Virtual Instruments Worldwide, Inc.

Xangati, Inc

Other

Xerox Corporation

Virtual Instruments Corporation

Rangaswamy Jagannathan

Rosanna Lee

Derek Sanders

Kishor Kakatkar

Xiaohong Pan

Jing Liu

Elizaveta Tavastcherna

Michael Bello

Ricardo A Negrete

Vitoo Suwannakinthorn

Rick Haggart

Hong Zhu

Steve B Cousins

Roy Nakashima

Jeff Breidenbach

David Messina

Darek Sanders

Jing Lui

A method comprising discovering workload attributes and identify dependencies, receiving utilization performance measurements including memory utilization measurements of at least a subset of workloads, grouping workloads based on the workload attributes, the dependencies, and the utilization performance measurements into affinity groups, determining at least one representative synthetic workload for each affinity group, each representative synthetic workload including a time slice of a predetermined period of time when there are maximum performance values for any number of utilization performance measurements among virtual machines of that particular affinity group, determining at least one cloud service provider (CSP)'s cloud services based on performance of the representative synthetic workloads, and generating a report for at least one of the representative synthetic workloads, the report identifying the at least one of the representative synthetic workloads and the at least one CSP's cloud services including cloud workload cost.

Port configuration for cloud migration readiness

Systems and methods for cloud migration readiness

A monitoring device responds to status data to detect storms, analysis, and to attempt to remediate those storms. The monitoring device several types of storms, for each of which it has a technique for analysis of the storm. The monitoring device can determine if the storm is due to resource contention, excess or unbalanced performance activity, or network degradation. Once analyzed, the monitoring device analyzes the storm, and attempts to remediate the cause of the storm.

Storm detection, analysis, remediation, and other network behavior

A monitoring device responds to status data pushed from a network device, and also manages a link with another network device, the link allowing the monitoring device to pull status data from the second network device. The monitoring device receives packets including status, the data indicating activity for one or more clock ticks. The monitoring device can compute statistical measures, rather than the network device. The monitoring device maintains the status data in a buffer. The monitoring device lags actual activity, but has is more likely to capture delayed packets. The network device sends packets as wrappers, each wrapper indicating sets of status information. When the information in a wrapper crosses a clock tick boundary, the monitoring device allocates reported activity among clock ticks, assuming that activity follows a uniform distribution.

Cross silo time stitching

Push pull data collection

Computing devices, each of which monitors information in a monitoring environment, take on the role of a controller for some of them, separating the (real or virtual) elements of the environment into subsets. Computing devices provide their results to a unification device, which combines them into a monitoring parameter. Each computing device monitors its parameters based on a timestamp, so unification devices can determine whether results from those computing devices represent the same state of the environment. Unification devices divide the results from their computing devices into uniform durations. Even if results don't reflect the same environment state, unification devices can still approximate results for unification. Elements can be reassigned on time boundaries, or can be duplicated, with unification devices still able to unify results. Predicted queries can be pre-computed.

Parallel distributed networking

For example, as indicated in the Incorporated Disclosures, rapid change from relatively little use of network bandwidth, to heavy overuse of network bandwidth (combined with a large number of endpoints attempting to access that network bandwidth) can often indicate an email virus or other maladjusted use of the system. In such cases, it could be appropriate to generate an alert, and bring the change to the attention of a human expert, or otherwise escalate the problem. In this Application, the phrase 'best practices,' and variants thereof, can generally refer to business preferences (and to business rules imposed by management, or law or regulation), such as high utilization preferences often expressed by management. In such cases, one or more operators can enter descriptors of rules the system that it is desired the system follow, such as 'processor cycle utilization &#x3e;90%.' When these best practices are not followed, it could be appropriate to generate an alert, bring the change to the attention of a human expert, or otherwise escalate the problem. In this Application, the phrase &#x201c;profiling and alerting,&#x201d; and variants thereof, can generally refer to maintaining a profile of how the system normally behaves, absent stressors, and to identify any substantial changes from that profile. When a profile is seriously violated, it could be appropriate to generate an alert, bring the change to the attention of a human expert, or otherwise escalate the problem. In such cases, it can be appropriate to escalate the severity of the alert with increased severity of violation, increased time duration of the violation, and increased trajectory in the direction of further violation. In this Application, the phrase &#x201c;absolute capacity,&#x201d; and variants thereof, can refer to an objective measure of system capacity, such as whether the system has 1 TB or 4 TB of memory. The phrase &#x201c;relative capacity,&#x201d; and variants thereof, can refer to a subjective measure of system capacity, such as when the system has 4 TB of physical memory, but 3.5 TB of that memory are allocated to a high-priority task whose interruption or pause would be untoward.

Self-learning and best-practice profiling and alerting with relative and absolute capacity

Network monitoring views can be recorded, replayed and shared. A monitoring system receives monitoring data, in response to which a monitoring view can be constructed. The monitoring data is maintained in memory or mass storage. One or more monitoring views can later be constructed in response to user preferences, even if those users would not have known to select those particular preferences or construct those particular monitoring views at the time. The monitoring views constructed in response to that monitoring data can be presented to users in a simulation of the problem, as if it were occurring at that time. Users can send particular monitoring views to others, with both the effects that the monitoring view can be preserved as part of a trouble report, and the persons receiving the monitoring view can have useful insight into what problem the user has pointed to.

Recording, replay, and sharing of live network monitoring views

A network monitoring device responds to network status data (whether 'pushed' from the network device or 'pulled' from the network device) to maintain a buffer of saved status data. The network status data is reordered, manipulated, and presented to users of the network monitoring device in order. The monitoring device can provide a status report of the network environment. When network status data is delayed too long, the monitoring device can discard the network status data, or reduce its weighted consideration. The monitoring device attempts to balance accuracy and latency by adjusting wait time for network status data. The monitoring device maintains a record of the amount of network status data it receives and processes from each network device and each network device's ability to provide accurate and complete information to operators and users in a distributed network monitoring environment.

Enhanced flow processing

One network monitoring system maintains both information regarding historical activity and emergent activity of the network. Comparison of recent activity of the network with historical activity allows the system to determine whether recent network activity is within the realm of normal. The system maintains data structures representing a p.d.f. for observable values of network parameters. Such data structures are maintained both for historical and for emergent activity of the network. Recent activity can be compared with the p.d.f. for historical activity to aid in determining whether that recent activity is within the realm of normal. Network activity not within the realm of normal can include values of observable network parameters too high or too low to be consistent with historical activity of the network, or other values too unlikely to be consistent with historical activity at the network. Another network monitoring device includes data structures for maintaining information regarding historical activity of a network and emergent activity of a network. Those data structures include multiple types of observable values as well as multiple types of combinations of multiple observable values. The network monitoring device maintains those data structures including information regarding historical activity of a network and emergent activity of a network only for those source/destination pairs, and only for those nodes, for which maintaining that information would be substantially meaningful. Yet another network monitoring system includes data structures for maintaining information regarding historical activity of a network and emergent activity of a network. Those data structures include observable values for multiple profile dimensions, including source/destination address, application, location, and time. The data structures also include observable values for combinations of more than one of those multiple profile dimensions, including, e.g., (source address)&#xd7;(application), and the like. It is expected that only a relatively sparse set of combinations of more than one of those multiple profile dimensions would have meaningful information associated therewith. The network monitoring system maintains those data structures only for those combinations of more than one of those multiple profile dimensions for which maintaining that information would be substantially meaningful.

Symptom detection using behavior probability density, network monitoring of multiple observation value types, and network monitoring using orthogonal profiling dimensions

For example, as indicated in the Incorporated Disclosures, rapid change from relatively little use of network bandwidth, to heavy overuse of network bandwidth (combined with a large number of endpoints attempting to access that network bandwidth) can often indicate an email virus or other maladjusted use of the system. In such cases, it could be appropriate to generate an alert, and bring the change to the attention of a human expert, or otherwise escalate the problem. Best practices In this Application, the phrase 'best practices,' and variants thereof, can generally refer to business preferences (and to business rules imposed by management, or law or regulation), such as high utilization preferences often expressed by management. In such cases, one or more operators can enter descriptors of rules the system that it is desired the system follow, such as 'processor cycle utilization &#x3e;90%.' When these best practices are not followed, it could be appropriate to generate an alert, bring the change to the attention of a human expert, or otherwise escalate the problem. Profiling and alerting In this Application, the phrase &#x201c;profiling and alerting,&#x201d; and variants thereof, can generally refer to maintaining a profile of how the system normally behaves, absent stressors, and to identify any substantial changes from that profile. When a profile is seriously violated, it could be appropriate to generate an alert, bring the change to the attention of a human expert, or otherwise escalate the problem. In such cases, it can be appropriate to escalate the severity of the alert with increased severity of violation, increased time duration of the violation, and increased trajectory in the direction of further violation. Relative and absolute capaci-ty In this Application, the phrase &#x201c;absolute capacity,&#x201d; and variants thereof, can refer to an objective measure of system capacity, such as whether the system has 1 TB or 4 TB of memory. The phrase &#x201c;relative capacity,&#x201d; and variants thereof, can refer to a subjective measure of system capacity, such as when the system has 4 TB of physical memory, but 3.5 TB of that memory are allocated to a high-priority task whose interruption or pause would be untoward.

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Sunnyvale, CA, United States of America

Rangaswamy Jagannathan