  Resource optimisation is commonly used in workload management, ensuring
efficient and timely task completion utilising available resources. It serves
to minimise costs, prompting the development of numerous algorithms tailored to
this end. The majority of these techniques focus on scheduling and executing
workloads effectively within the provided resource constraints. In this paper,
we tackle this problem using another approach. We propose a novel framework D&A
to determine the number of cores required in completing a workload under time
constraint. We first preprocess a small portion of queries to derive the number
of required slots, allowing for the allocation of the remaining workloads into
each slot. We introduce a scaling factor in handling the time fluctuation issue
caused by random functions. We further establish a lower bound of the number of
cores required under this scenario, serving as a baseline for comparison
purposes. We examine the framework by computing personalised PageRank values
involving intensive computations. Our experimental results show that D&A
surpasses the baseline, achieving reductions in the required number of cores
ranging from 38.89% to 73.68% across benchmark datasets comprising millions of
vertices and edges.
