Its mission is to provide a flexible solution for both public and private clouds of any size, and for this matter two basic requirements are considered: clouds must be simple to implement and massively scalable. To meet these principles OpenStack is divided into different components that work together. With these APIs, services can communicate with each other and also allows a service to be replaced by another with similar characteristics, only if the form of communication is respected. This is a simplified view of the architecture, assuming that all the services are used in the most standard configuration. Is a Django web application, a web framework for perfectionists ;) Those familiar with this framework will find Horizon code easy to understand. Web Server Gateway Interface WSGI is a specification for application servers and web servers to communicate with web applications. The code is divided into reusable modules with logic, interaction with APIs, and presentation, to make customization in different sites easier. It also has a small database, SQLite3 by default, for some options, but most of the data are provided by the other services. This notion is fundamental to understand this service since the administration of objects is it main objective. Both files and objects have metadata associated with the data they contain, but objects are characterized by their extended metadata. Proxy server accepts incoming requests, like files to upload, modifications to metadata or container creation; it also serve files and container listing.
Container servers manage a mapping of containers, folders, within the object store service. Also replication services run to provide consistency and availability across the cluster, audit and update. An image is a single file containing the complete contents and structure of a storage medium. Images are frequently used as a distribution medium for operating systems and instances (one particular execution time) of them. In other words, snapshots are running instances that can be obtained by creating a new image based on the current state of the disk of a particular instance.
Every Keystone function has a pluggable backend which allows different ways to use the particular service. Probably you will want to open the image in another tab … There are too many relatioships! I’m sure that many things remain pending – I am aware that the description of Keystone is pretty poor, and I will improve it!
I understood the two-way arrows like both services can communicate with each other in a full-duplex style. About that, excellent work :) Your post gave me the tools needed to break the ice with OpenStack and get involved with the code in an easier way. And, please, add your suggestions here or by email, either way I’ll appreciate that a lot.
In any case I will be subscribing in your rss feed and I’m hoping you write once more soon!
We would appreciate it, it will help developer on this side, please excuse me IF have asked for more.
Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. More and more organizations are looking to the Cloud for their data and analytics projects, seeking to leverage elastic, scalable, and cost-efficient infrastructure without the need to procure or maintain in-house hardware. Pentaho provides a highly flexible platform for blending, orchestrating, and analyzing data from virtually any source, effectively reaching across system, application and organizational boundaries.


Enterprise analytic data management firm Cloudera has announced its Enterprise Data Hub, a unified Big Data platform.
The new hub is the fifth update to Cloudera’s Platform for Big Data and is available now as a public beta release, after launching at the O’Reilly Strata-Hadoop World conference this week in New York. Mike Olson, chairman and chief strategy officer at Cloudera explained during the launch that an organisation can run different types of analytical workloads against the data in the hub.
In addition, the hub integrates with existing systems and offers robust security governance, data protection, and management.
Cloudera said the Enterprise Hub brought together a number of different systems that enterprises often manage separately, including data warehouses, secure but easily accessible storage systems, databases for large-scale analytics and search systems. The company said although this kind of system is fine for traditional, light workloads that don’t change often, the modular approach is not suitable for companies that have to deal with fast growth of data volume and variety.
Cloudera said in a statement that a hybrid system is required in order to “acquire and combine any amount or type of data in its original fidelity, in one place, for as long as is necessary, and deliver insights to all kinds of users, as fast as possible. SAS, Revolution Analytics, Syncsort have already ported some of their software to the Enterprise Data Hub. Olson explained, "Over the last five years, we have worked closely with enterprises around the world to help them capture the value in the data they have. We are utilizing our new Linux benchmark suite and Ubuntu Server 12.04 LTS to take a look specifically at the compute performance of these instances.
As one can see, the second generation Amazon EC2 instance, the m3.2xlarge does show some impressive performance. Again we see the Amazon EC2 instance generally fall into line as to what we would expect from them.
Again, we can see a major difference in the first generation (m1) and second generation (m3) Amazon EC2 cloud instances here. 7-zip compression benchmarks were a mainstay in our Windows suite so we are including it again on the Linux side as a compression benchmark.
One thing immediately stands out here, and that is the xlarge and larger AWS cloud instances have much higher memory bandwidth. IO is the performance-limiting resource for many important cloud workloads and the different vendors currently use dramatically different storage infrastructure with dramatically different performance characteristics. Allows users to create their own networks and then link them to the devices of their choice.
This is not a file system, is more like a container that can store files and retrieve them later.
Each object is assigned a unique identifier which allows a server or end user to retrieve the object without needing to know the physical location of the data. But you may be right, it can be confusing… using two independent arrows seems clearer. It’s a great document that explains things both in detail and from a high-level overview. With the growing adoption of both Big Data and cloud computing, businesses need a robust platform that can deliver value from both on-premise and hosted data at enterprise scale.
It can store data as it is generated, even if the business is not sure how it will be used. Resoundingly, they have asked for a more secure, more reliable real-time data platform that streamlines their existing architectures and speeds up time to insight. For example, the Intel E5430 is not going to have AES-NI instructions which may be a big impact for some users. One can also see that the m3 instances are often out-performing the first generation m1.xlarge instance by a fairly solid margin. There are two main versions, one that tests single CPU performance on that tests multiple CPU performance.


It is also one where we saw issues last time with the Phoronix Test Suite and running on ARM CPUs. In fact, it is arguably becoming even more popular these days so we decided to include a pyBench benchmark in the results. Again, one can point to the stream triad, openssl and pybench results to see that there is clearly a benefit to the second generation Amazon EC2 compute instances. First, it is clear that Rackspace and Amazon are benchmarking their instances and pricing accordingly. For his day job, Patrick is a management consultant focused in the technology industry and has worked with numerous large hardware and storage vendors in the Silicon Valley. A little sunlight shined on the differences between clouds should help improve IO performance across the industry and help us get a better return on our IAAS spending. This approach is useful for automating and streamlining data storage in cloud computing environments.
The customer does not know where his car will be parked or how many times an attendant might move the car while the customer is dining. Having said that, I’ll try to make the Grizzly set of pictures more clear and maybe using the arrows as you suggest might be a good idea. As part of the feedback we received there, we benchmarked all of the Rackspace cloud compute instance types.
We then ran each benchmark five times on each of the five instances and averaged the results. The reason this is important is because the data bars in the above are generated from the full set of results and as one can see, there have been much faster setups in the lab. Prices stay relatively close for each performance tier between the Amazon EC2 cloud and the Rackspace cloud.
The goal of STH is simply to help users find some information about basic server building blocks. It is a bit more complicated because Amazon EBS from what I have seen can be very inconsistent.
I will use this when explaining to different people how things relate and break down as it illustrates that easily.
The Amazon AWS EC2 cloud computing node types have many different compute and memory configurations. Moving beyond that distinction, the other major trend is that the Amazon EC2 second generation compute instances are generally significantly faster than the first generation instances.
Today we are sharing benchmarks on all types of Micro, Standard and Second Generation Standard instances from Amazon. Needless to say, oftentimes these results are hard to reproduce unless one is trying to squeeze every ounce of performance from a machine they are optimizing for. A final food for thought to readers, the results for dedicated server hardware is what generally pushes bars lower. In the future, we will have these results as a baseline when looking at dedicated hardware so folks can make an informed decision on which infrastructure to run. Moving up to the dual socket and single socket Intel Xeon and AMD Opteron range is the force behind the data bars being so low.
Stay tuned for more results but the above will be our cloud computing baseline to compare against dedicated hardware. Specifically we are using the lowest bandwidth figure Triad test to compare memory throughput.



Wwdc 2014 icloud price waterhouse
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Comments

  1. 29.08.2014 at 15:54:15


    Have lots of hidden charges couple with their pricing plans why cloud.

    Author: LEDI_RAMIL_GENCLIK
  2. 29.08.2014 at 13:46:25


    In 2013, Forbes complexity of traditional file-based.

    Author: 545454545