Cloud, etc. All customer specific data is anonymized. • Customers use Sumo Logic to manage production applications and underlying infrastructure. Hence, this report provides a snapshot of the production application state. • The Sumo Logic analytics service runs on AWS. The experience and expertise of running this mission critical and massive service is also leveraged in this report. This report assumes that an app or -
Understanding Log Analytics at Scale O'reilly Report | Pure Storage
C om p lim e nt s of SECOND EDITION Understanding Log Analytics at Scale Log Data, Analytics & Management Matt Gillespie & Charles Givre REPORT SECOND EDITION Understanding Log Analytics at Scale Log Data, Analytics, and Management Matt Gillespie and Charles Givre Beijing Boston Farnham Sebastopol Tokyo Understanding Log Analytics at Scale by Matt Gillespie and Charles Givre Copyright © 2021 O’Reilly Media, Inc.. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com). For more infor‐ mation, contact our corporate/institutional sales department: 800-998-9938 or [email protected]. Acquisitions Editor: Jessica Haberman Proofreader: Abby Wheeler Development Editor: Michele Cronin Interior Designer: David Futato Production Editor: Beth Kelly Cover Designer: Randy Comer Copyeditor: nSight, Inc. Illustrator: Kate Dullea May 2021: Second Edition February 2020: First Edition Revision History for the Second Edition 2021-05-05: First Release The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Understanding Log Analytics at Scale, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. The views expressed in this work are those of the authors, and do not represent the publisher’s views. While the publisher and the authors have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the authors disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. -
Gigaom Radar for Cloud Observability V1.0
MARKET RADAR GigaOm Radar for Cloud Observability v1.0 ANDY THURAI | FEB 26, 2021 - 10:57 AM CST TOPIC: CLOUD INFRASTRUCTURE CREDIT: BAGOTAJ GigaOm Radar for Cloud Observability TABLE OF CONTENTS 1 Summary 2 Market Categories and Deployment Types 3 Key Criteria Comparison 4 GigaOm Radar 5 Vendor Insights 6 Analyst’s Take 7 About Andy Thurai 8 About GigaOm 9 Copyright GigaOm Radar for Cloud Observability v1.0 2 1. Summary Observability is an emerging set of practices, platforms, and tools that goes beyond monitoring to provide insight into the internal state of systems by analyzing external outputs. It’s a concept that has its roots in 19th century control theory concepts and is rapidly gaining traction today. Of course, monitoring has been a core function of IT for decades, but old approaches have become inadequate for a variety of reasons—cloud deployments, agile development methodology, continuous deployments, and new DevOps practices among them. These have changed the way systems, infrastructure, and applications need to be observed so events and incidents can be acted upon quickly. At the heart of the observability concept is a very basic premise: quickly learn what happens within your IT to avoid extended outages. And in the unfortunate event of an outage, you need to ensure that you can get to the root cause of it fast. Outages are measured by Mean Time To Resolution (MTTR) and it is the goal of the observability concept to drive the MTTR value to as close to zero as possible. No surprise, building resilient service delivery systems that are available with high uptime is the ultimate end goal for any business. -
Application Data Analytics for Modern Applications How Sumo Logic’S Unified Machine Data Platform Changes the Management Game
Application Data Analytics for Modern Applications How Sumo Logic’s Unified Machine Data Platform Changes the Management Game An ENTERPRISE MANAGEMENT ASSOCIATES® (EMA™) White Paper Prepared for Sumo Logic July 2016 IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Application Data Analytics for Modern Applications: How Sumo Logic’s Unified Machine Data Platform Changes the Management Game Table of Contents Overview ................................................................................................................................................. 1 Telemetry, Big Operational Data, and the Real-Time Business ................................................................ 2 Modern Application Environments Require a New Approach to Application Management ................... 4 Sumo Logic: Cloud-Native Unified Logs and Metrics ............................................................................. 6 EMA Perspective ...................................................................................................................................... 8 ©2016 Enterprise Management Associates, Inc. All Rights Reserved. | www.enterprisemanagement.com Application Data Analytics for Modern Applications: How Sumo Logic’s Unified Machine Data Platform Changes the Management Game Overview telemetry – an automated communications process by which measurements and other data are collected at remote or inaccessible points and are subsequently transmitted to receiving equipment for monitoring1 The word “telemetry” has traditionally -
GCP Cloud Foundations #Whoami
GCP Cloud Foundations #whoami Federico Fregosi Current: Principal Consultant - Technical Past: [email protected] https://www.linkedin.com/in/federico-fregosi/ About Contino Contino is a leading transformation consultancy that helps large, heavily-regulated enterprises to become fast, agile and competitive. 360+ 5 300+ 150+ People Global offices Engagements Customers The deepest pool of We can scale rapidly More DevOps Specializing in helping the DevOps, data & cloud to support diverse transformation executed world's leading brands transformation talent client requirements than any other professional accelerate digital in the industry across the globe services firm transformation 3 Agenda 01 | Cloud Foundations 02 | Organization Structure & Resource Deployment 03 | Authentication & Authorization 04 | Networking 05 | Secrets Management 06 | Logging 07 | Operating Model 08 | FinOps - Billing 09 | Q&A 4 Why Do You Need Cloud Foundations? Landing zones enable management of standardised GCP projects, which in turn control your Virtual Private Clouds (VPCs) and consumption of GCP cloud services. ● Prevents Project Sprawl: Project provision can be managed as cloud engagement increases ● Minimises Engineering Overhead: Eliminating manual changes reduces complexity and enables scalability and consistency ● Enables Scaling by Design: Management of services and infrastructure in public cloud is made simple by the use of a well-designed landing zone ● Accelerates Consumption of Cloud Services: Allows for GCP projects to be provisioned with a -
Designing Phelkstat: Big Data Analytics for System Event Logs
Designing PhelkStat: Big Data Analytics for System Event Logs Mohammed Salman∗, Brian Welch∗ and Joseph Tront∗ David Raymondy, Randy Marchanyy ∗Department of Electrical and Computer Engineering y IT Security Office Virginia Tech, Blacksburg Virginia Tech, Blacksburg Abstract—With wider adoption of micro-service based archi- tectures in cloud and distributed systems, logging and monitoring costs have become increasingly relevant topics of research. There are a large number of log analysis tools such as the ELK(ElasticSearch, Logstash and Kibana) stack, Apache Spark, Sumo Logic, and Loggly, among many others. These tools have been deployed to perform anomaly detection, diagnose threats, optimize performance, and troubleshoot systems. Due to the real- time and distributed nature of logging, there will always be a need to optimize the performance of these tools; this performance can be quantified in terms of compute, storage, and network utilization. As part of the Information Technology Security Lab at Virginia Tech, we have the unique ability to leverage production data from the university network for research and testing. We analyzed the workload variations from two production systems at Virginia Tech, finding that the maximum workload is about four times the average workload. Therefore, a static configuration can lead to an inefficient use of resources. To address this, we propose PhelkStat: a tool to evaluate the temporal and spatial attributes of system workloads, using clustering algorithms to categorize the current workload. Using PhelkStat, system parameters can be automatically tweaked based on the workload. This paper reviews publicly available system event log datasets from supercomputing clusters and presents a statistical analysis of these datasets. -
Leveraging Clustering and Natural Language Processing to Overcome Variety Issues in Log Management
Leveraging Clustering and Natural Language Processing to Overcome Variety Issues in Log Management Tobias Eljasik-Swoboda1 a, and Wilhelm Demuth2 1ONTEC AG, Ernst-Melchior-Gasse 24/DG, 1100 Vienna, Austria 2SCHOELLER NETWORK CONTROL GmbH, Ernst-Melchior-Gasse 24/DG, 1100 Vienna, Austria [email protected], [email protected] Keywords: Industrial Applications of AI, Intelligence and Cybersecurity, Machine Learning, Natural Language Processing, Trainer/Athlete Pattern, Log Analysis, Log Management, Event Normalization, Security Information and Event Management, Big Data Abstract: When introducing log management or Security Information and Event Management (SIEM) practices, organizations are frequently challenged by Gartner’s 3 Vs of Big Data: There is a large volume of data which is generated at a rapid velocity. These first two Vs can be effectively handled by current scale-out architectures. The third V is that of variety which affects log management efforts by the lack of a common mandatory format for log files. Essentially every component can log its events differently. The way it is logged can change with every software update. This paper describes the Log Analysis Machine Learner (LAMaLearner) system. It uses a blend of different Artificial Intelligence techniques to overcome variety issues and identify relevant events within log files. LAMaLearner is able to cluster events and generate human readable representations for all events within a cluster. A human being can annotate these clusters with specific labels. After these labels exist, LAMaLearner leverages machine learning based natural language processing techniques to label events even in changing log formats. Additionally, LAMaLearner is capable of identifying previously known named entities occurring anywhere within the logged event as well identifying frequently co-occurring variables in otherwise fixed log events. -
Softlayer Technologies, Inc
SOFTLAYER TECHNOLOGIES, INC. SOC 3 SYSTRUST FOR SERVICE ORGANIZATIONS REPORT SYSTEM DESCRIPTION OF THE PLATFORM SERVICES RELEVANT TO SECURITY AND AVAILABILITY TABLE OF CONTENTS REPORT OF INDEPENDENT ACCOUNTANTS .......................................................................................... 1 MANAGEMENT’S ASSERTION REGARDING THE EFFECTIVENESS OF ITS CONTROLS OVER THE PLATFORM SERVICES BASED ON THE AICPA TRUST PRINCIPLES AND CRITERIA FOR SECURITY AND AVAILABILITY ...................................................................................................................................... 2 DESCRIPTION OF SOFTLAYER TECHNOLOGIES, INC.’S PLATFORM SERVICES ............................... 3 BOUNDARIES OF PLATFORM SERVICES ................................................................................................ 5 REPORT OF INDEPENDENT ACCOUNTANTS To the Management of SoftLayer Technologies, Inc.: Scope We have examined management’s assertion that SoftLayer Technologies, Inc. (SoftLayer) during the period November 1, 2013, through October 31, 2014, maintained effective controls over the Platform Services (described in the attached system/service description) to provide reasonable assurance that: the system as defined, was protected against unauthorized access (both physical and logical); and the system as defined, was available for operation and use as committed or agreed; based on the criteria for security and availability set forth in the AICPA’s TSP Section 100, Trust Services Principles, Criteria, and Illustrations -
Rfp Template for Selecting a Micro-Segmentation Solution
RFP TEMPLATE FOR SELECTING A MICRO-SEGMENTATION SOLUTION This Request for Proposal template is comprised of common requirements and questions from buyers when selecting a solution for micro-segmentation. Use it to identify your solution criteria, clarify unknowns, and prioritize potential trade-offs. SECTION 1: COMPUTING ENVIRONMENT 1.01 Does the solution support workloads deployed on bare-metal (physical servers)? Are there any limitations? 1.02 Which OS platforms are supported (Windows, Linux, AIX, Solaris, etc.)? Are there any limitations? 1.03 Does the solution support workloads deployed as virtual machines on any virtualization platform or hypervisor? Which hypervisors are supported? Do you have feature parity among hypervisor support? Please explain any feature parity if it exists. 1.04 What public clouds are supported (AWS, EC2, S3, Azure, GCP)? Are there any limitations with each provider that the solution supports? Do you have feature parity among various providers? 1.05 What private cloud stacks are supported (IBM SoftLayer, Nutanix, etc.)? Are there any limitations? 1.06 Does the solution support workloads deployed as containers? Which container platforms are supported? Describe how the solution supports, visualizes, and secures containers. 1.07 Does the solution work on any network infrastructure without dependencies on hardware, such as VMware servers or Cisco switches, or requiring changes to the network? 1.08 Is the solution able to recognize and secure homegrown and legacy applications? If yes, how does it do this? SECTION 2: VISIBILITY 2.01 Does the solution provide an operations-level “big picture” view of locations with interactive drill-down capabilities for segmentation administration? 2.02 Does the solution provide an application-centric view of the connectivity and relationships of applications and their workload components? Note: This is an application dependency map, not a network map.