Presentation of the Ph. D. dissertation SLA-Driven Cloud Computing Domain Representation and Management. This presentation explains a new methodology for the representation and management of Cloud services using SLA fragments. Cloud resources are described as independent SLA fragments, which are composed on the fly to create complete Cloud services.
An architecture for the management of Cloud services is also presented.
Cloudcompaas, an open source SLA-driven framework is introduced. Cloudcompaas implements the methodology and architecture presented earlier and enables the management of the complete lifecycle of Cloud services.
Finally a set of experiments to validate the utility and performance of the contributions is presented.
This document discusses load balancing in cloud computing. It begins by defining cloud computing and some of its key characteristics like broad network access, rapid elasticity, and pay-as-you-go pricing. It then discusses how load balancing can improve performance in distributed cloud environments by redistributing load, improving response times, and better utilizing resources. The document outlines different load balancing techniques like virtual machine migration and throttled load balancing using a load balancer, virtual machines, and a data center controller. It also proposes a trust and reliability based algorithm that prioritizes data centers for load balancing based on calculated trust values that consider factors like initialization time, machine performance, and fault rates.
Presentazione durante il Cloud Community Day del 22 luglio 2013 presso il Politecnico di Milano.
http://www.eurocloud.it/index.php/component/content/article/190-cloud-communities-day
A WebLogic Server cluster consists of multiple WebLogic Server instances running simultaneously and working together to provide increased scalability and reliability. A cluster appears to clients to be a single WebLogic Server instance. Server instances in a cluster can run on the same machine or different machines. Clusters provide high availability through application failover and scalability by adding additional server instances. Key elements of a cluster include load balancing of requests across server instances and replication of HTTP session and EJB states.
CloudStack is an open source cloud computing platform that allows users to build and manage virtualized cloud environments. It provides tools for provisioning virtual machines, managing networks and storage, and monitoring resource usage. CloudStack's architecture includes components like hypervisors, primary storage, secondary storage, clusters, zones, and a management server. It offers both an administrative web interface and APIs for management and integration.
This document summarizes strategies for scaling a Ruby on Rails application. It discusses starting with shared hosting and moving to dedicated servers, scaling the database horizontally using replication or clustering, scaling the web servers by adding more application servers behind a load balancer, implementing user clusters to shard user data, adding caching at various levels using solutions like Squid, Memcached, and fragment caching, and using elastic cloud architectures on services like Amazon EC2. The key steps are horizontal scaling of databases, vertical and horizontal scaling of application servers, implementing user sharding and caching to optimize performance, and using elastic cloud services for on-demand scaling.
AWS Study Group - Chapter 07 - Integrating Application Services [Solution Arc...QCloudMentor
This document provides an overview of several AWS application services including SQS, SNS, Cognito, API Gateway, and WebSockets. It describes how SQS uses queues to asynchronously and reliably deliver messages between distributed components. SNS is a pub/sub messaging service that decouples systems using an event-driven model. Cognito provides authentication, authorization, and user management for web and mobile apps. API Gateway acts as a facade and endpoint for RESTful APIs. WebSockets in AWS can enable real-time communication using services like IoT and AppSync.
This document discusses cloud architecture patterns and provides examples to address common problems in cloud applications. It begins with an overview of common problem areas such as availability, data consistency, scalability, security and resiliency. It then describes and provides code samples for several cloud design patterns, including the queue-based load leveling pattern to handle variable workloads, the retry pattern to address transient faults, and the static content hosting pattern to optimize storage of static resources.
This document discusses load balancing in cloud computing. It begins by defining cloud computing and some of its key characteristics like broad network access, rapid elasticity, and pay-as-you-go pricing. It then discusses how load balancing can improve performance in distributed cloud environments by redistributing load, improving response times, and better utilizing resources. The document outlines different load balancing techniques like virtual machine migration and throttled load balancing using a load balancer, virtual machines, and a data center controller. It also proposes a trust and reliability based algorithm that prioritizes data centers for load balancing based on calculated trust values that consider factors like initialization time, machine performance, and fault rates.
Presentazione durante il Cloud Community Day del 22 luglio 2013 presso il Politecnico di Milano.
http://www.eurocloud.it/index.php/component/content/article/190-cloud-communities-day
A WebLogic Server cluster consists of multiple WebLogic Server instances running simultaneously and working together to provide increased scalability and reliability. A cluster appears to clients to be a single WebLogic Server instance. Server instances in a cluster can run on the same machine or different machines. Clusters provide high availability through application failover and scalability by adding additional server instances. Key elements of a cluster include load balancing of requests across server instances and replication of HTTP session and EJB states.
CloudStack is an open source cloud computing platform that allows users to build and manage virtualized cloud environments. It provides tools for provisioning virtual machines, managing networks and storage, and monitoring resource usage. CloudStack's architecture includes components like hypervisors, primary storage, secondary storage, clusters, zones, and a management server. It offers both an administrative web interface and APIs for management and integration.
This document summarizes strategies for scaling a Ruby on Rails application. It discusses starting with shared hosting and moving to dedicated servers, scaling the database horizontally using replication or clustering, scaling the web servers by adding more application servers behind a load balancer, implementing user clusters to shard user data, adding caching at various levels using solutions like Squid, Memcached, and fragment caching, and using elastic cloud architectures on services like Amazon EC2. The key steps are horizontal scaling of databases, vertical and horizontal scaling of application servers, implementing user sharding and caching to optimize performance, and using elastic cloud services for on-demand scaling.
AWS Study Group - Chapter 07 - Integrating Application Services [Solution Arc...QCloudMentor
This document provides an overview of several AWS application services including SQS, SNS, Cognito, API Gateway, and WebSockets. It describes how SQS uses queues to asynchronously and reliably deliver messages between distributed components. SNS is a pub/sub messaging service that decouples systems using an event-driven model. Cognito provides authentication, authorization, and user management for web and mobile apps. API Gateway acts as a facade and endpoint for RESTful APIs. WebSockets in AWS can enable real-time communication using services like IoT and AppSync.
This document discusses cloud architecture patterns and provides examples to address common problems in cloud applications. It begins with an overview of common problem areas such as availability, data consistency, scalability, security and resiliency. It then describes and provides code samples for several cloud design patterns, including the queue-based load leveling pattern to handle variable workloads, the retry pattern to address transient faults, and the static content hosting pattern to optimize storage of static resources.
Enhancing minimal virtual machine migration in cloud environmenteSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Stateful streaming and the challenge of stateYoni Farin
The different challenges of working with state in a distributed streaming data pipeline and how we solve it with the 3S architecture and Kafka streams stores based on rocksDB
AWS Study Group - Chapter 10 - Matching Supply and Demand [Solution Architect...QCloudMentor
This chapter discusses how to match computing resource supply and demand on AWS. It covers Elastic Load Balancing (ELB) and its three types - classic, application, and network load balancers. It also discusses AWS Auto Scaling, which allows automatically scaling computing resources up or down based on demand. Key attributes of ELB like stateless/stateful, internet-facing/internal-facing, and cross-zone load balancing are explained.
The document discusses how WebLogic Server uses a single common thread pool that prioritizes and schedules work. The thread pool size adjusts automatically based on historical throughput statistics to maximize performance while reducing complexity compared to custom thread pools. Work is prioritized according to user-defined rules and run-time metrics like request processing time and rates.
Building Eventing Systems for Microservice Architecture Yaroslav Tkachenko
In Bench Accounting we heavily use various events as first class citizens: notifications, in-app TODO lists (and messaging solution in future) rely on the eventing framework we built. Recently we’ve migrated our old legacy eventing system to the new framework with a focus on microservices architecture. We’ve chosen event sourcing approach as well as tools like Akka, Camel, ActiveMQ, Slick and Postgres (JSONB).
In this presentation I would like to share high-level overview of the system, implementation details and challenges we’ve faced.
Content delivery networks (CDNs) facilitate content delivery to end users by using a centrally managed network of devices. The key components of building a CDN include content distribution, request routing, content delivery, and resource accounting. Content distribution involves placing content on delivery devices, request routing steers users to a close delivery node, content delivery handles protocol processing and quality of service, and resource accounting provides logging and billing. Cisco provides an integrated solution with products that address all components of building a CDN.
Spring Cloud is a collection of projects that simplify application development in cloud environments including microservices architectures. It builds on top of Spring Boot. Spring Cloud Connectors provides a uniform API for cloud applications to obtain information about services like databases and message brokers. Spring Cloud Config provides distributed configuration through a centralized Git repository and REST API. It allows applications to retrieve common and profile-specific settings. The Config Server stores configuration in Git and clients can refresh their settings dynamically through REST endpoints.
The 3.0 release of the Maginatics Cloud Storage Platform (MCSP) includes great improvements in Data Protection, Multi-tier Caching and APIs, as well as other significant new features that make Maginatics the ideal choice for enterprise businesses with demanding storage requirements.
This document discusses directory write leases in MagFS, a globally distributed file system. It introduces the concept of directory write leases, which allow clients to cache and execute namespace-modifying operations locally to improve performance over high-latency networks. Evaluation results show that directory write leases enable workloads to complete much faster with increasing network latency compared to synchronous approaches.
Maginatics @ SDC 2013: Architecting An Enterprise Storage Platform Using Obje...Maginatics
How did Maginatics build a strongly consistent and secure distributed file system? Niraj Tolia, Chief Architect at Maginatics, gave this presentation on the design of MagFS at the Storage Developer Conference on September 16, 2013.
For more information about MagFS—The File System for the Cloud, visit maginatics.com or contact us directly at info@maginatics.com.
Markus Günther provides an overview of Apache Kafka. Kafka is a distributed publish-subscribe messaging system that supports topic access semantics. Producers publish data to topics and consumers subscribe to topics of interest to consume data at their own pace. Kafka uses a persistent commit log to implement messaging, with publishers appending messages and consumers reading sequentially. It supports at-least-once and exactly-once delivery guarantees.
Service Stampede: Surviving a Thousand ServicesAnil Gursel
How many services do you have? 5, 10, 100? How do you even run large number of services? A micro service may be relatively simple. But services also mean distributed systems, which are inherently complex. 5 services are complex. A thousand services across many generations are at least 200 times as complex. How do we deal with such complexity?
This talk discusses service architecture at Internet scale, the need for larger transaction density, larger horizontal and vertical scale, more predictable latencies under stress, and the need for standardization and visibility. We’ll dive into how we build our latest generation service infrastructure based on Scala and Akka to serve the needs of such a large scale ecosystem.
Lastly, have the cake and eat it too. No, we’re not keeping all the goodies only to ourselves. They are all there for you in open source.
1) The document provides tips for optimizing performance on WebSphere DataPower devices by adjusting caching, enabling persistent connections, using processing rules efficiently, optimizing MQ and XSLT configurations, and leveraging synchronous and asynchronous actions appropriately.
2) It recommends creating a "facade service" to monitor and shape requests to external services like logging servers to prevent slow responses from impacting core transactions. This facade service would use monitors and service level management policies to control latencies.
3) Using separate delegate services with monitoring is suggested to avoid direct connections to external services that could become slow and bottleneck transactions if they degrade in performance.
The document provides an overview of WebLogic Server topology, configuration, and administration. It describes key concepts such as domains, servers, clusters, Node Manager, and machines. It also covers configuration files, administration tools like the Administration Console and WLST, and some sample configuration schemes for development, high availability, and simplified administration.
Kafka summit SF 2019 - the art of the event-streaming appNeil Avery
Have you ever imagined what it would be like to build a massively scalable streaming application on Kafka, the challenges, the patterns and the thought process involved? How much of the application can be reused? What patterns will you discover? How does it all fit together? Depending upon your use case and business, this can mean many things. Starting out with a data pipeline is one thing, but evolving into a company-wide real-time application that is business critical and entirely dependent upon a streaming platform is a giant leap. Large-scale streaming applications are also called event streaming applications. They are classically different from other data systems; event streaming applications are viewed as a series of interconnected streams that are topologically defined using stream processors; they hold state that models your use case as events. Almost like a deconstructed realtime database.
In this talk, I step through the origins of event streaming systems, understanding how they are developed from raw events to evolve into something that can be adopted at an organizational scale. I start with event-first thinking, Domain Driven Design to build data models that work with the fundamentals of Streams, Kafka Streams, KSQL and Serverless (FaaS). Building upon this, I explain how to build common business functionality by stepping through patterns for Scalable payment processing Run it on rails: Instrumentation and monitoring Control flow patterns (start, stop, pause) Finally, all of these concepts are combined in a solution architecture that can be used at enterprise scale. I will introduce enterprise patterns such as events-as-a-backbone, events as APIs and methods for governance and self-service. You will leave talk with an understanding of how to model events with event-first thinking, how to work towards reusable streaming patterns and most importantly, how it all fits together at scale.
Website availability and performance have a direct business impact. Find out what kind of configurations can make WordPress sites scalable, highly available, bulletproof against any downtimes or malware attacks. Avoid a bad user experience caused by the downtime with a help of pre-packaged WordPress cluster that includes automated server scaling and database replication, built-in HTTP/3 ready CDN and SSL, integrated WAF and layer-7 anti-DDoS filtering, as well as a set of other features required for high availability and security of your sites.
More details based on the presentation are covered in the webinar https://youtu.be/NPyx2VBbUos
WordPress Cluster installation guide https://jelastic.com/blog/wordpress-hosting-enterprise-high-availability-auto-scaling/
WordPress Standalone Installation Guide https://jelastic.com/blog/wordpress-hosting-standalone-container/
How to migrate to Jelastic WordPress hosting https://jelastic.com/blog/migrate-wordpress-site/
Send a request to get access to Jelastic WordPress cluster https://jelastic.com/managed-auto-scalable-clusters-for-business/#wordpress
This document discusses scaling applications in the AWS cloud. It begins with an overview of AWS services like EC2, S3, RDS, and ELB. It then walks through creating a simple cloud application and database, and improving it by separating components, adding redundancy, caching, and autoscaling. A real-world example is shown using Vert.x, Kinesis, Docker, and deployment scripts to dynamically scale a streaming data application across Availability Zones.
Why stop the world when you can change it? Design and implementation of Incre...confluent
Since its initial release, the Kafka group membership protocol has offered Connect, Streams and Consumer applications an ingenious and robust way to balance resources among distributed processes. The process of rebalancing, as it's widely known, allows Kafka APIs to define an embedded protocol for load balancing within the group membership protocol itself. Until now, rebalancing has been working under the simple assumption that every time a new group generation is created, the members join after first releasing all of their resources, getting a whole new load assignment by the time the new group is formed. This allows Kafka APIs to provide task fault-tolerance and elasticity on top of the group membership protocol. However, due to its side-effects on multi-tenancy and scalability this simple approach in rebalancing, also known as stop-the-world effect, is limiting larger scale deployments. Because of stop-the-world, application tasks get interrupted only for most of them to receive the same resources after rebalancing. In this technical deep dive, I'll discuss the proposition of Incremental Cooperative Rebalancing as a way to alleviate stop-the-world and optimize rebalancing in Kafka APIs. We'll cover: * The internals of Incremental Cooperative Rebalancing * Uses cases that benefit from Incremental Cooperative Rebalancing * Implementation in Kafka Connect * Performance results in Kafka Connect clusters
Cloud computing that provides cheap and pay-as-you-go computing resources is rapidly gaining momentum as an alternative to traditional IT Infrastructure. As more and more consumers delegate their tasks to cloud providers, Service Level Agreements(SLA) between consumers and providers emerge as a key aspect. Due to the dynamic nature of the cloud, continuous monitoring on Quality of Service (QoS)
attributes is necessary to enforce SLAs. Also numerous other factors such as trust (on the cloud provider) come into consideration, particularly for enterprise customers that may outsource its critical data. This complex nature of the cloud landscape warrants a sophisticated means of managing SLAs. This paper proposes a mechanism for managing SLAs in a cloud computing environment using the Web Service Level Agreement(WSLA) framework, developed for SLA monitoring and SLA enforcement
in a Service Oriented Architecture (SOA). We use the third
party support feature of WSLA to delegate monitoring and enforcement tasks to other entities in order to solve the trust issues. We also present a real world use case to validate our proposal.
The document discusses the SLA-Ready project which aims to deliver a Common Reference Model (CRM) for cloud SLAs and best practices to support customers. It outlines the key elements of the SLA-Ready CRM and describes an SLA repository and readiness index being developed. The document also provides an update on related ISO/IEC 19086 standards for cloud SLAs, noting contributions made to develop the standards.
Enhancing minimal virtual machine migration in cloud environmenteSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Stateful streaming and the challenge of stateYoni Farin
The different challenges of working with state in a distributed streaming data pipeline and how we solve it with the 3S architecture and Kafka streams stores based on rocksDB
AWS Study Group - Chapter 10 - Matching Supply and Demand [Solution Architect...QCloudMentor
This chapter discusses how to match computing resource supply and demand on AWS. It covers Elastic Load Balancing (ELB) and its three types - classic, application, and network load balancers. It also discusses AWS Auto Scaling, which allows automatically scaling computing resources up or down based on demand. Key attributes of ELB like stateless/stateful, internet-facing/internal-facing, and cross-zone load balancing are explained.
The document discusses how WebLogic Server uses a single common thread pool that prioritizes and schedules work. The thread pool size adjusts automatically based on historical throughput statistics to maximize performance while reducing complexity compared to custom thread pools. Work is prioritized according to user-defined rules and run-time metrics like request processing time and rates.
Building Eventing Systems for Microservice Architecture Yaroslav Tkachenko
In Bench Accounting we heavily use various events as first class citizens: notifications, in-app TODO lists (and messaging solution in future) rely on the eventing framework we built. Recently we’ve migrated our old legacy eventing system to the new framework with a focus on microservices architecture. We’ve chosen event sourcing approach as well as tools like Akka, Camel, ActiveMQ, Slick and Postgres (JSONB).
In this presentation I would like to share high-level overview of the system, implementation details and challenges we’ve faced.
Content delivery networks (CDNs) facilitate content delivery to end users by using a centrally managed network of devices. The key components of building a CDN include content distribution, request routing, content delivery, and resource accounting. Content distribution involves placing content on delivery devices, request routing steers users to a close delivery node, content delivery handles protocol processing and quality of service, and resource accounting provides logging and billing. Cisco provides an integrated solution with products that address all components of building a CDN.
Spring Cloud is a collection of projects that simplify application development in cloud environments including microservices architectures. It builds on top of Spring Boot. Spring Cloud Connectors provides a uniform API for cloud applications to obtain information about services like databases and message brokers. Spring Cloud Config provides distributed configuration through a centralized Git repository and REST API. It allows applications to retrieve common and profile-specific settings. The Config Server stores configuration in Git and clients can refresh their settings dynamically through REST endpoints.
The 3.0 release of the Maginatics Cloud Storage Platform (MCSP) includes great improvements in Data Protection, Multi-tier Caching and APIs, as well as other significant new features that make Maginatics the ideal choice for enterprise businesses with demanding storage requirements.
This document discusses directory write leases in MagFS, a globally distributed file system. It introduces the concept of directory write leases, which allow clients to cache and execute namespace-modifying operations locally to improve performance over high-latency networks. Evaluation results show that directory write leases enable workloads to complete much faster with increasing network latency compared to synchronous approaches.
Maginatics @ SDC 2013: Architecting An Enterprise Storage Platform Using Obje...Maginatics
How did Maginatics build a strongly consistent and secure distributed file system? Niraj Tolia, Chief Architect at Maginatics, gave this presentation on the design of MagFS at the Storage Developer Conference on September 16, 2013.
For more information about MagFS—The File System for the Cloud, visit maginatics.com or contact us directly at info@maginatics.com.
Markus Günther provides an overview of Apache Kafka. Kafka is a distributed publish-subscribe messaging system that supports topic access semantics. Producers publish data to topics and consumers subscribe to topics of interest to consume data at their own pace. Kafka uses a persistent commit log to implement messaging, with publishers appending messages and consumers reading sequentially. It supports at-least-once and exactly-once delivery guarantees.
Service Stampede: Surviving a Thousand ServicesAnil Gursel
How many services do you have? 5, 10, 100? How do you even run large number of services? A micro service may be relatively simple. But services also mean distributed systems, which are inherently complex. 5 services are complex. A thousand services across many generations are at least 200 times as complex. How do we deal with such complexity?
This talk discusses service architecture at Internet scale, the need for larger transaction density, larger horizontal and vertical scale, more predictable latencies under stress, and the need for standardization and visibility. We’ll dive into how we build our latest generation service infrastructure based on Scala and Akka to serve the needs of such a large scale ecosystem.
Lastly, have the cake and eat it too. No, we’re not keeping all the goodies only to ourselves. They are all there for you in open source.
1) The document provides tips for optimizing performance on WebSphere DataPower devices by adjusting caching, enabling persistent connections, using processing rules efficiently, optimizing MQ and XSLT configurations, and leveraging synchronous and asynchronous actions appropriately.
2) It recommends creating a "facade service" to monitor and shape requests to external services like logging servers to prevent slow responses from impacting core transactions. This facade service would use monitors and service level management policies to control latencies.
3) Using separate delegate services with monitoring is suggested to avoid direct connections to external services that could become slow and bottleneck transactions if they degrade in performance.
The document provides an overview of WebLogic Server topology, configuration, and administration. It describes key concepts such as domains, servers, clusters, Node Manager, and machines. It also covers configuration files, administration tools like the Administration Console and WLST, and some sample configuration schemes for development, high availability, and simplified administration.
Kafka summit SF 2019 - the art of the event-streaming appNeil Avery
Have you ever imagined what it would be like to build a massively scalable streaming application on Kafka, the challenges, the patterns and the thought process involved? How much of the application can be reused? What patterns will you discover? How does it all fit together? Depending upon your use case and business, this can mean many things. Starting out with a data pipeline is one thing, but evolving into a company-wide real-time application that is business critical and entirely dependent upon a streaming platform is a giant leap. Large-scale streaming applications are also called event streaming applications. They are classically different from other data systems; event streaming applications are viewed as a series of interconnected streams that are topologically defined using stream processors; they hold state that models your use case as events. Almost like a deconstructed realtime database.
In this talk, I step through the origins of event streaming systems, understanding how they are developed from raw events to evolve into something that can be adopted at an organizational scale. I start with event-first thinking, Domain Driven Design to build data models that work with the fundamentals of Streams, Kafka Streams, KSQL and Serverless (FaaS). Building upon this, I explain how to build common business functionality by stepping through patterns for Scalable payment processing Run it on rails: Instrumentation and monitoring Control flow patterns (start, stop, pause) Finally, all of these concepts are combined in a solution architecture that can be used at enterprise scale. I will introduce enterprise patterns such as events-as-a-backbone, events as APIs and methods for governance and self-service. You will leave talk with an understanding of how to model events with event-first thinking, how to work towards reusable streaming patterns and most importantly, how it all fits together at scale.
Website availability and performance have a direct business impact. Find out what kind of configurations can make WordPress sites scalable, highly available, bulletproof against any downtimes or malware attacks. Avoid a bad user experience caused by the downtime with a help of pre-packaged WordPress cluster that includes automated server scaling and database replication, built-in HTTP/3 ready CDN and SSL, integrated WAF and layer-7 anti-DDoS filtering, as well as a set of other features required for high availability and security of your sites.
More details based on the presentation are covered in the webinar https://youtu.be/NPyx2VBbUos
WordPress Cluster installation guide https://jelastic.com/blog/wordpress-hosting-enterprise-high-availability-auto-scaling/
WordPress Standalone Installation Guide https://jelastic.com/blog/wordpress-hosting-standalone-container/
How to migrate to Jelastic WordPress hosting https://jelastic.com/blog/migrate-wordpress-site/
Send a request to get access to Jelastic WordPress cluster https://jelastic.com/managed-auto-scalable-clusters-for-business/#wordpress
This document discusses scaling applications in the AWS cloud. It begins with an overview of AWS services like EC2, S3, RDS, and ELB. It then walks through creating a simple cloud application and database, and improving it by separating components, adding redundancy, caching, and autoscaling. A real-world example is shown using Vert.x, Kinesis, Docker, and deployment scripts to dynamically scale a streaming data application across Availability Zones.
Why stop the world when you can change it? Design and implementation of Incre...confluent
Since its initial release, the Kafka group membership protocol has offered Connect, Streams and Consumer applications an ingenious and robust way to balance resources among distributed processes. The process of rebalancing, as it's widely known, allows Kafka APIs to define an embedded protocol for load balancing within the group membership protocol itself. Until now, rebalancing has been working under the simple assumption that every time a new group generation is created, the members join after first releasing all of their resources, getting a whole new load assignment by the time the new group is formed. This allows Kafka APIs to provide task fault-tolerance and elasticity on top of the group membership protocol. However, due to its side-effects on multi-tenancy and scalability this simple approach in rebalancing, also known as stop-the-world effect, is limiting larger scale deployments. Because of stop-the-world, application tasks get interrupted only for most of them to receive the same resources after rebalancing. In this technical deep dive, I'll discuss the proposition of Incremental Cooperative Rebalancing as a way to alleviate stop-the-world and optimize rebalancing in Kafka APIs. We'll cover: * The internals of Incremental Cooperative Rebalancing * Uses cases that benefit from Incremental Cooperative Rebalancing * Implementation in Kafka Connect * Performance results in Kafka Connect clusters
Cloud computing that provides cheap and pay-as-you-go computing resources is rapidly gaining momentum as an alternative to traditional IT Infrastructure. As more and more consumers delegate their tasks to cloud providers, Service Level Agreements(SLA) between consumers and providers emerge as a key aspect. Due to the dynamic nature of the cloud, continuous monitoring on Quality of Service (QoS)
attributes is necessary to enforce SLAs. Also numerous other factors such as trust (on the cloud provider) come into consideration, particularly for enterprise customers that may outsource its critical data. This complex nature of the cloud landscape warrants a sophisticated means of managing SLAs. This paper proposes a mechanism for managing SLAs in a cloud computing environment using the Web Service Level Agreement(WSLA) framework, developed for SLA monitoring and SLA enforcement
in a Service Oriented Architecture (SOA). We use the third
party support feature of WSLA to delegate monitoring and enforcement tasks to other entities in order to solve the trust issues. We also present a real world use case to validate our proposal.
The document discusses the SLA-Ready project which aims to deliver a Common Reference Model (CRM) for cloud SLAs and best practices to support customers. It outlines the key elements of the SLA-Ready CRM and describes an SLA repository and readiness index being developed. The document also provides an update on related ISO/IEC 19086 standards for cloud SLAs, noting contributions made to develop the standards.
Outsourcing SLA versus Cloud SLA by Jurian BurgersITpreneurs
1. There are seven key topics to consider when drafting a cloud computing service level agreement (SLA) compared to a regular outsourcing SLA: chain liability, contract duration, exit strategy, security, sharing resources, internet dependability, and financial model.
2. The cloud SLA should address liability if the cloud service provider subcontracts work and limit the provider's responsibility. It also needs flexibility in contract duration and a clear exit strategy for returning data.
3. The cloud SLA must require evidence of security standards and controls for access, backups, and data integrity. It should also define how multi-tenant access is controlled.
4. The SLA needs clarity on responsibilities for internet connections and
This document discusses cloud computing and service level agreements. It begins by defining different types of cloud computing models like SaaS, PaaS, and IaaS. It then discusses how cloud computing differs from traditional on-premise storage by addressing issues like data location, custody, and multi-tenancy. The document outlines important considerations for service level agreements including security, data encryption, privacy, regulatory compliance, and transparency. It emphasizes that SLAs should define metrics and responsibilities to ensure the cloud provider delivers the promised level of service. Finally, it cautions that moving to the cloud requires understanding issues like security, portability, accessibility, and data location laws.
Este documento presenta las estructuras de control de selección en Java, incluyendo las instrucciones condicionales if, if-else, y switch. Explica cómo funcionan estas instrucciones y proporciona ejemplos de su uso para tomar decisiones basadas en condiciones. También introduce el operador ternario como otra forma de expresar condiciones de forma concisa.
El documento describe los métodos en Java, incluyendo métodos de objeto y métodos de clase. Explica las características de cada tipo de método como la visibilidad, los parámetros, el cuerpo y cómo se llaman. También proporciona ejemplos de cómo definir y usar métodos en una clase.
Este documento describe los tipos de datos de clases y referencias en Java. Introduce los conceptos de clases, objetos y variables de referencia. Explica cómo se inicializan los atributos de un objeto, cómo se representan los objetos en memoria y las diferencias entre variables primitivas y de referencia. También cubre temas como la copia y comparación de objetos, el recolector de basura y el uso de atributos estáticos para almacenar información de clase.
Este documento describe algunas clases predefinidas en Java como String, Math y las clases envoltorio. La clase String permite manipular cadenas de caracteres mediante métodos como length(), substring(), equals(), etc. La clase Math contiene constantes y métodos estáticos para operaciones matemáticas como seno, coseno, logaritmos, raíz cuadrada y generación de números aleatorios. Las clases envoltorio como Integer, Float y Double permiten tratar valores elementales como objetos con funcionalidades adicionales.
Este documento resume los conceptos básicos de entrada y salida en Java. Explica que la entrada y salida se realiza a través de flujos y describe los flujos predefinidos System.in y System.out. Detalla los métodos println y printf para la salida formateada y explica cómo la clase Scanner permite la entrada desde teclado de diferentes tipos de datos de forma sencilla.
El documento describe los conceptos básicos de objetos, clases y programación en Java, incluyendo la estructura de una clase con atributos y métodos, la creación y uso de objetos, y la edición, compilación y ejecución de programas en Java. Se provee un ejemplo de clase Circulo que define los atributos y métodos de un círculo, y un ejemplo de programa PrimerPrograma que crea objetos Circulo y los añade a una pizarra.
Este documento introduce los tipos de datos fundamentales en Java, incluyendo tipos numéricos (enteros y reales), tipo carácter y tipo lógico. Explica las variables, constantes, expresiones y asignación. También cubre temas como compatibilidad y conversión de tipos, operadores aritméticos y de comparación, y bloques de instrucciones.
This document discusses metrics that can be used at various phases of the product development process, including code metrics during development, test addition metrics, test execution metrics, test coverage metrics, stability trends, prediction metrics, and bug metrics. The metrics are intended to provide objective indicators of product quality, check progress, aid decisions about phase exits/entries, and trigger actions for improvement. Key metrics discussed include code complexity, test coverage, bug trends, and achieving a goal of zero open bugs for release.
The Path To Cloud - an Infograph on Cloud MigrationInApp
Public cloud use makes up 18% of cloud adoption, while private cloud accounts for 71% and hybrid models 6%. On average, cloud users leverage 1.5 public clouds and 1.7 private clouds, and are experimenting with 1.5 additional public clouds and 1.3 private clouds. Security is no longer the top cloud challenge, replaced by concerns about reliability, tech support, price, and reputation. The document is an infographic from InApp summarizing trends in cloud adoption from surveys by Right Scale and Amazon.
Innovation with Open Source: The New South Wales Judicial Commission experienceLinuxmalaysia Malaysia
Innovation with Open Source: The New South Wales Judicial Commission experience. MyGOSSCON 2008. Mr. Murali Sagi
Director,
Information Management & Corporate Services,
JUDICIAL COMMISSION OF NSW, SYDNEY, AUSTRALIA
The document discusses data service level agreements (SLAs) in public cloud environments. It explains that achieving availability, consistency, and scalability is challenging due to Brewer's CAP theorem. It reviews strategies for relational and NoSQL databases to handle these tradeoffs, including dropping consistency or availability depending on needs. Code examples demonstrate typical operations for Cassandra, MongoDB, and Neo4J NoSQL databases. The conclusion recommends choosing solutions based on requirements and migrating to NoSQL as needed to address scaling issues.
SLACC is a decision support system that aims to help cloud providers and users negotiate service level agreements (SLAs) by estimating key performance indicators (KPIs) and service level objectives (SLOs). It analyzes historical data and information about a provider's IT infrastructure to evaluate what levels of availability, response time, and other parameters a provider can likely offer or accept. The system is intended to enhance SLA specificity and support negotiation processes without directly interfering with existing cloud architectures.
reliability based design optimization for cloud migrationNishmitha B
reliability based design optimization for cloud migration is an application designed to manage applications..more precisely legacy applications..whose extraction n magmt. is crucial n troublesome.
Massimiliano Raks, Naples University on SPECS: Secure provisioning of cloud s...SLA-Ready Network
The cloud is both a risk and an opportunity depending on the service. Despite the opportunities, security is a top concern for a growing number of cloud service customers, and rightfully so. A key challenge is representing security and measuring it in a service level agreement? How can a cloud service provider grant the security level? And how can a cloud service customer automatically enforce it?
Prof. Massimiliano Raks, University of Naples, talks us through Security Service Level Agreement (SecureSLAs), looking at
Security SLA Negotiation, Security SLA (Automatic) Enforcement and Security SLA Continuous Monitoring with the SPECS platform for SecSLAs.
SLA Basics describes service level agreements (SLAs) which define non-functional requirements for cloud services. SLAs consist of service level objectives (SLOs) evaluated using key performance indicators (KPIs) with thresholds. Automated SLA protection uses policy rules to evaluate KPIs periodically and trigger actions if conditions are met. SLAs are important in cloud computing to ensure customers receive the expected quality of service, as cloud providers may overcommit resources leading to variable performance without proper SLAs.
Software Defined Service Networking (SDSN) - by Dr. Indika KumaraThejan Wijesinghe
The document discusses Software-Defined Service Networking (SDSN) as an approach for managing multi-tenant cloud applications. SDSN defines service networks using configuration and regulation designs. The configuration design describes the topology and connections between roles. The regulation design describes how interaction messages are routed and regulated. SDSN supports sharing services among tenants with variations by defining virtual service networks from collaboration units with different configurations and regulations. The SDSN middleware aims to minimize gaps between design-time models and runtime, support enactment of multiple virtual networks on the same physical network, and enable policy-based management.
This document discusses definitions and concepts related to cloud computing. It begins by looking at definitions from NIST and WhatIs.com, which describe cloud computing as enabling on-demand access to configurable computing resources via a network. The document then covers central ideas like utility computing, service-oriented architecture (SOA), and service level agreements (SLAs). It discusses properties and characteristics of clouds like scalability, availability, reliability, manageability, interoperability, performance, and accessibility. Finally, it delves into concepts that enable these properties, such as virtualization, parallel computing, load balancing, fault tolerance, and system monitoring.
Chapter 1 Introduction to Cloud Computingnewbie2019
The document discusses cloud computing, including definitions from various sources, properties and characteristics of cloud computing, and service and deployment models. It defines cloud computing as on-demand access to shared configurable computing resources over the internet. The key properties discussed are high scalability, availability, reliability, manageability, interoperability, accessibility, and optimization through techniques like virtualization, parallel computing, and load balancing. It outlines service models of SaaS, PaaS, and IaaS and deployment models of private, public, hybrid and community clouds.
The document discusses performance evaluation of different cloud computing architectures and deployment models. It begins by defining cloud architecture and deployment models, including public, private and hybrid clouds as well as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). It then discusses defining test scenarios, identifying architectures and models to evaluate, and preparing a report on the performance evaluation methodology, test results and analysis. The document also provides a literature review on previous research related to evaluating cloud platforms, characteristics of cloud deployment models, components of cloud architecture, and algorithms for handling constraints. It concludes by identifying research gaps in evaluating specific deployment models, a lack of real-world evaluations, limited research
إن الحوسبة السحابية تعني توفير موارد تقنية المعلومات حسب الطلب عبر الإنترنت مع تسعير التكلفة حسب الاستخدام. فبدلاً من شراء مراكز البيانات الفعلية وامتلاكها والاحتفاظ بها، يمكنك الاستفادة من الخدمات التكنولوجية، مثل إمكانيات الحوسبة، والتخزين، وقواعد البيانات، بأسلوب يعتمد على الاحتياجات لديك، وذلك من خلال جهة موفرة للخدمات السحابية مثل Amazon Web Services (AWS
This document discusses scheduling in cloud computing environments and summarizes an experimental study comparing different task scheduling policies in virtual machines. It begins with introductions to cloud computing, architectures, and virtualization. It then presents the problem statement of improving application performance under varying resource demands through efficient scheduling. The document outlines simulations conducted using the CloudSim toolkit to evaluate scheduling algorithms like shortest job first, round robin, and a proposed algorithm incorporating machine processing speeds. It presents the implementation including a web interface and concludes that round robin scheduling distributes jobs equally but can cause fragmentation, while the proposed algorithm aims to overcome limitations of existing approaches.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Performance and Cost Analysis of Modern Public Cloud ServicesMd.Saiedur Rahaman
The document outlines a performance and cost analysis of modern public cloud services. It discusses the problem statement of selecting public cloud providers based on performance and cost. It then covers the performance and cost analysis of various public clouds, comparing metrics like response time, elasticity, upload/download speeds, and scaling latency across different providers and operating systems. Various existing approaches for analyzing public cloud performance and costs are also summarized and compared.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Score based deadline constrained workflow scheduling algorithm for cloud systemsijccsa
Cloud Computing is the latest and emerging trend in information technology domain. It offers utility- based
IT services to user over the Internet. Workflow scheduling is one of the major problems in cloud systems. A
good scheduling algorithm must minimize the execution time and cost of workflow application along with
QoS requirements of the user. In this paper we consider deadline as the major constraint and propose a
score based deadline constrained workflow scheduling algorithm that executes workflow within
manageable cost while meeting user defined deadline constraint. The algorithm uses the concept of score
which represents the capabilities of hardware resources. This score value is used while allocating
resources to various tasks of workflow application. The algorithm allocates those resources to workflow
application which are reliable and reduce the execution cost and complete the workflow application within
user specified deadline. The experimental results show that score based algorithm exhibits less execution
time and also reduces the failure rate of workflow application within manageable cost. All the simulations
have been done using CloudSim toolkit.
This document proposes a new Cloud Elasticity as a Service (CES) framework in OpenStack for efficiently managing cloud infrastructure utilization. CES allows cloud administrators to define policies with configurable quality-of-service parameters. It periodically validates policies by collecting monitoring data and automatically scales resources up or down using templates when policy conditions are met, without human intervention. The framework was tested by increasing load on a virtual machine and observing CES scale it up by triggering the policy template as CPU usage exceeded thresholds.
Dynamic congestion management system for cloud service brokerIJECEIAES
The cloud computing model offers a shared pool of resources and services with diverse models presented to the clients through the internet by an on-demand scalable and dynamic pay-per-use model. The developers have identified the need for an automated system (cloud service broker (CSB)) that can contribute to exploiting the cloud capability, enhancing its functionality, and improving its performance. This research presents a dynamic congestion management (DCM) system which can manage the massive amount of cloud requests while considering the required quality for the clients’ requirements as regulated by the service-level policy. In addition, this research introduces a forwarding policy that can be utilized to choose high-priority calls coming from the cloud service requesters and passes them by the broker to the suitable cloud resources. The policy has made use of one of the mechanisms that are used by Cisco to assist the administration of the congestion that might take place at the broker side. Furthermore, the DCM system is used to help in provisioning and monitoring the works of the cloud providers through the job operation. The proposed DCM system was implemented and evaluated by using the CloudSim tool.
Cloud computing allows users to access computing resources over the network. It has several key characteristics including on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service. There are three main service models (Software as a Service, Platform as a Service, and Infrastructure as a Service) and four deployment models (private cloud, community cloud, public cloud, and hybrid cloud). Achieving high performance, availability, and manageability in cloud computing requires techniques like virtualization, parallel processing, fault tolerance, load balancing and automation.
JPJ1403 A Stochastic Model To Investigate Data Center Performance And QoS I...chennaijp
We are good ieee java projects development center in chennai and pondicherry. We guided advanced java techonolgies projects of cloud computing, data mining, Secure Computing, Networking, Parallel & Distributed Systems, Mobile Computing and Service Computing (Web Service).
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/java-projects/
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
SLALOM Webinar Final Technical Outcomes Explanined "Using the SLALOM Technica...Oliver Barreto Rodríguez
SLALOM organized two live sessions to present the final versions of our legal terms and technical specifications for #Cloud #SLAs. The sessions provide examples showing how to practically apply SLALOM to improve current practice in the industry for # Cloud #SLAs and support development of cloud computing metrics.
The first webinar covered SLALOM Technical track "Using metrics to improve Cloud SLAs".
DOTNET 2013 IEEE CLOUDCOMPUTING PROJECT A stochastic model to investigate dat...IEEEGLOBALSOFTTECHNOLOGIES
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
CAKE: Sharing Slices of Confidential Data on BlockchainClaudio Di Ciccio
Presented at the CAiSE 2024 Forum, Intelligent Information Systems, June 6th, Limassol, Cyprus.
Synopsis: Cooperative information systems typically involve various entities in a collaborative process within a distributed environment. Blockchain technology offers a mechanism for automating such processes, even when only partial trust exists among participants. The data stored on the blockchain is replicated across all nodes in the network, ensuring accessibility to all participants. While this aspect facilitates traceability, integrity, and persistence, it poses challenges for adopting public blockchains in enterprise settings due to confidentiality issues. In this paper, we present a software tool named Control Access via Key Encryption (CAKE), designed to ensure data confidentiality in scenarios involving public blockchains. After outlining its core components and functionalities, we showcase the application of CAKE in the context of a real-world cyber-security project within the logistics domain.
Paper: https://doi.org/10.1007/978-3-031-61000-4_16
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
3. INTRODUCTION
• Cloud computing is becoming widely adopted in the last
years.
• Many concerns arise regarding Cloud services.
Specifically we focus on the following.
– Representation of Cloud services (e.g. an application that
depends on a software stack deployed on aVM).
– Delivery of QoS on multitier Clouds.
• These concerns motivate the Ph.D. dissertation.
– Definition and implementation of a methodology for the
representation of Cloud services and QoS rules.
• Service Level Agreements (SLAs) are proposed as a vehicle for the
management of QoS on Cloud.
• SLAs can be extended to define Cloud Services.
3
4. CONCEPTS
• Cloud resource.
– A resource (e.g. network, server, storage, etc.) served by a
Cloud system.
• Cloud service.
– A capability provided by a Cloud system. Can range from a
single resource (VM) to a complete system formed by
multiple resources.
• QoS.
– Measure of the performance of a system according to a set of
indicators.
• SLA.
– Contract between a provider and a user defining the
delivered service, as well as conditions and guarantees in the
QoS.
4
5. OBJECTIVES
• Define a generic and extensible methodology for the
description of Cloud services.
– Model of Cloud resources.
– SLAs as an unified representation of Cloud services.
• Design a SLA-driven architecture for the management of
Cloud services.
– Performs provision, scheduling and allocation of resources (passive
features).
– Performs assessment of QoS and elasticity operations (active
features).
• Implement the Cloudcompaas framework, an open source
implementation of the methodology and architecture.
• Evaluate the performance and benefits of the developments
with a set of experiments.
5
6. THREE ACTOR ROLES
• Service developer
– Person who describes and
registers a service in
Cloudcompaas (e.g. application
software).
• Service provider.
– Person who creates an instance
of the Cloud service, and pays
for its deployment and
management.
• Service user.
– Persons that directly makes use
of the Cloud service capabilities.
• These roles may be filled by
the same or different persons.
6
8. HIERARCHICAL MODEL OF RESOURCES
AT DIFFERENT LEVELS
• A simple and extensible model of resources has
been defined to support the Cloudcompaas
methodology.
• It defines resources according to the NIST’s three
levels of Cloud Computing.
• It uses a hierarchical organization of resources.
Some resources are aggregation of others.
• It also includes metadata. Metadata defines
information about the Cloud service or resources
(e.g. number of replicas).
8
9. HIERARCHICAL MODEL OF RESOURCES
AT DIFFERENT LEVELS: IAAS
• AVM is composed of Physical resources.
• Default resources: Cores, Memory, Network and
Architecture.
9
10. HIERARCHICAL MODEL OF RESOURCES
AT DIFFERENT LEVELS: PAAS
• AVirtual Container is
composed by a
hierarchy of software
components.
• The same software
component cannot
appear twice on the
same composition.
10
11. METHODOLOGY FORTHE
DESCRIPTION OF CLOUD SERVICES
• WS-Agreement used as SLA language.
• WS-Agreement describes each service as a SLA
document.
– Defines SLA template, offer and instance documents.
– Defines a schema with the different sections of a SLA.
• Our methodology maps each section of a SLA to a
part of a Cloud service.
– ServiceTerms describe passive features (e.g. resources).
– GuaranteeTerms describe active features (e.g. QoS
rules).
– Creation Constraints represent relationships and
dependences between elements.
11
12. VM DESCRIPTION
<ServiceDescriptionTerm>
<VirtualMachine Name="large">
<PhysicalResource Name="Memory">
1024
</PhysicalResource>
<PhysicalResource Name="Cores">
2
</PhysicalResource>
<PhysicalResource Name="Network">
public
</PhysicalResource>
<PhysicalResource
Name="Architecture">
x86_64
</PhysicalResource>
</VirtualMachine>
</ServiceDescriptionTerm>
• Defined by the
Cloducompaas.
• Represents a largeVM
with 1024MB of RAM, 2
cores and a public
network.
12
13. METHODOLOGY FORTHE
DESCRIPTION OF CLOUD SERVICES
• WS-Agreement used as SLA language.
• WS-Agreement describes each service as a SLA
document.
– Defines a schema with the different sections of a SLA.
• Our methodology maps each section of a SLA to a
part of a Cloud service.
– ServiceTerms describe passive features (e.g. resources).
– GuaranteeTerms describe active features (e.g. QoS
rules).
– Creation Constraints represent relationships and
dependences between elements.
13
14. QOS RULE DESCRIPTION
<GuaranteeTerm Name="SCALE_OUT">
<QualifyingCondition>
MAX_REPLICAS gt ACT_REPLICAS
</QualifyingCondition>
<ServiceLevelObjective>
<KPITarget>
<CustomServiceLevel>
list.avg(CPUPERC) le 90
</CustomServiceLevel>
</KPITarget>
</ServiceLevelObjective>
</GuaranteeTerm>
• Defined by
Cloudcompaas.
• Represents an elasticity
rule.
• If the average CPU
usage of all replicas is
higher than 90%, deploy
a new replica.
14
15. METHODOLOGY FORTHE
DESCRIPTION OF CLOUD SERVICES
• WS-Agreement used as SLA language.
• WS-Agreement describes each service as a SLA
document.
– Defines a schema with the different sections of a SLA.
• Our methodology maps each section of a SLA to a
part of a Cloud service.
– ServiceTerms describe passive features (e.g. resources).
– GuaranteeTerms describe active features (e.g. QoS
rules).
– Creation Constraints represent relationships and
dependences between elements.
15
16. REQUIREMENTS DESCRIPTION
<CreationConstraints>
<Item Name=“hardware">
<Location>
/VirtualMachine/
[@Name='‘large'']
</Location>
</Item>
<Item Name="java">
<Location>
/VirtualContainer/
VirtualRuntime/[@Name]
</Location>
<ItemConstraint>
<ExactlyOne>
<enumeration value=“openjdk-7-jre"/>
<enumeration value=“openjdk-6-jre"/>
<ExactlyOne>
</ItemConstraint>
</Item>
</CreationConstraints>
• Defined by the Service
developer.
• Describes requirements of
a Cloud resource.This
resource requires a large
VM and a Java
runtime, either version 7
or 6.
• The location tag points to
the element that is being
restricted. Item constraint
define the possible values.
16
17. METHODOLOGY FORTHE
DESCRIPTION OF CLOUD SERVICES
• SLA languages represent services as complete documents, predefined
by the provider.
– Services must be manually predefined by the provider.
– Produces a combinatorial explosion of services.
• Our methodology introduces the novel concept of SLA fragment.
– A SLA fragment is a section of the SLA defined as a stand-alone
document.
– SLA fragments define individual resources, not complete services.
– Can be combined together to describe services.
• Our methodology composes SLA fragments in response to a Service
provider query for Cloud resources, in order to generate a Cloud
service. It has the following advantages.
– Reduces operational and maintenance expenses.
– Each element is self-contained.
– Improves flexibility.
17
19. METHODOLOGY FORTHE
DESCRIPTION OF CLOUD SERVICES
• SLA languages represent services as complete documents, predefined
by the provider.
– Services must be manually predefined by the provider.
– Produces a combinatorial explosion of services.
• Our methodology introduces the novel concept of SLA fragment.
– A SLA fragment is a section of the SLA defined as a stand-alone
document.
– SLA fragments define individual resources, not complete services.
– Can be combined together to describe services.
• Our methodology composes SLA fragments in response to a Service
provider query for Cloud resources, in order to generate a Cloud
service. It has the following advantages.
– Reduces operational and maintenance expenses.
– Each element is self-contained.
– Improves flexibility.
19
22. METHODOLOGY FORTHE
DESCRIPTION OF CLOUD SERVICES
• SLA languages represent services as complete documents, predefined
by the provider.
– Services must be manually predefined by the provider.
– Produces a combinatorial explosion of services.
• Our methodology introduces the novel concept of SLA fragment.
– A SLA fragment is a section of the SLA defined as a stand-alone
document.
– SLA fragments define individual resources, not complete services.
– Can be combined together to describe services.
• Our methodology composes SLA fragments in response to a Service
provider query for Cloud resources, in order to generate a Cloud
service. It has the following advantages.
– Reduces operational and maintenance expenses.
– Each element is self-contained.
– Improves flexibility.
22
23. SLA FRAGMENT COMPOSITION
• Service providers query the system requesting Cloud resources.
• SLA fragments are composed according to a set of constraints.
– Semantic constraints introduced by the data model.
– The query parameters introduced by the provider.
• This problem is an instance of a decision problem.These
problems have exponential execution time.
23
Yes
Cores: 1
No
Cores: 2
No
Runtime
Java
Yes
Runtime
Python
Yes
VM:
small
No
VM:
medium
Yes
RAM:
256mb
No
RAM:
512mb
SLA fragments representing Cloud resources
Should this fragment be added to the solution?
…
…
24. SLA COMPOSITION ALGORITHM
• We have designed an algorithm that explores the SLA
fragments as a search tree.
– The algorithm is recursive. Certain SLA fragments are an
aggregation of other fragments, and therefore spawn
composition subproblems.
– Non-terminal elements are fragments which are aggregation
of others.Terminal elements are not.
24
26. OPTIMIZATIONS TOTHE ALGORITHM
• The complexity can be reduced using heuristics and
focusing on particular instances.
– Dynamic programming: Prevents the recursive combinatorial
problems from repeating themselves. Reuses the solutions
from previous searches.
– Branch and bound:The number of fragments is used as an
estimator to guide the search. Stops as soon as a local
minimum is found.
• Ad-hoc optimizations.
– Using semantic restrictions and data structure.
– Using provider restrictions.
• These optimizations reduce the experimental running
time to polynomial instead of exponential.
26
28. ARCHITECTURE
• Architecture composed by
distributed, loosely-coupled
components where each one
fulfills an specific role.
• SLA-driven means that all
the interactions in the
system are performed by
means of SLAs.
• As a framework, it relies on
third party providers to
deploy resources.
• It provides a SLA-driven
layer on top of existing
tools.
28
Orchestrator Catalog
Platform
Connector
SLA Manager
Service
Connector
Infrastructure
Connector
Monitor
ONE Virtual
Container
User-defined
services
Service provider
29. ARCHITECTURE
• Components are implemented
as JavaWeb Services running in
ApacheTomcat.
• They provide RESTful
interfaces using Apache Wink.
• The SLA Manager and Monitor
components use theWSAG4J
framework to implement WS-
Agreement.
• The Infrastructure Connector
interfaces with ONE using its
API.
• The Catalog implements the
database using HSQLDB.
29
Orchestrator Catalog
Platform
Connector
SLA Manager
Service
Connector
Infrastructure
Connector
Monitor
ONE Virtual
Container
User-defined
services
Service provider
30. ARCHITECTURE
• SLA Manager
– Search: retrieves a new
SLA.
– Create: checks an SLA
offer, request service
deployment, register
SLA.
– Query: retrieves the state
of a running SLA.
– Delete: deallocates a
service, delete the
instance.
30
Orchestrator Catalog
Platform
Connector
SLA Manager
Service
Connector
Infrastructure
Connector
Monitor
ONE Virtual
Container
User-defined
services
1
Service provider
31. ARCHITECTURE
• SLA Manager
– Search: retrieves a new
SLA.
– Create: checks an SLA
offer, request service
deployment, register
SLA.
– Query: retrieves the state
of a running SLA.
– Delete: deallocates a
service, delete the
instance.
31
Orchestrator Catalog
Platform
Connector
SLA Manager
Service
Connector
Infrastructure
Connector
Monitor
ONE Virtual
Container
User-defined
services
1
2
3
4
Service provider
32. ARCHITECTURE
• SLA Manager
– Search: retrieves a new
SLA.
– Create: checks an SLA
offer, request service
deployment, register
SLA.
– Query: retrieves the state
of a running SLA.
– Delete: deallocates a
service, delete the
instance.
32
Orchestrator Catalog
Platform
Connector
SLA Manager
Service
Connector
Infrastructure
Connector
Monitor
ONE Virtual
Container
User-defined
services
1
Service provider
33. ARCHITECTURE
• SLA Manager
– Search: retrieves a new
SLA.
– Create: checks an SLA
offer, request service
deployment, register
SLA.
– Query: retrieves the state
of a running SLA.
– Delete: deallocates a
service, delete the
instance.
33
Orchestrator Catalog
Platform
Connector
SLA Manager
Service
Connector
Infrastructure
Connector
Monitor
ONE Virtual
Container
User-defined
services
1
3
4
2
Service provider
34. ARCHITECTURE
• Monitor
– Registers SLAs for
assessing active features.
– Assesses the state of the
SLAs periodically, in
monitoring intervals.
34
Orchestrator Catalog
Platform
Connector
SLA Manager
Service
Connector
Infrastructure
Connector
Monitor
ONE Virtual
Container
User-defined
services
Service provider
35. DYNAMIC SERVICE MANAGEMENT
• The monitor performs three operations
periodically while an SLA is active.
– Update the SLA state.
Retrieves the monitoring information (e.g. CPU and
memory usage) and updates the state of the service.
– Evaluate the QoS rules.
Use the monitoring information to evaluate the QoS rules.
– Performs self-management operations.
If a QoS is violated, executes corrective actions. Accounts
the usage of resources and bills the user.
35
36. ARCHITECTURE
• Orchestrator
– Assesses the passive
features of Cloud services.
– Global coordinator of
resources and services.
– View of all Cloud providers.
– Scheduling of Cloud
services.
– Delegates on different
connectors the deployment
of services on specific
provider.
– If no resources are
available, the SLA is
rejected.
36
Orchestrator Catalog
Platform
Connector
SLA Manager
Service
Connector
Infrastructure
Connector
Monitor
ONE Virtual
Container
User-defined
services
Service provider
37. ARCHITECTURE
• Connectors
– Façade to underlying Cloud
providers.
– Provides an uniform
interface.
– Uses plug-ins to support
different providers.
– Checks for
compatibility/availability of
resources.
– Translates from the SLA
representation to the
provider specific
representation.
– Configures the resources.
Relies on third party tools
to perform these actions.
37
Orchestrator Catalog
Platform
Connector
SLA Manager
Service
Connector
Infrastructure
Connector
Monitor
ONE Virtual
Container
User-defined
services
Service provider
38. ARCHITECTURE
• Catalog
– Stores relevant
information regarding
every element on the
framework
(SLAs, monitoring
information, etc.).
– Globally accessible.
– Provides a RESTful API.
Third party monitoring
systems store information.
38
Orchestrator Catalog
Platform
Connector
SLA Manager
Service
Connector
Infrastructure
Connector
Monitor
ONE Virtual
Container
User-defined
services
Service provider
40. USE CASE
• Resolution of a complete use case by
Cloudcompaas.
– Description, deployment and management of a
Cloud service.
• Validation of the Cloudcompaas methodology.
– Shows the qualitative benefits of this approach.
• Measure of the performance of the QoS
assessment capabilities of Cloudcompaas.
– Calculates the benefit of providing elasticity to a
Cloud service.
40
41. SERVICE DEVELOPER
• A developer registers an
application in
Cloudcompaas.
– jLinpack, a Java
implementation of
Linpack.
• The developer registers
the application bundle
and SLA fragment in
Cloudcompaas.
<Template>
<Service Name="jLinpack">
<ServiceDescription>
A Java implementation of the Linpack benchmark.
</ServiceDescription>
<CreationConstraints>
<Item Name="JavaVR">
<Location>
/VirtualContainer/VirtualRuntime[@Name=‘’openjdk-6-jre'']
</Location>
</Item>
</CreationConstraints>
</Service>
<GuaranteeTerm Name="JLINPACK-PRICE">
<ServiceLevelObjective>
<KPITarget>
<KPIName>STATE</KPIName>
<CustomServiceLevel>
JLINPACK_STATE eq 'Ready‘
</CustomServiceLevel>
</KPITarget>
</ServiceLevelObjective>
<BusinessValueList>
<Reward>
<AssessmentInterval>
<TimeInterval>PT1M</TimeInterval>
</AssessmentInterval>
<ValueExpression>0.001*ACT_REPLICAS</ValueExpression>
</Reward>
</BusinessValueList>
</GuaranteeTerm>
</Template>
41
42. SERVICE PROVIDER
• A provider deploys jLinpack instances to serve
users.
• He queries the system in order to retrieve SLAs
that describe a jLinpack Cloud service.
• The provider issues a query using the SLA
Manager REST interface.
GET slamanager/agreement/template?
include=Service+jLinpack
• Cloudcompaas returns three SLAs, each one
with a differentVM configuration.
42
43. ELASTICITY RULES
• The provider wants to add
elasticity capabilities to
jLinpack.
• He chooses the QoS rules that
control the elasticity for the
service from an ontology.
• These rules are predefined by
Cloudcompaas.
• The rules determine when new
replicas should be deployed
based on monitoring
information.
– E.g. if the average CPU load is
higher tan 90%, deploy a new
replica.
<GuaranteeTerm Name="SCALE_OUT">
<QualifyingCondition>
MAX_REPLICAS gt ACT_REPLICAS
</QualifyingCondition>
<ServiceLevelObjective>
<KPITarget>
<CustomServiceLevel>
list.avg(CPUPERC) le 90
</CustomServiceLevel>
</KPITarget>
</ServiceLevelObjective>
</GuaranteeTerm>
43
44. SERVICE USERS
• A user sends a request to jLinpack service.
• Several users can be served concurrently by a
replica.The higher the number of users, the
higher the response time.
• If a request takes more than 10 seconds to
complete, the request times out and it counts as
a failure.
• Replicas are balanced by an ad-hoc load-
balancing service.
44
45. QOS ASSESSMENT
• The jLinpackCloud service has been deployed in a local Cloudcompaas
deployment.The serviceVMs are deployed in a ONE 3.2 on-premise Cloud.
• The number of user requests per unit of time has been modelled after the user
load profile of different EGI scenarios (Chemistry, Fusion).These load profiles
have been scaled to fit the experiment size.
• Two experiments for different user loads.
• Two configurations, fixed and elastic.
– Fixed: 7 replicas for the complete experiment.
– Elastic:Variable number of replicas (1-7) managed by Cloudcompaas elasticity
rules.
• Metrics:
– Price of the service.
– Number of failed user requests.
– Average revenue per user ARPU: Metric used in telecommunications to measure
the revenue produced by a single user.
– Break-even point Be: Profit per user that yields the same profit for both
configurations.
45
48. METHODOLOGY
• Simulates an on-premise cloud of 20 machines that allocatesVM
for users.
• Users request a certain quantity of CPU and Memory and the
system provides them with aVM that most closely matches their
request.
• Two scenarios:
• Static templates:The system provides user with 7 predefinedVM
templates.
• Composed templates:The system compose templates for each user
request, using 64 fragments for CPU and Memory.
• Metrics.
• Average number of active nodes.
• Rejected users.
48
49. EXPERIMENTAL RESULTS
METHODOLOGY UTILITY
• Parameters of the simulation to produce different configurations.
– Node capacity (memory and cores).
– Arrival rate λ.
– VM time to live (TTL).
• The values for number of active nodes and rejection rate are
consistently lower for the composed scenario.
49
50. EXPERIMENT CONCLUSIONS
• The use case highlights the qualitative benefits of Cloudcompaas.
– The Service developer can describe his service independently of other resources.
– The Service provider only needs to specify his requirements to search for Cloud
services.
– Cloudcompaas doesn’t need to explicitly predefine Cloud services for each
resource.Avoids combinatorial explosion.
• The elastic configuration yields a lower price and a higher number
of failures. For services that expect a small profit per user the
tradeoff is positive.
• The performance of the elastic configuration highly depends on the
load profile.
– The same configuration produce different results depending on the load.
– Works best for highly variable or unpredictable loads.
• The Cloudcompaas methodology is able to improve the utilization
of resources in a Cloud deployment by better adjusting the resource
assignment to users.
50
52. CONCLUDING REMARKS
• Generic and extensible methodology for the
representation of Cloud services using SLAs.
• SLA-driven architecture for Cloud Services management.
• Cloudcompaas, an open source framework
implementation.
• Experiments validating the benefits of the methodology
and framework.
• Future work:
– Restriction representation system.
– Negotiation protocol.
– Decision making system.
52
53. CONTRIBUTIONS
• Journal papers
– Andrés García and Ignacio Blanquer, "Cloud domain
representation using SLA composition", Journal of Grid
Computing,Accepted, 2014, Impact factor 1.603, Q1
– Miguel Caballer et al., “CodeCloud: A Platform to Enable
Execution of Programming Models on the Clouds”
Journal of Systems and Software, DOI 10.1016/j.jss.2014.02.005,
2014, Impact factor 1.135, Q2
– Andrés García, Ignacio Blanquer andVicente Hernández, "SLA-
driven dynamic cloud resource management". Future Generation
Computer Systems (2013), 10.1016/j.future.2013.10.005, Impact
factor 1.864, Q1
– Andrés García et al., “Performance enhancement of a GIS-based
facility location problem using desktop grid infrastructure”. Earth
Science Informatics pp. 1-9 (2013). DOI 10.1007/s12145-013-0119-1,
Impact factor 0.404, Q4
53
54. CONTRIBUTIONS
• Conference papers
– Toni Mastelic, Ivona Brandic and Andrés García, “Towards Uniform Management of
Cloud Services by applying Model-Driven Development”,COMPSAC, Under Review
– MiguelCaballer, Andrés García, Germán Moltó and Carlos de Alfonso, “Towards SLA-
driven Management of Cloud Infrastructures to Elastically Execute Scientic
Applications”, Ibergrid 2012.
– AndrésGarcía, Carlos de Alfonso, andVicente Hernández, “Overview of current
commercial PaaS platforms”, “IWCCTA 2011 - InternationalWorkshop on Cloud
Computing,Technology and Applications”, inside the framework of the conference
“ICSOFT 2011 – 6º International Conference on Software and DataTechnologies”, July
2011
– AndrésGarcía et al., “Biomass@UPV: Computacional Resources Optimization of GIS-
based Applications using a BOINC Infraestructure”, 3rd Iberian Grid Infrastructure
Conference Proceedings, May 2009
– AndrésGarcía, Carlos de Alfonso andVicente Hernández, “Design of a Platform of
Virtual Service Containers for Service Oriented Cloud Computing”,CGW 2009
Proceedings. March 2010
54
55. CONTRIBUTIONS
• Research projects
– (2011-2013) Servicio avanzados para el despliegue y contextualización de aplicaciones
virtualizadas para dar soporte a modelos de programación en entornos Cloud. Ministerio de
Educación y Ciencia, Gobierno de España. Ref. TIN2010-17804
– (2006-2008) Supporting and structuring Healthgrid activities & research in Europe:
Developing a roadmap. European Commission. Ref. 027694
• Research visit
– 01/02/2013~30/04/2013. Distributed Systems Group,Technische Universität Wien. Integration
of the M4Cloud tool with the Cloudcompaas framework. Supervisor: Ivona Brandić.
• Marie Curie ITN postdoc position at IBM Haifa Labs. 18 months from May 2014 to
October 2015.
• Cloudcompaas framework.
– http://www.grycap.upv.es/compaas/
55
56. CONTRIBUTIONS
56
• GitHub repository of
source code.
• All code available under
a BSD 3-clause license.
• Redmine used to track
bugs and features.