Leveraging joint allocation of multidimensional resources for distributed task assignment

Edge computing has changed the landscape of telecommunication networks. Different from cloud computing in which thousands of servers are centralized in a remote site, computation and storage resources are deployed at the network edge in edge computing, reducing the end-to-end latency and the amount of transmitting data in metro/backbone networks significantly. Due to the limited resource capacity in a single edge node and the requirements of distributed applications, some applications are supposed to be decomposed into multiple interdependent tasks and executed in distributed resource-constrained nodes. Assigning tasks to geographically distributed edge nodes is quite challenging because of the allocation of multidimensional resources (i.e., computation, storage, and transmission) as well as constraints of the interdependency between different tasks. Strategies that take only one factor into account for optimization will cause improper task assignments, leading to higher end-to-end latency and lower resource utilization efficiency. To solve this problem, we formulate a mathematical model aiming at minimizing the job completion time by jointly considering the availability of multidimensional resources and the interdependency among different tasks. We obtain optimal results in small topology by using optimization software that validates the correctness of the proposed mathematical model. Furthermore, we analyze the complexity and design of a practical algorithm by narrowing the searching space in large-scale topology. Simulation results present its effectiveness over greedy algorithms. Finally, we conduct a proof-of-concept experiment to validate the feasibility of the proposed strategy.