The document summarizes insights from a 2008 workshop between researchers and leaders in the commercial cloud computing community. It discusses how the researchers had to revise their definition of cloud computing based on the perspectives shared by speakers from IBM, Microsoft, and eBay. While some current research topics seemed less important to the companies, the speakers also brought up new questions for researchers regarding challenges like managing infrastructure at massive scales.
This document discusses different types of computing models including cloud computing, grid computing, utility computing, distributed computing, and cluster computing. It provides details on each model, including definitions, key characteristics, and examples. The document also evaluates cloud computing in terms of business drivers for adoption such as business growth, efficiency, customer experience, and assurance. It explains the NIST cloud computing model including deployment models (private, public, hybrid, community clouds) and service models (SaaS, PaaS, IaaS). Finally, it discusses differences between cloud computing, grid computing and cluster computing and provides a note on characteristics and properties of cloud computing.
Datacenter and cloud architectures continue to evolve to address the needs of large-scale multi-tenant data centers and clouds. These needs are centered around dimensions such as scalability in computing, storage, and bandwidth, scalability in network services, efficiency in resource utilization, agility in service creation, cost efficiency, service reliability, and security. Data centers are interconnected across the wide area network via routing and transport technologies to provide a pool of resources, known as the cloud. High-speed optical interfaces and dense wavelength-division multiplexing optical transport are used to provide for high-capacity transport intra- and inter-datacenter. This presentation will provide some brief descriptions on the working principles of Cloud & Data Center Networks.
With expanding volumes of knowledgeable production and the variability of themes and roots, shapes and languages, most detectable issues related to the delivery of storage space for the information and the variety of treatment strategies in addition to the problems related to the flow of information and methods
go down and take an interest in the advantage of them face the researchers. In any case, such a great significance comes with a support of a great infrastructure that includes large data centers comprising thousands of server units and other supporting equipment. The cloud is not a small, undeveloped branch of it, it is a type of computing that is based on the internet, an image from the internet. Cloud Computing is a
developed technology, cloud computing, possibly offers an overall economic benefit, in that end users shares a large, centrally achieved pool of storing and computing resources, rather than owning and managing their own systems. But, it needs to be environment friendly also. This review paper gives a general overview of cloud computing, also it describes cloud computing, architecture of cloud computing, characteristics of cloud computing, and different services and deployment model of cloud computing. This paper is for anyone who will have recently detected regarding cloud computing and desires to grasp a lot of regarding cloud computing.
Nabil Sultan. The disruptive and democratizing credentials of cloud computingCBOD ANR project U-PSUD
The disruptive and democratizing credentials of cloud computing
Nabil Sultan
International conference on
“DATA, DIGITAL BUSINESS MODELS, CLOUD COMPUTING AND ORGANIZATIONAL DESIGN”
24-25 November 2014 ,
Université Paris –Sud
Challenges Management and Opportunities of Cloud DBAinventy
Research Inventy provides an outlet for research findings and reviews in areas of Engineering, Computer Science found to be relevant for national and international development, Research Inventy is an open access, peer reviewed international journal with a primary objective to provide research and applications related to Engineering. In its publications, to stimulate new research ideas and foster practical application from the research findings. The journal publishes original research of such high quality as to attract contributions from the relevant local and international communities.
Distributed Large Dataset Deployment with Improved Load Balancing and Perform...IJERA Editor
Cloud computing is a prototype for permitting universal, appropriate, on-demand network access. Cloud is a
method of computing where enormously scalable IT-enabled proficiencies are delivered „as a service‟ using
Internet tools to multiple outdoor clients. Virtualization is the establishment of a virtual form of something such
as computing device or server, an operating system, or network devices and storage device. The different names
for cloud data management are DaaS Data as a service, Cloud Storage, and DBaaS Database as a service. Cloud
storage permits users to store data, information in documents formats. iCloud, Google drive, Drop box, etc. are
most common and widespread cloud storage methods. The main challenges connected with cloud database are
fault tolerance, scalability, data consistency, high availability and integrity, confidentiality and many more.
Load balancing improves the performance of the data center. We propose an architecture which provides load
balancing to the cloud database. We introduced a load balancing server which calculates the load of the data
center using our proposed algorithm and distributes the data accordingly to the different data centers.
Experimental results showed that it also improve the performance of the cloud system.
The document provides an overview of cloud computing including:
- Definitions of distributed computing, cluster computing, utility computing, and cloud computing as trends in computing.
- A brief history of cloud computing including early concepts in the 1960s and milestones like Salesforce.com in 1999 and Amazon Web Services in 2002.
- Descriptions of the types of cloud including public, private, hybrid, and community clouds.
- Explanations of cloud service models including Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS).
- Discussions of cloud storage and advantages and disadvantages of cloud computing.
- Real-life examples of
This document discusses different types of computing models including cloud computing, grid computing, utility computing, distributed computing, and cluster computing. It provides details on each model, including definitions, key characteristics, and examples. The document also evaluates cloud computing in terms of business drivers for adoption such as business growth, efficiency, customer experience, and assurance. It explains the NIST cloud computing model including deployment models (private, public, hybrid, community clouds) and service models (SaaS, PaaS, IaaS). Finally, it discusses differences between cloud computing, grid computing and cluster computing and provides a note on characteristics and properties of cloud computing.
Datacenter and cloud architectures continue to evolve to address the needs of large-scale multi-tenant data centers and clouds. These needs are centered around dimensions such as scalability in computing, storage, and bandwidth, scalability in network services, efficiency in resource utilization, agility in service creation, cost efficiency, service reliability, and security. Data centers are interconnected across the wide area network via routing and transport technologies to provide a pool of resources, known as the cloud. High-speed optical interfaces and dense wavelength-division multiplexing optical transport are used to provide for high-capacity transport intra- and inter-datacenter. This presentation will provide some brief descriptions on the working principles of Cloud & Data Center Networks.
With expanding volumes of knowledgeable production and the variability of themes and roots, shapes and languages, most detectable issues related to the delivery of storage space for the information and the variety of treatment strategies in addition to the problems related to the flow of information and methods
go down and take an interest in the advantage of them face the researchers. In any case, such a great significance comes with a support of a great infrastructure that includes large data centers comprising thousands of server units and other supporting equipment. The cloud is not a small, undeveloped branch of it, it is a type of computing that is based on the internet, an image from the internet. Cloud Computing is a
developed technology, cloud computing, possibly offers an overall economic benefit, in that end users shares a large, centrally achieved pool of storing and computing resources, rather than owning and managing their own systems. But, it needs to be environment friendly also. This review paper gives a general overview of cloud computing, also it describes cloud computing, architecture of cloud computing, characteristics of cloud computing, and different services and deployment model of cloud computing. This paper is for anyone who will have recently detected regarding cloud computing and desires to grasp a lot of regarding cloud computing.
Nabil Sultan. The disruptive and democratizing credentials of cloud computingCBOD ANR project U-PSUD
The disruptive and democratizing credentials of cloud computing
Nabil Sultan
International conference on
“DATA, DIGITAL BUSINESS MODELS, CLOUD COMPUTING AND ORGANIZATIONAL DESIGN”
24-25 November 2014 ,
Université Paris –Sud
Challenges Management and Opportunities of Cloud DBAinventy
Research Inventy provides an outlet for research findings and reviews in areas of Engineering, Computer Science found to be relevant for national and international development, Research Inventy is an open access, peer reviewed international journal with a primary objective to provide research and applications related to Engineering. In its publications, to stimulate new research ideas and foster practical application from the research findings. The journal publishes original research of such high quality as to attract contributions from the relevant local and international communities.
Distributed Large Dataset Deployment with Improved Load Balancing and Perform...IJERA Editor
Cloud computing is a prototype for permitting universal, appropriate, on-demand network access. Cloud is a
method of computing where enormously scalable IT-enabled proficiencies are delivered „as a service‟ using
Internet tools to multiple outdoor clients. Virtualization is the establishment of a virtual form of something such
as computing device or server, an operating system, or network devices and storage device. The different names
for cloud data management are DaaS Data as a service, Cloud Storage, and DBaaS Database as a service. Cloud
storage permits users to store data, information in documents formats. iCloud, Google drive, Drop box, etc. are
most common and widespread cloud storage methods. The main challenges connected with cloud database are
fault tolerance, scalability, data consistency, high availability and integrity, confidentiality and many more.
Load balancing improves the performance of the data center. We propose an architecture which provides load
balancing to the cloud database. We introduced a load balancing server which calculates the load of the data
center using our proposed algorithm and distributes the data accordingly to the different data centers.
Experimental results showed that it also improve the performance of the cloud system.
The document provides an overview of cloud computing including:
- Definitions of distributed computing, cluster computing, utility computing, and cloud computing as trends in computing.
- A brief history of cloud computing including early concepts in the 1960s and milestones like Salesforce.com in 1999 and Amazon Web Services in 2002.
- Descriptions of the types of cloud including public, private, hybrid, and community clouds.
- Explanations of cloud service models including Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS).
- Discussions of cloud storage and advantages and disadvantages of cloud computing.
- Real-life examples of
A cross referenced whitepaper on cloud computingShahzad
The document defines cloud computing and its basic elements including SaaS, PaaS, IaaS, and utility computing. It discusses essential cloud characteristics like on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service. The document also covers cloud deployment models, platforms, applications, and criticism of cloud computing.
Cloud computing refers to a model of network computing where applications and services run on remote servers that are accessed over the internet. With cloud computing, computing resources such as processing power, storage, and applications are provided as an online service rather than being located on a local device. Key benefits of cloud computing include reduced costs, flexibility and scalability, as computing resources can be dynamically allocated on demand. Popular cloud services include software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS). Major cloud providers include Amazon Web Services, Google Cloud, Microsoft Azure, IBM Cloud, and Oracle Cloud.
Efficient and reliable hybrid cloud architecture for big databaseijccsa
The objective of our paper is to propose a Cloud computing framework which is feasible and necessary for
handling huge data. In our prototype system we considered national ID database structure of Bangladesh
which is prepared by election commission of Bangladesh. Using this database we propose an interactive
graphical user interface for Bangladeshi People Search (BDPS) that use a hybrid structure of cloud
computing handled by apache Hadoop where database is implemented by HiveQL. The infrastructure
divides into two parts: locally hosted cloud which is based on “Eucalyptus” and the remote cloud which is
implemented on well-known Amazon Web Service (AWS). Some common problems of Bangladesh aspect
which includes data traffic congestion, server time out and server down issue is also discussed.
The past decade has seen increasingly ambitious and successful methods for outsourcing computing. Approaches such as utility computing, on-demand computing, grid computing, software as a service, and cloud computing all seek to free computer applications from the limiting confines of a single computer. Software that thus runs "outside the box" can be more powerful (think Google, TeraGrid), dynamic (think Animoto, caBIG), and collaborative (think FaceBook, myExperiment). It can also be cheaper, due to economies of scale in hardware and software. The combination of new functionality and new economics inspires new applications, reduces barriers to entry for application providers, and in general disrupts the computing ecosystem. I discuss the new applications that outside-the-box computing enables, in both business and science, and the hardware and software architectures that make these new applications possible.
The document provides an overview of the evolution of cloud computing from its roots in mainframe computing, distributed systems, grid computing, and cluster computing. It discusses how hardware virtualization, Internet technologies, distributed computing concepts, and systems management techniques enabled the development of cloud computing. The document then describes several early technologies and models such as time-shared mainframes, distributed systems, grid computing, and cluster computing that influenced the development of cloud computing.
This document is a seminar report on cloud computing submitted by Vishnuvarunan.T. It provides an introduction to cloud computing, discussing its key characteristics including on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service. It also covers cloud service models such as Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). The document discusses cloud deployment models including private cloud, community cloud, public cloud, and hybrid cloud. It notes some benefits of cloud computing like cost savings and scalability, as well as challenges around security, privacy, lack of standards, and compliance concerns.
This document provides a 3 paragraph summary of a seminar report on cloud computing submitted by Rahul Gupta to his professor Shraddha Khenka. The report acknowledges those who contributed to advancements in internet and computing technologies that enable cloud computing. It includes an introduction to cloud computing, comparisons to other technologies, economics of cloud computing, architectural layers, key features, deployment models, and issues. The summary covers the essential topics and information presented in the seminar report on cloud computing.
Cloud computing allows users to access computing resources like servers, storage, databases, networking, software, analytics and more over the internet. It provides scalability, reliability and cost savings. There are different cloud service models like Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). Users can choose between public, private or hybrid cloud deployment models based on their needs.
Innovation for Participation - Paul De Decker, Sun Microsystemsrobinwauters
The document discusses Sun Microsystems' strategy of providing an open source software stack called Solaris AMP (Apache, MySQL, PHP) that is optimized to run on their Solaris operating system. It promotes the benefits of the Solaris operating system and tools to help speed development and deployment. Additionally, it outlines Sun's approach of providing many free and open source software options along with support services to gain customers.
Cloud computing is basically storing and accessing data and sharing resources over the internet rather than having local servers or personal device to handle applications.
Cloud computing is Internet based development and use of computer technology. It is a style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet. Users need not have knowledge of, expertise in, or control over the technology infrastructure "in the cloud" that supports them. Cloud computing is a hot topic all over the world nowadays, through which customers can access information and computer power via a web browser. As the adoption and deployment of cloud computing increase, it is critical to evaluate the performance of cloud environments. Currently, modeling and simulation technology has become a useful and powerful tool in cloud computing research community to deal with these issues. Cloud simulators are required for cloud system testing to decrease the complexity and separate quality concerns. Cloud computing means saving and accessing the data over the internet instead of local storage. In this paper, we have provided a short review on the types, models and architecture of the cloud environment.
This document provides an overview of cloud computing, including its basic functioning, characteristics, service models (IaaS, PaaS, SaaS), types of clouds (private, public, hybrid, multi-cloud, community), and advantages and disadvantages. Cloud computing allows on-demand access to shared configurable computing resources via the internet. It provides various capabilities for users to store and process data in third-party data centers. The main service models are infrastructure as a service, platform as a service, and software as a service.
This document compares and contrasts cloud computing and grid computing. Grid computing refers to cooperation between multiple computers and servers to boost computational power, with a focus on high-capacity CPU tasks. Cloud computing delivers on-demand access to shared computing resources like networks, servers, storage and applications via the internet. Key differences include grid computing having a lower level of abstraction and scalability compared to cloud computing. Cloud computing also has stronger fault tolerance, is more widely accessible via the internet, and offers real-time services through its utility-based pricing model.
LOCALITY SIM: CLOUD SIMULATOR WITH DATA LOCALITY ijccsa
Cloud Computing (CC) is a model for enabling on-demand access to a shared pool of configurable computing resources. Testing and evaluating the performance of the cloud environment for allocating, provisioning, scheduling, and data allocation policy have great attention to be achieved. Therefore, using cloud simulator would save time and money, and provide a flexible environment to evaluate new research
work. Unfortunately, the current simulators (e.g., CloudSim, NetworkCloudSim, GreenCloud, etc..) deal with the data as for size only without any consideration about the data allocation policy and locality. On the other hand, the NetworkCloudSim simulator is considered one of the most common used simulators because it includes different modules which support needed functions to a simulated cloud environment,
and it could be extended to include new extra modules. According to work in this paper, the NetworkCloudSim simulator has been extended and modified to support data locality. The modified simulator is called LocalitySim. The accuracy of the proposed LocalitySim simulator has been proved by building a mathematical model. Also, the proposed simulator has been used to test the performance of the
three-tire data center as a case study with considering the data locality feature
Cloud computing is an emerging technology that uses remote servers and the internet to maintain data and applications. It provides computing resources like storage, servers, and enterprise applications delivered over the internet. The cloud offers an on-demand, flexible environment that saves corporations money while providing scalable, secure access to resources from any internet-connected device. Popular cloud services include Google Apps, Amazon Web Services, and Microsoft Azure.
Cloud computing provides on-demand access to shared computing resources like networks, servers, storage, applications and services. It has essential characteristics like on-demand self-service, broad network access, resource pooling and rapid elasticity. The cloud services models include Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). The deployment models are private cloud, community cloud, public cloud and hybrid cloud.
This chapter introduces cloud computing and discusses its key concepts. It describes cloud computing as a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. It discusses the delivery models of Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). The chapter also outlines some of the benefits of cloud computing as well as challenges and ethical issues that need to be addressed for its successful adoption.
This document introduces the concept of Variability as a Service (VaaS) which allows Software as a Service (SaaS) providers to outsource variability management in their multi-tenant applications to VaaS providers. It presents the VaaS meta-model and architecture which defines the process of variability specification and execution between SaaS providers, tenants, and VaaS providers. SaaS providers can model variability in their applications using the VaaS meta-model and store it with a VaaS provider. Tenants then customize the application by selecting variants, with their choices stored in customization documents. At runtime, application variability is resolved by the VaaS provider using the variability model and customizations
This course provides a comprehensive study of cloud computing concepts across infrastructure, platform, software and business process as a service models through hands-on assignments and projects. Students will learn how to configure cloud infrastructure and develop applications on various cloud platforms. Topics also include cloud security, high performance computing, and leveraging software and business process as a service solutions to build business applications in the cloud.
A cross referenced whitepaper on cloud computingShahzad
The document defines cloud computing and its basic elements including SaaS, PaaS, IaaS, and utility computing. It discusses essential cloud characteristics like on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service. The document also covers cloud deployment models, platforms, applications, and criticism of cloud computing.
Cloud computing refers to a model of network computing where applications and services run on remote servers that are accessed over the internet. With cloud computing, computing resources such as processing power, storage, and applications are provided as an online service rather than being located on a local device. Key benefits of cloud computing include reduced costs, flexibility and scalability, as computing resources can be dynamically allocated on demand. Popular cloud services include software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS). Major cloud providers include Amazon Web Services, Google Cloud, Microsoft Azure, IBM Cloud, and Oracle Cloud.
Efficient and reliable hybrid cloud architecture for big databaseijccsa
The objective of our paper is to propose a Cloud computing framework which is feasible and necessary for
handling huge data. In our prototype system we considered national ID database structure of Bangladesh
which is prepared by election commission of Bangladesh. Using this database we propose an interactive
graphical user interface for Bangladeshi People Search (BDPS) that use a hybrid structure of cloud
computing handled by apache Hadoop where database is implemented by HiveQL. The infrastructure
divides into two parts: locally hosted cloud which is based on “Eucalyptus” and the remote cloud which is
implemented on well-known Amazon Web Service (AWS). Some common problems of Bangladesh aspect
which includes data traffic congestion, server time out and server down issue is also discussed.
The past decade has seen increasingly ambitious and successful methods for outsourcing computing. Approaches such as utility computing, on-demand computing, grid computing, software as a service, and cloud computing all seek to free computer applications from the limiting confines of a single computer. Software that thus runs "outside the box" can be more powerful (think Google, TeraGrid), dynamic (think Animoto, caBIG), and collaborative (think FaceBook, myExperiment). It can also be cheaper, due to economies of scale in hardware and software. The combination of new functionality and new economics inspires new applications, reduces barriers to entry for application providers, and in general disrupts the computing ecosystem. I discuss the new applications that outside-the-box computing enables, in both business and science, and the hardware and software architectures that make these new applications possible.
The document provides an overview of the evolution of cloud computing from its roots in mainframe computing, distributed systems, grid computing, and cluster computing. It discusses how hardware virtualization, Internet technologies, distributed computing concepts, and systems management techniques enabled the development of cloud computing. The document then describes several early technologies and models such as time-shared mainframes, distributed systems, grid computing, and cluster computing that influenced the development of cloud computing.
This document is a seminar report on cloud computing submitted by Vishnuvarunan.T. It provides an introduction to cloud computing, discussing its key characteristics including on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service. It also covers cloud service models such as Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). The document discusses cloud deployment models including private cloud, community cloud, public cloud, and hybrid cloud. It notes some benefits of cloud computing like cost savings and scalability, as well as challenges around security, privacy, lack of standards, and compliance concerns.
This document provides a 3 paragraph summary of a seminar report on cloud computing submitted by Rahul Gupta to his professor Shraddha Khenka. The report acknowledges those who contributed to advancements in internet and computing technologies that enable cloud computing. It includes an introduction to cloud computing, comparisons to other technologies, economics of cloud computing, architectural layers, key features, deployment models, and issues. The summary covers the essential topics and information presented in the seminar report on cloud computing.
Cloud computing allows users to access computing resources like servers, storage, databases, networking, software, analytics and more over the internet. It provides scalability, reliability and cost savings. There are different cloud service models like Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). Users can choose between public, private or hybrid cloud deployment models based on their needs.
Innovation for Participation - Paul De Decker, Sun Microsystemsrobinwauters
The document discusses Sun Microsystems' strategy of providing an open source software stack called Solaris AMP (Apache, MySQL, PHP) that is optimized to run on their Solaris operating system. It promotes the benefits of the Solaris operating system and tools to help speed development and deployment. Additionally, it outlines Sun's approach of providing many free and open source software options along with support services to gain customers.
Cloud computing is basically storing and accessing data and sharing resources over the internet rather than having local servers or personal device to handle applications.
Cloud computing is Internet based development and use of computer technology. It is a style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet. Users need not have knowledge of, expertise in, or control over the technology infrastructure "in the cloud" that supports them. Cloud computing is a hot topic all over the world nowadays, through which customers can access information and computer power via a web browser. As the adoption and deployment of cloud computing increase, it is critical to evaluate the performance of cloud environments. Currently, modeling and simulation technology has become a useful and powerful tool in cloud computing research community to deal with these issues. Cloud simulators are required for cloud system testing to decrease the complexity and separate quality concerns. Cloud computing means saving and accessing the data over the internet instead of local storage. In this paper, we have provided a short review on the types, models and architecture of the cloud environment.
This document provides an overview of cloud computing, including its basic functioning, characteristics, service models (IaaS, PaaS, SaaS), types of clouds (private, public, hybrid, multi-cloud, community), and advantages and disadvantages. Cloud computing allows on-demand access to shared configurable computing resources via the internet. It provides various capabilities for users to store and process data in third-party data centers. The main service models are infrastructure as a service, platform as a service, and software as a service.
This document compares and contrasts cloud computing and grid computing. Grid computing refers to cooperation between multiple computers and servers to boost computational power, with a focus on high-capacity CPU tasks. Cloud computing delivers on-demand access to shared computing resources like networks, servers, storage and applications via the internet. Key differences include grid computing having a lower level of abstraction and scalability compared to cloud computing. Cloud computing also has stronger fault tolerance, is more widely accessible via the internet, and offers real-time services through its utility-based pricing model.
LOCALITY SIM: CLOUD SIMULATOR WITH DATA LOCALITY ijccsa
Cloud Computing (CC) is a model for enabling on-demand access to a shared pool of configurable computing resources. Testing and evaluating the performance of the cloud environment for allocating, provisioning, scheduling, and data allocation policy have great attention to be achieved. Therefore, using cloud simulator would save time and money, and provide a flexible environment to evaluate new research
work. Unfortunately, the current simulators (e.g., CloudSim, NetworkCloudSim, GreenCloud, etc..) deal with the data as for size only without any consideration about the data allocation policy and locality. On the other hand, the NetworkCloudSim simulator is considered one of the most common used simulators because it includes different modules which support needed functions to a simulated cloud environment,
and it could be extended to include new extra modules. According to work in this paper, the NetworkCloudSim simulator has been extended and modified to support data locality. The modified simulator is called LocalitySim. The accuracy of the proposed LocalitySim simulator has been proved by building a mathematical model. Also, the proposed simulator has been used to test the performance of the
three-tire data center as a case study with considering the data locality feature
Cloud computing is an emerging technology that uses remote servers and the internet to maintain data and applications. It provides computing resources like storage, servers, and enterprise applications delivered over the internet. The cloud offers an on-demand, flexible environment that saves corporations money while providing scalable, secure access to resources from any internet-connected device. Popular cloud services include Google Apps, Amazon Web Services, and Microsoft Azure.
Cloud computing provides on-demand access to shared computing resources like networks, servers, storage, applications and services. It has essential characteristics like on-demand self-service, broad network access, resource pooling and rapid elasticity. The cloud services models include Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). The deployment models are private cloud, community cloud, public cloud and hybrid cloud.
This chapter introduces cloud computing and discusses its key concepts. It describes cloud computing as a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. It discusses the delivery models of Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). The chapter also outlines some of the benefits of cloud computing as well as challenges and ethical issues that need to be addressed for its successful adoption.
This document introduces the concept of Variability as a Service (VaaS) which allows Software as a Service (SaaS) providers to outsource variability management in their multi-tenant applications to VaaS providers. It presents the VaaS meta-model and architecture which defines the process of variability specification and execution between SaaS providers, tenants, and VaaS providers. SaaS providers can model variability in their applications using the VaaS meta-model and store it with a VaaS provider. Tenants then customize the application by selecting variants, with their choices stored in customization documents. At runtime, application variability is resolved by the VaaS provider using the variability model and customizations
This course provides a comprehensive study of cloud computing concepts across infrastructure, platform, software and business process as a service models through hands-on assignments and projects. Students will learn how to configure cloud infrastructure and develop applications on various cloud platforms. Topics also include cloud security, high performance computing, and leveraging software and business process as a service solutions to build business applications in the cloud.
This document presents a literature analysis of Business Process as a Service (BPaaS) and proposes an architecture for BPaaS. The summary is:
1) A literature review finds increasing publications on BPaaS since 2013, focusing on business perspectives like outsourcing processes to the cloud, and development perspectives like implementation technologies.
2) An example application is presented that was built on top of the OpenStack cloud platform using its RESTful APIs, demonstrating how external applications can utilize cloud services.
3) An architecture for BPaaS is proposed that adds a new layer to existing cloud computing reference architectures to represent business processes and services, with RESTful APIs enabling connections between applications and components.
Big Data: Concepts, techniques et démonstration de Apache Hadoophajlaoui jaleleddine
C'est une initiation au Big Data, qui est présenté dans un workshop organisé en 12 Décembre 2015 par un club TB3C (Tunisian Big Data Cloud Computing Community) au sein de ISSAT de Sousse
This document summarizes a study on a new dynamic load balancing approach in cloud environments. It begins by outlining some of the major challenges of load balancing in cloud systems, including uneven distribution of workloads across CPUs. It then proposes a new approach with three main components: 1) A queueing and job assignment process that prioritizes assigning jobs to faster CPUs, 2) A timeout chart to determine when jobs should be migrated or terminated to avoid delays, and 3) Use of a "super node" to act as a proxy and backup in case other nodes fail. The approach is intended to more efficiently distribute jobs and help cloud systems maintain optimal performance. Finally, the document discusses how this approach could be integrated into existing cloud architectures
cloud computing is a growing field in computer science. This ppt can help the beginners understand it. contains information about PaaS, Iaas, SaaS and other concepts of Cloud Computing.It also contains a video on cloud computing.
Cloud computing allows users to access technology services over the Internet on an as-needed basis. It provides on-demand access to shared computing resources like networks, servers, storage, databases, software, analytics and more without users having to maintain the infrastructure. The key characteristics of cloud computing include on-demand self-service, broad network access, resource pooling, rapid elasticity and measured service. The document discusses the history and components of cloud computing.
The document provides an overview of cloud architecture, services, and storage. It defines cloud architecture as the components and relationships between databases, software, applications, and other resources leveraged to solve business problems. The main components are on-premise resources, cloud resources, software/services, and middleware. Three common cloud service models are also defined - Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). Amazon Simple Storage Service (S3) is discussed as a cloud storage service that stores unlimited data in buckets with fine-grained access controls and analytics capabilities.
It's a simple presentation I did it with my friend Khawlah Al-Mazyd last year as a one topic should we cover it through doing Advanced Network course.
2010 - King Saud Universty
Riyadh - Saudi Arabia
This document provides an overview of cloud computing, including definitions, examples of cloud services, basic concepts around service and deployment models, and advantages and disadvantages. Specifically, it defines cloud computing as on-demand access to computer resources without direct management. It lists common cloud services like Google Drive, Dropbox, and AWS. It also describes the main service models of SaaS, PaaS, and IaaS and deployment models of public, private, and hybrid clouds. Finally, it outlines advantages like flexibility and cost savings as well as disadvantages like lack of control and potential bandwidth issues.
Cloud computing involves accessing applications and data storage over the internet instead of on a local computer. It provides scalable resources, software, and data storage through large distributed server networks. Key elements include clients that access cloud services, data centers that house servers, and distributed servers across multiple locations. Common cloud services are Software as a Service (SaaS), Platform as a Service (PaaS), and Hardware as a Service (HaaS). Cloud deployment options include private, public, hybrid, and community clouds depending on the organization and intended users.
The document discusses elastic data warehousing in the cloud. It begins with an introduction to data warehousing and cloud computing. Cloud computing offers benefits like reduced costs, expertise, and elasticity. However, challenges include data import/export performance, low-end cloud nodes, latency, and loss of control. The goal is an elastic data warehousing system that can automatically scale resources based on usage, saving money. It will provide overviews of traditional data warehousing and current cloud offerings to analyze the potential for elastic data warehousing in the cloud.
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1. TOWARDS A CLOUD COMPUTING RESEARCH AGENDA
Ken Birman, Gregory Chockler, Robbert van Renesse
Abstract
The 2008 LADIS workshop on Large Scale Distributed Systems brought together leaders from the
commercial cloud computing community with researchers working on a variety of topics in
distributed computing. The dialog yielded some surprises: some hot research topics seem to be of
limited near-term importance to the cloud builders, while some of their practical challenges seem to
pose new questions to us as systems researchers. This brief note summarizes our impressions.
Workshop Background
LADIS is an annual workshop focusing on the state of the art in distributed systems. The workshops
are by invitation, with the organizing committee setting the agenda. In 2008, the committee
included ourselves, Eliezer Dekel, Paul Dantzig, Danny Dolev, and Mike Spreitzer. The workshop
website, at http://www.cs.cornell.edu/projects/ladis2008/, includes the detailed agenda, white papers,
and slide sets [23]; proceedings are available electronically from the ACM Portal web site [22].
LADIS 2008 Topic
The 2008 LADIS topic was Cloud Computing, and more specifically:
• Management infrastructure tools (examples would include Chubby [4], Zookeeper [28], Paxos
[24], [20], Boxwood [25], Group Membership Services, Distributed Registries, Byzantine
State Machine Replication [6], etc),
• Scalable data sharing and event notification (examples include Pub-Sub platforms, Multicast
[35], Gossip [34], Group Communication [8], DSM solutions like Sinfonia [1], etc),
• Network-Level and other resource-managed technologies (Virtualization and Consolidation,
Resource Allocation, Load Balancing, Resource Placement, Routing, Scheduling, etc),
• Aggregation, Monitoring (Astrolabe [33], SDIMS [36], Tivoli, Reputation).
In 2008, LADIS had three keynote speakers, one of whom shared his speaking slot with a colleague:
• Jerry Cuomo, IBM Fellow, VP, and CTO for IBM’s Websphere product line. Websphere is
IBMs flagship product in the web services space, and consists of a scalable platform for
deploying and managing demanding web services applications. Cuomo has been a key player
in the effort since its inception.
• James Hamilton, at that time a leader within Microsoft’s new Cloud Computing Initiative.
Hamilton came to the area from a career spent designing and deploying scalable database
systems and clustered data management platforms, first at Oracle and then at Microsoft.
(Subsequent to LADIS, he joined Amazon.com.)
• Franco Travostino and Randy Shoup, who lead eBay’s architecture and scalability effort.
Both had long histories in the parallel database arena before joining eBay and both
participated in eBay’s scale-out from early in that company’s launch.
We won’t try and summarize the three talks (slide sets for all of them are online at the LADIS web
site, and additional materials such as blogs and videotaped talks at [18], [29]). Rather, we want to
2. focus on three insights we gained by comparing the perspectives articulated in the keynote talks with
the cloud computing perspective represented by our research speakers:
• We were forced to revise our “definition” of cloud computing.
• The keynote speakers seemingly discouraged work on some currently hot research topics.
• Conversely, they left us thinking about a number of questions that seem new to us.
Cloud Computing Defined
Not everyone agrees on the meaning of cloud computing. Broadly, the term has an “outward
looking” and an “inward looking” face. From the perspective of a client outside the cloud, one could
cite the Wikipedia definition:
Cloud computing is Internet (cloud) based development and use of computer technology
(computing), whereby dynamically scalable and often virtualized resources are provided as a
service over the Internet. Users need not have knowledge of, expertise in, or control over the
technology infrastructure "in the cloud" that supports them.
The definition is broad enough to cover everything from web search to photo sharing to social
networking. Perhaps the key point is simply that cloud computing resources should be accessible by
the end user anytime, anywhere, and from any platform (be it a cell phone, mobile computing
platform or desktop).
The outward facing side of cloud computing has a growing set of associated standards. By and large:
• Cloud resources are accessed from browsers, “minibrowsers” running JavaScript/AJAX or
similar code, or at a program level using web services standards. For example, many cloud
platforms employ SOAP as a request encoding standard, and HTTP as the preferred way to
actually transmit the SOAP request to the cloud platform, and to receive a reply.
• Although the client thinks of the cloud as a single entity, the implementation typically
requires one or more data centers, composed of potentially huge numbers of service instances
running on a large amount of hardware. Inexpensive commodity PCs structured into clusters
are popular. A typical data center has an outward facing bank of servers with which client
systems interact directly. Cloud systems implement a variety of DNS and load-
balancing/routing mechanisms to control the routing of client requests to actual servers.
• The external servers, which often run in a “demilitarized zone” (outside any firewall),
perform “business logic.” This typically involves extracting the client request and
parallelizing it within some set of services that do the work. The server then collects replies,
combines them into a single “result,” and sends it back to the client.
There is also an inside facing perspective:
• A cloud service is implemented by some sort of pool of servers that either share a database
subsystem or replicate data [14]. The replication technology is very often supported by
some form of scalable, high-speed update propagation technology, such as publish-subscribe
message bus (in web services, the term Enterprise Service Bus or ESB is a catch-all for such
mechanisms).
3. • Cloud platforms are highly automated: management of these server pools (including such
tasks as launching servers, shutting them down, load balancing, failure detection and handling)
are performed by standardized infrastructure mechanisms.
• A cloud system will often provide its servers with some form of shared global file system, or
in-memory store services. For example, Google’s GFS [16], Yahoo!’s HDFS [3],
Amazon.com’s S3 [2], memcached [26], and Amazon Dynamo [13] are widely cited. These
are specific solutions; the more general statement is simply that servers share files, databases,
and other forms of content.
• Server pools often need ways to coordinate when shared configuration or other shared state is
updated. In support of this many cloud systems provide some form of locking or atomic
multicast mechanism with strong properties [4], [28]. Some very large-scale services use
tools like Distributed Hash Tables (DHTs) to rapidly find information shared within a pool of
servers, or even as part of a workload partitioning scheme (for example, Amazon’s
shopping-cart service uses a DHT to spread the shopping cart function over a potentially
huge number of server machines).
We’ve focused the above list on the interactive side of a data center, which supports the clustered
server pools with which clients actually interact. But these in turn will often depend upon “back
office” functionality: activities that run in the background and prepare information that will be used
by the servers actually handling client requests. At Google, these back office roles include computing
search indices. Examples of widely known back-office supporting technologies include:
• Scheduling mechanisms that assign tasks to machines, but more broadly, play the role of
provisioning the data center as a whole. As we’ll see below, this aspect of cloud computing is
of growing importance because of its organic connection to power consumption: both to spin
disks and run machines, but also because active machines produce heat and demand cooling.
Scheduling, it turns out, comes down to “deciding how to spend money.”
• Storage systems that include not just the global file system but also scalable database systems
and other scalable transactional subsystems and middleware such as Google’s BigTable [7],
which provides an extensive (conceptually unlimited) table structure implemented over GFS.
• Control systems for large-scale distributed data processing like MapReduce [11] and
DryadLINQ [37].
• Archival data organization tools, applications that compress information or compute
indexes, applications that look for duplicate versions of objects, etc.
In summary, cloud computing lacks any crisp or simple definition. Trade publications focus on cloud
computing as a realization of a form of ubiquitous computing and storage, in which such functionality
can be viewed as a new form of cyber-supported “utility”. One often reads about the cloud as an
analog of the electric power outlet or the Internet itself. From this perspective, the cloud is defined
not by the way it was constructed, but rather by the behavior it offers. Technologists, in turn, have a
tendency to talk about the components of a cloud (like GFS, BigTable, Chubby) but doing so can lose
track of the context in which those components will be used – a context that is often very peculiar
when compared with general enterprise computing systems.
Is the Distributed Systems Research Agenda Relevant?
We would like to explore this last point in greater detail. If the public perception of the cloud is
largely oblivious to the implementation of the associated data centers, the research community can
4. seem oblivious to the way mechanisms are used. Researchers are often unaware that cloud systems
have overarching design principles that guide developers towards a cloud-computing mindset quite
distinct from what we may have been familiar with from our work in the past, for example on
traditional client-server systems or traditional multicast protocols. Failing to keep the broader
principles in mind can have the effect of overemphasizing certain cloud computing components or
technologies, while losing track of the way that the cloud uses those components and technologies.
Of course if the use was arbitrary or similar enough to those older styles of client-server system, this
wouldn’t matter. But because the cloud demands obedience to those overarching design goals, what
might normally seem like mere application-level detail instead turns out to be dominant and to have
all sorts of lower level implications.
Just as one could criticize the external perspective (“ubiquitous computing”) as an oversimplification,
LADIS helped us appreciate that when the research perspective overlooks the roles of our
technologies, we can sometimes wander off on tangents by proposing “new and improved” solutions
to problems that actually run contrary to the overarching spirit of the cloud mechanisms that will use
these technologies.
To see how this can matter, consider the notion of distributed systems consistency. The research
community thinks of consistency in terms of very carefully specified models such as the
transactional database model, atomic broadcast, Consensus, etc. We tend to reason along the
following lines: Google uses Chubby (a locking service) and Chubby uses State Machine Replication
based on Paxos. Thus Consensus, an essential component of State Machine Replication, should be
seen as a legitimate cloud computing topic: Consensus is “relevant” by virtue of its practical
application to a major cloud computing infrastructure. We then generalize: research on Consensus,
new Consensus protocols and tools, alternatives to Consensus are all “cloud computing topics”.
While all of this is true, our point is that Consensus, for Google, wasn’t the goal. Sure, locking
matters in Google, this is why they built a locking service. But the bigger point is that even though
large data centers need locking services, if one can trust our keynote speakers, application developers
are under huge pressure not to use them. We’re reminded of the old story of the blind men touching
the elephant. When we reason that “Google needed Chubby, so Consensus as used to support locking
is a key cloud computing technology,” we actually skip past the actual design principle and jump
directly to the details: this way of building a locking service versus that one. In doing so, we lose
track of the broader principle, which is that distributed locking is a bad thing that must be avoided!
This particular example is a good one because, as we’ll see shortly, if there was a single overarching
theme within the keynote talks, it turns out to be that strong synchronization of the sort provided
by a locking service must be avoided like the plague. This doesn’t diminish the need for a tool like
Chubby; when locking actually can’t be avoided, one wants a reliable, standard, provably correct
solution. Yet it does emphasize the sense in which what we as researchers might have thought of as
the main point (“the vital role of consistency and Consensus”) is actually secondary in a cloud
setting. Seen in this light, one realizes that while research on Consensus remains valuable, it was a
mistake to portray it as if it was research on the most important aspect of cloud computing.
Our keynote speakers made it clear that in focusing overly narrowly, the research community often
misses the bigger point. This is ironic: most of the researchers who attended LADIS are the sorts of
people who teach their students to distinguish a problem statement from a solution to that problem,
and yet by overlooking the reasons that cloud platforms need various mechanisms, we seem to be
5. guilty of fine-tuning specific solutions without adequately thinking about the context in which they
are used and the real needs to which they respond – aspects that can completely reshape a problem
statement. To go back to Chubby: once one realizes that locking is a technology of last resort, while
building a great locking service is clearly the right thing to do, one should also ask what research
questions are posed by the need to support applications that can safely avoid locking. Sure,
Consensus really matters, but if we focus too strongly on it, we risk losing track of its limited
importance in the bigger picture.
Let’s look at a second example just to make sure this point is clear. During his LADIS keynote,
Microsoft’s James Hamilton commented that for reasons of autonomous control, large data centers
have adopted a standard model resembling the well-known Recovery-Oriented Computing (ROC)
paradigm [27], [5]. In this model, every application must be designed with a form of automatic fault
handling mechanism. In short, this mechanism suspects an application if any other component
complains that it is misbehaving. Once suspected by a few components, or suspected strenuously by
even a single component, the offending application is rebooted – with no attempt to warn its clients
or ensure that the reboot will be graceful or transparent or non-disruptive. The focus apparently is
on speed: just push the reboot button. If this doesn’t clear the problem, James described a series of
next steps: the application might be automatically reinstalled on a fresh operating system instance,
or even moved to some other node—again, without the slightest effort to warn clients.
What do the clients do? Well, they are forced to accept that services behave this way, and
developers code around the behavior. They try and use idempotent operations, or implement ways
to resynchronize with a server when a connection is abruptly broken.
Against this backdrop, Hamilton pointed to the body of research on transparent task migration:
technology for moving a running application from one node to another without disrupting the
application or its clients. His point? Not that the work in question isn’t good, hard, or publishable.
But simply that cloud computing systems don’t need such a mechanism: if a client can (somehow)
tolerate a service being abruptly restarted, reimaged or migrated, there is no obvious value to adding
“transparent online migration” to the menu of options. Hamilton sees this as analogous to the end-
to-end argument: if a low level mechanism won’t simplify the higher level things that use it, how can
one justify the complexity and cost of the low level tool?
Earlier, we noted that although it wasn’t really our intention when we organized LADIS 2008,
Byzantine Consensus turned out to be a hot topic. It was treated, at least in passing, by surprisingly
many LADIS researchers in their white papers and talks. Clearly, our research community is not
only interested in Byzantine Consensus, but also perceives Byzantine fault tolerance to be of value in
cloud settings.
What about our keynote speakers? Well, the quick answer is that they seemed relatively uninterested
in Consensus, let alone Byzantine Consensus. One could imagine many possible explanations. For
example, some industry researchers might be unaware of the Consensus problem and associated
theory. Such a person might plausibly become interested once they learn more about the importance
of the problem. Yet this turns out not to be the case for our four keynote speakers, all of whom
have surprisingly academic backgrounds, and any of whom could deliver a nuanced lecture on the
state of the art in fault-tolerance.
6. The underlying issue was quite the opposite: the speakers believe themselves to understand something
we didn’t understand. They had no issue with Byzantine Consensus, but it just isn’t a primary
question for them. We can restate this relative to Chubby. One of the LADIS attendees commented
at some point that Byzantine Consensus could be used to improve Chubby, making it tolerant of
faults that could disrupt it as currently implemented. But for our keynote speakers, enhancing
Chubby to tolerate such faults turns out to be of purely academic interest. The bigger – the
overarching – challenge is to find ways of transforming services that might seem to need locking into
versions that are loosely coupled and can operate correctly without locking [18] – to get Chubby
(and here we’re picking on Chubby: the same goes for any synchronization protocol) off the critical
path.
The principle in question was most clearly expressed by Randy Shoup, who presented the eBay
system as an evolution that started with a massive parallel database, but then diverged from the
traditional database model over time. As Shoup explained, to scale out, eBay services started with
the steps urged by Jim Gray in his famous essay on terminology for scalable systems [14]: they
partitioned the enterprise into multiple disjoint subsystems, and then used small clusters to parallelize
the handling of requests within these. But this wasn’t enough, Shoup argued, and eventually eBay
departed from the transactional ACID properties entirely, moving towards a decentralized
convergence behavior in which server nodes are (as much as possible) maintained in loosely
consistent but transiently divergent states, from which they will converge back towards a consistent
state over time.
Shoup argued, in effect, that scalability and robustness in cloud settings arises not from tight
synchronization and fault-tolerance of the ACID type, but rather from loose synchronization and
self-healing convergence mechanisms.
Shoup was far from the only speaker to make this point. Hamilton, for example, commented that
when a Microsoft cloud computing group wants to use a strong consistency property in a service…
his executive team had the policy of sending that group home to find some other way to build the
service. As he explained it, one can’t always completely eliminate strong ACID-style consistency
properties, but the first principle of successful scalability is to batter the consistency mechanisms
down to a minimum, move them off the critical path, hide them in a rarely visited corner of the
system, and then make it as hard as possible for application developers to get permission to use
them. As he said this, Shoup beamed: he has the same role at eBay.
The LADIS audience didn’t take these “fighting words” passively. Alvisi and Guerraoui both pointed
out that Byzantine fault-tolerance protocols are more and more scalable and more and more
practical, citing work to optimize these protocols for high load, sustained transaction streams, and to
create optimistic variants that will terminate early if an execution experiences no faults [10], [21].
Yet the keynote speakers pushed back, reiterating their points. Shoup, for example, noted that much
the same can be said of modern transaction protocols: they too scale well, can sustain extremely high
transaction rates, and are more and more optimized for typical execution scenarios. Indeed, these
are just the kinds of protocols on which eBay depended in its early days, and that Hamilton “cut his
teeth” developing at Oracle and then as a technical leader of the Microsoft SQL server team. But for
Shoup performance isn’t the reason that eBay avoids these mechanisms. His worry is that no matter
how fast the protocol, it can still cause problems.
7. This is a surprising insight: for our research community, the prevailing assumption has been that
Byzantine Protocols would be used pervasively if only people understood that they no longer need to
be performance limiting bottlenecks. But Shoup’s point is that eBay avoids them for a different
reason. His worry involves what could be characterized as “spooking correlations” and “self
synchronization”. In effect, any mechanism capable of “coupling” the behavior of multiple nodes
even loosely would increase the risk that the whole data center might begin to thrash.
Shoup related stories about the huge effort that eBay invested to eliminate convoy effects, in which
large parts of a system go idle waiting for some small number of backlogged nodes to work their way
through a seemingly endless traffic jam. Then he spoke of feedback oscillations of all kinds:
multicast storms, chaotic load fluctuations, thrashing. And from this, he reiterated, eBay had learned
the hard way that any form of synchronization must be limited to small sets of nodes and used rarely.
In fact, the three of us are aware of this phenomenon from projects on which we’ve collaborated
over the years. We know of many episodes in which data center operators have found their large-
scale systems debilitated by internal multicast “storms” associated with publish-subscribe products
that destabilized on a very large scale, ultimately solving those problems by legislating that UDP
multicast would not be used as a transport. The connection? Multicast storms are another form of
self-synchronizing, destructive behavior that can arise when coordinated actions (in this case, loss
recovery for a reliable multicast protocol) are unleashed on a large scale.
Thus for our keynote speakers, “fear of synchronization” was an overarching consideration that in
their eyes, mattered far more than the theoretical peak performance of such-and-such an atomic
multicast or Consensus protocol, Byzantine-tolerant or not. In effect, the question that mattered
wasn’t actually performance, but rather the risk of destabilization that even using mechanisms such
as these introduces.
Reflecting on these comments, which were echoed by Cuomo and Hamilton in other contexts, we
find ourselves back in that room with the elephant. Perhaps as researchers focused on the
performance and scalability of multicast protocols, or Consensus, or publish-subscribe, we’re in the
position of mistaking the tail of the beast for the critter itself. Our LADIS keynote speakers weren’t
naïve about the properties of the kinds of protocols on which we work. If anything, we’re the ones
being naïve, about the setting in which those protocols are used.
To our cloud operators, the overarching goal is scalability, and they’ve painfully learned one
overarching principle of scale: decoupling. The key is to enable nodes to quietly go about their work,
asynchronously receiving streams of updates from the back-office systems, synchronously handling
client requests, and avoiding even the most minor attempt to interact with, coordinate with, agree
with or synchronize with other nodes. However simple or fast a consistency mechanism might be,
they still view such mechanisms as potential threats to this core principle of decoupled behavior.
And thus their insistency on asynchronous convergence as an alternative to stronger consistency:
yes, over time, one wants nodes to be consistent. But putting consistency ahead of decoupling is,
they emphasized, just wrong.
Towards a Cloud Computing Research Agenda
Our workshop may have served to deconstruct some aspects of the traditional research agenda, but it
also left us with elements of a new agenda – and one not necessarily less exciting than the one we are
being urged by these leaders to shift away from. Some of the main research themes that emerge are:
8. 1. Power management. Hamilton was particularly emphatic on this topic, arguing that a ten-fold
reduction in the power needs of data centers may be possible if we can simply learn to build
systems that are optimized with power management as their primary goal, and that this savings
opportunity may be the most exciting way to have impact today [15]. Examples of ideas that
Hamilton floated were:
o Explore ways to simply do less during surge load periods.
o Explore ways to migrate work in time. The point here was that load on modern cloud
platforms is very cyclical, with infrequent peaks and deep valleys. It turns out that the
need to provide acceptable quality of service during the peaks inflates costs continuously:
even valley time is made more expensive by the need to own a power supply able to
handle the peaks, a number of nodes adequate to handle surge loads, a network
provisioned for worst-case demand, etc. Hamilton suggested that rather than think about
task migration for fault-tolerance (a topic mentioned above), we should be thinking about
task decomposition with the goal of moving work from peak to trough. Hamilton’s
point was that in a heavily loaded data center coping with a 95% peak load, surprisingly
little is really known about the actual tasks being performed. As in any system, a few
tasks probably represent the main load, so one could plausibly learn a great deal – perhaps
even automatically. Having done this, one could attack those worst-case offenders.
Maybe they can precompute some data, or defer some work to be finished up later, when
the surge has ended. The potential seems to be very great, and the topic largely
unexplored.
o Even during surge loads, some machines turn out to be very lightly loaded. Hamilton
argued that if one owns a node, it should do its share of the work. This argues for
migrating portions of some tasks in space: breaking overloaded services into smaller
components that can operate in parallel and be shifted around to balance load on the
overall data center. Here, Hamilton observed that we lack software engineering solutions
aimed at making it easy for the data center development team to delay these decisions
until late in the game. After all, when building an application it may not be at all clear
that, three years down the road, the application will account for most of the workload
during surge loads that in turn account for most of the cost of the data center. Thus, long
after an application is built, one needs ways to restructure it with power management as a
goal.
2. New models and protocols for convergent consistency [32]. As noted earlier, Shoup
energetically argued against traditional consistency mechanisms related to the ACID properties,
and grouped Consensus into this technology area. But it was not so clear to us what alternative
eBay would prefer, and in fact we see this as a research opportunity.
o We need to either adapt existing models for convergent behavior (self-stabilization,
perhaps, or the forms of probabilistic convergence used in some gossip protocols) to
create a formal model that could capture the desired behavior of loosely coupled systems.
Such a model would let us replace “loose consistency” with strong statements about
precisely when a system is indeed loosely consistent, and when it is merely broken!
o We need a proof methodology and metrics for comparison, so that when distinct teams
solve this new problem statement, we can convince ourselves that the solutions really
work and compare their costs, performance, scalability and other properties.
o Conversely, the Byzantine Consensus community has value on the table that one would
not wish to sweep to the floor. Consider the recent, highly publicized, Amazon.com
outage in which that company’s S3 storage system was disabled for much of a day when a
9. corrupted value slipped into a gossip-based subsystem and was then hard to eliminate
without fully restarting the subsystem – one needed by much of Amazon, and hence a
step that forced Amazon to basically shut down and restart. The Byzantine community
would be justified, we think, in arguing that this example illustrates not just a weakness in
loose consistency, but also a danger associated with working in a model that has never
been rigorously specified. It seems entirely feasible to import ideas from Byzantine
Consensus into a world of loose consistency; indeed, one can imagine a system that
achieves “eventual Byzantine Consensus.” One of the papers at LADIS (Rodrigues et al.
[30], [31]) presented a specification of exactly such a service. Such steps could be fertile
areas for further study: topics close enough to today’s hot areas to publish upon, and yet
directly relevant to cloud computing.
3. Not enough is known about stability of large-scale event notification platforms, management
technologies, or other cloud computing solutions. As we scale these kinds of tools to encompass
hundreds or thousands of nodes spread over perhaps tens of data centers, worldwide, we as
researchers can’t help but be puzzled: how do our solutions work today, in such settings?
o Very large-scale eventing deployments are known to be prone to destabilizing behavior –
a communications-level equivalent of thrashing. Not known are the conditions that
trigger such thrashing, the best ways to avoid it, the general styles of protocols that
might be inherently robust or inherently fragile, etc.
o Not very much is known about testing protocols to determine their scalability. If we
invent a solution, how can we demonstrate its relevance without first taking leave of our
day jobs and signing on at Amazon, Google, MSN or Yahoo? Today, realistically, it
seems nearly impossible to validate scalable protocols without working at some company
that operates a massive but proprietary infrastructure.
o Another emerging research direction looks into studying subscription patterns exhibited
by the nodes participating in a large-scale publish-subscribe system. Researchers (including
the authors of this article) are finding that in real-world workloads, the subscription
patters associated with individual nodes are highly correlated, forming clusters of nearly
identical or highly similar subscriptions. These structures can be discovered and exploited
(through e.g., overlay network clustering [9], [17], or channelization [35]). LADIS
researchers reported on opportunities to amortize message dissemination costs by
aggregating multiple topics and nodes, with the potential of dramatically improving
scalability and stability of a pub-sub system.
4. Our third point leads to an idea that Mahesh Balakrishnan has promoted: we should perhaps begin
to treat virtualization as a first-class research topic even with respect to seemingly remote
questions such as the scalability of an eventing solution or a tolerating Byzantine failures. The
point Mahesh makes runs roughly as follows:
o For reasons of cost management and platform management, the data center of the future
seems likely to be fully virtualized.
o Today, one assumes that it makes no sense to talk about a scalable protocol that was
actually evaluated on 200 virtual nodes hosted on 4 physical ones: one presumes that
internal scheduling and contention effects could be more dominant than the scalability of
the protocols per se. But perhaps tomorrow, it will make no sense to talk about
protocols that aren’t designed for virtualized settings in which nodes will often be co-
located. After all, if Hamilton is right and cost factors will dominate all other decisions
in all situations, how could this not be true for nodes too?
10. o Are there deep architectural principles waiting to be uncovered – perhaps even entirely
new operating systems or virtualization architectures – when one thinks about support
for massively scalable protocols running in such settings?
5. In contrast to enterprise systems, the only economically sustainable way of supporting Internet
scale services is to employ a huge hardware base consisting entirely of cheap off-the-shelf
hardware components, such as low-end PC’s and network switches. As Hamilton pointed out, this
reflects simple economies of scale: i.e., it is much cheaper to obtain the necessary computational
and storage power by putting together a bunch of inexpensive PC’s than to invest into a high-end
enterprise level equipment, such as a mainframe. This trend has important architectural
implications for cloud platform design:
o Scalability emerges as a crosscutting concern affecting all the building blocks used in cloud
settings (and not restricted to those requiring strong consistency). Those blocks should be
either redesigned with scalability in mind (e.g., by using peer-to-peer techniques and/or
dynamically adjustable partitioning), or replaced with new middleware abstractions known
to perform well when scaled out.
o As we scale systems up, sheer numbers confront us with growing frequency of faults
within the cloud platform as a whole. Consequently, cloud services must be designed
under assumption that they will experience frequent and often unpredictable failures.
Services must recover from failures autonomously (without human intervention), and this
implies that cloud computing platforms must offer standard, simple and fast recovery
procedures [18]. We pointed to a seeming connection to recovery oriented computing
(ROC) [27], yet ROC was proposed in much smaller scale settings. A rigorously specified,
scalable form of ROC is very much needed.
o Those of us who design protocols for cloud settings may need to think hard about churn
and handling of other forms of sudden disruptions, such as sudden load surges. Existing
protocols are too often prone to destabilized behaviors such as oscillation, and this may
prevent their use in large data centers, where such events run the risk of disrupting even
applications that don’t use those protocols directly.
We could go on at some length, but these points already touch on the highlights we gleaned from the
LADIS workshop. Clearly, cloud computing is here to stay, and poses tremendously interesting
research questions and opportunities. The distributed systems community, up until now at least, owns
just a portion of this research space (indeed, some of the topics mentioned above are entirely outside
of our area, or at best tangential).
LADIS 2009
In conclusion, LADIS 2008 seems to have been an unqualified success, and indeed, a far more
thought-provoking workshop than we three have attended in some time. The key was that LADIS
generated spirited dialog between distributed systems researchers and practitioners, but also that the
particular practitioners who participated shared so much of our background and experience. When
researchers and system builders meet, there is often an impedance mismatch, but in the case of
LADIS 2008 we managed to fill a room with people who share a common background and way of
thinking, and yet see the cloud computing challenge from very distinct perspectives.
11. LADIS 2009 is now being planned running just before the ACM Symposium on Operating Systems in
October 2009, at Big Sky Resort in Utah. In contrast to the two previous workshops, the papers are
being solicited through both an open Call For Papers, and targeted solicitation. If SOSP 2009 isn’t
already enough of an attraction, we would hope that readers of this essay might consider LADIS 2009
to be absolutely irresistible! You are most cordially invited to submit a paper and attend the
workshop. More information can be found at http://www.sigops.org/sosp/sosp09/workshops-
cfp/ladis09-cfp.pdf and http://www.cs.cornell.edu/projects/ladis2009.
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