Technology – BidElastic https://bidelastic.com Optimize your cloud computing costs Wed, 09 Jun 2021 23:19:21 +0000 en-US hourly 1 https://wordpress.org/?v=4.5.32 Cloud Monitor https://bidelastic.com/2016/01/26/cloud-monitor/ https://bidelastic.com/2016/01/26/cloud-monitor/#respond Tue, 26 Jan 2016 20:48:28 +0000 http://localhost/wordpress/?p=270

Cloud Monitor is a secure web application that ingests raw EC2 Watch feeds and provides the customer with access to interactive reports on: 

Instance hierarchy based on platform, environment, type, name and instance metadata.

  • Functional group: Testing, staging, pre-production, production and demonstration.
  • Production group of instance functionalities such as ingest and encode.
  • Instance ID.
  • Measure time.
  • Instance type, for example t2.micro, c3.large or m3.medium.

Default measures automatically made available include:

  • Costs.
  • CPU usage.
  • Incoming Network Transfer.
  • Outgoing Network Transfer.
  • Disk read and write operations.
  • CPU credit balance for burstable t2.* instances.

Custom measures requiring running code on the server to generate:

  • Memory usage.
  • CPU usage for every running process.
  • Any other custom metrics specific to customer needs.

Cloud Monitor reports can be downloaded in PDF format with the underlying data available in XLS.

]]> https://bidelastic.com/2016/01/26/cloud-monitor/feed/ 0 Optimizing of simulation computations on the Amazon EC2 spot market https://bidelastic.com/2016/01/26/optimizing-of-simulation-computations-on-the-amazon-ec2-spot-market/ Tue, 26 Jan 2016 14:07:45 +0000 http://localhost/wordpress/?p=278 The BidElastic team published an article in a special issue of Simulation Modelling Practice and Theory. The article describes some of the technology and principles used by Bid Server to plan and provision EC2 spot instances. 

Using the Amazon spot price market can significantly lower the execution costs of large-scale simulations that require millions of computations. However, Amazon can interrupt computations on the spot price market when user bids are too low. To complete computations without incurring high costs, a bidding algorithm should be developed that balances costs and completion time of computations.

We have developed such an algorithm by identifying drivers of spot prices on Amazon EC2 and using these insights to propose an adaptive bidding strategy to minimize computation costs and delays due to computation termination simultaneously. It turns out that bidding close to spot prices and dynamically switching between instances is an efficient and simple strategy. To develop and test other bidding strategies on the Amazon spot price market, we have also built a simulator of the EC2 spot pricing mechanism.

The article is available as Open Access on Science Direct.

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