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AWS Broke the Internet Again or, Better, a Typo | @CloudExpo #AI #ML #DL
An AI-defined infrastructure can help to avoid service disruptions
By: Rene Buest
Mar. 23, 2017 10:00 AM
Amazon Web Services (AWS) broke the Internet again or better "a typo". On February 28, 2017, an Amazon S3 service disruption in AWS' oldest region US-EAST-1 shuts down several major websites and services like Slack, Trello, Quora, Business Insider, Coursera and Time Inc. Other users were reporting that they were also unable to control devices which were connected via the Internet of Things since IFTTT was also down. Those kinds of disruptions are becoming more and more business critical for today's digital economy. To prevent these situations, cloud users should always consider the shared responsibility model in the public cloud. However, there are also ways where Artificial Intelligence (AI) can help. This article describes that an AI-defined Infrastructure respectively an AI-powered IT management system can help to avoid service disruptions of public cloud providers.
Amazon S3 Service Disruption - What has happened
"The Amazon Simple Storage Service (S3) team was debugging an issue causing the S3 billing system to progress more slowly than expected. At 9:37AM PST, an authorized S3 team member using an established playbook executed a command which was intended to remove a small number of servers for one of the S3 subsystems that is used by the S3 billing process. Unfortunately, one of the inputs to the command was entered incorrectly and a larger set of servers was removed than intended. The servers that were inadvertently removed supported two other S3 subsystems. One of these subsystems, the index subsystem, manages the metadata and location information of all S3 objects in the region. This subsystem is necessary to serve all GET, LIST, PUT, and DELETE requests. The second subsystem, the placement subsystem, manages allocation of new storage and requires the index subsystem to be functioning properly to correctly operate. The placement subsystem is used during PUT requests to allocate storage for new objects. Removing a significant portion of the capacity caused each of these systems to require a full restart. While these subsystems were being restarted, S3 was unable to service requests. Other AWS services in the US-EAST-1 Region that rely on S3 for storage, including the S3 console, Amazon Elastic Compute Cloud (EC2) new instance launches, Amazon Elastic Block Store (EBS) volumes (when data was needed from a S3 snapshot), and AWS Lambda were also impacted while the S3 APIs were unavailable."
Bottom line, a typo crashed the AWS powered Internet! AWS outages already have a long history and the more AWS customers running their web infrastructure on the cloud giant, the more issues end customers will experience in the future. According to SimilarTech only Amazon S3 is already used by 152,123 websites and 124,577 unique domains.
However, following the philosophy of "Everything fails all the time (Werner Vogels, CTO Amazon.com)" means if you are using AWS you must "Design for Failure". Something cloud role model and video on demand provider Netflix is doing in perfection. In doing so, Netflix has developed its Simian Army an open source toolset everyone can use to run a cloud infrastructure on AWS high-available.
Netflix "simply" uses the two levels of redundancy AWS offers. Multiple regions and multiple availability zones (AZ). Multiple regions are the masterclass of using AWS, very complex and sophisticated since you must build and manage entire separated infrastructure environments within AWS' worldwide distributed cloud infrastructure. Multiple AZs are the preferred and "easiest" way for high availability (HA) on AWS. In this case, the infrastructure is built within more than one data center (AZ). In doing so, a single region HA architecture is deployed in at least two or more AZs - a load balancer in front of it is controlling the data traffic.
However, even if "typos" shouldn't happen the recent accident shows, that human error is still the biggest issue running IT systems. In addition, you can blame AWS only to a certain extend since the public cloud is about shared responsibility.
Shared Responsibility in the Public Cloud
The customer is responsible for the operations and security of the logical environment. This includes:
Thus, the customer is responsible for the operations and security of his own infrastructure environment and the systems, applications, services, as well as stored data on top of it. However, providers like Amazon Web Services or Microsoft Azure provide comprehensive tools and services customers can use e.g. to encrypt their data as well as ensure identity and access controls. In addition, enablement services (micro services) exist that customers can adopt to develop own applications more quickly and easily.
In doing so, the customer is all alone in its area of responsibility and thus must take self-responsibility. However, this part of the shared responsibility can be done by an AI-defined IT management system respectively an AI-defined Infrastructure.
An AI-defined Infrastructure can help to avoid Service Disruptions
To put this into the context of an AWS service disruption:
Frankly speaking, everything described above is no magic. Like every new born organism an AI-defined Infrastructure needs to be trained but afterwards can work autonomously as well as can detect anomalies as well as service disruptions in the public cloud and solve them. Therefore, you need the knowledge of experts who have a deep understanding of AWS and how the cloud works in general. These experts need to teach the General AI with their contextual knowledge that includes not only what, when and where but also why. They have to teach the AI with atomic pieces (Knowledge Items, KI) that can be indexed and prioritized by the AI. Context and indexing enable this KIs to be combined to form many solutions.
KIs created by various IT experts create pooled expertise that is further optimized by machine selection of best knowledge combinations for problem resolution. This type of collaborative learning improves process time task by task. However, the number of possible permutations grows exponentially with added knowledge. Connected to a knowledge core, the General AI continuously optimizes performance by eliminating unnecessary steps and even changing routes based on other contextual learning. And the bigger the semantic graph and knowledge core gets, the better and more dynamically the infrastructure can act in terms of service disruptions.
On a final note, do not underestimate the "power of we"! Our research at Arago revealed that with an overlap of 33 percent in basic knowledge, this knowledge can and is used outside a specific organizational environment, i.e. across different client environments. The reuse of knowledge within a client is up to 80 percent. Thus, exchanging basic knowledge within a community becomes imperative from an efficiency perspective and improve the abilities of the General AI.
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