Interrupt handling system 102 may receive notifications (e.g., via communication subsystem 112) of potential disruptions to ongoing processes. Specifically, the system may receive, using a controller process of a plurality of controller processes within a cloud computing system, an interrupt indicator from a worker process of a plurality of worker processes. The interrupt indicator may signal a potential interruption to the worker process. Each worker process of the plurality of worker processes may be associated with a corresponding controller process of the plurality of controller processes. Each controller process may manage a corresponding worker process. For example, the interrupt detection subsystem may monitor network traffic for interrupt signals and pass them to the communication subsystem to relay to the appropriate controller process.

FIG. 2 illustrates an excerpt of interrupt message 200 that may be used to communicate interrupt information between components of computing environment 100. Interrupt message 200 may include an interrupt identifier 203, a timestamp 206, an interrupt type 209, and interrupt data 212. Communication subsystem 112 of interrupt handling system 102 may receive the interrupt indicator in the form of interrupt message 200. Interrupt identifier 203 may be a unique numerical identifier for each interrupt event, such as “12” as shown in FIG. 2. In some implementations interrupt identifier 203 may be an alphanumeric string or another suitable identifier. Timestamp 206 may record the precise date and time when the interrupt occurred. The time stamp may allow interrupt handling system 102 to count elapsed time from the interrupt occurring. Interrupt type 209 may specify the classification or category of the interrupt event, while interrupt data 212 may store specific payload information or details associated with the interrupt event. For example, interrupt type may indicate with the interrupt was caused by the cloud computing system or by something occurring within the worker process itself.

Interrupt detection subsystem 114 may analyze the received interrupt message 200 to determine the nature and significance of the potential interruption. In some implementations, interrupt type 209 may indicate that the worker process 104 is subject to a spot interrupt from the cloud computing system. A spot interrupt may give a 2-minute warning before terminating the process, allowing interrupt handling system 102 to take appropriate actions to preserve the state of the worker process 104.

In some implementations, interrupt handling system 102 may be part of a controller process within the cloud computing system. The cloud computing system may include a plurality of controller processes, each associated with one or more worker processes. When interrupt handling system 102 receives an interrupt indicator from its associated worker process (e.g., worker process 104), it may initiate a series of actions to manage the potential interruption and ensure the preservation of computational progress.

In some implementations, interrupt handling system 102 may evaluate the likelihood of process termination based on received indicators. Specifically, the system may determine (e.g., using interrupt detection subsystem), based on the interrupt indicator, whether the worker process is expected to be halted and removed by the cloud computing system within a predetermined time period. The cloud computing system may periodically halt and remove processes to manage assigned computing resources. For example, the system may analyze metadata in the interrupt indicator to estimate whether the worker process will be terminated within the next two minutes based on current resource allocation policies.

Interrupt type 209 may be used for determining the nature and origin of each interrupt. The interrupt type field within the interrupt message structure may contain information that allows the interrupt handling system to classify the interrupt based on various categories. These categories may include system-level interrupts, process-specific interrupts, or resource-related interrupts. By analyzing the interrupt type, the system may distinguish between interrupts originating externally within the cloud computing infrastructure and those resulting from operations performed by worker processes.

In some implementations, the interrupt type may indicate whether the interrupt was generated by the cloud computing system itself, such as in cases of resource reallocation, system maintenance, or spot instance termination warnings. For example, a spot interrupt type may signal that the worker process is subject to imminent termination due to changes in resource availability. Alternatively, the interrupt type may reveal that the interruption originated from within a worker process, potentially due to computational errors, task completion, or resource requests. This granular classification of interrupt types may enable the interrupt handling system to apply appropriate response strategies, such as initiating checkpointing procedures for system-originated interrupts or diagnosing and addressing issues for worker process-generated interrupts. The ability to accurately identify and categorize interrupt sources may enhance the system's capacity to manage workloads efficiently and maintain operational continuity in dynamic cloud environments.

Checkpoint generation subsystem 116 may use the information from the interrupt message 200 to begin preparing for a potential checkpoint operation. For example, by analyzing interrupt type 209 and interrupt data 212, checkpoint generation subsystem 116 may determine the urgency of the situation and prioritize its checkpoint generation activities accordingly. The interrupt handling system may request status updates from potentially affected processes. Specifically, based on determining that the worker process is expected to be halted and removed by the cloud computing system, interrupt handling system 102 may transmit (e.g., using communication subsystem 112) a status request to the worker process. For example, communication subsystem 112 may send a message to the worker process asking for its current progress and any completed results.

The status request transmitted by interrupt handling system 102 to the worker process may be structured as a lightweight, machine-readable message formatted in JSON or a similar data interchange format. This request may include fields such as a unique request identifier, a timestamp of when the request was initiated, and the ID of the worker process being queried. The status request may also specify which types of information are being requested, such as the current task ID, percentage of task completion, number of completed subtasks, estimated time to completion, and any error codes or messages encountered during execution.

Upon receiving the status request, the worker process may respond with a comprehensive snapshot of its current state. This response may include details such as the worker's process ID, the ID of the task currently being executed, a list of completed task IDs with their corresponding computational results, and a list of pending task IDs. The response may also contain resource utilization metrics such as CPU usage, memory consumption, and network activity. In some implementations, the worker process may include additional metadata such as checkpointing frequency, last successful checkpoint timestamp, and any performance metrics relevant to the specific computational tasks being performed. This detailed status information may enable interrupt handling system 102 to make informed decisions about how to manage the potential interruption and preserve the progress of the worker process.

Aa discussed above, the interrupt handling system may receive completed work from the affected worker process. Specifically, in response to the status request, interrupt handling system 102 (e.g., via communication subsystem 112) may receive, from the worker process, a first set of computational results for a first set of tasks that have been completed by the worker process. For example, the worker process may return partial results for three out of ten assigned computational tasks that it has finished so far.

In the context of financial trading activities, computational results processed by the worker process may encompass a wide range of outcomes. For numerical values, these results may include profit and loss calculations, such as a $15,000 profit on a particular trade or a $7,500 loss on another. Trade volumes may be reported, indicating the number of shares or contracts traded, such as 5,000 shares of a specific stock or 200 futures contracts. Price changes may be calculated and expressed as percentages or absolute values, for example, a 3.2% increase in a commodity price or a $2.50 decrease in a stock's value.

Trade risk assessments may form another category of computational results. These may include evaluations of market volatility risk, such as “Elevated risk due to geopolitical tensions affecting oil prices” or “Low risk in stable blue-chip equities market.” Liquidity risk calculations may result in outputs like “High risk: thinly traded small-cap stock” or “Minimal liquidity risk in major currency pair trading.” Counterparty risk assessments may produce results such as “Moderate risk: new trading partner with limited credit history” or “Negligible counterparty risk with AAA-rated financial institution.”

The worker process may also generate results related to trade execution and performance. These may include successful trade confirmations like “Limit order executed: Bought 1000 shares at $50.25” or “Options contract sold at specified strike price.” Partial fills may be reported as “65% of buy order filled at market open” or “Remaining 30% of sell order pending execution.” Failed or problematic trades may result in outputs such as “Order rejected due to insufficient funds” or “Trade execution delayed due to circuit breaker activation.” Additionally, the computational results may include performance metrics like “Trading algorithm outperformed benchmark by 2.3%” or “High-frequency trading strategy achieved 98.5% successful execution rate in volatile market conditions.”

FIG. 3 illustrates an excerpt of a task data structure 300 that may be received by interrupt handling system 102 in response to a status request sent to a worker process. Task data structure 300 may contain multiple fields including a task identifier 303 and a computational result 306. Other fields may be included in task data structure 300. Task identifier 303 may include multiple unique identifiers, which may represent distinct computational tasks assigned to the worker process. Task identifier may be numerical, alphanumeric, or other suitable identifier types. Computational result 306 field may contain corresponding results for each task (e.g., numerical and/or alphanumeric output).

When interrupt handling system 102 receives this data structure, it may process the information to determine which tasks have been completed and what results have been generated. The system may analyze the task identifiers to track progress and identify any tasks that may still be pending or incomplete. The association between tasks and their results is maintained through the alignment of each task identifier 303 and computational result 306. This structure allows the interrupt handling system to efficiently match each task with its corresponding output.

Upon receiving and processing this task data structure, the interrupt handling system may use this information to generate a checkpoint, update its internal state regarding task completion, or make decisions about how to handle potential interruptions to the worker process. The system may also use this data to prepare for task redistribution or result aggregation, if necessary.

The interrupt handling system may identify remaining work for the affected process. Specifically, interrupt handling system 102 may (e.g., via checkpoint generation subsystem 116) determine, based on the first set of computational results, a second set of tasks that have not been completed by the worker process. The first set of tasks and the second set of tasks may have been assigned to the worker process. For example, if the worker process was assigned ten total tasks and returned results for three, the system may determine that seven tasks remain incomplete.

FIG. 4 illustrates an excerpt of a task data structure 400 that may be used by the interrupt handling system to identify tasks that have not been completed by a worker process. Task data structure 400 may include a task identifier 403 and a computational result 406. Task data structure 400 may include other fields, as necessary. This structure provides a clear and efficient way to track the status of multiple tasks assigned to a worker process.

In task data structure 400, the task identifier 403 may unique numerical or other types of identifiers for each task. The corresponding computational result 406 for each task. Null values may indicate that the tasks have not yet been completed or that results have not been received. By analyzing this data structure, the interrupt handling system is able to quickly identify which tasks lack computational results and may therefore be considered incomplete.

The organization of task data structure 400 allows the interrupt handling system to efficiently determine the second set of tasks that have not been completed by the worker process. By comparing the task identifiers with their corresponding computational results, the system may generate a list of tasks that require further processing or reassignment. This information may be crucial for checkpoint generation subsystem 116 when creating a checkpoint data structure that accurately reflects the current state of task completion and ensures that no unfinished work is lost during an interrupt event.

In some implementations, interrupt handling system 102 may analyze task completion status to identify unfinished work. Specifically, interrupt handling system 102 may determine, based on the first set of computational results, a second set of tasks that have not been completed by worker process 104. The first set of tasks and the second set of tasks may have been assigned to worker process 104. For example, checkpoint generation subsystem 116 may compare the received results against the original task list to identify outstanding tasks.

FIG. 4 illustrates an excerpt of task data structure 400 that may be used by interrupt handling system 102 to identify tasks that have not been completed by worker process 104. Task data structure 400 may include task identifier 403 and computational result 406. Task data structure 400 may include other fields, as necessary. This structure may provide a clear and efficient way to track the status of multiple tasks assigned to worker process 104.

In task data structure 400, task identifier 403 may contain unique numerical or other types of identifiers for each task. Computational result 406 may correspond to each task. Null values in computational result 406 may indicate that the tasks have not yet been completed or that results have not been received. By analyzing task data structure 400, interrupt handling system 102 may quickly identify which tasks lack computational results and may therefore be considered incomplete.

In some implementations, organization of task data structure 400 may allow interrupt handling system 102 to efficiently determine the second set of tasks that have not been completed by worker process 104. By comparing task identifiers 403 with corresponding computational results 406, interrupt handling system 102 may generate a list of tasks that require further processing or reassignment. This information may be crucial for checkpoint generation subsystem 116 when creating a checkpoint data structure that accurately reflects the current state of task completion and ensures that no unfinished work is lost during an interrupt event.

In some implementations, interrupt handling system 102 may handle both external and internal disruption events. For external disruption events, such as scaled down infrastructure or spot removal, interrupt detection subsystem 114 may receive signals from the cloud computing environment indicating imminent resource reallocation. In response, interrupt handling system 102 may initiate the task completion analysis process to identify and preserve progress on incomplete tasks. For internal disruption events, such as calculation crashes or out of memory errors, communication subsystem 112 may receive error messages from worker process 104. Interrupt handling system 102 may then analyze task data structure 400 to determine which tasks were affected by the internal error and require reprocessing or error handling.

The interrupt handling system may create a record of completed and remaining work. Specifically, the system may generate, for the worker process, a checkpoint data structure that indicates that the first set of tasks has been completed and that the second set of tasks has not been completed. For example, checkpoint generation subsystem 116 may create a file listing completed tasks with their results and uncompleted tasks marked as pending.

FIG. 5 illustrates an excerpt of a checkpoint data structure 500 that includes three distinct fields: task identifier 503, computational result 506, and task data 509. More or less fields may be included in a given checkpoint data structure. This structure provides a comprehensive format for tracking and managing computational tasks, their outcomes, and associated data within a cloud computing environment. Task identifier 503 field may contain unique numerical, alphanumerical, or other suitable identifiers for each task. These identifiers allow the system to uniquely reference and track individual computational tasks within the broader workflow. Computational result 506 field shows the status or outcome of calculations for each task, indicating a completed task with an associated result, while subsequent entries may represent tasks that are still in progress or have not yet been executed. Task data 509 field may contain additional information related to each task. This field may store various types of metadata, parameters, or other task-specific information that is relevant to the execution or management of the task.

The organization of these fields within the checkpoint data structure 500 allows the interrupt handling system to efficiently capture and manage the state of multiple computational tasks. By combining task identifiers, results, and associated data in a single structure, the system can quickly assess the progress of work, identify completed and pending tasks, and preserve critical information in the event of an interruption or system failure. This comprehensive approach to checkpointing may enhance the resilience and continuity of operations within the cloud computing environment.

Checkpoint generation subsystem 116 of interrupt handling system 102 may populate checkpoint data structure 500 based on information received from worker process 104. For completed tasks, checkpoint generation subsystem 116 may input the task identifier into task identifier 503, the corresponding result into computational result 506, and any relevant task-specific data into task data 509. For uncompleted tasks, checkpoint generation subsystem 116 may input the task identifier into task identifier 503, leave computational result 506 as null or empty, and include any available task data in task data 509.

In some cases, interrupt handling system 102 may include remoting hooks to determine if calculations need to be distributed across multiple pods 108a-108n. These remoting hooks may analyze the complexity and resource requirements of tasks to decide if they should be executed locally or distributed for parallel processing. The decision made by the remoting hooks may be reflected in task data 509 of checkpoint data structure 500.

Interrupt handling system 102 may update checkpoint data structure 500 when worker process 104 completes a subsequent task. Communication subsystem 112 may receive a task completion message from worker process 104. Checkpoint generation subsystem 116 may then update checkpoint data structure 500 by moving the completed task from the set of uncompleted tasks to the set of completed tasks, and updating the corresponding computational result 506 and task data 509 fields.

In some implementations, interrupt handling system 102 may include optimization hooks to determine if distributed calculation steps can be grouped together and run on a single processor. These optimization hooks may analyze the dependencies between tasks and their resource requirements to identify opportunities for consolidation. The grouping decisions made by the optimization hooks may be recorded in task data 509 of checkpoint data structure 500.

Interrupt handling system 102 may use directed acyclic graphs to structure and parallelize complex calculations. These graphs may represent the dependencies and flow of calculations, allowing for efficient distribution and execution of tasks across multiple pods 108a-108n. The structure of these graphs may be encoded within task data 509 of checkpoint data structure 500, providing a comprehensive representation of the computational workflow.

By maintaining and updating checkpoint data structure 500, interrupt handling system 102 may ensure that computational progress is preserved even in the event of interruptions or failures in worker process 104. This structured approach to checkpointing may enhance the resilience and efficiency of distributed computations within computing environment 100.

The interrupt handling system may save the completed work and progress record. Specifically, the system may transmit, to be recorded within the cloud computing system, a computational payload including the first set of computational results and the checkpoint data structure. For example, the communication subsystem may send the partial results and checkpoint file to a central database within the cloud to be stored for later retrieval and processing.

The transmission of the computational payload may serve multiple purposes within the cloud computing system. Interrupt handling system 102 may ensure that completed work is preserved even if worker process 104 is terminated. By storing the first set of computational results, the cloud computing system may avoid redundant recalculation of completed tasks. The checkpoint data structure may provide a snapshot of the overall task progress, allowing for efficient task resumption or redistribution if needed.

In some cases, interrupt handling system 102 may adjust removal priorities for worker processes based on their execution time and task completion status. Interrupt handling system 102 may raise the removal priority of a worker process that has been executing for a given time period, indicating that the worker process has become more important. Conversely, interrupt handling system 102 may lower the removal priority of a worker process that has completed one or more operations within its assigned task list.

In some implementations, interrupt handling system 102 may receive a controller interrupt indicating that the controller process will be halted and removed by the cloud computing system. Based on receiving the controller interrupt, interrupt handling system 102 may signal to the worker process 104 to stop further processing. This signaling may help prevent data loss or inconsistencies that could occur if worker process 104 continues executing without proper management. Furthermore, interrupt handling system 102 may raise removal priorities for multiple worker processes after a predetermined time has passed. This adjustment may help balance resource allocation and ensure fair treatment of long-running processes within the cloud computing environment.

Interrupt handling system 102 may initiate new controller and worker processes with assigned task lists and removal priorities. Interrupt handling system 102 may assign a new task list to a new worker process via a new controller process. Initially, interrupt handling system 102 may assign a first removal priority to the new worker process, which may be used by the cloud computing system when determining which processes to halt and remove based on resource constraints.

Interrupt handling system 102 may generate the checkpoint data structure by inputting the first set of tasks into the checkpoint data structure. The system may assign, within the checkpoint data structure, a corresponding computational result to each task in the first set of tasks. Interrupt handling system 102 may also input the second set of tasks into the checkpoint data structure. The system may generate, within the checkpoint data structure, a corresponding flag for each task in the second set of tasks. The corresponding flag may indicate that a corresponding task has not been completed.

In some implementations, interrupt handling system 102 may populate the checkpoint data structure 500 with task information, as discussed above. For completed tasks, the system may input the task identifier into the task identifier 503. The system may then assign the corresponding result to the computational result 506. Any relevant task-specific data may be included in the task data 509. For uncompleted tasks, the system may input the task identifier into the task identifier 503. The computational result 506 may be left as null or empty. Any available task data may be included in the task data 509.

The checkpoint generation subsystem 116 may update the checkpoint data structure 500 when a worker process 104 completes a subsequent task. The communication subsystem 112 may receive a task completion message from the worker process 104. The checkpoint generation subsystem 116 may then update the checkpoint data structure 500. The completed task may be moved from the set of uncompleted tasks to the set of completed tasks. The corresponding computational result 506 and task data 509 fields may be updated. This structured approach to checkpointing may enhance the resilience and efficiency of distributed computations within the computing environment 100. The system may ensure that computational progress is preserved even in the event of interruptions or failures in the worker process 104.

In some implementations, interrupt handling system 102 may receive a task completion message indicating that a subsequent task of the second set of tasks has been completed by worker process 104. The task completion message may include a corresponding computational result. Communication subsystem 112 may handle the reception of this message from worker process 104.

Upon receiving the task completion message, checkpoint generation subsystem 116 may update checkpoint data structure 500. The system may remove the subsequent task from the second set of tasks, which represents uncompleted tasks. The system may then add the subsequent task to the first set of tasks, which represents completed tasks. This operation may effectively move the task from an “uncompleted” status to a “completed” status within the data structure.

Interrupt handling system 102 may update the corresponding computational result 506 field in the checkpoint data structure 500. The system may associate the newly completed task with its corresponding result received in the task completion message. This update may involve modifying the entry in the computational result 506 field from a null or empty value to the actual result of the computation. Checkpoint generation subsystem 116 may also update the task data 509 field for the completed task. This update may include modifying or adding relevant metadata, execution parameters, or other task-specific information that may be useful for future reference or analysis.

Furthermore, interrupt handling system 102 may generate an updated checkpoint data structure. This updated structure may reflect the current state of task completion, including the newly completed task and its associated data. Interrupt handling system 102 (e.g., using communication subsystem 112) may then transmit the updated checkpoint data structure to be recorded within the cloud computing system. This transmission may ensure that the latest task completion information is preserved and accessible within computing environment 100. The system may store this updated structure in a central database or another suitable storage location within the cloud infrastructure.

By maintaining and transmitting these updated checkpoint data structures, interrupt handling system 102 may provide a robust mechanism for tracking task progress and preserving computational results. This approach may enhance the system's ability to handle interruptions, resume tasks efficiently, and maintain data integrity across distributed computing processes.

In some implementations, interrupt handling system 102 may receive a controller interrupt at the controller process. This controller interrupt may indicate that the controller process will be halted and removed by the cloud computing system. The interrupt may be received as a result of the cloud computing system may periodically halting and removing processes to manage assigned computing resources.

Based on receiving the controller interrupt, interrupt handling system 102 may signal to worker process 104 to stop further processing. This signaling may be performed by the communication subsystem 112. Communication subsystem 112 may transmit a stop processing message to worker process 104 over the network 150.

Worker process 104 may receive the stop processing signal and initiate a graceful shutdown procedure. This procedure may include completing any in-progress atomic operations, flushing data buffers, and releasing any held resources. Worker process 104 may also generate checkpoint data for checkpoint data structure 500 before ceasing operations.

Interrupt handling system 102 may monitor the worker process 104 for confirmation of the shutdown. Interrupt detection subsystem 114 may wait for a shutdown confirmation message from worker process 104. If the confirmation is not received within a predetermined timeout period, interrupt handling system 102 may determine that the worker process is unresponsive (e.g., has crashed) and stop processing any data received from that worker process.

Interrupt handling system 102 may also transmit the final checkpoint data structures for worker process 104 to a persistent storage location within the cloud computing system. This transmission may be handled by communication subsystem 112. The persistent storage may be a distributed database or a cloud storage service that ensures data durability. By signaling the worker process 104 to stop processing when the controller process is about to be halted, interrupt handling system 102 may help prevent data loss or inconsistencies. This approach may ensure that worker process 104 does not continue executing without proper management, which could lead to orphaned or inconsistent computational states within the cloud computing environment.

Interrupt handling system 102 may initiate a new controller process and a new worker process within the cloud computing system. Interrupt handling system 102 may assign a new task list to the new worker process via the new controller process. Interrupt handling system 102 may also assign a first removal priority to the new worker process. The cloud computing system may halt and remove worker processes and controller processes based on an assigned removal priority. That is, worker processes with lower priority will be removed first before worker processes with a higher priority. In some implementations, instead of a priority, a removal cost may be used. Thus, the cloud computing system may remove worker processes with lower removal costs first.

Subsequently to assigning the first removal priority, interrupt handling system 102 may determine that the new worker process has been executing for a given time period. Based on determining that the new worker process has been executing for the given time period, interrupt handling system 102 may raise the first removal priority to indicate that the new worker process has become more important than when the first removal priority was assigned.

Interrupt handling system 102 may determine that one or more operations within the new task list have been completed by the new worker process and may generate a new checkpoint data structure for the new worker process. Interrupt handling system 102 may then assign a second removal priority of a lower value indicating that the new worker process is less important than when the first removal priority was raised. That is, when a checkpoint is created, if a worker process is removed, less work will be lost. Thus, the priority and/or removal cost is now lower. In some implementations, interrupt handling system 102 may determine that a predetermined amount of time has passed. Based on the determination, interrupt handling system 102 may raise a plurality of removal priorities for a plurality of worker processes that have not completed execution. That is, the longer a worker process is active without a checkpoint, the more important it becomes and the more costlier (in terms of work lost) it becomes to remove the particular worker process.

Interrupt handling system 102 may determine that one or more operations within the new task list have been completed by the new worker process. Based on this determination, interrupt handling system 102 may generate a new checkpoint data structure for the new worker process. This checkpoint data structure may include information about completed tasks, their results, and any relevant metadata.

After generating the new checkpoint data structure, interrupt handling system 102 may assign a second removal priority to the new worker process. This second removal priority may have a lower value than the first removal priority. The lower value may indicate that the new worker process is less important than when the first removal priority was assigned. That is, interrupt handling system 102 may adjust the removal priority based on the creation of checkpoints. When a checkpoint is created, the potential loss of work if the worker process is removed may be reduced. This reduction in potential work loss may be reflected in the lowered removal priority or cost.

In some implementations, interrupt handling system 102 may use a removal cost metric instead of a removal priority. The cloud computing system may remove worker processes with lower removal costs first. After creating a checkpoint, interrupt handling system 102 may lower the removal cost of the worker process, reflecting the reduced impact of potential removal. Interrupt handling system 102 may periodically reassess the removal priorities or costs of worker processes. The system may determine that a predetermined amount of time has passed since the last checkpoint or priority adjustment. Based on this determination, interrupt handling system 102 may raise the removal priorities for a plurality of worker processes that have not completed execution or created recent checkpoints.

As worker processes continue to execute without creating checkpoints, they may become more important in terms of preserving computational progress. Interrupt handling system 102 may reflect this increased importance by raising the removal priority or increasing the removal cost over time. This adjustment may help protect long-running processes from premature termination. Interrupt handling system 102 may help maintain a balance between efficient resource allocation and preserving computational progress. By dynamically adjusting removal priorities based on checkpoint creation and execution time, the system may optimize the use of cloud computing resources while minimizing the risk of losing significant amounts of completed work.

In some implementations, interrupt handling system 102 this component may be called Pod Defender. The Pod Defender may be deployed with all pods and may have awareness of what each pod is doing. This awareness may allow interrupt handling system 102 to make informed decisions about the status of worker processes.

As discussed above, the Pod Defender may track how long a worker process 104 has been working and how long it has been since the last checkpoint. This information may be used to assess the potential impact of halting and removing worker process 104. For example, if worker process 104 has been running for a long time without a recent checkpoint, interrupt handling system 102 may determine that, if removed, the computational cost would be high.

Furthermore, the Pod Defender may periodically update a pod deletion cost for each container. The pod deletion cost may be used by the cloud computing system to determine which pods to remove when managing resources. Interrupt handling system 102 may use this information to estimate the likelihood of a worker process 104 being halted and removed. To balance the need for up-to-date information with constraints on API call budgets, the Pod Defender may update the pod deletion cost every 5-10 minutes. This update frequency may allow interrupt handling system 102 to maintain reasonably current information about the status of worker processes without overwhelming the cloud computing system's API.

The pod deletion cost may be represented as an integer value. This integer may correspond to the number of minutes of unsaved work for a given worker process 104. For instance, if worker process 104 has been running for 30 minutes since its last checkpoint, the pod deletion cost may be set to 30. Interrupt handling system 102 may use this value to prioritize which worker processes are most critical to protect from interruption.

By considering these factors, interrupt handling system 102 may make informed decisions about whether a worker process 104 is likely to be halted and removed. This determination may guide subsequent actions, such as initiating checkpoints or redistributing workloads, to minimize data loss and maintain computational efficiency in the face of potential interruptions.

FIG. 6 shows an example computing system that may be used in accordance with some embodiments of this disclosure. In some instances, computing system 600 is referred to as a computer system 600. A person skilled in the art would understand that those terms may be used interchangeably. The components of FIG. 6 may be used to perform some or all operations discussed in relation to FIGS. 1-5. Furthermore, various portions of the systems and methods described herein may include or be executed on one or more computer systems similar to computing system 600. Further, processes and modules described herein may be executed by one or more processing systems similar to that of computing system 600.

Computing system 600 may include one or more processors (e.g., processors 610a-610n) coupled to system memory 620, an input/output (I/O) device interface 630, and a network interface 640 via an I/O interface 650. A processor may include a single processor, or a plurality of processors (e.g., distributed processors). A processor may be any suitable processor capable of executing or otherwise performing instructions. A processor may include a central processing unit (CPU) that carries out program instructions to perform the arithmetical, logical, and input/output operations of computing system 600. A processor may execute code (e.g., processor firmware, a protocol stack, a database management system, an operating system, or a combination thereof) that creates an execution environment for program instructions. A processor may include a programmable processor. A processor may include general or special purpose microprocessors. A processor may receive instructions and data from a memory (e.g., system memory 620). Computing system 600 may be a uni-processor system including one processor (e.g., processor 610a), or a multi-processor system including any number of suitable processors (e.g., 610a-610n). Multiple processors may be employed to provide for parallel or sequential execution of one or more portions of the techniques described herein. Processes, such as logic flows, described herein may be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating corresponding output. Processes described herein may be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field-programmable gate array) or an ASIC (application-specific integrated circuit). Computing system 600 may include a plurality of computing devices (e.g., distributed computer systems) to implement various processing functions.

I/O device interface 630 may provide an interface for connection of one or more I/O devices 660 to computer system 600. I/O devices may include devices that receive input (e.g., from a user) or output information (e.g., to a user). I/O devices 660 may include, for example, a graphical user interface presented on displays (e.g., a cathode ray tube (CRT) or liquid crystal display (LCD) monitor), pointing devices (e.g., a computer mouse or trackball), keyboards, keypads, touchpads, scanning devices, voice recognition devices, gesture recognition devices, printers, audio speakers, microphones, cameras, or the like. I/O devices 660 may be connected to computer system 600 through a wired or wireless connection. I/O devices 660 may be connected to computer system 600 from a remote location. I/O devices 660 located on remote computer systems, for example, may be connected to computer system 600 via a network and network interface 640.

Network interface 640 may include a network adapter that provides for connection of computer system 600 to a network. Network interface 640 may facilitate data exchange between computer system 600 and other devices connected to the network. Network interface 640 may support wired or wireless communication. The network may include an electronic communication network, such as the Internet, a local area network (LAN), a wide area network (WAN), a cellular communications network, or the like.

System memory 620 may be configured to store program instructions 670 or data 680. Program instructions 670 may be executable by a processor (e.g., one or more of processors 610a-610n) to implement one or more embodiments of the present techniques. Program instructions 670 may include modules of computer program instructions for implementing one or more techniques described herein with regard to various processing modules. Program instructions may include a computer program (which in certain forms is known as a program, software, software application, script, or code). A computer program may be written in a programming language, including compiled or interpreted languages, or declarative or procedural languages. A computer program may include a unit suitable for use in a computing environment, including as a stand-alone program, a module, a component, or a subroutine. A computer program may or may not correspond to a file in a file system. A program may be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, subprograms, or portions of code). A computer program may be deployed to be executed on one or more computer processors located locally at one site, or distributed across multiple remote sites and interconnected by a communication network.

System memory 620 may include a tangible program carrier having program instructions stored thereon. A tangible program carrier may include a non-transitory computer-readable storage medium. A non-transitory computer-readable storage medium may include a machine-readable storage device, a machine-readable storage substrate, a memory device, or any combination thereof. A non-transitory computer-readable storage medium may include non-volatile memory (e.g., flash memory, ROM, PROM, EPROM, EEPROM memory), volatile memory (e.g., random access memory (RAM), static random-access memory (SRAM), synchronous dynamic RAM (SDRAM)), bulk storage memory (e.g., CD-ROM and/or DVD-ROM, hard drives), or the like. System memory