Patent Application
20230168935
This disclosure relates to dynamic strategy based parallelization of user requests.
Cloud computing systems have increased in popularity as storage of large quantities of data in the cloud becomes more common. Users of the cloud computing systems can store, retrieve, and search large quantities of data by executing operations on distributed computing resources. In some instances, an operation may be delayed because a majority of the computing resources are unavailable due to execution of other operations. In other instances, however, the computing resources remain idle and unutilized in the cloud computing system. Thus, with the increasingly large quantity of data stored on the cloud, managing availability of the computing resources to perform operations on the data is often a cumbersome process.
One aspect of the disclosure provides a computer-implemented method that when executed on data processing hardware causes the data processing hardware to perform operations for parallelization of user requests. The operations include receiving a search request to search a portion of a data store and splitting the search request into a plurality of sub-searches. When executed, each sub-search of the plurality of sub-searches is configured to search a different respective sub-portion of the portion of the data store. The operations also include selecting a first bucket from a plurality of buckets based on the plurality of sub-searches split from the search request. Each bucket of the plurality of buckets is associated with a respective amount of available resources capable of executing a corresponding maximum number of sub-searches in parallel. The operations also include allocating, to the selected first bucket, a first execution set of sub-searches selected from the plurality of sub-searches. Here, a number of sub-searches in the first execution set of sub-searches is less than or equal to the corresponding maximum number of sub-searches that the respective amount of available resources associated with the selected first bucket is capable of executing in parallel. The operations also include executing, in parallel, each sub-search in the first execution set of sub-searches in using the respective amount of available resources associated with the selected first bucket.
Implementations of the disclosure may include one or more of the following optional features. In some implementations, when a number of the sub-searches in the plurality of sub-searches split from the search request is less than or equal to the corresponding maximum number of sub-searches that the respective amount of available resources associated with the selected first bucket is capable of executing in parallel. The first execution set of sub-searches includes all of the sub-searches in the plurality of sub-searches split from the search request. In some examples, the operations further include determining whether the first bucket is available. In these examples, when the first bucket is available the operations also include allocating the first execution set of sub-searches to the selected first bucket and executing, in parallel, each sub-search in the first execution set of sub-searches using the respective amount of available resources associated with the selected first bucket.
In some implementations, when the first bucket is not available, the operations further include selecting a second bucket from the plurality of buckets and determining whether the second bucket is available. The corresponding maximum number of sub-searches that the respective amount of available resources associated with the selected second bucket is capable of executing in parallel is less than the corresponding maximum number of sub-searches that the respective amount of available resources associated with the first bucket is capable of executing in parallel. In these implementations, when the second bucket is available, the operations also include allocating a second execution set of sub-searches selected from the plurality of sub-searches to the selected second bucket and executing, in parallel, each sub-search in the second execution set of sub-searches using the respective amount of available resources associated with the selected second bucket. Here, a number of sub-searches in the second execution set of sub-searches is less than or equal to the corresponding maximum number of sub-searches that the respective amount of available resources associated with the selected second bucket is capable of executing in parallel. In some examples, when the second bucket is not available, the operations further include selecting a third bucket from the plurality of buckets and determining whether the third bucket is available. The corresponding maximum number of sub-searches that the respective amount of available resources associated with the selected third bucket is capable of executing in parallel is one. In these examples, when the third bucket is available, the operations also include allocating a single sub-search selected from the plurality of sub-searches to the selected third bucket and executing the single sub-search using the respective amount of available resources associated with the selected third bucket.
After allocating the second execution set of sub-searches to the selected second bucket, the operations may include: identifying one or more sub-searches from the plurality of sub-searches that were not selected for inclusion in the second execution set of sub-searches; waiting until execution of each sub-search in the second execution set of sub-searches using the respective amount of available resources associated with the selected second bucket is complete; allocating the identified one or more sub-searches from the plurality of sub-searches that were not selected for inclusion in the second execution set of sub-searches to the selected second bucket; and executing each sub-search of the identified one or more sub-searches in parallel using the respective amount of available resources associated with the selected second bucket. The search request may include a raw log search of an Internet Protocol (IP) address.
Optionally, the operations may further include: receiving, for each sub-search of the plurality of sub-searches, a respective result; compiling each received result to generate a composite search result; and returning the composite search result to a user associated with the search request. In some examples, after allocating the first execution set of sub-searches to the selected first bucket, the operations further include setting the selected first bucket as not available. In some implementations, after execution of each sub-search in the first execution set of sub-searches using the respective amount of available resources associated with the selected first bucket is complete, the operations further include setting the selected first bucket as available.
Another aspect of the disclosure provides a system that includes data processing hardware and memory hardware storing instructions that when executed on the data processing hardware causes the data processing hardware to perform operations. The operations include receiving a search request to search a portion of a data store and splitting the search request into a plurality of sub-searches. When executed, each sub-search of the plurality of sub-searches is configured to search a different respective sub-portion of the portion of the data store. The operations also include selecting a first bucket from a plurality of buckets based on the plurality of sub-searches split from the search request. Each bucket of the plurality of buckets is associated with a respective amount of available resources capable of executing a corresponding maximum number of sub-searches in parallel. The operations also include allocating, to the selected first bucket, a first execution set of sub-searches selected from the plurality of sub-searches. Here, a number of sub-searches in the first execution set of sub-searches is less than or equal to the corresponding maximum number of sub-searches that the respective amount of available resources associated with the selected first bucket is capable of executing in parallel. The operations also include executing, in parallel, each sub-search in the first execution set of sub-searches in using the respective amount of available resources associated with the selected first bucket.
Implementations of the disclosure may include one or more of the following optional features. In some implementations, when a number of the sub-searches in the plurality of sub-searches split from the search request is less than or equal to the corresponding maximum number of sub-searches that the respective amount of available resources associated with the selected first bucket is capable of executing in parallel. The first execution set of sub-searches includes all of the sub-searches in the plurality of sub-searches split from the search request. In some examples, the operations further include determining whether the first bucket is available. In these examples, when the first bucket is available the operations also include allocating the first execution set of sub-searches to the selected first bucket and executing, in parallel, each sub-search in the first execution set of sub-searches using the respective amount of available resources associated with the selected first bucket.
In some implementations, when the first bucket is not available, the operations further include selecting a second bucket from the plurality of buckets and determining whether the second bucket is available. The corresponding maximum number of sub-searches that the respective amount of available resources associated with the selected second bucket is capable of executing in parallel is less than the corresponding maximum number of sub-searches that the respective amount of available resources associated with the first bucket is capable of executing in parallel. In these implementations, when the second bucket is available, the operations also include allocating a second execution set of sub-searches selected from the plurality of sub-searches to the selected second bucket and executing, in parallel, each sub-search in the second execution set of sub-searches using the respective amount of available resources associated with the selected second bucket. Here, a number of sub-searches in the second execution set of sub-searches is less than or equal to the corresponding maximum number of sub-searches that the respective amount of available resources associated with the selected second bucket is capable of executing in parallel. In some examples, when the second bucket is not available, the operations further include selecting a third bucket from the plurality of buckets and determining whether the third bucket is available. The corresponding maximum number of sub-searches that the respective amount of available resources associated with the selected third bucket is capable of executing in parallel is one. In these examples, when the third bucket is available, the operations also include allocating a single sub-search selected from the plurality of sub-searches to the selected third bucket and executing the single sub-search using the respective amount of available resources associated with the selected third bucket.
After allocating the second execution set of sub-searches to the selected second bucket, the operations may include: identifying one or more sub-searches from the plurality of sub-searches that were not selected for inclusion in the second execution set of sub-searches; waiting until execution of each sub-search in the second execution set of sub-searches using the respective amount of available resources associated with the selected second bucket is complete; allocating the identified one or more sub-searches from the plurality of sub-searches that were not selected for inclusion in the second execution set of sub-searches to the selected second bucket; and executing each sub-search of the identified one or more sub-searches in parallel using the respective amount of available resources associated with the selected second bucket. The search request may include a raw log search of an Internet Protocol (IP) address.
Optionally, the operations may further include: receiving, for each sub-search of the plurality of sub-searches, a respective result; compiling each received result to generate a composite search result; and returning the composite search result to a user associated with the search request. In some examples, after allocating the first execution set of sub-searches to the selected first bucket, the operations further include setting the selected first bucket as not available. In some implementations, after execution of each sub-search in the first execution set of sub-searches using the respective amount of available resources associated with the selected first bucket is complete, the operations further include setting the selected first bucket as available.
The details of one or more implementations of the disclosure are set forth in the accompanying drawings and the description below. Other aspects, features, and advantages will be apparent from the description and drawings, and from the claims.
Like reference symbols in the various drawings indicate like elements.
Cloud computing systems are growing in popularity as users store and retrieve more data from cloud computing databases. In some instances, users request searches of large amounts data (i.e., hundreds of gigabytes) from cloud databases and expect minimal latency in receiving the requested data. However, search requests over large databases require significant computing resources thereby incurring latency before returning search results to the user. In some examples, cloud computing systems split large search requests into multiple sub-searches and execute the multiple sub-searches in parallel to reduce latency of the search results. Executing multiple sub-searches in parallel, however, can consume a significant amount of the available computing resources. In some instances, large search requests consume all of the computing resources thereby prohibiting other searches from being conducted until computing resources are freed.
Conventional techniques address this issue by either executing the sub-searches one at a time (e.g., sequentially) or by fixing a maximum number of sub-searches that may be executed in parallel. While these techniques address the issue of a single large search request consuming a significant majority of the available computing resources, neither, however, optimize utilization of computing resources by minimizing latency of search results while also maximizing utilization of computing resources. More specifically, limiting the number of sub-searches that can execute in parallel may lead to unnecessary latency (i.e., the searches take longer) while other computing resources are unutilized and could be used to increase the number of sub-searches run in parallel (i.e., reduce the time required to run the search).
Implementations herein are directed towards methods and systems of a dynamic strategy for parallelization of user requests. In particular, a cloud computing environment executes a splitter that splits search requests into a plurality of sub-searches and a selector that allocates computing resources to the plurality of sub-searches based on an availability of computing resources. Thus, the cloud computing environment optimizes utilization of the computing resources by executing the plurality of sub-searches that each searches a separate sub-portion of a database. A plurality of concurrency buckets each represent a differing amount of computing resources that can execute the plurality of sub-searches. The selector allocates the amount of computing resources for a selected bucket to execute the plurality of sub-searches in parallel. Notably, the methods and systems described herein increase computing resource utilization without allowing a single search request to consume all of the computing resources.
Referring now to
The cloud computing environment 140 may execute a splitter 150, a selector 201, an executor 160, and a data store 170. A data store is a repository for persistently storing and managing collections of data which include not just repositories like databases, but also simpler store types, such as simple files, emails, etc. In the examples shown, the data store is a database, but other forms of data storage are possible as well. The data store 170 stores data in the cloud computing environment 140 and may be accessed by the user 10. In other examples, the cloud computing environment 140 includes a file system (not shown) in addition to or in lieu of the data store 170. The file system may store unstructured and/or unrelated data while the data store 170 stores structured and/or related data. Accordingly, the cloud computing environment 140 may store indexed data (i.e., structured data) and/or raw log data (i.e., unstructured data). In some examples, the data store 170 is overlain on the storage resources 146 to allow scalable use of the data store 170 by one or more of the user devices 102 or the computing resources 144. For example, the computing resources 144 can execute storage and retrieval operations on data stored in the data store 170.
In some implementations, the user 10 transmits a search request 104 to the cloud computing environment 140 via the network 112 to search a portion of the data store 170. In some examples, the search request 104 includes a request to search raw logs (i.e., unstructured search) associated with an Internet Protocol (IP) address. Unstructured searches include more comprehensive searches of the data store 170 and take a longer time to execute (e.g., consume more computing resources 144) as compared to structured searches. The user 10 may specify one or more attributes of the search request 104 which determines the portion of the data store 170 to search to satisfy the search request 104. Attributes may include a search timeline, IP address, data format, or any other attribute associated with data stored on the data store 170. For example, the search request 104 may request all raw logs associated with a particular IP address for a particular time frame such as from the past year. In this example, the portion of the data store 170 associated with the search request 104 is all of the raw logs for the particular IP address from the past year.
The splitter 150 receives the search request 104 and is configured to split the search request 104 into a plurality of sub-searches 152, 152a-n. When executed, each sub-search 152 of the plurality of sub-searches 152 is configured to search a different respective sub-portion of the portion of the data store 170. In some examples, the splitter 150 splits the search request 104 into a smallest common sub-portion of the data store 170. Optionally, the user 10 may configure the smallest common sub-portion corresponding to the sub-searches 152. For example, the user 10 may specify whether the splitter 150 splits the search request 104 into sub-searches 152 that include sub-portions each corresponding to a month (i.e., a search of a month's worth of data), week (i.e., a search of a week's worth of data), day (i.e., a search of a day's worth of data), hour (i.e., a search of an hour's worth of data), etc. of the data store 170. The sub-portion of the data store 170 to search may correspond to any attribute of the search request 104. Continuing with the above example, the splitter 150 receives the search request 104 that requests data from the past year and the splitter 150 splits the search request 104 into twelve one-month sub-searches 152. Here, each sub-search 152 includes a separate sub-portion (e.g., one month of data) of the portion (e.g., one year of data) of the data store 170 associated with the search request 104. The splitter 150 sends the plurality of sub-searches 152 to the selector 201.
The selector 201 is configured to select an available concurrency bucket 202 from a plurality of concurrency buckets 202, 202a—n based on a quantity of the plurality of sub-searches 152 (i.e., a concurrency bucket 202 not currently assigned or allocated to any sub-searches 152). Each concurrency bucket (also referred to herein as just “bucket”) 202 of the plurality of buckets 202 is associated with a respective amount of available computing resources 144 capable of executing a corresponding maximum number of sub-searches 152 in parallel. In particular, for a large quantity of sub-searches 152, the selector 201 selects a bucket 202 with greater amount of resources 144 and, for a small quantity of sub-searches 152, the selector 201 selects a bucket with fewer resources 144. In the example shown, each bucket 202a—c is associated with a respective amount of available computing resources 144, 144a—c (i.e., resources such as processing and/or memory resources). Accordingly, the amount of available computing resources 144 associated with a respective bucket 202 refers to the portion of available resources from the total computing resource 144 the respective bucket 202 can execute. For example, a first bucket 202, 202a may be associated with a first amount of resources 144, 144a (e.g., sixty percent of the total computing resource 144) capable of executing six sub-searches 152 in parallel, a second bucket 202, 202b associated with a second amount of resources 144, 144b (e.g., thirty percent of the total computing resource 144) capable of executing three sub-searches 152 in parallel, and a third bucket 202, 202c associated with a third amount of resources 144, 144c (e.g., ten percent of the total computing resource 144) capable of executing one sub-search 152 in parallel. (i.e., 144, 144a. Here, the first amount of resources 144a is greater than the second and third amount of resources 144b, 144c and the second amount of resources 144b is greater than the third amount of resources 144c.
Accordingly, each of the buckets 202 represents a sufficient portion of resources 144 to execute a specific number of sub-searches 152 from the data store 170. For example, when the sub-searches 152 each represent a search of a day's worth of data, one concurrency bucket 202 may represent the amount of computing resources 144 necessary to execute thirty sub-searches 152 (i.e., a month's worth of data) and another bucket 202 may represent the amount of computing resources 144 necessary to execute seven sub-searches 152 (i.e., a week's worth of data).
In some examples, only a portion of resources 144 of a bucket 202 execute in parallel. For example, a bucket 202 may be associated with an amount of resources 144 capable of executing one hundred sub-searches 152. In this example, the bucket 202 is only capable of executing fifty sub-searches 152 in parallel for a single search request 104. Accordingly, a first portion of the bucket 202 may execute up to fifty sub-searches 152 in parallel for a first search request 104 and a second portion of the bucket may execute up to fifty sub-searches in parallel for a second search request 104. In other examples, all of the resources 144 associated with a bucket 202 are capable of executing in parallel.
In some implementations, the selector 201 is configured to select an available bucket 202 from the plurality of buckets 202 that represents the greatest amount of resources 144. Additionally or alternatively, the selector 201 may select the available bucket 202 from the plurality of buckets 202 based on the quantity of the plurality of sub-searches 152. By selecting the bucket 202 that represents the greatest amount of resources 144, the selector 201 helps to minimize execution time of the search requests 104 and maximize utilization of the resources 144. In the example shown, the first bucket 202a, the second bucket 202b, and the third bucket 202c are each available (i.e., not currently assigned or allocated to any sub-searches 152). In this case, the selector 201 selects the first bucket 202a (denoted by the solid lines) while the second and third buckets 202b, 202c are not selected (denoted by the dashed lines), as the first bucket 202a represents the largest number of resources 144.
Optionally, the selector 201 may determine whether the selected bucket (i.e., first bucket) 202a is available. In some implementations, the selector 201 determines which buckets 202 are available prior to selecting a bucket 202. The selector 201 may determine whether a bucket 202 is available based on the availability of the computing resources 144 represented by the bucket 202. That is, if there is not a sufficient number of resources 144 available to satisfy the amount of sub-searches a bucket 202 is configured to execute, the bucket 202 may be deemed unavailable. In this instance, the selector 201 determines whether the bucket 202 with the next greatest amount of resources 144 is available. In other implementations, the selector 201 determines which buckets 202 are available after selecting a bucket 202.
In some examples, the selector 201 determines whether the quantity of the plurality of sub-searches 152 to be executed in parallel satisfies a threshold value prior to selecting the bucket 202. The threshold value may be proportional to the number of sub-searches 152 a bucket 202 can execute in parallel. In these examples, the threshold value ensures that the selector 201 selects the bucket 202 with the least amount of resources 144 capable of executing the quantity of the plurality of sub-searches 152. For example, when one bucket 202 represents resources 144 to execute thirty sub-searches 152 in parallel and a second bucket 202 represents resources 144 to execute seven sub-searches 152 in parallel, the threshold value for the first bucket 202 may be eight. As such, the threshold value of eight ensures that the selector does not select the first bucket 202 when the plurality of sub-searches 152 only includes eight or less sub-searches 152 when the second bucket 202 is available and capable of executing the eight or less sub-searches 152. That is, the selector 201 may only select the first bucket 202 when the number of sub-searches 152 is greater than or equal to eight. In this example, allocating a plurality of sub-searches 152 including three sub-searches to the first bucket 202 would be wasteful if the second bucket 202 is also available and capable of executing all of the sub-searches 152. Thus, one or more of the plurality of buckets 202 may be unavailable for selection when the quantity of the plurality of sub-searches 152 does not satisfy the threshold value.
In some instances, the selector 201 may determine that the first bucket 202a is available but the quantity of the plurality of sub-searches 152 does not satisfy a threshold value of a first bucket 202a. Thus, the selector 201 determines whether a second bucket 202b is available. Here, the selector 201 may determine that the second bucket 202b is not available. In this instance, the selector 201 may select the first bucket 202a even though the quantity of the plurality of sub-searches 152 does not satisfy the threshold value of the first bucket 202a because the second bucket 202b (e.g., bucket 202 with the next greatest amount of resources 144) is not available. When the plurality of sub-searches 152 does not satisfy the threshold value of a particular bucket 202, the selector 201 may first determine whether any of the buckets 202 with a greater amount of resources 144 than the particular bucket 202 are available before determining whether any of the buckets 202 with a fewer amount of resources 144 than the particular bucket 202 are available.
With continued reference to the example of
In this example, the selector 201 transmits the first execution set 152S1 of sub-searches 152 and the first amount of resources 144a represented by the first bucket 202a to the executor 160. The executor 160 executes each sub-search 152 of the first execution set 152S1 in parallel using the first amount of resources 144a. That is, the executor 160 executes each sub-search 152 to retrieve results from the corresponding sub-portion from the data store 170. For each sub-search 152 of the plurality of sub-searches 152, the executor 160 receives a respective result 172 from the data store 170. The respective result 172 corresponds to the sub-portion of the respective sub-search 152. The executor 160 compiles each of the received results 172 to generate a composite search result 162. Continuing with the example above, the executor 160 receives results 172 for each one month sub-search 152 of the twelve sub-searches 152 and compiles the results 172 to generate the composite search result 162 for the past year search request 104.
In some implementations, after executing each sub-search 152 in the first execution set 152S1 of sub-searches 152 using the first amount of resources 144a is complete, the selector 201 sets an indicator that indicates the first bucket 202a is available. Optionally, the executor 160 may communicate the composite search result 162 to the user 10 via the network 112. The user device 102 may display the received composite search result 162 to the user 10 via a graphical user interface (GUI).
By allocating sub-searches 152 to resources 144 dynamically based on the quantity of sub-searches 152 and the availability of the resources 144, usage of the resources 144 becomes more predictable and may in some instances flatten the usage of the resources over a longer duration. Moreover, a maximum number of resources 144 may be allocated to search requests 104 when the resources 144 are available without leading to starvation of resources 144 for other search requests 104. In some implementations, when all computing resources 144 are allocated (i.e., none of the buckets 202 are available), the cloud computing environment 140 prioritizes shorter duration search requests 104. Prioritizing the shorter duration search requests 104 allows the shorter duration searches to execute normally while longer duration search requests 104 may incur increased latency.
Referring now to
An exemplary schematic view 200 (
Referring now to
When the third bucket 202c is available, the selector 201 allocates a first single sub-search 152a (i.e., a fourth execution set) selected from the plurality of sub-searches 152 to the third amount of resources 144c associated with the third bucket 203c. The selector 201 sends the first single sub-search 152a of the plurality of sub-searches 152 and the third amount of computing resources 144c associated with the third bucket 202c to the executor 160. The executor 160 executes the first single sub-search 152a and receives a result 172 for the first single sub-search 152a. Notably, the executor 160 does not yet generate a composite search result 162 because the executor 160 has not executed the second through ninth sub-searches 152b-i. Thus, the executor 160 cannot generate the composite search result 162 corresponding to the entire portion of the data store 170 associated with the search request 104.
Referring now to
It is also understand that examples herein include only two or three buckets 202. However, the system 100 may include any number of buckets 202, including multiple buckets of the same size (i.e., capable of executing the same number of sub-searches 152 in parallel). For example, a system may include five buckets 202 capable of executing thirty sub-searches 152 in parallel, ten buckets 202 capable of executing seven sub-searches 152 in parallel, and one hundred buckets 202 capable of sequentially executing sub-searches 152. There may be any number of sizes of buckets (e.g., years, months, weeks, days, hours, minutes, etc.).
Referring now to
In some implementations, one bucket 202 of the plurality of buckets 202 is underutilized. In particular, an underutilized bucket 202 refers to a bucket 202 that has idle computing resources 144 (e.g., the idle computing resources 144 are not executing sub-searches 152). Optionally, computing resources 144 may only be identified as idle when the computing resources 144 do not execute at least one sub-search 152 for a threshold amount of time. In these implementations, one or more other buckets 202 of the plurality of buckets 202 may be over utilized. As such, it is a more efficient utilization of all of the computing resources 144 in this scenario to merge all of, or some of, the computing resources 144 from the underutilized bucket 202 to the one or more other buckets 202.
Continuing with the example, when the resource utilization rate satisfies the utilization threshold value (e.g., the utilization rate exceeds the utilization rate threshold value), the cloud computing environment 140 proceeds to operation 625 and does not change the configuration of the plurality of buckets 202. Alternatively, When the resource utilization fails to satisfy the utilization threshold value (e.g., the utilization rate does not exceed the utilization rate threshold value), the cloud computing environment 140 proceeds to operation 620 to determine whether there is an underutilized bucket 202 from the plurality of buckets 202.
In some instances, when there is not an underutilized bucket 202 from the plurality of buckets 202, the cloud computing environment 140 proceeds to operation 625 and does not change the configuration of the plurality of buckets 202. At operation 630, when there is an underutilized bucket 202 in the plurality of buckets 202, the cloud computing environment 140 determines whether the resource utilization satisfies a first minimum threshold value. If the resource utilization fails to satisfy the first min threshold value (e.g., the resource utilization exceeds the first minimum threshold value), the cloud computing environment 140 proceeds to operation 640. At operation 640, the cloud computing environment 140 determines whether the resource utilization satisfies a second minimum threshold value (e.g., the resource utilization is between the first minimum threshold value and the second minimum threshold value).
Referring now to specifically to
In other examples, the cloud computing environment 140 proceeds to operation 645 when the resource utilization satisfies a second minimum threshold value (e.g., the resource utilization is greater than the first minimum threshold value and does not exceed the second threshold value). For example, the second minimum threshold value may represent between forty percent resource utilization and sixty percent resources utilization for all of the computing resources 144. At operation 645, the cloud computing environment 140 merges the identified underutilized bucket 202 with a bucket 202 from the plurality of buckets 202 that has a medium amount of computing resources 144 (e.g., medium concurrency bucket 202). Here, buckets 202 with medium amounts of computing resources 144 may have an amount computing resources 144 between a minimum resource value and a maximum resource value.
In some examples, when the resource utilization fails to satisfy the second minimum threshold value (e.g., the resource utilization exceeds the second minimum threshold value), the cloud computing environment 140 proceeds to operation 650 and merges the identified underutilized bucket 202 with a bucket 202 with the next greatest amount of computing resources 144 than the identified underutilized bucket 202. 144. For example, if the identified underutilized bucket 202 is the second bucket 202b (
Referring now specifically to
In other examples, the cloud computing environment 140 proceeds to operation 660 when the resource utilization satisfies the second minimum threshold value. At operation 660, the cloud computing environment 140 shifts the subset of quote from the identified underutilized bucket 202 to a bucket 202 from the plurality of buckets 202 that has a medium amount of computing resources 144 (e.g., medium concurrency bucket 202). Here, buckets 202 with medium amounts of computing resources 144 may have an amount computing resources 144 between a minimum resource value and a maximum resource value. In some examples, when the resource utilization fails to satisfy the second minimum threshold value, the cloud computing environment 140 proceeds to operation 665 and shifts the subset of quota from the identified underutilized bucket 202 to a bucket 202 with the next greatest amount of computing resources 144 than the identified underutilized bucket 202.
In some implementations, one bucket 202 of the plurality of buckets 202 is over utilized. In particular, an over utilized bucket 202 refers to a bucket 202 that satisfies a threshold percentage of its computing resources executing sub-searches 152. In these implementations, it may be efficient to split the computing resources 144 of the over utilized bucket 202 into two or more bucket 202.
In some implementations, when the transformation rate fails to satisfy the transformation threshold value (e.g., the transformation rate does not exceed the transformation threshold value), the cloud computing environment 140 proceeds to operation 715 (
Referring now to specifically to
In some examples, when the resource utilization fails to satisfy the first maximum threshold value (e.g., the resource utilization does not exceed the first maximum threshold value), the cloud computing environment 140 proceeds to operation 740 and determines whether the resource utilization satisfies a second maximum threshold value (e.g., the resource utilization is between the first maximum threshold value and the second maximum threshold value). For example, the second maximum threshold value may represent eighty-five percent resource utilization for all of the computing resources 144. Here, when the resource utilization satisfies the second maximum threshold value, the cloud computing environment 140 proceeds to operation 745 and splits the identified over utilized bucket 202 into two or more buckets 202. In particular, the cloud computing environment 140 determines an amount of computing resources 144 for each of the two or more buckets 202 that the cloud computing environment 140 splits the identified over utilized bucket 202 into based on the amount of resource utilization. Next, the cloud computing environment 140 splits the identified over utilized bucket 202 into the two buckets. Notably, a first bucket 202 of the two or more buckets 202 includes the same amount of computing resources 144 as the identified over utilized bucket. Moreover, a second bucket 202 of the two or more buckets 202 includes an amount of computing resources 144 between the two buckets 202 with the least amount of computing resources 144 above a second specified threshold. Here, the second specified threshold is less than the first specified threshold. Put another way, the cloud computing environment 140 adds the second bucket 202 of the two or more buckets 202 to the plurality of buckets 202.
In some implementations, when the resource utilization fails to satisfy the second maximum threshold value (e.g., the resource utilization does not exceed the second maximum threshold value), the cloud computing environment 140 proceeds to operation 760 and splits the identified over utilized bucket 202 into two or more buckets 202. In particular, the cloud computing environment 140 determines an amount of computing resources 144 for each of the two or more buckets 202 that the cloud computing environment 140 splits the identified over utilized bucket 202 into based on the amount of resource utilization. Next, the cloud computing environment 140 splits the identified over utilized bucket 202 into the two buckets. Notably, a first bucket 202 of the two or more buckets 202 includes the same amount of computing resources 144 as the identified over utilized bucket 202. Moreover, a second bucket 202 of the two or more buckets 202 includes an amount of computing resources 144 as a bucket 202 with the next least amount of computing resources 144 than the identified over utilized bucket 202. For example, if the identified over utilized bucket 202 is the second bucket 202b (
Referring now specifically to
In some implementations, the cloud computing environment 140 determines that plurality of buckets 202 include too many (or not enough) computing resources 144 overall. That is, not just one bucket 202 of the plurality of buckets 202 include too many (or not enough) computing resources 144, but the entire system 100 (
Thereafter, at operation 825, the cloud computing environment determines whether the resource utilization of the system satisfies a first system threshold (e.g., over eighty percent). When the resource utilization of the system satisfies the first system threshold (e.g., resource utilization is over eighty percent), the cloud computing environment 140 proceeds to operation 830 and distributes the additional capacity of computing resources 144 to multiple buckets 202 with low amount of computing resources 144. If the resource utilization of the system does not satisfy the first system threshold (e.g., resource utilization is below eighty percent), at operation 835, the cloud computing environment 140 determines whether the resource utilization satisfies a second system threshold (e.g., between fifty and eighty percent). When the resource utilization satisfies the second system threshold, the cloud computing environment 140 proceeds to operation 840 and distributes the additional computing resource 144 capacity to multiple medium concurrency buckets 202. However, if the resource utilization fails to satisfy the second system threshold, the cloud computing environment 140 proceeds to operation 845 and distributes the additional computing resource 144 capacity to each bucket 202 of the plurality of buckets 202.
Thereafter, at operation 825, the cloud computing environment determines whether the resource utilization of the system satisfies a first system threshold (e.g., over eighty percent). When the resource utilization of the system satisfies the first system threshold (e.g., resource utilization is over eighty percent), the cloud computing environment 140 proceeds to operation 860 and reduces computing resource 144 capacity from multiple buckets 202 with a high amount of computing resources 144. If the resource utilization of the system does not satisfy the first system threshold (e.g., resource utilization is below eighty percent), at operation 865, the cloud computing environment 140 determines whether the resource utilization satisfies a second system threshold (e.g., between fifty and eighty percent). When the resource utilization satisfies the second system threshold, the cloud computing environment 140 proceeds to operation 865 and reduces computing resource 144 capacity from multiple buckets 202 with a medium amount of computing resources 144. However, if the resource utilization fails to satisfy the second system threshold, the cloud computing environment 140 proceeds to operation 870 and reduces computing resource capacity evenly from each bucket 202 of the plurality of buckets 202. Here, reducing capacity from a bucket 202 refers to removing computing resources 144 that are currently allocated to the respective bucket 202.
The computing device 1000 includes a processor 1010, memory 1020, a storage device 1030, a high-speed interface/controller 1040 connecting to the memory 1020 and high-speed expansion ports 1050, and a low speed interface/controller 1060 connecting to a low speed bus 1070 and a storage device 1030. Each of the components 1010, 1020, 1030, 1040, 1050, and 1060, are interconnected using various busses, and may be mounted on a common motherboard or in other manners as appropriate. The processor 1010 can process instructions for execution within the computing device 1000, including instructions stored in the memory 1020 or on the storage device 1030 to display graphical information for a graphical user interface (GUI) on an external input/output device, such as display 1080 coupled to high speed interface 1040. In other implementations, multiple processors and/or multiple buses may be used, as appropriate, along with multiple memories and types of memory. Also, multiple computing devices 1000 may be connected, with each device providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system).
The memory 1020 stores information non-transitorily within the computing device 1000. The memory 1020 may be a computer-readable medium, a volatile memory unit(s), or non-volatile memory unit(s). The non-transitory memory 1020 may be physical devices used to store programs (e.g., sequences of instructions) or data (e.g., program state information) on a temporary or permanent basis for use by the computing device 1000. Examples of non-volatile memory include, but are not limited to, flash memory and read-only memory (ROM)/programmable read-only memory (PROM)/erasable programmable read-only memory (EPROM)/electronically erasable programmable read-only memory (EEPROM) (e.g., typically used for firmware, such as boot programs). Examples of volatile memory include, but are not limited to, random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), phase change memory (PCM) as well as disks or tapes.
The storage device 1030 is capable of providing mass storage for the computing device 1000. In some implementations, the storage device 1030 is a computer-readable medium. In various different implementations, the storage device 1030 may be a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations. In additional implementations, a computer program product is tangibly embodied in an information carrier. The computer program product contains instructions that, when executed, perform one or more methods, such as those described above. The information carrier is a computer- or machine-readable medium, such as the memory 1020, the storage device 1030, or memory on processor 1010.
The high speed controller 1040 manages bandwidth-intensive operations for the computing device 1000, while the low speed controller 1060 manages lower bandwidth-intensive operations. Such allocation of duties is exemplary only. In some implementations, the high-speed controller 1040 is coupled to the memory 1020, the display 1080 (e.g., through a graphics processor or accelerator), and to the high-speed expansion ports 1050, which may accept various expansion cards (not shown). In some implementations, the low-speed controller 1060 is coupled to the storage device 1030 and a low-speed expansion port 1090. The low-speed expansion port 1090, which may include various communication ports (e.g., USB, Bluetooth, Ethernet, wireless Ethernet), may be coupled to one or more input/output devices, such as a keyboard, a pointing device, a scanner, or a networking device such as a switch or router, e.g., through a network adapter.
The computing device 1000 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a standard server 1000a or multiple times in a group of such servers 1000a, as a laptop computer 1000b, or as part of a rack server system 1000c.
Various implementations of the systems and techniques described herein can be realized in digital electronic and/or optical circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms “machine-readable medium” and “computer-readable medium” refer to any computer program product, non-transitory computer readable medium, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine-readable signal” refers to any signal used to provide machine instructions and/or data to a programmable processor.
The processes and logic flows described in this specification can be performed by one or more programmable processors, also referred to as data processing hardware, executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows can also be performed by special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit). Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read only memory or a random access memory or both. The essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks. However, a computer need not have such devices. Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
To provide for interaction with a user, one or more aspects of the disclosure can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube), LCD (liquid crystal display) monitor, or touch screen for displaying information to the user and optionally a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending documents to and receiving documents from a device that is used by the user; for example, by sending web pages to a web browser on a user's client device in response to requests received from the web browser.
A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the disclosure. Accordingly, other implementations are within the scope of the following claims.
