1. A bone plate for bone fixation, comprising:
a plate body defining a plurality of openings for receiving fasteners that secure the plate body to bone, the plurality of openings including an oblong opening defining a long axis; and
a locking element configured to be disposed in threaded engagement with a fastener extending at least from the oblong opening, then through the locking element and into bone, the locking element being connected slidably to the plate body, in the absence of the fastener, for movement at least substantially parallel to the long axis.
2. The bone plate of claim 1, wherein the plate body includes an inner face defining a cavity that at least partially receives the locking element, and wherein the plate body supports the locking element in the cavity when the inner face is facing downward.
3. The bone plate of claim 1, wherein the locking element includes at least one hook portion, and wherein the locking element is connected to the plate body using the at least one hook portion.
4. The bone plate of claim 3, wherein the locking element includes a bent metal sheet.
5. The bone plate of claim 1, wherein the locking element defines a through-hole with an internal thread.
6. A system for bone fixation, comprising:
a plate body defining a plurality of openings for receiving fasteners that secure the plate body to bone; and
a locking element for receiving a fastener that locks to the locking element while extending through an opening of the plate body and into bone, the locking element being disposed inward of the opening and being connected movably to the plate body in the absence of the fastener.
7. The system of claim 6, wherein the opening is oblong and defines a long axis, and wherein the locking element is slidable at least substantially parallel to the long axis.
8. The system of claim 7, wherein the opening includes a sloped countersink surface.
9. The system of claim 6, the locking element being a first locking element, further comprising one or more additional locking elements that form a set of locking elements collectively with the first locking element, wherein the set of locking elements is connected to the plate body, and wherein each locking element is movable individually.
10. The system of claim 6, wherein the plate body defines a long axis and a length measured parallel to the long axis, and wherein the locking element is capable of sliding motion at least substantially parallel to the long axis, with a range of motion restricted to only a subset of the length.
11. The system of claim 6, wherein the plate body includes an inner face defining a cavity that at least partially receives the locking element, and wherein the plate body supports the locking element in the cavity when the inner face is facing downward.
12. The system of claim 6, wherein the locking element is hooked to the plate body.
13. The system of claim 12, wherein the locking element is formed of a bent sheet of metal.
14. The system of claim 6, wherein the locking element defines a through-hole with an internal thread.
15. The system of claim 6, wherein the plate body has an outer face, and wherein the locking element is configured such that at least a portion of the locking element moves closer to the outer face as the fastener is tightened fully against the plate body.
16. The system of claim 6, further comprising a bone screw for placement through the opening of the plate body, into threaded engagement with the locking element, and into bone.
17. A method of bone fixation, comprising:
connecting a plate body of a bone plate to a first portion of bone;
placing a fastener into a second portion of bone from an oblong opening of the plate body and through a locking element disposed between the oblong opening and the second portion of bone, such that the fastener connects the bone plate to the second portion of bone and is locked to the locking element; and
moving the fastener, the locking element, and the second portion of bone collectively relative to the plate body and the first portion of bone, to urge the first and second portions of bone toward one another while the fastener remains locked to the locking element.
18. The method of claim 17, the fastener being a bone screw having a head, the plate body including a sloped countersink surface extending generally along the oblong opening, wherein the step of moving includes a step of turning the bone screw such that the head of the fastener travels along the sloped countersink surface.
19. The method of claim 17, further comprising a step of selecting a bone plate including the plate body connected to the locking element in the absence of the fastener, wherein the steps of connecting, placing, and moving are performed with the bone plate after the step of selecting.
20. The method of claim 17, the first and second portions of bone being arranged longitudinally along a bone, wherein the step of moving includes a step of urging the first and second portions toward one another longitudinally in relation to the bone.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A method for optimizing a prediction of resource usage of multiple applications running in a virtual environment, comprising:
providing a predetermined set of benchmarks;
executing the predetermined set of benchmarks in a native hardware system in which the application natively resides;
executing the predetermined set of benchmarks in the virtual environment;
collecting first traces of first resource utilization metrics in the native hardware system based on the execution of the predetermined set of benchmarks in the native hardware system;
collecting second traces of second resource utilization metrics in the virtual environment based on the execution of the predetermined set of benchmarks in the virtual environment;
generating a first prediction model that maps a first selected set of the first traces of a selected one of the first resource utilization metrics to the second traces of resource utilization metrics;
generating a second prediction model that maps a second selected set of the first traces of the selected first resource utilization metric to the second traces of resource utilization metrics;
collecting first application traces of resource utilization metrics in the native hardware system based on an execution of a first application in the native hardware system;
collecting second application traces of resource utilization metrics in the native hardware system based on an execution of a second application in the native hardware system;
aggregating the first application traces of the first application and the second application traces of the second application into combined application traces of resource utilization metrics; and
predicting a combined resource usage of the first and second applications running in the virtual environment by applying the first and second prediction models to the combined application traces of resource utilization metrics.
2. The method of claim 1, wherein applying the first and second prediction models comprises:
applying each of the first and second prediction models to the combined traces of resource utilization metrics based on an inputout rate of a selected one of the resource utilization metrics.
3. The method of claim 1, further comprising:
generating a third prediction model that maps the first and second selected sets of the first traces of the selected first metric to the second traces of resource utilization metrics; and
predicting a combined resource usage of the first and second applications running in the virtual environment by applying the third prediction model to each of the collected first and second traces of resource utilization metrics.
4. The method of claim 3, wherein predicting the combined resource usage by applying the third prediction model comprises:
applying the third prediction model to the collected first application traces of resource utilization metrics to estimate a first resource usage of the first application running in the virtual environment;
applying the third prediction model to the collected second application traces of resource utilization metrics to estimate a second resource usage of the second application running in the virtual environment; and
predicting the combined resource usage of the first and second applications by aggregating the first and second resource usage.
5. The method of claim 3, further comprising:
calculating a first error associated with the first prediction model;
calculating a second error associated with the second prediction model; and
calculating a third error associated with the third prediction model.
6. The method of claim 5, wherein the step of predicting the combined resource usage by applying a combination of the first and second prediction models is performed upon a first condition based on a comparison of the third error against a sum of the first and second errors.
7. The method of claim 6, wherein the step of predicting the combined resource usage by applying the third prediction model is performed upon a second different condition based on a comparison of the third error against the sum of the first and second errors.
8. The method of claim 1, wherein aggregating the first and second traces of resource utilization metrics of the first and second applications comprises:
aggregating each of the first traces of resource utilization metrics to each of the second traces of resource utilization metrics when the first and second traces are of the same resource utilization metric.
9. The method of claim 1, wherein:
generating the first prediction model comprises generating the first prediction model for each virtual machine guest domain and each driver domain in the virtual environment; and
generating the second prediction model comprises generating the second prediction model for each virtual machine guest domain and each driver domain in the virtual environment.
10. The method of claim 1, wherein:
generating the first prediction model comprises generating the first prediction model for each virtual machine guest domain and a hypervisor in the virtual environment; and
generating the second prediction model comprises generating the second prediction model for each virtual machine guest domain and the hypervisor in the virtual environment.
11. A system for optimizing a prediction of resource usage of multiple applications running in a virtual environment, comprising:
a first hardware platform implementing a native hardware system in which an application natively resides and executes, the native hardware system operating to execute a predetermined set of benchmarks that includes at least one of a network-intensive workload and a disk-intensive workload;
a second hardware platform implementing a virtual environment therein, the virtual environment operating to execute the predetermined set of benchmarks;
a third hardware platform operating to collect first resource usage traces from the first hardware platform that result from the execution of the predetermined set of benchmarks in the native hardware system and second resource usage traces from the second hardware platform that result from the execution of the predetermined set of benchmarks in the virtual environment;
wherein the third hardware platform further operating to generate at least one prediction model that predicts a resource usage of each of the multiple applications executing in the virtual environment based on the collected first and second resource usage traces; and
wherein the third hardware platform further operating to:
a) collect first application traces of resource utilization metrics in the native hardware system based on an execution of a first one of the multiple applications in the native hardware system;
b) collect second application traces of resource utilization metrics in the native hardware system based on an execution of a second one of the multiple applications in the native hardware system;
c) apply the at least one prediction model to the collected first application traces of the first application to calculate a first estimate resource usage in the virtual environment by the first application;
d) apply the at least one prediction model to the collected second application traces of the second application to calculate a second estimate resource usage in the virtual environment by the second application; and
e) predicting an overall resource usage in the virtual environment by the first and second applications by aggregating the first and second estimate resource usages.
12. The system of claim 11, wherein the third hardware platform further operating to:
generate a third prediction model that maps a first selected set of the first traces of a selected one of the first resource utilization metrics to the second traces of resource utilization metrics;
generate a fourth prediction model that maps a second different selected set of the first traces of the selected first resource utilization metric to the second traces of resource utilization metrics;
aggregate the first application traces of the first application and the second application traces of the second application into combined application traces of resource utilization metrics; and
predict an overall resource usage of the first and second applications running in the virtual environment by applying the first and second prediction models to different sets of the combined application traces of resource utilization metrics.
13. The system of claim 11, wherein the virtual environment includes a driver domain and a virtual machine (VM) guest domain, and the second resource usage traces comprises:
a percentage of processor utilization in the virtual environment that is attributed to a utilization of hardware processing resources in the second hardware platform by the driver domain; and
a percentage of processor utilization in the virtual environment that is attributed to a utilization of hardware processing resources in the second hardware platform by the VM guest domain.
14. The system of claim 11, wherein the virtual environment includes a hypervisor and a virtual machine (VM) guest domain, and the second resource usage traces comprises:
a percentage of processor utilization in the virtual environment that is attributed to a utilization of hardware processing resources in the second hardware platform by the hypervisor; and
a percentage of processor utilization in the virtual environment that is attributed to a utilization of hardware processing resources in the second hardware platform by the VM guest domain.
15. A computer readable medium on which is encoded programming code executed by a computerized system for:
providing a predetermined set of benchmarks;
executing the predetermined set of benchmarks in a native hardware system in which the application natively resides;
executing the predetermined set of benchmarks in the virtual environment;
collecting first traces of first resource utilization metrics in the native hardware system based on the execution of the predetermined set of benchmarks in the native hardware system;
collecting second traces of second resource utilization metrics in the virtual environment based on the execution of the predetermined set of benchmarks in the virtual environment;
generating a first prediction model that maps a first selected set of the first traces of a selected one of the first resource utilization metrics to the second traces of resource utilization metrics;
generating a second prediction model that maps a second selected set of the first traces of the selected first resource utilization metric to the second traces of resource utilization metrics;
collecting first application traces of resource utilization metrics in the native hardware system based on an execution of a first application in the native hardware system;
collecting second application traces of resource utilization metrics in the native hardware system based on an execution of a second application in the native hardware system;
aggregating the first application traces of the first application and the second application traces of the second application into combined application traces of resource utilization metrics; and
predicting a combined resource usage of the first and second applications running in the virtual environment by applying the first and second prediction models to the combined application traces of resource utilization metrics.