This document discusses the management of services across heterogeneous environments from monolithic applications to microservices to serverless functions. Effective management depends on the application architecture and infrastructure to satisfy non-functional requirements like quality of service. The DITAS platform allows developers to design data-intensive applications for deployment on cloud and edge environments using data and computation movement strategies to optimize data utility based on requirements.
Optimizing Monitorability of Multi-cloud ApplicationsMonica Vitali
This document proposes an approach to optimize the monitorability of applications deployed across multiple cloud providers. It aims to maximize the number of application metrics that can be monitored while minimizing costs, based on each cloud provider's monitoring capabilities. A matchmaking algorithm finds the best deployment by considering users' monitoring requirements, providers' offered metrics and costs, and estimating metrics that are not directly measurable. The approach was validated on a sample deployment and showed response times around 19 seconds for optimizing monitoring of 4 VMs with 7 metrics each across 7 cloud providers.
The document proposes a distributed monitoring system to manage energy efficiency and quality of service in cloud applications. It addresses the issues that monitoring data becomes too large to analyze centrally due to volume and velocity. The system distributes data collection and analysis across nodes to reduce network usage and improve scalability. A distributed algorithm learns relationships between monitored indicators using Bayesian networks, providing energy efficiency analysis and improvements in a way that grows linearly rather than exponentially with data volume.
Applications of cloud computing for power systemsObul Naidu
This document discusses applications of cloud computing for power systems. It begins by introducing cloud computing and describing types of cloud computing and cloud service models. It then discusses using cloud computing for energy monitoring systems with smart meters, cloud-based SCADA configurations for power grids, and the advantages of using cloud applications for smart grids versus without cloud. Some advantages of cloud computing for power systems include backup and restore of data, improved collaboration, low maintenance costs, and unlimited storage capacity. Potential disadvantages include reliance on internet connectivity and security issues. The document concludes that cloud computing provides supercomputing power for broad access and analysis of power system data across organizations.
This document discusses applications of cloud computing for power systems. It begins with an introduction to cloud computing and its potential benefits for power systems. It then describes different types of cloud computing models including public, private, and hybrid clouds. It also discusses the three main cloud service models: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). The document provides examples of cloud-based power system applications such as energy monitoring systems, SCADA configurations, and comparisons of smart grids with and without cloud integration. It concludes that cloud computing can help power systems by providing scalable computing resources and facilitating data processing and system monitoring.
This document discusses the management of services across heterogeneous environments from monolithic applications to microservices to serverless functions. Effective management depends on the application architecture and infrastructure to satisfy non-functional requirements like quality of service. The DITAS platform allows developers to design data-intensive applications for deployment on cloud and edge environments using data and computation movement strategies to optimize data utility based on requirements.
Optimizing Monitorability of Multi-cloud ApplicationsMonica Vitali
This document proposes an approach to optimize the monitorability of applications deployed across multiple cloud providers. It aims to maximize the number of application metrics that can be monitored while minimizing costs, based on each cloud provider's monitoring capabilities. A matchmaking algorithm finds the best deployment by considering users' monitoring requirements, providers' offered metrics and costs, and estimating metrics that are not directly measurable. The approach was validated on a sample deployment and showed response times around 19 seconds for optimizing monitoring of 4 VMs with 7 metrics each across 7 cloud providers.
The document proposes a distributed monitoring system to manage energy efficiency and quality of service in cloud applications. It addresses the issues that monitoring data becomes too large to analyze centrally due to volume and velocity. The system distributes data collection and analysis across nodes to reduce network usage and improve scalability. A distributed algorithm learns relationships between monitored indicators using Bayesian networks, providing energy efficiency analysis and improvements in a way that grows linearly rather than exponentially with data volume.
Applications of cloud computing for power systemsObul Naidu
This document discusses applications of cloud computing for power systems. It begins by introducing cloud computing and describing types of cloud computing and cloud service models. It then discusses using cloud computing for energy monitoring systems with smart meters, cloud-based SCADA configurations for power grids, and the advantages of using cloud applications for smart grids versus without cloud. Some advantages of cloud computing for power systems include backup and restore of data, improved collaboration, low maintenance costs, and unlimited storage capacity. Potential disadvantages include reliance on internet connectivity and security issues. The document concludes that cloud computing provides supercomputing power for broad access and analysis of power system data across organizations.
This document discusses applications of cloud computing for power systems. It begins with an introduction to cloud computing and its potential benefits for power systems. It then describes different types of cloud computing models including public, private, and hybrid clouds. It also discusses the three main cloud service models: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). The document provides examples of cloud-based power system applications such as energy monitoring systems, SCADA configurations, and comparisons of smart grids with and without cloud integration. It concludes that cloud computing can help power systems by providing scalable computing resources and facilitating data processing and system monitoring.
Applications of big data in electrical energy systemObul Naidu
Big data technology is used to analyze large and complex datasets from sources in electrical power systems. This data comes from phasor measurement units, smart meters, and other intelligent electronic devices. The data has characteristics of volume, variety, and velocity. It is analyzed to extract useful information for applications like faster decision making, fraud and fault detection, load forecasting, and power generation management. Some disadvantages include potential hacking or cybersecurity issues. Overall, big data analysis provides benefits for managing the smart grid but also faces security challenges.
Application-Aware Big Data Deduplication in Cloud EnvironmentSafayet Hossain
The document proposes AppDedupe, a distributed deduplication framework for cloud environments that exploits application awareness, data similarity, and locality. AppDedupe uses a two-tiered routing scheme with application-aware routing at the director level and similarity-aware routing at the client level. It builds application-aware similarity indices with super-chunk fingerprints to speed up intra-node deduplication efficiently. Evaluation results show that AppDedupe consistently outperforms state-of-the-art schemes in deduplication efficiency and achieving high global deduplication effectiveness.
Thesis Presentation on Energy Efficiency Improvement in Data CentersMonica Vitali
The document proposes a methodology to assess and improve energy efficiency in data centers through self-adaptation. It involves (1) defining goals/indicators to measure performance and energy efficiency, (2) learning relationships between goals using Bayesian networks, and (3) learning how actions impact goals to select optimal actions. A simulation is used to test the approach under different loads and configurations. The goal is to maximize efficiency while respecting service level agreements.
Grid computing involves distributing computing resources across a network to tackle large problems. The Worldwide LHC Computing Grid (WLCG) was established to support the Large Hadron Collider (LHC) experiment, which produces around 15 petabytes of data annually. The WLCG uses a four-tiered model, with raw data stored at Tier-0 (CERN), copies distributed to Tier-1 data centers, computational resources provided by Tier-2 centers, and Tier-3 facilities providing additional analysis capabilities. This distributed model has proven effective in supporting the first year of LHC data collection and analysis through globally shared computing resources.
What began as tiny baby steps to bridge gaps of human limitations like memory, cognitive fluctuations has today turned into a virtual revolution where an invisible world of 0’s and 1’s is home to the most important bytes and pieces of our lives. Replacing the need for local servers, cloud computing emerged as the matrix within which multiple remote servers coordinated together to store, manage and administer data. The two other baits in the cloud computing movement is the live/real-time information updating and light-speed transfer of data. Initially, it was the center for large-scale corporations. In the last few years, small-scale and medium-sized organizations have also joined the army of cloud computing era. The analytics of the cloud computing culture swing between the spectrums ranging from the sustainability of cloud computing to the increased carbon emissions due to servers.
Read the full blog here:
http://suyati.com/culture-of-cloud-computing-a-green-move-or-eco-death/
Or reach us at: jghosh@suyati.com
This document summarizes international collaboration on developing and deploying smart power grids. It discusses smart grid research areas like applications, infrastructure, communication systems, and security. It also outlines EPRI's smart grid demonstrations that deploy new technologies like distributed energy resources, storage, demand response, and renewable generation across multiple sites. The demonstrations aim to integrate these resources at different levels and test interoperability. Coordination efforts include use case sharing, cost-benefit analysis frameworks, and advisory groups for smart grid projects. Contact information is provided for the director of EPRI's smart grid research.
International Journal on Cloud Computing: Services and Architecture (IJCCSA)ijccsa
Cloud computing helps enterprises transform business and technology. Companies have begun to look for solutions that would help reduce their infrastructures costs and improve profitability. Cloud computing is becoming a foundation for benefits well beyond IT cost savings. Yet, many business leaders are concerned about cloud security, privacy, availability, and data protection. To discuss and address these issues, we invite researches who focus on cloud computing to shed more light on this emerging field.
Grid computing involves applying the computing resources of many networked computers to a single large problem simultaneously. It allows for resource sharing and coordinated problem solving across dynamic virtual organizations. Idle systems on a network and their wasted CPU cycles can be united into a single large virtual system for efficient resource sharing at runtime through grid computing techniques. The document provides an example of a local area network of 20 systems where 10 are idle and 5 use low CPU, and how grid computing could efficiently utilize their wasted CPU cycles. It also outlines the major business areas that benefit from grid computing like life sciences, financial services, education, and engineering.
Applications of big data in electrical energy system documentObul Naidu
This document provides an overview of applications of big data in electrical energy systems. It discusses big data technology, power systems components, characteristics of big data such as volume, velocity, variety and veracity. It describes analyzing big data and various sources of data in power systems from intelligent electronic devices like smart meters, phasor measurement units and SCADA systems. It also discusses the role of big data in power systems and some applications like predictive maintenance, fault detection and renewable energy forecasting.
An Efficient Cluster-Tree Based Data Collection Scheme for Large Mobile Wirel...Nexgen Technology
CIDT is a cluster-based data collection scheme for large mobile wireless sensor networks. It constructs a data collection tree based on cluster head locations to minimize energy usage, end-to-end delay, and traffic for cluster heads. The data collection nodes in the tree simply collect and deliver data packets to the sink. Simulation results show CIDT provides better quality of service than existing methods in terms of energy consumption, throughput, end-to-end delay, and network lifetime for mobile sensor networks.
International Journal on Cloud Computing: Services and Architecture (IJCCSA)ijccsa
Cloud computing helps enterprises transform business and technology. Companies have begun to look for solutions that would help reduce their infrastructures costs and improve profitability. Cloud computing is becoming a foundation for benefits well beyond IT cost savings. Yet, many business leaders are concerned about cloud security, privacy, availability, and data protection. To discuss and address these issues, we invite researches who focus on cloud computing to shed more light on this emerging field. This peer-reviewed open access Journal aims to bring together researchers and practitioners in all security aspects of cloud-centric and outsourced computing, including (but not limited to)
International Journal on Cloud Computing: Services and Architecture (IJCCSA)ijccsa
Cloud computing helps enterprises transform business and technology. Companies have begun to look for solutions that would help reduce their infrastructures costs and improve profitability. Cloud computing is becoming a foundation for benefits well beyond IT cost savings. Yet, many business leaders are concerned about cloud security, privacy, availability, and data protection. To discuss and address these issues, we invite researches who focus on cloud computing to shed more light on this emerging field. This peer-reviewed open access Journal aims to bring together researchers and practitioners in all security aspects of cloud-centric and outsourced computing, including (but not limited to):
Fog computing may help to save energy in cloud computingieeepondy
Fog computing may help to save energy in cloud computing
+91-9994232214,7806844441, ieeeprojectchennai@gmail.com,
www.projectsieee.com, www.ieee-projects-chennai.com
IEEE PROJECTS 2016-2017
-----------------------------------
Contact:+91-9994232214,+91-7806844441
Email: ieeeprojectchennai@gmail.com
2nd International Conference on Cloud, Big Data and Web Services (CBW 2021)ijwscjournal
2nd International Conference on Cloud, Big Data and Web Services (CBW 2021) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Cloud, Big Data and Web services. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of Cloud, Big Data and web services.
Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.
International Journal on Cloud Computing: Services and Architecture (IJCCSA)ijccsa
Cloud computing helps enterprises transform business and technology. Companies have begun to look for solutions that would help reduce their infrastructures costs and improve profitability. Cloud computing is becoming a foundation for benefits well beyond IT cost savings. Yet, many business leaders are concerned about cloud security, privacy, availability, and data protection. To discuss and address these issues, we invite researches who focus on cloud computing to shed more light on this emerging field. This peer-reviewed open access Journal aims to bring together researchers and practitioners in all security aspects of cloud-centric and outsourced computing
International Journal on Cloud Computing: Services and Architecture (IJCCSA)ijccsa
Cloud computing helps enterprises transform business and technology. Companies have begun to look for solutions that would help reduce their infrastructures costs and improve profitability. Cloud computing is becoming a foundation for benefits well beyond IT cost savings. Yet, many business leaders are concerned about cloud security, privacy, availability, and data protection. To discuss and address these issues, we invite researches who focus on cloud computing to shed more light on this emerging field.
2nd International Conference on Cloud, Big Data and Web Services (CBW 2021)albert ca
2nd International Conference on Cloud, Big Data and Web Services (CBW 2021) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Cloud, Big Data and Web services. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of Cloud, Big Data and web services.
This document provides an overview of distributed computing paradigms such as cloud computing, jungle computing, and fog computing. It defines distributed computing as utilizing multiple autonomous computers located across different areas to solve large problems. Cloud computing is described as internet-based computing using shared online resources and data storage. Jungle computing combines distributed systems for high performance, while fog computing extends cloud computing to network edges for low latency applications. The document discusses characteristics, architectures, advantages and disadvantages of these paradigms.
Grid and Cloud Computing Lecture-2a.pptxDrAdeelAkram2
The document discusses grid architecture and tools. It covers the hourglass model of grid architecture, which focuses on core services to enable diverse solutions. It also discusses the layered grid architecture with four layers - fabric, connectivity, collective, and application. Simulation tools for modeling grid environments like GridSim are presented. The document then discusses clouds and defines cloud computing. Key aspects of clouds like scalability, virtualization, and on-demand services are covered. It compares clouds to grids and other paradigms. Finally, it introduces service-oriented architecture and defines the characteristics of services.
Applications of big data in electrical energy systemObul Naidu
Big data technology is used to analyze large and complex datasets from sources in electrical power systems. This data comes from phasor measurement units, smart meters, and other intelligent electronic devices. The data has characteristics of volume, variety, and velocity. It is analyzed to extract useful information for applications like faster decision making, fraud and fault detection, load forecasting, and power generation management. Some disadvantages include potential hacking or cybersecurity issues. Overall, big data analysis provides benefits for managing the smart grid but also faces security challenges.
Application-Aware Big Data Deduplication in Cloud EnvironmentSafayet Hossain
The document proposes AppDedupe, a distributed deduplication framework for cloud environments that exploits application awareness, data similarity, and locality. AppDedupe uses a two-tiered routing scheme with application-aware routing at the director level and similarity-aware routing at the client level. It builds application-aware similarity indices with super-chunk fingerprints to speed up intra-node deduplication efficiently. Evaluation results show that AppDedupe consistently outperforms state-of-the-art schemes in deduplication efficiency and achieving high global deduplication effectiveness.
Thesis Presentation on Energy Efficiency Improvement in Data CentersMonica Vitali
The document proposes a methodology to assess and improve energy efficiency in data centers through self-adaptation. It involves (1) defining goals/indicators to measure performance and energy efficiency, (2) learning relationships between goals using Bayesian networks, and (3) learning how actions impact goals to select optimal actions. A simulation is used to test the approach under different loads and configurations. The goal is to maximize efficiency while respecting service level agreements.
Grid computing involves distributing computing resources across a network to tackle large problems. The Worldwide LHC Computing Grid (WLCG) was established to support the Large Hadron Collider (LHC) experiment, which produces around 15 petabytes of data annually. The WLCG uses a four-tiered model, with raw data stored at Tier-0 (CERN), copies distributed to Tier-1 data centers, computational resources provided by Tier-2 centers, and Tier-3 facilities providing additional analysis capabilities. This distributed model has proven effective in supporting the first year of LHC data collection and analysis through globally shared computing resources.
What began as tiny baby steps to bridge gaps of human limitations like memory, cognitive fluctuations has today turned into a virtual revolution where an invisible world of 0’s and 1’s is home to the most important bytes and pieces of our lives. Replacing the need for local servers, cloud computing emerged as the matrix within which multiple remote servers coordinated together to store, manage and administer data. The two other baits in the cloud computing movement is the live/real-time information updating and light-speed transfer of data. Initially, it was the center for large-scale corporations. In the last few years, small-scale and medium-sized organizations have also joined the army of cloud computing era. The analytics of the cloud computing culture swing between the spectrums ranging from the sustainability of cloud computing to the increased carbon emissions due to servers.
Read the full blog here:
http://suyati.com/culture-of-cloud-computing-a-green-move-or-eco-death/
Or reach us at: jghosh@suyati.com
This document summarizes international collaboration on developing and deploying smart power grids. It discusses smart grid research areas like applications, infrastructure, communication systems, and security. It also outlines EPRI's smart grid demonstrations that deploy new technologies like distributed energy resources, storage, demand response, and renewable generation across multiple sites. The demonstrations aim to integrate these resources at different levels and test interoperability. Coordination efforts include use case sharing, cost-benefit analysis frameworks, and advisory groups for smart grid projects. Contact information is provided for the director of EPRI's smart grid research.
International Journal on Cloud Computing: Services and Architecture (IJCCSA)ijccsa
Cloud computing helps enterprises transform business and technology. Companies have begun to look for solutions that would help reduce their infrastructures costs and improve profitability. Cloud computing is becoming a foundation for benefits well beyond IT cost savings. Yet, many business leaders are concerned about cloud security, privacy, availability, and data protection. To discuss and address these issues, we invite researches who focus on cloud computing to shed more light on this emerging field.
Grid computing involves applying the computing resources of many networked computers to a single large problem simultaneously. It allows for resource sharing and coordinated problem solving across dynamic virtual organizations. Idle systems on a network and their wasted CPU cycles can be united into a single large virtual system for efficient resource sharing at runtime through grid computing techniques. The document provides an example of a local area network of 20 systems where 10 are idle and 5 use low CPU, and how grid computing could efficiently utilize their wasted CPU cycles. It also outlines the major business areas that benefit from grid computing like life sciences, financial services, education, and engineering.
Applications of big data in electrical energy system documentObul Naidu
This document provides an overview of applications of big data in electrical energy systems. It discusses big data technology, power systems components, characteristics of big data such as volume, velocity, variety and veracity. It describes analyzing big data and various sources of data in power systems from intelligent electronic devices like smart meters, phasor measurement units and SCADA systems. It also discusses the role of big data in power systems and some applications like predictive maintenance, fault detection and renewable energy forecasting.
An Efficient Cluster-Tree Based Data Collection Scheme for Large Mobile Wirel...Nexgen Technology
CIDT is a cluster-based data collection scheme for large mobile wireless sensor networks. It constructs a data collection tree based on cluster head locations to minimize energy usage, end-to-end delay, and traffic for cluster heads. The data collection nodes in the tree simply collect and deliver data packets to the sink. Simulation results show CIDT provides better quality of service than existing methods in terms of energy consumption, throughput, end-to-end delay, and network lifetime for mobile sensor networks.
International Journal on Cloud Computing: Services and Architecture (IJCCSA)ijccsa
Cloud computing helps enterprises transform business and technology. Companies have begun to look for solutions that would help reduce their infrastructures costs and improve profitability. Cloud computing is becoming a foundation for benefits well beyond IT cost savings. Yet, many business leaders are concerned about cloud security, privacy, availability, and data protection. To discuss and address these issues, we invite researches who focus on cloud computing to shed more light on this emerging field. This peer-reviewed open access Journal aims to bring together researchers and practitioners in all security aspects of cloud-centric and outsourced computing, including (but not limited to)
International Journal on Cloud Computing: Services and Architecture (IJCCSA)ijccsa
Cloud computing helps enterprises transform business and technology. Companies have begun to look for solutions that would help reduce their infrastructures costs and improve profitability. Cloud computing is becoming a foundation for benefits well beyond IT cost savings. Yet, many business leaders are concerned about cloud security, privacy, availability, and data protection. To discuss and address these issues, we invite researches who focus on cloud computing to shed more light on this emerging field. This peer-reviewed open access Journal aims to bring together researchers and practitioners in all security aspects of cloud-centric and outsourced computing, including (but not limited to):
Fog computing may help to save energy in cloud computingieeepondy
Fog computing may help to save energy in cloud computing
+91-9994232214,7806844441, ieeeprojectchennai@gmail.com,
www.projectsieee.com, www.ieee-projects-chennai.com
IEEE PROJECTS 2016-2017
-----------------------------------
Contact:+91-9994232214,+91-7806844441
Email: ieeeprojectchennai@gmail.com
2nd International Conference on Cloud, Big Data and Web Services (CBW 2021)ijwscjournal
2nd International Conference on Cloud, Big Data and Web Services (CBW 2021) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Cloud, Big Data and Web services. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of Cloud, Big Data and web services.
Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.
International Journal on Cloud Computing: Services and Architecture (IJCCSA)ijccsa
Cloud computing helps enterprises transform business and technology. Companies have begun to look for solutions that would help reduce their infrastructures costs and improve profitability. Cloud computing is becoming a foundation for benefits well beyond IT cost savings. Yet, many business leaders are concerned about cloud security, privacy, availability, and data protection. To discuss and address these issues, we invite researches who focus on cloud computing to shed more light on this emerging field. This peer-reviewed open access Journal aims to bring together researchers and practitioners in all security aspects of cloud-centric and outsourced computing
International Journal on Cloud Computing: Services and Architecture (IJCCSA)ijccsa
Cloud computing helps enterprises transform business and technology. Companies have begun to look for solutions that would help reduce their infrastructures costs and improve profitability. Cloud computing is becoming a foundation for benefits well beyond IT cost savings. Yet, many business leaders are concerned about cloud security, privacy, availability, and data protection. To discuss and address these issues, we invite researches who focus on cloud computing to shed more light on this emerging field.
2nd International Conference on Cloud, Big Data and Web Services (CBW 2021)albert ca
2nd International Conference on Cloud, Big Data and Web Services (CBW 2021) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Cloud, Big Data and Web services. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of Cloud, Big Data and web services.
This document provides an overview of distributed computing paradigms such as cloud computing, jungle computing, and fog computing. It defines distributed computing as utilizing multiple autonomous computers located across different areas to solve large problems. Cloud computing is described as internet-based computing using shared online resources and data storage. Jungle computing combines distributed systems for high performance, while fog computing extends cloud computing to network edges for low latency applications. The document discusses characteristics, architectures, advantages and disadvantages of these paradigms.
Grid and Cloud Computing Lecture-2a.pptxDrAdeelAkram2
The document discusses grid architecture and tools. It covers the hourglass model of grid architecture, which focuses on core services to enable diverse solutions. It also discusses the layered grid architecture with four layers - fabric, connectivity, collective, and application. Simulation tools for modeling grid environments like GridSim are presented. The document then discusses clouds and defines cloud computing. Key aspects of clouds like scalability, virtualization, and on-demand services are covered. It compares clouds to grids and other paradigms. Finally, it introduces service-oriented architecture and defines the characteristics of services.
An Efficient Cloud Scheduling Algorithm for the Conservation of Energy throug...IJECEIAES
Method of broadcasting is the well known operation that is used for providing support to different computing protocols in cloud computing. Attaining energy efficiency is one of the prominent challenges, that is quite significant in the scheduling process that is used in cloud computing as, there are fixed limits that have to be met by the system. In this research paper, we are particularly focusing on the cloud server maintenance and scheduling process and to do so, we are using the interactive broadcasting energy efficient computing technique along with the cloud computing server. Additionally, the remote host machines used for cloud services are dissipating more power and with that they are consuming more and more energy. The effect of the power consumption is one of the main factors for determining the cost of the computing resources. With the idea of using the avoidance technology for assigning the data center resources that dynamically depend on the application demands and supports the cloud computing with the optimization of the servers in use.
Inroduction to grid computing by gargi shankar vermagargishankar1981
Grid computing allows for sharing and coordination of distributed computer resources to address large-scale computation problems. It enables dynamic, scalable, and inexpensive access to computing power by connecting computers and other resources together with open standards. Key aspects of grid computing include dependable, consistent, pervasive, and inexpensive access to high-end computational capabilities through coordination of distributed and often heterogeneous resources not subject to centralized control.
IEEE Projects 2013 For ME Cse Seabirds ( Trichy, Thanjavur, Karur, Perambalur )SBGC
ieee projects 2013 for me cse trichy, ieee projects 2013 for me cse Karur, ieee projects 2013 for me cse chennai, ieee projects 2013 for me cse, ieee projects, ieee projects for cse, ieee projects 2013, ieee projects 2013 for me cse Thanjavur, ieee projects 2013 for me cse Perambalur,
This document provides information about IEEE projects provided by SEABIRDS. It lists the domains and technologies that projects are available in, including Java, J2ME, J2EE, .NET, MATLAB, and NS2. It describes the two categories of project assistance provided - for students with their own project ideas or selecting from their list. It lists the types of students they provide projects for, including various engineering and technology degrees as well as business and management degrees. It also lists the deliverables and support provided for projects.
Ieee projects-2013-2014-title-list-for-me-be-mphil-final-year-studentsPruthivi Rajan
This document provides information about IEEE projects provided by SEABIRDS. It lists the domains and technologies that projects are available in, including Java, J2ME, J2EE, .NET, MATLAB, and NS2. It describes the two categories of project assistance provided - for students with their own project ideas or selecting from their project list. Details are provided about the project deliverables and support provided. A list is then given of the engineering and technology degrees and programs that projects are available for.
GaruaGeo: Global Scale Data Aggregation in Hybrid Edge and Cloud Computing En...Otávio Carvalho
Research work published on the 9th International Conference on Cloud Computing and Services Science (CLOSER 2019) held at Heraklion, Crete.
The combination of Edge Computing devices and Cloud Computing resources brings the best of both worlds: Data aggregation closer to the source and scalable resources to grow the network on demand. However, the ability to leverage each time more powerful edge nodes to decentralize data processing and aggregation is still a significant challenge for both industry and academia. In this work, we extend the Garua platform to analyze the impact of a model for data aggregation in a global scale smart grid application dataset. The platform is extended to support global data aggregators that are placed nearly to the Edge nodes where data is being collected. This way, it is possible to aggregate data not only at the edge of the network but also pre-process data at nearby geographic areas, before sending data to be aggregated globally by global centralization nodes. The results of this work show that the implemented testbed application, through the usage of edge node aggregation, data aggregators geographically distributed and messaging windows, can achieve collection rates above 400 million measurements per second.
Parallel and Distributed System IEEE 2015 ProjectsVijay Karan
List of Parallel and Distributed System IEEE 2015 Projects. It Contains the IEEE Projects in the Domain Parallel and Distributed System for the year 2015
A Survey: Hybrid Job-Driven Meta Data Scheduling for Data storage with Intern...dbpublications
Cloud computing is a promising computing model that enables convenient and on demand network access to a shared pool of configurable computing resources. The first offered cloud service is moving data into the cloud: data owners let cloud service providers host their data on cloud servers and data consumers can access the data from the cloud servers. This new paradigm of data storage service also introduces new security challenges, because data owners and data servers have different identities and different business interests with map and reduce tasks in different jobs. Therefore, an independent auditing service is required to make sure that the data is correctly hosted in the Cloud. The goal is to improve data locality for both map tasks and reduce tasks, avoid job starvation, and improve job execution performance. Two variations are further introduced to separately achieve a better map-data locality and a faster task assignment. We conduct extensive experiments to evaluate and compare the two variations with current scheduling algorithms. The results show that the two variations outperform the other tested algorithms in terms of map-data locality, reduce-data locality, and network overhead without incurring significant overhead. In addition, the two variations are separately suitable for different Map Reduce workload scenarios and provide the best job performance among all tested algorithms in cloud computing data storage.
M.Phil Computer Science Parallel and Distributed System ProjectsVijay Karan
List of Parallel and Distributed System IEEE 2006 Projects. It Contains the IEEE Projects in the Domain Parallel and Distributed System for M.Phil Computer Science students.
M phil-computer-science-parallel-and-distributed-system-projectsVijay Karan
List of Parallel and Distributed System IEEE 2006 Projects. It Contains the IEEE Projects in the Domain Parallel and Distributed System for M.Phil Computer Science students.
Efficient architectural framework of cloud computing Souvik Pal
This document discusses an efficient architectural framework for cloud computing. It begins by providing background on cloud computing and discusses challenges such as security, privacy, and reliability. It then proposes a new architectural framework that separates infrastructure as a service (IaaS) into three sub-modules: IaaS itself, a hypervisor monitoring environment (HME), and resources as a service (RaaS). The HME acts as middleware between IaaS and physical resources, using a hypervisor to allocate resources from a pool managed by RaaS. This proposed framework is intended to improve performance and access speed for cloud computing.
This document provides 6 IEEE project summaries in the domain of Java and cloud computing/data mining. The summaries are:
1. A decentralized access control scheme for secure cloud data storage that supports anonymous authentication.
2. A performance analysis framework for distributed file systems that qualitatively and quantitatively evaluates performance.
3. Approaches to guarantee trustworthy transactions on cloud servers by enforcing policy consistency constraints.
4. A scalable MapReduce approach for anonymizing large datasets to satisfy privacy requirements like k-anonymity.
5. A resource allocation scheme for a self-organizing cloud that achieves maximized utilization and optimal execution efficiency.
6. An attribute-based encryption framework for flexible
Agent based Aggregation of Cloud Services- A Research Agendaidescitation
-Cloud computing has come to the forefront as it
overcomes some of the issues in computing such as storage
space and processing power. It enables ubiquitous accessing
and processing of information without the need of excessive
computing facilities. In this work, we plan to brief some of the
issues in aggregating the cloud services, discovering futuristic
cloud service requests, develop a repository of the same and
propose an agent based Quality of Service (QoS) provisioning
system for cloud clients.
M.E Computer Science Parallel and Distributed System ProjectsVijay Karan
List of Parallel and Distributed System IEEE 2006 Projects. It Contains the IEEE Projects in the Domain Parallel and Distributed System for M.E Computer Science students.
Parallel and Distributed System IEEE 2015 ProjectsVijay Karan
List of Parallel and Distributed System IEEE 2015 Projects. It Contains the IEEE Projects in the Domain Parallel and Distributed System for the year 2015
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
DITAS@CCW2018
1. Customised Data as a Service for
multiple and conflicting data intensive
applications in cloud/edge
environments
13th Cloud Control Workshop, 13-15 June 2018
Discussion session on
Vitali Monica, Politecnico di Milano
monica.vitali@polimi.it
2. What???
Cloud computing is getting old but edge is still too young
We need to manage both data and computation between heterogeneous
resources
Heterogeneous customers = heterogeneous requirements
GOAL
Distributed management and allocation of computation and data with
heterogeneous resources
3. Some definitions
Edge Computing
The network layer encompassing the end devices and their users, to provide, for
example, local computing capability on a sensor, metering or some other devices that
are network-accessible.
Fog Computing
A layered model for enabling ubiquitous access to a shared continuum of scalable
computing resources. The model facilitates the deployment of distributed,
latency-aware applications and services
REF: Fog Computing Conceptual Model, Recommendations of the National Institute of Standards and
Technology https://doi.org/10.6028/NIST.SP.500-325
7. Challenges and research directions
● Do existing frameworks support us? (e.g., kubernetes) - INFRASTRUCTURE
○ Kubernetes is at the moment the more complete framework. It has to be fed with
knowledge of capabilities of the resources. Rewrite kubernetes scheduler to better
manage heterogeneity. What edge devices can really do? Virtual kubelets
● Flexible but shared model to design customers requirements (e.g.,
kubernetes) - DESIGN
○ A guided process to define requirements for both QoS and Quality of Data. It is needed to
put constraints but it causes a limitation in the approach (and assumptions)
● Coupling monitoring data and user requirements’ satisfaction - DESIGN
○ Using the same process to define also capabilities of the data provider. Use a goal model
to express capabilities and requirements and perform matching between the two.
● Managing access to shared resources: centralized vs distributed decision
system - RUNTIME
○ Both approaches have good and bad aspects. A hybrid approach is probably better. To be
investigated how to fit decision making on edge devices.