Abstract: A system and method place an incoming workload within a data center having infrastructure elements (IEs) for execution. Instrumentation data are collected for both individual IEs in the data center, and workload instances executing on each of these IEs. These data are used to train a future load model according to machine learning techniques, especially supervised learning. Future loads, in turn, are used to train a ranking model that ranks IEs according to suitability to execute additional workloads. After receiving an incoming workload, the first model is used to predict, for each IE, the load on its computing resources if the workload were executed on that IE. The resulting predicted loads are then fed into the second model to predict the best ranking of IEs, and the workload is placed on the highest-ranked IE that is available to execute the workload.

Abstract: A system and method mediate transfer of encrypted data files between local applications and external computer systems. Application containers perform cryptographic operations using stored credentials to decrypt data coming from these external systems and configurably forward them to the local applications, and to encrypt data sent from the local applications to the external systems. Access to this encryption-as-a-service (EaaS) functionality is gated by a fingerprint service that classifies requests by security level, and detects anomalous requests. Security classification is performed by a supervised machine learning algorithm, while anomalous request detection is performed by unsupervised machine learning algorithm. Stored keys are monitored, and when they near expiration or are damaged, embodiments proactively undertake key renewal and key exchange with the external computer systems. Containerization enables key storage in multiple vaults, thereby making such storage vendor-agnostic.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes receiving a corpus of historical recycling settlement data regarding a plurality of recycled assets, the historical recycling settlement data including information pertaining to a recycling of each asset of the plurality of recycled assets, wherein the information pertaining to the recycling includes a recovery value of each recycled asset. The method also includes generating a training dataset from the corpus of historical recycling settlement data, the training dataset including a plurality of training samples, each training sample of the plurality of training samples corresponding to a recycled asset, and training a recovery value prediction module using the plurality of training samples. Once trained, the recovery value prediction module can predict a recovery value of a provided asset.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes receiving a corpus of historical order fulfillment data regarding a plurality of completed orders for one or more products, the historical order fulfillment data including an actual delivery time for each product in a completed order, and identifying, from the corpus of historical order fulfillment data, a plurality of features for a product, the plurality of features correlated with an actual delivery time for the product. The method also includes generating a training dataset using the identified plurality of features, the training dataset including a plurality of training samples, each training sample of the plurality of training samples corresponding to a product and including one or more identified features and the actual delivery time for the product. The method may include training the delivery time prediction module using the plurality of training samples.

Abstract: A system and method place an incoming workload within a data center having infrastructure elements (IEs) for execution. Instrumentation data are collected for both individual IEs in the data center, and workload instances executing on each of these IEs. These data are used to train a future load model according to machine learning techniques, especially supervised learning. Future loads, in turn, are used to train a ranking model that ranks IEs according to suitability to execute additional workloads. After receiving an incoming workload, the first model is used to predict, for each IE, the load on its computing resources if the workload were executed on that IE. The resulting predicted loads are then fed into the second model to predict the best ranking of IEs, and the workload is placed on the highest-ranked IE that is available to execute the workload.

Abstract: A system and method reorder execution of a test suite to be performed on a given device according to an initial testing order. Each testing sequence in the test suite is analyzed for dependencies between test cases, and these dependencies are recorded in directed graphs. Next, a machine learning algorithm, such as the random forest algorithm, is trained on multi-dimensional historical testing data according to several testing parameters to predict success or failure of any given test. The trained algorithm is used to predict, for a given device under test, which of the test cases are likely to fail, and to compute a confidence value for each such prediction. The directed graphs then are reorganized so that graphs containing tests most likely to fail are executed early in the test suite, according to a modified testing order that accounts for both test dependencies and the confidence values.

Abstract: A system and method mediate transfer of encrypted data files between local applications and external computer systems. Application containers perform cryptographic operations using stored credentials to decrypt data coming from these external systems and configurably forward them to the local applications, and to encrypt data sent from the local applications to the external systems. Access to this encryption-as-a-service (EaaS) functionality is gated by a fingerprint service that classifies requests by security level, and detects anomalous requests. Security classification is performed by a supervised machine learning algorithm, while anomalous request detection is performed by unsupervised machine learning algorithm. Stored keys are monitored, and when they near expiration or are damaged, embodiments proactively undertake key renewal and key exchange with the external computer systems. Containerization enables key storage in multiple vaults, thereby making such storage vendor-agnostic.