Abstract: An edge computing system comprises: a cloud computing system; an edge processing function; a connection between the edge processing function and the cloud computing system; a backend server within the cloud computing system. An assessment module is configured to receive information about processing goals, and processing capabilities of the backend server and the edge processing function. The assessment module derives a set of possible interfaces and corresponding functionality splits defining a division of processing activity between the backend server and the edge processing function. Based on a received measurement of bandwidth and/or of latency on the connection, the assessment module selects an interface and corresponding functionality split, and downloads them to the edge processing function and the backend server.

Abstract: An edge computing system comprises: a cloud computing system; an edge processing function; a connection between the edge processing function and the cloud computing system; a backend server within the cloud computing system. An assessment module is configured to receive information about processing goals, and processing capabilities of the backend server and the edge processing function. The assessment module derives a set of possible interfaces and corresponding functionality splits defining a division of processing activity between the backend server and the edge processing function. Based on a received measurement of bandwidth and/or of latency on the connection, the assessment module selects an interface and corresponding functionality split, and downloads them to the edge processing function and the backend server.

Abstract: An edge computing system comprises: a cloud computing system; an edge processing function; a connection between the edge processing function and the cloud computing system; a backend server within the cloud computing system. An assessment module is configured to receive information about processing goals, and processing capabilities of the backend server and the edge processing function. The assessment module derives a set of possible interfaces and corresponding functionality splits defining a division of processing activity between the backend server and the edge processing function. Based on a received measurement of bandwidth and/or of latency on the connection, the assessment module selects an interface and corresponding functionality split, and downloads them to the edge processing function and the backend server.

Abstract: An edge computing system comprises: a cloud computing system; an edge processing function; a connection between the edge processing function and the cloud computing system; a backend server within the cloud computing system. An assessment module is configured to receive information about processing goals, and processing capabilities of the backend server and the edge processing function. The assessment module derives a set of possible interfaces and corresponding functionality splits defining a division of processing activity between the backend server and the edge processing function. Based on a received measurement of bandwidth and/or of latency on the connection, the assessment module selects an interface and corresponding functionality split, and downloads them to the edge processing function and the backend server.

Abstract: An edge computing system comprises: a cloud computing system; an edge processing function; a connection between the edge processing function and the cloud computing system; a backend server within the cloud computing system. An assessment module is configured to receive information about processing goals, and processing capabilities of the backend server and the edge processing function. The assessment module derives a set of possible interfaces and corresponding functionality splits defining a division of processing activity between the backend server and the edge processing function. Based on a received measurement of bandwidth and/or of latency on the connection, the assessment module selects an interface and corresponding functionality split, and downloads them to the edge processing function and the backend server.

Abstract: An edge computing system comprises: a cloud computing system; an edge processing function; a connection between the edge processing function and the cloud computing system; a backend server within the cloud computing system. An assessment module is configured to receive information about processing goals, and processing capabilities of the backend server and the edge processing function. The assessment module derives a set of possible interfaces and corresponding functionality splits defining a division of processing activity between the backend server and the edge processing function. Based on a received measurement of bandwidth and/or of latency on the connection, the assessment module selects an interface and corresponding functionality split, and downloads them to the edge processing function and the backend server.

Abstract: Methods, systems, and computer-readable media provide for predicting an effectiveness of a cost saving opportunity. According to embodiments of the method, a feasibility score, a financial impact score, and a risk score are determined. An effectiveness score is determined based on the feasibility score, the financial impact score, and the risk score. The feasibility score may indicate an anticipated ease with which to implement each cost saving opportunity. The financial impact score may indicate an anticipated cost savings resulting from implementing each cost saving opportunity. The risk score may indicate an anticipated level of business risk incurred by implementing each of the plurality of cost saving opportunities. The effectiveness score, which may be represented in one or two dimensions, indicates the anticipated effectiveness of each cost saving opportunity based on the feasibility score, the financial impact score, and the risk score.

Abstract: Methods, systems, and computer-readable media provide for determining an application risk rating. According to embodiments, a method for determining an application risk rating is provided. According to the method, a technology risk score is determined. The technology risk score indicates a technology status associated with a business system. A capacity risk score is determined. The capacity risk score indicates a capacity status associated with the business system. A business risk score is determined. The business risk score indicates a criticality of a function provided by the business system. An application risk rated is determined based on the technology risk score, the capacity risk score, and the business risk score.

Abstract: Lubricant compositions for use in automotive engine oils comprising a combination of a specific base stock or mixture of base stocks and a friction reducing additive to improve fuel economy and fuel economy longevity of the automotive engine oil. The friction reducing additive is a specific partial polyol ester and may also include a specific saturated primary amide.