The transition to cloud computing offers a large number of benefits, such as lower capital costs and a highly agile environment. Yet, the development of software engineering practices has not kept pace with this change. The design and runtime behavior of cloud based services and the underlying cloud infrastructure are largely decoupled from one another, which limits both the efficiency of the cloud environment and the Quality of Service which can be delivered to the hosted applications. This paper describes the innovative concepts being developed by CloudWave to utilize the principles of DevOps to create an execution analytics cloud infrastructure where, through the use of programmable monitoring and online data abstraction, much more relevant information for the optimization of the ecosystem is obtained. Required optimizations are subsequently negotiated between the applications and the cloud infrastructure to obtain coordinated adaption of the ecosystem. Additionally, the project is developing the technology for a Feedback Driven Development Standard Development Kit which will utilize the data gathered through execution analytics to supply developers with a powerful mechanism to shorten application development cycles. Abstract-The transition to cloud computing offers a large number of benefits, such as lower capital costs and a highly agile environment. Yet, the development of software engineering practices has not kept pace with this change. Moreover, the design and runtime behavior of cloud based services and the underlying cloud infrastructure are largely decoupled from one another.This paper describes the innovative concepts being developed by CloudWave to utilize the principles of DevOps to create an execution analytics cloud infrastructure where, through the use of programmable monitoring and online data abstraction, much more relevant information for the optimization of the ecosystem is obtained. Required optimizations are subsequently negotiated between the applications and the cloud infrastructure to obtain coordinated adaption of the ecosystem. Additionally, the project is developing the technology for a Feedback Driven Development Standard Development Kit which will utilize the data gathered through execution analytics to supply developers with a powerful mechanism to shorten application development cycles.

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Many applications have security vulnerabilities that can be exploited. It is practically impossible to find all of them due to the NP-complete nature of the testing problem. Security solutions provide defenses against these attacks through continuous application testing, fast-patching of vulnerabilities, automatic deployment of patches, and virtual patching detection techniques deployed in network and endpoint security tools. These techniques are limited by the need to find vulnerabilities before the 'black hats'. We propose an innovative technique to virtually patch vulnerabilities before they are found. We leverage testing techniques for supervised-learning data generation, and show how artificial intelligence techniques can use this data to create predictive deep neural-network models that read an application's input and predict in real time whether it is a potential malicious input. We set up an ahead-of-threat experiment in which we generated data on old versions of an application, and then evaluated the predictive model accuracy on vulnerabilities found years later. Our experiments show ahead-of-threat detection on LibXML2 and LibTIFF vulnerabilities with 91.3% and 93.7% accuracy, respectively. We expect to continue work on this field of research and provide ahead-of-threat virtual patching for more libraries. Success in this research can change the current state of endless racing after application vulnerabilities and put the defenders one step ahead of the attackers.

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