# US 2018/0024868 A1 · Computer resource allocation to workloads in an information technology environment

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 2018/0024868 A1
- Document ID: US20180024868A1
- Status: Published application
- Kind: A1
- Application number: 15/218,386
- Filed: 2016-07-25
- Published: 2018-01-25
- Earliest claimed: 2016-07-25
- Assignees: Accenture Global Solutions Limited
- Applicants: Accenture Global Solutions Limited
- Inventors: Kunal MEHTA; David M. Klimek; Farah Lalani; Justin Melde; Kate Wilson; Brian Svehaug
- Web page: https://patentgrep.com/patent/US20180024868A1

## CPC

- G06F 9/5083: {Techniques for rebalancing the load in a distributed system}
- G06F 9/505: {considering the load}
- G06F 11/3006: {where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems (multiprogramming arrangements G06F9/46; allocation of resources G06F9/50)}
- G06F 11/34: Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation {; Recording or statistical evaluation of user activity, e.g. usability assessment}
- G06F 11/3442: {for planning or managing the needed capacity}
- G06F 11/3447: {Performance evaluation by modeling}
- G06F 11/3452: {Performance evaluation by statistical analysis}
- G06F 16/24569: {Query processing with adaptation to specific hardware, e.g. adapted for using GPUs or SSDs}
- G06F 16/24578: {using ranking}
- G06F 16/283: {Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP}
- G06F 16/285: {Clustering or classification}
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

According to an example of the present disclosure, an information technology (IT) environment, such as a data center, a cloud services platform or other type of computing environment, may include computer resources that are dynamically allocated to workloads. A model is generated to estimate performance and allocate computer resources to the workloads.

## Claims

### Claim 1

An information technology system operable to provision computer resources for workloads, the information technology system comprising:
a configuration and management server to:
allocate subsets of computer resources from a pool of computer resources to workloads; and
capture performance metrics for each subset of computer resources assigned to one of the workloads;
a performance prediction server to:
identify performance metrics from the captured performance metrics for a model;
determine weights for the identified performance metrics;
generate a model based on the identified performance metrics and the weights; and
apply the model to the captured performance metrics to predict performance for each of the workloads,
wherein the configuration and management server is further to:
determine the allocation of the subset of computer resources for each of the workloads based on the predict performance for each of the workloads.

### Claim 2 (depends on 1)

The information technology system of claim 1, wherein the workloads comprise web hosting services.

### Claim 3 (depends on 2)

The information technology system of claim 2, wherein the identified metrics comprise categories of metrics including metrics approximating end-to-end response time observed by an end user downloading a web page, metrics evaluating caching of web pages, and metrics associated with aborted requests for web pages.

### Claim 4 (depends on 1)

The information technology system of claim 1, wherein the performance metrics measure the performance of the subset of computer resources from the point of view of end users.

### Claim 5 (depends on 1)

The information technology system of claim 1, wherein to generate the model, the performance prediction server is to:
determine categories of the identified performance metrics; and
determine a weighting for each category, wherein the identified metrics in each category are weighted according to the category weighting.

### Claim 6 (depends on 5)

The information technology system of claim 5, wherein the category weighting comprises a range of weights and the performance prediction server is to modify the model by selecting a new weight for a plurality of the categories within the range for each category.

### Claim 7 (depends on 1)

The information technology system of claim 1, wherein the predicted performance is a single score for each workload.

### Claim 8

A system comprising:
at least one processor;
a non-transitory computer readable medium storing machine readable instructions executable by the at least one processor, wherein the machine readable instructions comprise:
a data import and management module to collect and normalize metrics, and store the metrics in a database;
a computation module to:
identify a set of the metrics, determine weights for the set of metrics, and generate a model based on the set of metrics and weights;
apply the model to the collected metrics to predict performance; and
adjust at least one of the weights and the set of metrics in the model based on at least one of the collected metrics and user input.

### Claim 9 (depends on 8)

The system of claim 8, wherein the set of metrics comprise categories of metrics, wherein the categories include customer experience, cost and revenue, and the weights are associated with each category.

### Claim 10 (depends on 8)

The system of claim 8, wherein the computation module is to determine actionable insights based on the predicted performance.

### Claim 11 (depends on 8)

The system of claim 8, wherein the predicted performance is for a customer representative or a computing resource in an information technology infrastructure.

### Claim 12 (depends on 8)

The system of claim 8, wherein the machine readable instructions comprise a gamification module to:
determine a challenge based on predicted performances determined by the model; and
generate a leader board based on the challenge and the predicted performances.

### Claim 13 (depends on 8)

The system of claim 8, wherein the machine readable instructions comprise a machine learning function to predict sentiment based on collected data associated with customer feedback.

### Claim 14 (depends on 8)

The system of claim 8, wherein the predicted performance comprises a single score.

### Claim 15

A system comprising:
a server to capture performance metrics for each subset of computer resources assigned to one of the workloads;
a performance prediction server to:
identify performance metrics from the captured performance metrics for a model;
determine weights for the identified performance metrics;
generate a model based on the identified performance metrics and the weights; and
apply the model to the captured performance metrics to predict performance for each of a plurality of agents,
wherein the server is further to:
determine allocation of workload for each agent based on the predict performance for each agent.

### Claim 16 (depends on 15)

The system of claim 15, wherein the performance metrics measure performance of the subset of computer resources from the point of view of a customers or end users.

### Claim 17 (depends on 15)

The system of claim 15, wherein to generate the model, the performance prediction server is to:
determine categories of the identified performance metrics; and
determine a weighting for each category, wherein the identified metrics in each category are weighted according to the category weighting.

### Claim 18 (depends on 17)

The system of claim 17, wherein the category weighting comprises a range of weights and the performance prediction server is to modify the model by selecting a new weight for a plurality of the categories within the range for each category.

### Claim 19 (depends on 18)

The system of claim 18, wherein the category weighting is based on customer or end user feedback.

### Claim 20 (depends on 15)

The system of claim 15, wherein the predicted performance is a single score for each workload.