Abstract: Virtual redundancy for active-standby cloud applications is disclosed herein. A virtual machine (“VM”) placement scheduling system is disclosed herein. The system can compute, for each standby VM of a plurality of available standby VMs, a minimum required placement overlap delta to meet an entitlement assurance rate (“EAR”) threshold. The system can compute a minimum number of available VM slots for activating each standby VM to meet the EAR threshold. For each standby VM of a given application, the system can filter out any server of a plurality of servers that does not meet criteria. If a given server meets the criteria, the system can add the given server to a candidate list; sort, in descending order, the candidate list by the minimum required placement overlap delta and the number of available virtual machine slots; and select, from the candidate list of servers, a candidate server from atop the candidate list.

Abstract: Virtual redundancy for active-standby cloud applications is disclosed herein. A virtual machine (“VM”) placement scheduling system is disclosed herein. The system can compute, for each standby VM of a plurality of available standby VMs, a minimum required placement overlap delta to meet an entitlement assurance rate (“EAR”) threshold. The system can compute a minimum number of available VM slots for activating each standby VM to meet the EAR threshold. For each standby VM of a given application, the system can filter out any server of a plurality of servers that does not meet criteria. If a given server meets the criteria, the system can add the given server to a candidate list; sort, in descending order, the candidate list by the minimum required placement overlap delta and the number of available virtual machine slots; and select, from the candidate list of servers, a candidate server from atop the candidate list.

Abstract: Methods and systems are disclosed for providing cloud services to multiple customers in a cloud. One embodiment includes receiving a number of requests for the cloud services from the multiple customers simultaneously or substantially simultaneously; prioritizing the requests based on a probability distribution of actually deploying a service, a budget of the customers, and an expected demand of the requested service based on the probability distribution; generating a number of cloud configurations along with a number of Service Level Agreements (SLAs) for the customers based on prioritization of the requests, a class & past behavior of the customers, and a current demand of the cloud services, the SLAs of the customers include differentiated price offering; recommending the cloud configurations and the SLAs to the customers; allowing the customers to negotiate terms of the SLAs; and providing the cloud services based on the negotiated SLAs to the customers.

Abstract: Concepts and technologies are disclosed herein for an application deployment engine. A processor that executes an application deployment engine can receive an application request. The processor can obtain network topology data that indicates availability of resources of a data center, an application template associated with the application, and a running time during which an application placement plan is to be identified out of a large number of placement scenarios within the running time. The application template can describe an application flow path associated with the application. The processor can identify the application placement plan, where the application placement plan can include an optimal placement of the application at the data center, before a given running time expires by pruning the large search space. The processor can generate a command to effect deployment of the application in accordance with the application placement plan.

Abstract: Concepts and technologies are disclosed herein for an application deployment engine. A processor that executes an application deployment engine can receive an application request. The processor can obtain network topology data that indicates availability of resources of a data center, an application template associated with the application, and a running time during which an application placement plan is to be identified out of a large number of placement scenarios within the running time. The application template can describe an application flow path associated with the application. The processor can identify the application placement plan, where the application placement plan can include an optimal placement of the application at the data center, before a given running time expires by pruning the large search space. The processor can generate a command to effect deployment of the application in accordance with the application placement plan.

Abstract: A method includes, for components of an application, identifying a plurality of groups including a first group and a second group, The method also includes nesting the first group into the second group to create a nested group, based at least on a level of the first group not exceeding a level of the second group. The method includes deploying the application by placing the components of the nested group within a network in accordance with a restriction of the nested group. The components include at least one virtual machine.

Abstract: Methods and systems are provided for determining performance characteristics of an application processing system. The method comprises monitoring throughput of a plurality of resources of the system in a selected time window. A change rate is detected in the throughput of the resources, respectively, representative of a change to constancy of workload in at least some of the resources. Such a change in constancy comprises a knee point of a plot of resource usage comprising load relative to throughput. The time of the change rate is identified within the time window. A relatively first to occur of a plurality of resources knee points is determined wherein the resource corresponding to the first to occur is determined to have a fully loaded throughput within the multi-tier processing system. The determination of the first to occur knee point comprises pinpointing a bottleneck starting point within the application processing system.

Abstract: Methods and systems for determining prices of customized virtual machines required to process customer-specified workloads are disclosed. A count of instances of the customized virtual machines, required to process the customer-specified workloads is determined, based on a configuration of the customized virtual machines. The instances of the customized virtual machines are consolidated on virtual machine servers. Further, the prices of the customized virtual machines are determined based on a count of the virtual machine servers, unused resources in the virtual machine servers, and unused resources in the customized virtual machines. The determined prices are recommended to the customer. Further, at least one of the prices of the customized virtual machines or the configuration of at least one or more customized virtual machines is modified, based on a response to the recommendation received from the customer.

Abstract: A method, non-transitory computer readable medium, and apparatus for adapting resources of the cluster of nodes for a real-time streaming workflow are disclosed. For example, the method receives a notification that a node of the cluster of nodes associated with an instance of a process of the real-time streaming workflow is predicted to be a bottleneck, identifies a number of hops to send a resource statement when the bottleneck is predicted that minimizes a ripple effect associated with transmitting the resource statement, transmits the resource statement to at least one or more nodes of the cluster of nodes within the number of hops, receives a response from one of the at least one or more nodes within the cluster of nodes and adapts a resource usage to the at least one of the one or more nodes within the cluster of nodes that the response was received from.

Abstract: A method, non-transitory computer readable medium, and apparatus for estimating a completion time for a MapReduce job are disclosed. For example, the method builds a general MapReduce performance model, computes one or more performance characteristics of each one of one or more benchmark workloads, computes one or more performance characteristics of the MapReduce job in the known processing system, selects a subset of the one or more benchmark workloads that have similar performance characteristics as the one or more performance characteristics of the MapReduce job, targets a cluster of processing nodes in a distributed processing system, computes one or more performance characteristics of the subset of the one or more benchmark workloads in the cluster of processing nodes and estimates the completion time for the MapReduce job.

Abstract: Concepts and technologies are disclosed herein for an application deployment engine. A processor that executes an application deployment engine can receive an application request. The processor can obtain network topology data that indicates availability of resources of a data center, an application template associated with the application, and a running time during which an application placement plan is to be identified out of a large number of placement scenarios within the running time. The application template can describe an application flow path associated with the application. The processor can identify the application placement plan, where the application placement plan can include an optimal placement of the application at the data center, before a given running time expires by pruning the large search space. The processor can generate a command to effect deployment of the application in accordance with the application placement plan.

Abstract: A method provides a recommendation for a cloud configuration for deploying a service. In response to receiving a service request, a database is searched for a cloud configuration. The search is performed by acquiring a resource usage pattern for the service. The resource usage pattern is mapped to a multidimensional space, which is searched for a previously deployed cloud configuration having a similar resource usage pattern. In response to finding a previously deployed cloud configuration, the activity management is determined for the previously deployed cloud configuration. A recommendation is made based on the activity management.

Abstract: The disclosed embodiments relate to systems and methods for method and systems for sub-allocating computational resources. A first computing device receives information associated with a first set of computational resources from a cloud infrastructure. The first set of computational resources has been allocated to the first computing device by the cloud infrastructure. A first set of parameters associated with a workflow received by the first computing device is determined. The first set of parameters is indicative of a need of the first set of computational resources by the first computing device. One or more computational resources from the first set of computational resources are sub-allocated based on the determined first set of parameters.

Abstract: A method, a system, and a computer program product for translating a document are disclosed. A document in a source language is received and text snippets are extracted from the same. The text snippets are sent to a first set of remote workers for translation and a second set of remote workers for validation. The words in the validated text snippets are assigned a probability score. The words with the highest probability score are combined to generate the translated document.

Abstract: Methods and systems for recommending one or more computational resources. A portion of computational resources is determined from a set of computational resources associated with a datacenter based on a user-profile associated with a user, from one or more users, and the set of computational resources. The user-profile comprises at least one of a required performance level, a required load, or a cost constraint. The one or more computational resources are recommended from the portion of computational resources, in response to requests received from the one or more users.

Abstract: An embodiment generally relates to systems and methods for improving system performance by reducing fragility of computing systems. A processing module can identify separate ensemble files each comprising interpretations, by separate entities of a workflow, of a phrase in a file. The processing module can compare the interpretations to determine if the interpretations are the same or essentially the same. If the interpretations are neither the same nor essentially the same, a subsequent entity in the workflow can create a new file that replaces an associated interpretation of the phrase with a common interpretation. The subsequent entity can proceed with an intended operation.

Abstract: Methods and systems for scalable extraction of episode rules using incremental episode tree construction in a multi-application event space comprise compiling events from multiple, different domain logs into in a universal log file, rolling domain-dependent and domain-independent windows through the universal log file to identify distinct event-pattern episodes, adding episodes to an episode tree data structure, pruning less frequent episodes from the episode tree, analyzing the episode tree to identify frequent episode rules, and applying the frequent episode rules to future interactions with users.

Abstract: Methods and systems are disclosed for providing cloud services to multiple customers in a cloud. One embodiment includes receiving a number of requests for the cloud services from the multiple customers simultaneously or substantially simultaneously; prioritizing the requests based on a probability distribution of actually deploying a service, a budget of the customers, and an expected demand of the requested service based on the probability distribution; generating a number of cloud configurations along with a number of Service Level Agreements (SLAs) for the customers based on prioritization of the requests, a class & past behavior of the customers, and a current demand of the cloud services, the SLAs of the customers include differentiated price offering; recommending the cloud configurations and the SLAs to the customers; allowing the customers to negotiate terms of the SLAs; and providing the cloud services based on the negotiated SLAs to the customers.

Abstract: A method for computing the energy rating for cloud-based software services is disclosed. For each of the service, following steps are performed. The method includes identifying configuration parameters impacting the energy consumption. The method further includes determining a value for each configuration parameter. Further, the method includes determining a relative energy rating using a pre-determined equation, based on the values of the configuration parameter. Finally, the method includes assigning a discrete value based on the range of the relative energy rating.

Abstract: Methods and systems of performing data mining may include receiving a plurality of web log records and a plurality of call log records; associating one or more web log records with a call log record, wherein the associated user for each of the associated one or more web log records and the call log record are the same; identifying one or more patterns among the web log records for the plurality of call log records, wherein each pattern comprises one or more web accesses, a time stamp at which each of the one or more web accesses is performed and the call topic for the call log record; identifying one or more web log records associated with a new call, and predicting a call topic for the new call based on at least one pattern and the one or more web log records.