Abstract: The described implementations relate to processing of electronic data. One implementation is manifested as a system that can include an inference engine and at least one processing device configured to execute the inference engine. The inference engine can be configured to perform automated detection of concepts expressed in failure logs that include unstructured data. For example, the inference engine can analyze text of support tickets or diary entries relating to troubleshooting of an electronic network to obtain concepts identifying problems, actions, or activities. The inference engine can also be configured to generate output that reflects the identified concepts, e.g., via a visualization or queryable programming interface.

Abstract: An optimization framework for hosting sites that dynamically places application instances across multiple hosting sites based on the energy cost and availability of energy at these sites, application SLAs (service level agreements), and cost of network bandwidth between sites, just to name a few. The framework leverages a global network of hosting sites, possibly co-located with renewable and non-renewable energy sources, to dynamically determine the best datacenter (site) suited to place application instances to handle incoming workload at a given point in time. Application instances can be moved between datacenters subject to energy availability and dynamic power pricing, for example, which can vary hourly in day-ahead markets and in a time span of minutes in realtime markets.

Abstract: Described herein are various technologies pertaining to providing assistance to an operator in a data center with respect to failures in the data center. An alarm is received, and a failing device is identified based upon content of the alarm. Failure conditions of the alarm are mapped to a failure symptom that may be exhibited by the failing device, and troubleshooting options previously employed to mitigate the failure symptom are retrieved from historical data. Labels are respectively assigned to the troubleshooting options, where a label is indicative of a probability that a troubleshooting option to which the label has been assigned will mitigate the failure symptom.

Abstract: Techniques for generating a client-side Application Programming Interface (API) are described herein. The techniques may include analyzing source code that is related to an API of a service provider and/or content that describes the source code and/or the API of the service provider. The analysis may identify characteristics of the API of the service provider, such as routines, characteristics of the routines, data constructs, characteristics of the data constructs, and so on. The techniques may also include generating a representation to represent the characteristics of the API of the service provider and generating a client-side API based on the representation. The client-side API may include a library of client-side routines and/or data constructs that provide access to routines and/or data constructs that are made available via the API of the service provider.

Abstract: This document describes techniques for dynamically placing computing jobs. These techniques enable reduced financial and/or energy costs to perform computing jobs at data centers.

Abstract: Disclosed herein is a system and method for searching or processing queries for searching for documents contained in a domain specific knowledge base. The system takes a query and generates from the query a modified version of the query by passing the query through one or more filters in a query processor. The query processor adds or removes terms from the query. The query processor can add or recognize that two words that appear to be separate words actually identify a specific software entity or can determine that a number appearing in a query is not just a number but refers to a specific version or a number relevant to the specific problem.

Abstract: Disclosed herein is a system and method for generating an enhanced index for documents contained in a knowledge base. Documents or configuration snapshots are tokenized and then passed through a number of filters. The filters modify the token stream to generate an enhanced token stream for the document by removing words from the stream and adding domain level knowledge to the tokens in the stream. The token stream is then added to the index for the document so that searches can be completed against the knowledge base when searches are generated from configuration snapshots or from inputted search queries.

Abstract: A hierarchical network analytics system operated by a computing device or system is described. In some example techniques, the analytics system may determine results of a plurality of first level analyses each based at least in part on results of a respective plurality of data queries that return respective subsets of a plurality of types of network data. The analytics system may determine a result of a second level analysis based at least in part on results of the plurality of first level analyses.

Abstract: An elastic scaling cloud-hosted batch application system and method that performs automated elastic scaling of the number of compute instances used to process batch applications in a cloud computing environment. The system and method use automated elastic scaling to minimize job completion time and monetary cost of resources. Embodiments of the system and method use a workload-driven approach to estimate a work volume to be performed. This is based on task arrivals and job execution times. Given the work volume estimate, an adaptive controller dynamically adapts the number of compute instances to minimize the cost and completion time. Embodiments of the system and method also mitigate startup delays by computing a work volume in the near future and gradually starting up additional compute instances before they are needed. Embodiments of the system and method also ensure fairness among batch applications and concurrently executing jobs.

Abstract: Optimization mechanism that dynamically splits the computation in an application (e.g., cloud), that is, which parts run on a client (e.g., mobile) and which parts run on servers in a datacenter. This optimization can be based on application characteristics, network connectivity (e.g., latency, bandwidth, etc.) between the client and the datacenter, power or energy available at the client, size of the application objects, load in the datacenter, security and privacy concerns (e.g., cannot share all data on the client with the datacenter), and other criteria, as desired.

Abstract: The subject disclosure is directed towards verifying correct middlebox operation/behavior, including while the middlebox is running in a network. Probe traffic is sent to a middlebox, with the middlebox output monitored to determine whether the middlebox correctly processed the traffic. For example, the verification may be directed towards evaluating that only legitimate traffic is passed, and that the legitimate traffic is correctly routed. Also described is the use of a summary data structure to track traffic flows, and the detection of routing loops.

Abstract: Aspects of the subject disclosure are directed towards automatically inferring the significant parts of bug reports, including by querying a knowledge base built by mining information from a large number of earlier bug reports. Common phrases in the earlier bug reports are filtered to provide a subset of phrases relevant to a bug domain, which are stored in the knowledge base and mapped to an ontology model of the knowledge base. When a new bug report is received for inference/analysis, the phrases therein are tagged based upon the ontology model, and the knowledge base queried with the tagged phrases to determine problems, activities and actions that are likely related to the new bug report.

Abstract: Aspects of the subject disclosure are directed towards protecting machines, such as virtual machines in a cloud datacenter, from receiving unwanted traffic, and also reducing bandwidth by eliminating redundant data transmissions. In one aspect, an agent intercepts packets from a source, and determines whether the destination is allowed to receive packets from the source, based upon a communication group membership. The agent also may drop packets based upon malware/fraud signatures. The agent also attempts to reduce bandwidth by replacing redundant content with identifiers (e.g., hashcodes), which a destination machine uses to rebuild the original content. A destination-side agent may perform the same or similar communication group membership and malware/fraud signature filtering operations, and reassemble redundancy-reduced content from received identifiers as needed.

Abstract: The described implementations relate to processing of electronic data. One implementation is manifest as a system that can include an event analysis component and one or more processing devices configured to execute the event analysis component. The event analysis component can be configured to obtain multiple events that are generated by network devices in a networking environment. The event analysis component can also be configured to identify impactful events from the multiple events. The impactful events can have associated device-level or link-level impacts. The event analysis component can also be configured to determine one or more failure metrics for an individual impactful event. The one or more failure metrics can include at least a first redundancy-related failure metric associated with redundant failovers in the networking environment.

Abstract: A data center system is described which includes multiple data centers powered by multiple power sources, including any combination of renewable power sources and on-grid utility power sources. The data center system also includes a management system for managing execution of computational tasks by moving data components associated with the computational tasks within the data center system, in lieu of, or in addition to, moving power itself. The movement of data components can involve performing pre-computation or delayed computation on data components within any data center, as well as moving data components between data centers. The management system also includes a price determination module for determining prices for performing the computational tasks based on different pricing models. The data center system also includes a “stripped down” architecture to complement its use in the above-summarized data-centric environment.

Abstract: Methods and systems for load balancing in a cluster storage system are disclosed herein. The method includes identifying a source node within the cluster storage system from which to move a number of data objects, wherein the source node includes a node with a total load exceeding a threshold value. The method also includes selecting the data objects to move from the source node, wherein the data objects are chosen such that the total load of the source node no longer exceeds the threshold value. The method further includes determining a target node within the cluster storage system based on a proximity to the source node and the total load of the target node and moving the data objects from the source node to the target node.

Abstract: A data center system is described which includes multiple data centers powered by multiple power sources, including any combination of renewable power sources and on-grid utility power sources. The data center system also includes a management system for managing execution of computational tasks by moving data components associated with the computational tasks within the data center system, in lieu of, or in addition to, moving power itself. The movement of data components can involve performing pre-computation or delayed computation on data components within any data center, as well as moving data components between data centers. The management system also includes a price determination module for determining prices for performing the computational tasks based on different pricing models. The data center system also includes a “stripped down” architecture to complement its use in the above-summarized data-centric environment.

Abstract: The described implementations relate to processing of electronic data. One implementation is manifest as one or more computer-readable storage devices comprising instructions which, when executed by one or more processing devices, cause the one or more processing devices to perform acts. The acts can include determining service levels provided by multiple network configurations, determining costs associated with the multiple network configurations, and evaluating the multiple network configurations based on both the costs and the service levels. The multiple network configurations can include redundantly-deployed devices. Furthermore, some implementations may determine cost/service level metrics that can be used to compare devices based on expected costs to provide a particular service level.

Abstract: The described implementations relate to processing of electronic data. One implementation is manifest as a system that that can include an event analysis component and one or more processing devices configured to execute the event analysis component. The event analysis component can be configured to obtain events from event logs, the events reflecting failures by one or more network devices in one or more data centers and characterize a service level of an application or a network device based on the events. For example, the event analysis component can be configured to characterize the availability of an application based on one or more network stamps of the application.

Abstract: Instances of a same application execute on different respective hosts in a cloud computing environment. Instances of a monitor application are distributed to concurrently execute with each application instance on a host in the cloud environment, which provides user access to the application instances. The monitor application may be generated from a specification, which may define properties of the application/cloud to monitor and rules based on the properties. Each rule may have one or more conditions. Each monitor instance running on a host, monitors execution of the corresponding application instance on that host by obtaining from the host information regarding values of properties on the host per the application instance. Each monitor instance may evaluate the local host information or aggregate information collected from hosts running other instances of the monitor application, to repeatedly determine whether a rule condition has been violated. On violation, a user-specified handler is triggered.