Aspects of the present disclosure are directed to orchestration of tasks in tenant clouds. In an embodiment, an orchestrator receives a task-group and a condition, with the task-group specifying multiple tasks. The orchestrator selects a group of resources satisfying the condition, with the group of resources being of a tenant cloud spanning multiple cloud infrastructures. The orchestrator invokes the task-group on the group of resources to cause each of the multiple tasks to be executed on each of the group of resources.

In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus may be a pod manager. The pod manager receives receive a request for composing a target composed-node. The pod manager employs a first set of pooled hardware resources of the computing pod to build the target composed-node. The pod manager determines to reserve a second set of pooled hardware resources of the computing pod for a backup node of the target composed-node. The pod manager determines that the target composed-node has failed. The pod manager employs the second set of pooled hardware resources to build the backup node.

Techniques are described for filtering and normalizing training data used to build a predictive auto scaling model used by a service provider network to proactively scale users' computing resources. Further described are techniques for identifying collections of computing resources that exhibit suitably predictable usage patterns such that a predictive auto scaling model can be used to forecast future usage patterns with reasonable accuracy and to scale the resources based on such generated forecasts. The filtering of training data and the identification of suitably predictable collections of computing resources are based in part on autocorrelation analyses, and in particular on “delayed” autocorrelation analyses, of time series data, among other techniques described herein.

Technologies for allocating resources of managed nodes to workloads to balance multiple resource allocation objectives include an orchestrator server to receive resource allocation objective data indicative of multiple resource allocation objectives to be satisfied. The orchestrator server is additionally to determine an initial assignment of a set of workloads among the managed nodes and receive telemetry data from the managed nodes. The orchestrator server is further to determine, as a function of the telemetry data and the resource allocation objective data, an adjustment to the assignment of the workloads to increase an achievement of at least one of the resource allocation objectives without decreasing an achievement of another of the resource allocation objectives, and apply the adjustments to the assignments of the workloads among the managed nodes as the workloads are performed. Other embodiments are also described and claimed.

Systems, apparatuses, methods, and computer-readable media, are provided for offloading computationally intensive tasks from one computer device to another computer device taking into account, inter alia, energy consumption and latency budgets for both computation and communication. Embodiments may also exploit multiple radio access technologies (RATs) in order to find opportunities to offload computational tasks by taking into account, for example, network/RAT functionalities, processing, offloading coding/encoding mechanisms, and/or differentiating traffic between different RATs. Other embodiments may be described and/or claimed.

According to an aspect, there is provided a computer-implemented method of operating a first node. The first node has an algorithm for evaluating input data from another node, with the input data having a plurality of different attributes. The method comprises receiving, from a second node, a proposal for the evaluation of a first set of input data by the algorithm; estimating the performance of the algorithm in evaluating the first set of input data based on the proposal; and outputting, to the second node, an indication of the estimated performance of the algorithm. A corresponding first node is also provided.

A system, method, and computer program product are provided for a determining a network situation in a communication network. In use, at least one threshold value of at least one operational parameter of a communication network is obtained, the at least one operational parameter representing at least one operational status of at least one of a computational device or a communication device. Additionally, log data of the communication network is obtained, the log data containing at least one value of the at least one operational parameter reported by at least one network entity of the communication network. The at least one value of the at least one operational parameter of the log data is compared with a corresponding threshold value of the at least one threshold value to form a detection of a network situation. Further, the detection of the network situation is reported if the at least one value of the at least one operational parameter of the log data traverses the corresponding threshold value of the at least one threshold value.

Systems, methods, and computer program products are provided for load balancing for processing large data sets. The method includes identifying a number of segments and a transaction data set comprising transaction data for a plurality of transactions, the transaction data for each transaction of the plurality of transactions comprising a transaction value, determining an entropy of the transaction data set based on the transaction value of each transaction of the plurality of transactions, segmenting the transaction data set into the number of segments based on the entropy of the transaction data set and balancing respective entropies of each segment of the number of segments, and distributing processing tasks associated with each segment of the number of segments to at least one processor of a plurality of processors to process each transaction in each respective segment.

In various embodiments, an isolation application determines processor assignment(s) based on a performance cost estimate. The performance cost estimate is associated with an estimated level of cache interference arising from executing a set of workloads on a set of processors. Subsequently, the isolation application configures at least one processor included in the set of processors to execute at least a portion of a first workload that is included in the set of workloads based on the processor assignment(s). Advantageously, because the isolation application generates the processor assignment(s) based on the performance cost estimate, the isolation application can reduce interference in a non-uniform memory access (NUMA) microprocessor instance.

An example method is provided for recommending VM configurations, including one or more servers upon which one or more VMs can run. A user wishing to run these VMs can request a recommendation for an appropriate server or set of servers. The user can indicate a category corresponding to the type of workload that pertains to the VMs. The system can receive the request and identify a pool of servers available to the user. Using industry specifications and benchmarks, the system can classify the available servers into multiple categories. Within those categories, similar servers can be clustered and then ranked based on their levels of optimization. The sorted results can be displayed to the user, who can select a particular server (or group of servers) and customize the deployment as needed. This process allows a user to identify and select an optimized setup quickly and accurately.