Auto-scaling Techniques for Elastic Data Stream ProcessingZbigniew Jerzak
An elastic data stream processing system is able to handle changes in workload by dynamically scaling out and
scaling in. This allows for handling of unexpected load spikes without the need for constant overprovisioning. One of the major challenges for an elastic system is to find the right point in time to scale in or to scale out. Finding such a point is difficult as it depends on constantly changing workload and system characteristics. In this paper we investigate the application of different auto-scaling techniques for solving this problem. Specifically: (1) we formulate basic requirements for an autoscaling technique used in an elastic data stream processing system, (2) we use the formulated requirements to select the best auto scaling techniques, and (3) we perform evaluation of the selected auto scaling techniques using the real world data. Our experiments show that the auto scaling techniques used in existing elastic data stream processing systems are performing worse than the strategies used in our work.
Energy proportionality is the key in order to reduce the Total Cost of Ownership (TCO) of Warehouse Scale Computer (WSC) systems, yet is difficult to achieve in practice. Typical WSC hardware usually does not meet this principle. Furthermore, critical services (e.g. billing) require all servers to remain up regardless the current traffic intensity. These two issues make existing power management technique ineffective at reducing energy use in a WSC dimension. We present Hybrid Performance-aware Power-capping Orchestrator (HyPPO), a distributed Observe Decide Act (ODA) control loop for optimizing energy proportionality of a distribute containerized infrastructures. This first version of HyPPO uses Kubernetes resource metrics (e.g. milli-cpus consumption) in order to dynamically adjust node power consumption, while respecting the Service Level Agreement (SLA) agreement defined by the containerized application owners.
CoolDC'16: Seeing into a Public Cloud: Monitoring the Massachusetts Open CloudAta Turk
Cloud users today have little visibility into the performance characteristics, power consumption, and utilization of cloud resources; and the cloud has little visibility into user application performance requirements and critical metrics such as response time and throughput. This paper outlines new efforts to reduce the information gap between the cloud users and the cloud. We first present a scalable monitoring platform to collect and retain rich information on a regional public cloud. Second, we present two motivating use cases that leverage the collected information: (1) Participation in emerging smart grid demand response programs in order to reduce datacenter energy costs and stabilize power grid demands, (2) budgeting available power to applications via peak shaving. This work is done in the context of the Massachusetts Open Cloud (MOC), a new public cloud project that has a central goal of enabling cloud research.
Auto-scaling Techniques for Elastic Data Stream ProcessingZbigniew Jerzak
An elastic data stream processing system is able to handle changes in workload by dynamically scaling out and
scaling in. This allows for handling of unexpected load spikes without the need for constant overprovisioning. One of the major challenges for an elastic system is to find the right point in time to scale in or to scale out. Finding such a point is difficult as it depends on constantly changing workload and system characteristics. In this paper we investigate the application of different auto-scaling techniques for solving this problem. Specifically: (1) we formulate basic requirements for an autoscaling technique used in an elastic data stream processing system, (2) we use the formulated requirements to select the best auto scaling techniques, and (3) we perform evaluation of the selected auto scaling techniques using the real world data. Our experiments show that the auto scaling techniques used in existing elastic data stream processing systems are performing worse than the strategies used in our work.
Energy proportionality is the key in order to reduce the Total Cost of Ownership (TCO) of Warehouse Scale Computer (WSC) systems, yet is difficult to achieve in practice. Typical WSC hardware usually does not meet this principle. Furthermore, critical services (e.g. billing) require all servers to remain up regardless the current traffic intensity. These two issues make existing power management technique ineffective at reducing energy use in a WSC dimension. We present Hybrid Performance-aware Power-capping Orchestrator (HyPPO), a distributed Observe Decide Act (ODA) control loop for optimizing energy proportionality of a distribute containerized infrastructures. This first version of HyPPO uses Kubernetes resource metrics (e.g. milli-cpus consumption) in order to dynamically adjust node power consumption, while respecting the Service Level Agreement (SLA) agreement defined by the containerized application owners.
CoolDC'16: Seeing into a Public Cloud: Monitoring the Massachusetts Open CloudAta Turk
Cloud users today have little visibility into the performance characteristics, power consumption, and utilization of cloud resources; and the cloud has little visibility into user application performance requirements and critical metrics such as response time and throughput. This paper outlines new efforts to reduce the information gap between the cloud users and the cloud. We first present a scalable monitoring platform to collect and retain rich information on a regional public cloud. Second, we present two motivating use cases that leverage the collected information: (1) Participation in emerging smart grid demand response programs in order to reduce datacenter energy costs and stabilize power grid demands, (2) budgeting available power to applications via peak shaving. This work is done in the context of the Massachusetts Open Cloud (MOC), a new public cloud project that has a central goal of enabling cloud research.
Intelligent Placement of Datacenters for Internet ServicesMaria Stylianou
Course: Execution Environments for Distributed Computing 6th Presentation (10-15min):
Intelligent Placement of Datacenters for Internet Services
Source: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5961695
dynamic resource allocation using virtual machines for cloud computing enviro...Kumar Goud
Abstract—Cloud computing allows business customers to scale up and down their resource usage based on needs., we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of “skewness” to measure the unevenness in the multidimensional resource utilization of a server. By minimizing imbalance, we will mix completely different of workloads nicely and improve the overall utilization of server resources. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this paper Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance.
Index Terms—Cloud computing, resource management, virtualization, green computing.
UnaCloud is an opportunistic based cloud infrastructure
(IaaS) that allows to access on-demand computing
capabilities using commodity desktops. Although UnaCloud
tried to maximize the use of idle resources to deploy virtual
machines on them, it does not use energy-efficient resource
allocation algorithms. In this paper, we design and implement
different energy-aware techniques to operate in an energyefficient
way and at the same time guarantee the performance
to the users. Performance tests with different algorithms and
scenarios using real trace workloads from UnaCloud, show how
different policies can change the energy consumption patterns
and reduce the energy consumption in opportunistic cloud
infrastructures. The results show that some algorithms can
reduce the energy-consumption power up to 30% over the
percentage earned by opportunistic environment.
Application of selective algorithm for effective resource provisioning in clo...ijccsa
Modern day continued demand for resource hungry services and applications in IT sector has led to
development of Cloud computing. Cloud computing environment involves high cost infrastructure on one
hand and need high scale computational resources on the other hand. These resources need to be
provisioned (allocation and scheduling) to the end users in most efficient manner so that the tremendous
capabilities of cloud are utilized effectively and efficiently. In this paper we discuss a selective algorithm
for allocation of cloud resources to end-users on-demand basis. This algorithm is based on min-min and
max-min algorithms. These are two conventional task scheduling algorithm. The selective algorithm uses
certain heuristics to select between the two algorithms so that overall makespan of tasks on the machines is
minimized. The tasks are scheduled on machines in either space shared or time shared manner. We
evaluate our provisioning heuristics using a cloud simulator, called CloudSim. We also compared our
approach to the statistics obtained when provisioning of resources was done in First-Cum-First-
Serve(FCFS) manner. The experimental results show that overall makespan of tasks on given set of VMs
minimizes significantly in different scenarios.
Detecting Lateral Movement with a Compute-Intense Graph KernelData Works MD
Cybersecurity Analytics on a D-Wave Quantum Computer
Effective cybersecurity analysis requires frequent exploration of graphs of many types and sizes, the computational cost of which can be overwhelming if not carefully chosen. After briefly introducing the D-Wave quantum computing system, we describe an analytic for finding “lateral movement” in an enterprise network, i.e., an intruder or insider threat hopping from system to system to gain access to more information. This analytic depends on maximum independent set, an NP-hard graph kernel whose computational cost grows exponentially with the size of the graph and so has not been widely used in cyber analysis. The growing strength of D-Wave’s quantum computers on such NP-hard problems will enable new analytics. We discuss practicalities of the current implementation and implications of this approach.
Steve Reinhardt has built hardware/software systems that deliver new levels of performance usable via conceptually simple interfaces, including Cray Research’s T3E distributed-memory systems, ISC’s Star-P parallel-MATLAB software, and YarcData/Cray’s Urika graph-analytic systems. He now leads D-Wave’s efforts working with customers to map early applications to D-Wave systems.
Empirical studies have revealed that a significant amount of energy is lost unnecessarily in the
network architectures, protocols, routers and various other network devices. Thus there is a need for techniques
to obtain green networking in the computer architecture which can lead to energy saving. Green networking is
an emerging phenomenon in the computer industry because of its economic and environmental benefits. Saving
energy leads to cost-cutting and lower emission of greenhouse gases which are apparently one of the major
threats to the environment. ’Greening’ as the name suggests is the process of constructing network architecture
in such a way so as to avoid unnecessary loss of power and energy due its various components and can be
implemented using various techniques out of which four are mentioned in this review paper, namely Adaptive
link rate (ALR), Dynamic Voltage and Frequency scaling(DVFS), Interface proxying and energy aware
applications and software.
Adaptive Digital Filter Design for Linear Noise Cancellation Using Neural Net...iosrjce
Noise is the most serious issue in the filters and adaptive filters are subjected to this unwanted
component. This paper deals with the problem of the adaptive noise and various adaptive algorithms functions
which when implemented practically shows that the noise is cancelled or removed by the neural network
approach using the exact random basis function. The adaptive filters are used to control the noise and it has a
linear input and output characteristics. This approach is done so as to get the minimum possible error so that to
obtain the error free desired signal. The designed filter will reduce this noise from measured signal by a
reference signal which is highly correlated with the noise signal. This approach gives excellent result for this
signal processing technique that removes or eliminates the linear noise from the different functions. The
simulation results are also mentioned so as to gives a vivid idea of reduced noise using neural networks
algorithm.
Quality of Service based Task Scheduling Algorithms in Cloud Computing IJECEIAES
In cloud computing resources are considered as services hence utilization of the resources in an efficient way is done by using task scheduling and load balancing. Quality of service is an important factor to measure the trustiness of the cloud. Using quality of service in task scheduling will address the problems of security in cloud computing. This paper studied quality of service based task scheduling algorithms and the parameters used for scheduling. By comparing the results the efficiency of the algorithm is measured and limitations are given. We can improve the efficiency of the quality of service based task scheduling algorithms by considering these factors arriving time of the task, time taken by the task to execute on the resource and the cost in use for the communication.
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTINGijdpsjournal
Cloud computing becomes an ideal computing paradigm for scientific and commercial applications. The
increased availability of the cloud models and allied developing models creates easier computing cloud
environment. Energy consumption and effective energy management are the two important challenges in
virtualized computing platforms. Energy consumption can be minimized by allocating computationally
intensive tasks to a resource at a suitable frequency. An optimal Dynamic Voltage and Frequency Scaling
(DVFS) based strategy of task allocation can minimize the overall consumption of energy and meet the
required QoS. However, they do not control the internal and external switching to server frequencies,
which causes the degradation of performance. In this paper, we propose the Real Time Adaptive EnergyScheduling (RTAES) algorithm by manipulating the reconfiguring proficiency of Cloud ComputingVirtualized Data Centers (CCVDCs) for computationally intensive applications. The RTAES algorithm
minimizes consumption of energy and time during computation, reconfiguration and communication. Our
proposed model confirms the effectiveness of its implementation, scalability, power consumption and
execution time with respect to other existing approaches.
Intelligent Placement of Datacenters for Internet ServicesMaria Stylianou
Course: Execution Environments for Distributed Computing 6th Presentation (10-15min):
Intelligent Placement of Datacenters for Internet Services
Source: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5961695
dynamic resource allocation using virtual machines for cloud computing enviro...Kumar Goud
Abstract—Cloud computing allows business customers to scale up and down their resource usage based on needs., we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of “skewness” to measure the unevenness in the multidimensional resource utilization of a server. By minimizing imbalance, we will mix completely different of workloads nicely and improve the overall utilization of server resources. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this paper Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance.
Index Terms—Cloud computing, resource management, virtualization, green computing.
UnaCloud is an opportunistic based cloud infrastructure
(IaaS) that allows to access on-demand computing
capabilities using commodity desktops. Although UnaCloud
tried to maximize the use of idle resources to deploy virtual
machines on them, it does not use energy-efficient resource
allocation algorithms. In this paper, we design and implement
different energy-aware techniques to operate in an energyefficient
way and at the same time guarantee the performance
to the users. Performance tests with different algorithms and
scenarios using real trace workloads from UnaCloud, show how
different policies can change the energy consumption patterns
and reduce the energy consumption in opportunistic cloud
infrastructures. The results show that some algorithms can
reduce the energy-consumption power up to 30% over the
percentage earned by opportunistic environment.
Application of selective algorithm for effective resource provisioning in clo...ijccsa
Modern day continued demand for resource hungry services and applications in IT sector has led to
development of Cloud computing. Cloud computing environment involves high cost infrastructure on one
hand and need high scale computational resources on the other hand. These resources need to be
provisioned (allocation and scheduling) to the end users in most efficient manner so that the tremendous
capabilities of cloud are utilized effectively and efficiently. In this paper we discuss a selective algorithm
for allocation of cloud resources to end-users on-demand basis. This algorithm is based on min-min and
max-min algorithms. These are two conventional task scheduling algorithm. The selective algorithm uses
certain heuristics to select between the two algorithms so that overall makespan of tasks on the machines is
minimized. The tasks are scheduled on machines in either space shared or time shared manner. We
evaluate our provisioning heuristics using a cloud simulator, called CloudSim. We also compared our
approach to the statistics obtained when provisioning of resources was done in First-Cum-First-
Serve(FCFS) manner. The experimental results show that overall makespan of tasks on given set of VMs
minimizes significantly in different scenarios.
Detecting Lateral Movement with a Compute-Intense Graph KernelData Works MD
Cybersecurity Analytics on a D-Wave Quantum Computer
Effective cybersecurity analysis requires frequent exploration of graphs of many types and sizes, the computational cost of which can be overwhelming if not carefully chosen. After briefly introducing the D-Wave quantum computing system, we describe an analytic for finding “lateral movement” in an enterprise network, i.e., an intruder or insider threat hopping from system to system to gain access to more information. This analytic depends on maximum independent set, an NP-hard graph kernel whose computational cost grows exponentially with the size of the graph and so has not been widely used in cyber analysis. The growing strength of D-Wave’s quantum computers on such NP-hard problems will enable new analytics. We discuss practicalities of the current implementation and implications of this approach.
Steve Reinhardt has built hardware/software systems that deliver new levels of performance usable via conceptually simple interfaces, including Cray Research’s T3E distributed-memory systems, ISC’s Star-P parallel-MATLAB software, and YarcData/Cray’s Urika graph-analytic systems. He now leads D-Wave’s efforts working with customers to map early applications to D-Wave systems.
Empirical studies have revealed that a significant amount of energy is lost unnecessarily in the
network architectures, protocols, routers and various other network devices. Thus there is a need for techniques
to obtain green networking in the computer architecture which can lead to energy saving. Green networking is
an emerging phenomenon in the computer industry because of its economic and environmental benefits. Saving
energy leads to cost-cutting and lower emission of greenhouse gases which are apparently one of the major
threats to the environment. ’Greening’ as the name suggests is the process of constructing network architecture
in such a way so as to avoid unnecessary loss of power and energy due its various components and can be
implemented using various techniques out of which four are mentioned in this review paper, namely Adaptive
link rate (ALR), Dynamic Voltage and Frequency scaling(DVFS), Interface proxying and energy aware
applications and software.
Adaptive Digital Filter Design for Linear Noise Cancellation Using Neural Net...iosrjce
Noise is the most serious issue in the filters and adaptive filters are subjected to this unwanted
component. This paper deals with the problem of the adaptive noise and various adaptive algorithms functions
which when implemented practically shows that the noise is cancelled or removed by the neural network
approach using the exact random basis function. The adaptive filters are used to control the noise and it has a
linear input and output characteristics. This approach is done so as to get the minimum possible error so that to
obtain the error free desired signal. The designed filter will reduce this noise from measured signal by a
reference signal which is highly correlated with the noise signal. This approach gives excellent result for this
signal processing technique that removes or eliminates the linear noise from the different functions. The
simulation results are also mentioned so as to gives a vivid idea of reduced noise using neural networks
algorithm.
Quality of Service based Task Scheduling Algorithms in Cloud Computing IJECEIAES
In cloud computing resources are considered as services hence utilization of the resources in an efficient way is done by using task scheduling and load balancing. Quality of service is an important factor to measure the trustiness of the cloud. Using quality of service in task scheduling will address the problems of security in cloud computing. This paper studied quality of service based task scheduling algorithms and the parameters used for scheduling. By comparing the results the efficiency of the algorithm is measured and limitations are given. We can improve the efficiency of the quality of service based task scheduling algorithms by considering these factors arriving time of the task, time taken by the task to execute on the resource and the cost in use for the communication.
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTINGijdpsjournal
Cloud computing becomes an ideal computing paradigm for scientific and commercial applications. The
increased availability of the cloud models and allied developing models creates easier computing cloud
environment. Energy consumption and effective energy management are the two important challenges in
virtualized computing platforms. Energy consumption can be minimized by allocating computationally
intensive tasks to a resource at a suitable frequency. An optimal Dynamic Voltage and Frequency Scaling
(DVFS) based strategy of task allocation can minimize the overall consumption of energy and meet the
required QoS. However, they do not control the internal and external switching to server frequencies,
which causes the degradation of performance. In this paper, we propose the Real Time Adaptive EnergyScheduling (RTAES) algorithm by manipulating the reconfiguring proficiency of Cloud ComputingVirtualized Data Centers (CCVDCs) for computationally intensive applications. The RTAES algorithm
minimizes consumption of energy and time during computation, reconfiguration and communication. Our
proposed model confirms the effectiveness of its implementation, scalability, power consumption and
execution time with respect to other existing approaches.
Suomen Taidoliiton Dan-leirillä 2012 aiheena oli reaktiokyky. Tässä on osa lauantain luennon kalvoista ja kokoelma käsiajanotolla mitattuja tekniikoiden kestoja.
Architecting a Cloud-Scale Identity FabricArinto Murdopo
Original article can be found here:
http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5719572&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel5%2F2%2F5731551%2F05719572.pdf%3Farnumber%3D5719572
Arviointi ja palaute on Suomen Taidoliiton valmentaja- ja ohjaajakoulutusjärjestelmän II-tasolle kuuluva koulutus, jossa perehdytään arviointimenetelmiin ja palautteenannon psykologiaan teoriassa ja käytännössä.
The counting system for small animals in japaneseCheyanneStotlar
This is a power point for the counting system the Japanes use for counting small animals. In this case it is describing the counting system for small fish.
Slides for sharing session with PPI Stockholm. The topic is about Distributed Computing, covering what it is, why it is important in our daily life and how we can utilize it in Indonesia.
40 Powers of 10 - Simulating the Universe with the DiRAC HPC Facilityinside-BigData.com
In this deck from the Swiss HPC Conference, Mark Wilkinson presents: 40 Powers of 10 - Simulating the Universe with the DiRAC HPC Facility.
"DiRAC is the integrated supercomputing facility for theoretical modeling and HPC-based research in particle physics, and astrophysics, cosmology, and nuclear physics, all areas in which the UK is world-leading. DiRAC provides a variety of compute resources, matching machine architecture to the algorithm design and requirements of the research problems to be solved. As a single federated Facility, DiRAC allows more effective and efficient use of computing resources, supporting the delivery of the science programs across the STFC research communities. It provides a common training and consultation framework and, crucially, provides critical mass and a coordinating structure for both small- and large-scale cross-discipline science projects, the technical support needed to run and develop a distributed HPC service, and a pool of expertise to support knowledge transfer and industrial partnership projects. The on-going development and sharing of best-practice for the delivery of productive, national HPC services with DiRAC enables STFC researchers to produce world-leading science across the entire STFC science theory program."
Watch the video: https://wp.me/p3RLHQ-k94
Learn more: https://dirac.ac.uk/
and
http://hpcadvisorycouncil.com/events/2019/swiss-workshop/agenda.php
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Grid optical network service architecture for data intensive applicationsTal Lavian Ph.D.
Integrated SW System Provide the “Glue”
Dynamic optical network as a fundamental Grid service in data-intensive Grid application, to be scheduled, to be managed and coordinated to support collaborative operations
From Super-computer to Super-network
In the past, computer processors were the fastest part
peripheral bottlenecks
In the future optical networks will be the fastest part
Computer, processor, storage, visualization, and instrumentation - slower "peripherals”
eScience Cyber-infrastructure focuses on computation, storage, data, analysis, Work Flow.
The network is vital for better eScience
An Architecture for Data Intensive Service Enabled by Next Generation Optical...Tal Lavian Ph.D.
DWDM-RAM - An architecture for data intensive Grids enabled by next generation dynamic optical networks, incorporating new methods for lightpath provisioning.
DWDM-RAM: An architecture designed to meet the
networking challenges of extremely large scale Grid applications.
Traditional network infrastructure cannot meet these demands,
especially, requirements for intensive data flows
DWDM-RAM Components Include:
Data management services
Intelligent middleware
Dynamic lightpath provisioning
State-of-the-art photonic technologies
Wide-area photonic testbed implementation
This is the 2nd defense of my Ph.D. double degree.
More details - https://kkpradeeban.blogspot.com/2019/08/my-phd-defense-software-defined-systems.html
We demonstrated how at Criteo we have introduced on our Mesos clusters:
* network isolation between our containers
* a network bandwidth custom resource patching all our frameworks (marathon and aurora).
This talk has been presented at MesosCon18 in SF.
Network-aware Data Management for Large Scale Distributed Applications, IBM R...balmanme
IBM Research – Talk – June 24, 2015
Title:
Network-aware Data Management for Large Scale Distributed Applications
Abstract:
As current technology enables faster storage devices and larger interconnect bandwidth, there is a substantial need for novel system design and middleware architecture to address increasing latency, scalability, and throughput requirements. In this talk, I will outline network-aware data management and present solutions based on my past experience in large-scale data migration between remote repositories.
I will first describe my experience in the initial evaluation of 100Gbps network as a part of the Advance Network Initiative project. We needed intense fine-tuning in network, storage, and application layers, to take advantage of the higher network capacity. End-system bottlenecks and system performance play an important role especially in many-core platforms. I will introduce a special data movement prototype, successfully tested in one of the first 100Gbps demonstrations, in which applications map memory blocks for remote data, in contrast to the send/receive semantics. This prototype was used to stream climate data over wide-area for in-memory application processing and visualization.
Within this scope, I will introduce a flexible network reservation algorithm for on-demand bandwidth guaranteed virtual circuit services. Flexible reservations find best path in a time-dependent dynamic network topology to support predictable application performance. I will then present a data-scheduling model with advance provisioning, in which data movement operations are defined with earliest start and latest completion times.
I will conclude my talk with a very brief overview of my other related projects on performance engineering, hyper-converged virtual storage, and optimization in control and data path for virtualized environments.
Virtualization in 4-4 1-4 Data Center Network.Ankita Mahajan
4-4 1-4 delivers great performance guarantees in traditional (non-virtualized) setting, due to location based static IP address allocation to all network elements.
Download this ppt first and then open in powerpoint to view without merged figures and with animations.
Similar to Intelligent Placement of Datacenter for Internet Services (20)
Project presentation for High Availability in YARN project. We propose to use MySQL Cluster (NDB) to tackle High Availability issue in YARN. We also developed benchmark framework to investigate whether MySQL Cluster (NDB) is better than Apache's proposed storage (ZooKeeper and HDFS)
Full project report will be uploaded after I finish it.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Designing Great Products: The Power of Design and Leadership by Chief Designe...
Intelligent Placement of Datacenter for Internet Services
1. EEDC
34330
Execution Intelligent Placement of
Environments for Datacenter for Internet
Distributed Services
Computing
Master in Computer Architecture,
Networks and Systems - CANS
Homework number: 6
by
Arinto Murdopo – arinto@gmail.com
2. Problem Statement
where? dónde? di mana? oú? waar?
Data Center
dove? どこですか? πού? 在哪里?어디?
Response time, availability,
cost, environmental concerns
2
3. Proposed Solution
Framework
Produce tool to compare
Solve optimization efficiency and accuracy
Problem
Characterization
3
4. Framework
Efficiently select data center locations
Response Time
Minimize Cost
Consistency
Availability
4
5. Solve Optimization Problem
Problem formulation
Approaches:
• Simple Linear Programming (LP0)
• Pre-set Linear Programming (LP1)
• Brute force (Brute)
• Heuristic-based on LP (Heuristic)
• Simulated Annealing plus LP1 (SA+LP1)
• Optimzed SA + LP1 (OSA + LP1)
5
6. Placement Tool
Available Inputs:
MaxS
1/ratioServerUser
MAXLAT
MAXDELAY
MINAVAIL
area of interest
Granularity
existing data center
6
7. Placement Tool
Location-dependent data:
Network backbones: latency data from backbone ISP
Power plants, transmission lines, and CO2 emissions:
obtained from DOE
Electricity, land, water and temperature: obtained from DOE
as well
Missing data are obtained from neighboring location
7
8. Placement Tool
Datacenter characteristics:
Cooling : CRACs and Water Chillers for cooling
Connection: It costs $500k/mile of transmission
line, and $480k/mile of fiber. Amortization of 12
years
Building: Its costs depends of the maximum
power
Land: 6 K square feet per Megawatt
8
9. Placement Tool
Datacenter characteristics:
Water: 24K gallons of water per MW per day
Server: Each server costs $2000 (4 years
amortization), each interconnect switch costs
$20K (4 years amortization)
Staff: $0.05 per Watt per month. $100K per year
salary for 1K servers
9
14. Sample Output
Specifications: Results
1. 60 K servers Three locations :
2. Latency <= 60 ms 1. Seattle(A, 1789 servers)
3. Consistency Delay <= 85 ms 2. St. Louis (B, 22712 servers)
4. Minimum Availability = 5 nines 3. Oklahoma city(C, 5501 servers)
14
15. Evaluation of Chosen Approach
Based on this specification:
1. 60 K servers
2. Latency <= 60 ms
3. Consistency Delay <= 85 ms
4. Minimum Availability = 5 nines
15
21. Exploring Placement Tradeoff
Availability
It is usually cheaper to build networks out of less redundant datacenters
Tier II data centers are the best option
21
23. Exploring Placement Tradeoff
Green datacenters
Green network is less than $100k more expensive per
month than the cost-optimal network when the maximum
latency can be relatively high (> 70ms)
23
25. Conclusions
• Proposed and implemented optimization
framework for automatic data center
placement for Internet Services
• Characterized US regions
• Evaluated solutions based on the framework
25