AWS Rolls Out Cloud Management And Scalability Features For EC2

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Amazon Web Services unveiled a suite of new features for Amazon EC2 which will help users monitor cloud capacity, scale on demand, and spread incoming traffic across multiple web servers. Amazon originally announced these features last fall, but the applications didn’t enter public beta until today.

The first feature, Amazon CloudWatch, provides customers with real-time visibility into resource utilization, operational performance, and overall demand patterns—including metrics such as CPU utilization, disk reads and writes, and network traffic. The metrics are rolled-up at one minute intervals and are retained for two weeks.

Amazon’s scaling feature, Auto Scaling, allows you to automatically scale your Amazon EC2 capacity up or down according to metrics you receive from CloudWatch. With Auto Scaling, you can make sure that the number of Amazon EC2 instances you’re using scales up during demand spikes to maintain performance, and scales down automatically during demand lulls to minimize costs.

Elastic Load Balancing will automatically distribute incoming application traffic across multiple Amazon EC2 instances. The feature will detect problem instances within a group and will automatically reroutes traffic to working instances until the unhealthy instances have been restored. Users can also implement “Health Checks” to figure out the viability of each instance via pings and URL fetches.

The new features, which were added due to customer demand, are aimed to give EC2 customers greater visibility of the cloud while still being able to scale on demand, direct traffic and manage overloads easily. It appears that these features would be fairly useful for customers and definitely makes AWS a more powerful cloud computing platform.

  • mike

    Nice Leena. If I was a writer, I too would copy the press release. Since I am a AWS user/customer, I already knew this, about 5 hours before you wrote it.

    • Gio

      I for one am glad this got posted to TechCrunch.

  • Cost conscious

    This is a much needed addition. I was researching third party applications to do this. To find it native in AWS is awesome.

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  • Falafulu Fisi

    I haven’t used AWS but that’s what I intend to use. I like the solutions that AWS offers, but I doubt if it can scale for large scale real-time analytic. I attended a talk on AWS from a rep from Amazon and one of the question being asked at question time, if AWS can handle scientific computing. The Amazon rep wasn’t sure if their services can or can’t do intensive real-time computation like that, but he gave an answer that said, Amazon will no doubt be able to handle such numeric intensive computation in the near future. This talk was held last year (2008) and now it is a year after, perhaps their services can now handle that.

    • Hareem Haque

      Hello Falafulu Fisi

      I don’t know which specific environment you wish to setup. However, Amazon has recently announced Elastic Mapreduce (hadoop on AWS infrastructure) service.

      Many users are adopting Elatic Mapreduce for intensive computations.

      And many developers have ported MPI to EC2. So its all very much possible.

      • Falafulu Fisi


        Thanks for the tip. That’s what I thought that EC2 will be able to handle those high performance numerical linear algebra computations. Some of my codes are developed using the HpJava (a high performance development environment for Java), which is basically an extension of the standard Java.

    • Victoria Livschitz

      The original question has three key words:
      (a) large-scale
      (b) real-time
      (c) analytics

      Let me comment of each. Large-scale part is addressable by EC2 today. Nearly a year ago we ran Monte-Carlo simulations on EC2 to test scalability of the underlying platform given the perfect scalability of math, and it worked perfectly linearly up to 512 nodes (we didn’t run it further). I know of multi-thousand node computations and basically believe that scalability is not an issue. Here is a (now rather old) blog with more details on that benchmark:

      (b) Real-time can be the problem. First of all, there is a cost of starting up a cluster, which is several minutes. Once you have the computational cluster up and running, the scheduling can happen right away, depending on the speed of the scheduler. We have ran real-time schedules on EC2, doing things like annuity calculations. The other SIGNIFICANT issue related to real-timeness is QoS. Basically, you can’t control how loaded EC2 servers and network is at the time you ran your calculations. If it’s really oversubscribed, you’ll see significant performance degradation as compared to the best-case times seeing when the infrastructure is more available. Since there is no way to secure QoS, there is no way to guarantee resource availability or time completion. Hence, for some real-time applications, this is a significant problem.

      (c) Analytics. The algorithms that are
      1. Embarrasingly parallel
      2. Do not depend on data location or transfer
      3. Share nothing

      should be runnable on EC2. Algorithms that either
      1. Depend on data off-the-edge of the Cloud
      2. Use message-passing between the compute nodes, or
      3. Share some sort of state

      are going to be trouble to run on EC2 in any kind of real-time scenario.

      There is a large gray area in between where a lot is possible with a lot of level of effort ;-) I hope this helps.

      • Falafulu Fisi


        Very good tips and informative. It is good to know that your company consults in this area of high performance computing, because I may be looking for outside expertise to help in what I am doing at some stage (which depends on funding that I will get access to). I am assembling a team for a startup and our app is a real-time analytic that we must do it right from the beginning. Realtime analytic is a very challenging task in today’s computing environment. In the world of finance, computing the volatility of a single asset (ie, a single stock) using different algorithms that are based on linear algebra decomposition (ie, matrix factorisation) are very compute intensive. Eg, doing real-time GARCH is a memory intensive process. One can still do it non real-time (perhaps every 5 minutes or so) but this means that one has to accept losing a few important opportunities that have arised in the market without being exploited. There is always a trade that is registered in the market every seconds (or even less) or so, this means that the GARCH has to be recomputed everytime a single trade has been registered in the exchange, ie, every seconds. This is the problem with real-time analytic.

      • Victoria Livschitz

        It makes perfect sense to consider pure-play cloud deployment for green-field analytics apps, for both development & testing, as well as production deployment for resource-bursty apps, and many start-ups do just that. In some cases, companies can actually pass the cost of computing down to their clients – depending on their pricing model.

        We also work with very large financial services firms, getting their computational analytics on the cloud as well. By the time you launch your product, it may even be possible to take advantage of such specialized cloud service offerings as “analytics on the cloud”, offered by major cloud service providers.

        It is important to realize that even though EC2 is the biggest and most mature cloud provider out there – and today’s announcement proves once again their leadership – it is not the only game in town. For example, for real-time, on-demand
        analytics node start-up is still an issue. Joyent can get you down to under 30 sec start-up on a new node – which sometimes is the difference between feasible/not feasible approach. Other cloud providers offer better SLAs or better data access solution.

        Let us know if we can help you design a scalable, cloud-ready infrastructure. You’ll probably need to pick a good real-time scheduler too, and solve data transfer/access/management issues,
        amongst other things, and we specialize in this space.

  • Anton

    Bye bye…

  • Erik Schwartz

    I think this will make twitter better.

    • Hareem Haque

      I doubt that Twitter would use this service. With their traffic volume would it not be super expensive.


  • 墨尔本

    Looks great,
    it’s time to try ec2.

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  • Ken Oestreich

    Alas, Cassatt had this functionality over a year ago. Too bad they’re about to go bellyup. A few others are doing this too, but this now sets the bar for true elasticity.

  • ChipCorrera

    It doesn’t appear that they’re supporting sticky load balancing – a major gap for hosting an application that requires session affinity and caching. Any word on why not or when?

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