Methods and systems for distributed task scheduling in satellite cluster for earth observation data analytics

A system comprising:

• a memory storing instructions;
• one or more input/output (I/O) interfaces;
• and
• one or more hardware processors coupled to the memory via the one or more I/O interfaces, wherein the one or more hardware processors are configured by the instructions to:
• receive a plurality of tasks to be executed by a plurality of satellites present in a satellite cluster and one or more ground stations connected to the one or more satellites, a predefined soft real-time constraint defined for each task of the plurality of tasks, one or more resource requirements required for each task, and one or more resource capabilities of each of the one or more satellites and each of the one or more ground stations, wherein each task comprises a plurality of subtasks and one or more dependencies of each subtask on remaining subtasks of the plurality of subtasks;
• assign the plurality of tasks to the plurality of satellites present in the satellite cluster and the one or more ground stations, in a static phase and a dynamic phase in a sequence, using an optimization technique, to obtain an optimized distributed schedule of the plurality of tasks,
• wherein assigning the plurality of tasks in the static phase comprising:
• (i) identifying a first subtask among the plurality of subtasks for each task, wherein the first subtask is an earth observation data collection task;
• (ii) receiving one or more resources required for the first subtask of each task;
• (iii) identifying one or more qualifying satellites among the plurality of satellites, for the first subtask of each task, based on the one or more resource capabilities of each satellite, and the one or more resources required for the first subtask;
• (iv) calculating a value of a reward function for each of the one or more qualifying satellites, for executing the first subtask of each task, based on a set of operational attributes of an associated first subtask, wherein the reward function is defined in the optimization technique;
• and
• (v) selecting a qualifying satellite among the one or more qualifying satellites having a maximum value of the reward function, based on the predefined soft real-time constraint defined for each task, as a controller agent for each task of the plurality of tasks;
• and
• wherein scheduling the plurality of tasks in the dynamic phase comprising:
• (i) executing the first subtask of each task of the plurality of tasks, by an associated controller agent selected in the static phase;
• (ii) identifying a set of remaining subtasks for each task of the plurality of tasks, after the execution of the first subtask and removing the first subtask from an associated task;
• (iii) sorting one or more subtasks present in the set of remaining subtasks for each task of the plurality of tasks, in a topological order, based on the one or more dependencies of each subtask;
• (iv) if there is a single subtask present in the set of remaining subtasks for each task after sorting in the topological order, at a current level of execution of one or more execution levels, then:
• (a) executing the single subtask by the associated controller agent selected in the static phase, if one or more resource requirements of the single subtask matches with the one or more resource capabilities of the associated controller agent selected in the static phase and matches with the predefined soft real-time constraint;
• and
• (b) handing over the single subtask to other satellites for execution using a handover mechanism, if the one or more resource requirements of the single subtask does not match with the one or more resource capabilities of the associated controller agent selected in the static phase and does not match with the predefined soft real-time constraint;
• (v) if there are multiple subtasks present in the set of remaining subtasks for each task after sorting in the topological order, at the current level of execution of one or more execution levels, then:
• (a) executing the multiple subtasks present in the set of remaining subtasks for each task after sorting in the topological order, at the current level of execution, by the associated controller agent selected in the static phase, if one or more resource requirements of the multiple subtasks matches with the one or more resource capabilities of the associated controller agent selected in the static phase and matches with the predefined soft real-time constraint;
• and
• (b) handing over the multiple subtasks present in the set of remaining subtasks for each task after sorting in the topological order, at the current level of execution to other satellites for execution using a handover mechanism, if the one or more resource requirements of the multiple subtasks does not match with the one or more resource capabilities of the associated controller agent selected in the static phase and does not match with the predefined soft real-time constraint;
• and
• execute the plurality of tasks by the plurality of satellites present in the satellite cluster and the one or more ground stations, using the optimized distributed schedule.

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

• receiving a plurality of tasks to be executed by a plurality of satellites present in a satellite cluster and one or more ground stations connected to the one or more satellites, a predefined soft real-time constraint defined for each task of the plurality of tasks, one or more resource requirements required for each task, and one or more resource capabilities of each of the one or more satellites and each of the one or more ground stations, wherein each task comprises a plurality of subtasks and one or more dependencies of each subtask on remaining subtasks of the plurality of subtasks;
• assigning the plurality of tasks to the plurality of satellites present in the satellite cluster and the one or more ground stations, in a static phase and a dynamic phase in a sequence, using an optimization technique, to obtain an optimized distributed schedule of the plurality of tasks,
• wherein assigning the plurality of tasks in the static phase comprising:
• (i) identifying a first subtask among the plurality of subtasks for each task, wherein the first subtask is an earth observation data collection task;
• (ii) receiving one or more resources required for the first subtask of each task;
• (iii) identifying one or more qualifying satellites among the plurality of satellites, for the first subtask of each task, based on the one or more resource capabilities of each satellite, and the one or more resources required for the first subtask;
• (iv) calculating a value of a reward function for each of the one or more qualifying satellites, for executing the first subtask of each task, based on a set of operational attributes of an associated first subtask, wherein the reward function is defined in the optimization technique;
• and
• (v) selecting a qualifying satellite among the one or more qualifying satellites having a maximum value of the reward function, based on the predefined soft real-time constraint defined for each task, as a controller agent for each task of the plurality of tasks;
• and
• wherein scheduling the plurality of tasks in the dynamic phase comprising:
• (i) executing the first subtask of each task of the plurality of tasks, by an associated controller agent selected in the static phase;
• (ii) identifying a set of remaining subtasks for each task of the plurality of tasks, after the execution of the first subtask and removing the first subtask from an associated task;
• (iii) sorting one or more subtasks present in the set of remaining subtasks for each task of the plurality of tasks, in a topological order, based on the one or more dependencies of each subtask;
• (iv) if there is a single subtask present in the set of remaining subtasks for each task after sorting in the topological order, at a current level of execution of one or more execution levels, then:
• (a) executing the single subtask by the associated controller agent selected in the static phase, if one or more resource requirements of the single subtask matches with the one or more resource capabilities of the associated controller agent selected in the static phase and matches with the predefined soft real-time constraint;
• and
• (b) handing over the single subtask to other satellites for execution using a handover mechanism, if the one or more resource requirements of the single subtask does not match with the one or more resource capabilities of the associated controller agent selected in the static phase and does not match with the predefined soft real-time constraint;
• (v) if there are multiple subtasks present in the set of remaining subtasks for each task after sorting in the topological order, at the current level of execution of one or more execution levels, then:
• (a) executing the multiple subtasks present in the set of remaining subtasks for each task after sorting in the topological order, at the current level of execution, by the associated controller agent selected in the static phase, if one or more resource requirements of the multiple subtasks matches with the one or more resource capabilities of the associated controller agent selected in the static phase and matches with the predefined soft real-time constraint;
• and
• (b) handing over the multiple subtasks present in the set of remaining subtasks for each task after sorting in the topological order, at the current level of execution to other satellites for execution using a handover mechanism, if the one or more resource requirements of the multiple subtasks does not match with the one or more resource capabilities of the associated controller agent selected in the static phase and does not match with the predefined soft real-time constraint;
• and
• executing, via the one or more hardware processors, the plurality of tasks by the plurality of satellites present in the satellite cluster and the one or more ground stations, using the optimized distributed schedule.