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Method and apparatus for resource allocation when schedule changes are incorporated in real time

A plurality of resources, typically service operatives, are allocated to a plurality of tasks by a method in which initial information relating to the tasks to be allocated and the resources available to perform the tasks is provided. An initial series of schedules is first generated allocating resources to the tasks, and then modifying the individual schedule of at least one resource in response to updated information. Changes to individual schedules may be made in response to such updated information independently of the schedule generation. The initial, series of schedules may be generated in a two-stage process in which a rule-based system allocates tasks selected as being difficult to allocate (e.g., because they are linked to other tasks). then a stochastic (non-systematic) search system compiles the rest of the schedule. Periodically, the stochastic system may be interrupted to allow a further rule-based system to analyze the schedules created thus far, and fix the best ones in the schedule, so that the stochastic system can then concentrate on improving the remaining schedules. In order to allow the system to handle rapid changes in the requirements for tasks and the resources, on a scale faster than the time required to generate the schedules, a schedule modification system is arranged to make changes in the short term in between schedule updates delivered by the schedule generation system.
Owner:TRIMBLE MRM

A joint optimization method for task unloading and resource allocation in a mobile edge computing network

The invention discloses a joint optimization method for task unloading and resource allocation in a mobile edge computing network, which comprises the following steps of 1, establishing an OFDMA (Orthogonal Frequency Division Multiple Access)-based multi-MEC (Mobile Edge Computing) base station and a multi-user scene model, wherein the MEC base station supports the multi-user access; 2, introducing an unloading decision mechanism; Meanwhile, constructing a local calculation model and a remote calculation model, selecting a user needing to perform calculation unloading, and establishing a calculation task unloading and resource allocation scheme based on minimum energy consumption under the condition of meeting the time delay constraint according to the conditions; 3, carrying out variablefusion on three mutually constrained optimization variables, namely an unloading decision variable, a wireless resource distribution variable and a computing resource distribution variable, so as to simplify the problem; and 4, obtaining an unloading decision and a resource allocation result which enable the total energy consumption of the user in the MEC system to be minimum through a branch andbound algorithm. The method has the advantage that the energy consumption of the system can be effectively reduced on the premise that strict time delay limitation is guaranteed.
Owner:NANJING UNIV OF POSTS & TELECOMM
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