Aspects of workload reallocation within a software-defined data center (SDDC) undergoing an upgrade are described. As upgrades become available for services and other types of applications installed on a cluster of host devices within a data center, an upgrade of the installed services may be required for each of the host devices. During a cluster upgrade, the order in which hosts in the cluster are upgraded is determined as a function of evacuation costs and evacuation policies associated with each host device in the computing cluster. In addition, a maintenance cost associated with a workload needing to be evacuated from a host undergoing an upgrade is determined based on the upgrade sequence. The maintenance cost can then be used as a factor for selecting an optimal candidate host for migrating the workload to when the host the workload is currently running on is being upgraded.

A system for detecting malicious software, comprising at least one hardware processor adapted to: execute a tested software object in a plurality of computing environments each configured according to a different hardware and software configuration; monitor a plurality of computer actions performed in each of the plurality of computing environments when executing the tested software object; identify at least one difference between the plurality of computer actions performed in a first of the plurality of computing environments and the plurality of computer actions performed in a second of the plurality of computing environments; and instruct a presentation of an indication of the identified at least one difference on a hardware presentation unit.

Solutions for supporting storage using a multi-writer log-structured file system (LFS) are disclosed that include receiving incoming data from an object of a plurality of objects that are configured to simultaneously write to the LFS from different nodes; based at least on receiving the incoming data, determining whether sufficient free segments are available in a local segment usage table (SUT) for writing the incoming data; based at least on determining that insufficient free segments are available, requesting allocation of new free segments; writing the incoming data to a log; acknowledging the writing to the object; determining whether the log has accumulated a full segment of data; based at least on determining that the log has accumulated a full segment of data, writing the full segment of data to a first segment of the free segments; and updating the local SUT to mark the first segment as no longer free.

Load testing a service having a plurality of different states is provided. A multitude of simulated users accessing the service are divided into a plurality of cohorts. Simulated users within a given cohort share a similar personality type. A load test of the service is performed by applying a set of service requests from each respective cohort to the service. In response to a percentage of simulated users of each cohort encountering a particular state in the service, a user response is determined for the percentage of simulated users within each cohort at that particular state based on a probabilistic user behavior model corresponding to a personality type of each cohort such that user responses at that particular state are distributed in accordance with the probabilistic user behavior model. Distributed user responses at that particular state are applied to the load test in accordance with the probabilistic user behavior model.

Aspects of the present disclosure involve systems and methods for performing operations comprising providing a messaging application comprising a feature to a client device, the feature being implemented by operations having alternative complexity levels, wherein a first complexity level represents a first amount of device resources consumed by a first set of operations, and wherein a second complexity level represents a second amount of device resources consumed by a second set of operations; determining that the first configuration rule is satisfied by a first property of the client device; and in response to determining that the first configuration rule is satisfied by the first property of the client device, causing the feature to be implemented on the client device by the first set of operations having the first complexity level that consume a greater amount of device resources than the second set of operations having the second complexity level.

The embodiments herein describe hybrid parallelism techniques where a mix of data and model parallelism techniques are used to split the workload of a layer across an array of processors. When configuring the array, the bandwidth of the processors in one direction may be greater than the bandwidth in the other direction. Each layer is characterized according to whether they are more feature heavy or weight heavy. Depending on this characterization, the workload of an NN layer can be assigned to the array using a hybrid parallelism technique rather than using solely the data parallelism technique or solely the model parallelism technique. For example, if an NN layer is more weight heavy than feature heavy, data parallelism is used in the direction with the greater bandwidth (to minimize the negative impact of weight reduction) while model parallelism is used in the direction with the smaller bandwidth.

Methods and systems for managing the performance of workloads in a distributed system are disclosed. The distributed system may include any number of clients, deployments, and data sources operably to one another. To service the workloads, container instances may be deployed to various deployments. When deciding where to deploy the container instances, the hardware resources of the deployments and/or resource expectations associated with the container instances may be taken into account. By doing so, container instances may be more likely to be deployed to deployments that meet their resource expectations. The resource expectations may be embedded as metadata in resources specific build files.

Application link scaling method, apparatus and system are provided. The method includes obtaining an application link, the application link being a path formed by at least two associated applications for a service scenario; determining information of target resources required by capacity scaling for all applications in the application link; allocating respective resources to the applications according to the information of the target resources; and generating instances for the applications to according the respective resources. From the perspective of services, the method performs capacity assessment for related applications on a link as a whole, and capacity scaling of the entire link, thus fully utilizing resources, and preventing the applications from being called by other applications which results in insufficient resources. This ensures the applications not to become the vulnerability of a system, ensures the stability of the system, avoids allocating excessive resources to the applications, and reduces a waste of resources.

A system and method for scaling resources of a secondary network for disaster recovery uses a disaster recovery notification from a primary resource manager of a primary network to a secondary resource manager of the secondary network to generate a scale-up recommendation for additional resources to the secondary network. The additional resources are based on latest resource demands of workloads on the primary network included in the disaster recovery notification. A scale-up operation for the additional resources is then executed based on the scale-up recommendation from the secondary resource manager to operate the secondary network with the additional resources to run the workloads on the secondary network.

Monitoring a set of virtual users during performance testing and reporting virtual user operations data to ensure an ongoing and constant transactions per second load and providing test result data with evidence of constant transactions per second (TPS) during the test. Generating and executing performance tests under constant TPS includes restarting virtual users that are terminated during the performance tests.