Research on resource allocation algorithm for multi-objective optimization in OFDMA systems

AbstractCloud resource demands, especially some unclear and emergent resource demands, are growing rapidly with the development of cloud computing, big data and artificial intelligence. The traditional cloud resource allocation methods do not support the emergent mode in guaranteeing the timeliness and optimization of resource allocation. This paper proposes a resource allocation algorithm for emergent demands in cloud computing. After building the priority of resource allocation and the matching distances of resource performance and resource proportion to respond to emergent resource demands, a multi-objective optimization model of cloud resource allocation is established based on the minimum number of the physical servers used and the minimum matching distances of resource performance and resource proportion. Then, an improved evolutionary algorithm, RAA-PI-NSGAII, is presented to solve the multi-objective optimization model, which not only improves the quality and distribution uniformity of the solution set but also accelerates the solving speed. The experimental results show that our algorithm can not only allocate resources quickly and optimally for emergent demands but also balance the utilization of all kinds of resources.

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Energy-Spectrum Efficient Content Distribution in Fog-RAN Using Rate-Splitting, Common Message Decoding, and 3D-Resource Matching

10.36227/techrxiv.12933761.v2 ◽

2021 ◽

Author(s):

MD ZOHEB HASSAN

Keyword(s):

Resource Allocation ◽

Pareto Front ◽

Optimization Problem ◽

Trade Off ◽

Resource Allocation Algorithm ◽

Allocation Algorithm ◽

Multi Objective ◽

Resource Matching ◽

Rate Splitting ◽

Common Message

<div>Multi-objective resource allocation is studied for edge-caching enabled fog-radio access network. Notably, joint maximization of the energy-efficiency (EE) and spectrum-efficiency (SE) and interference management are investigated for distributing contents from the cache-enabled fog access points (F-APs) and cloud base station (CBS) to the user devices (UDs). In our envisioned system, the UDs are grouped into multiple non-overlapping device-clusters based on their locations. A rate-splitting with common message decoding based transmission strategy is applied to enable UDs of each device-cluster to receive data from a suitably selected F-AP and CBS over the same radio resource blocks. To maximize system EE and SE jointly, a multi-objective optimization problem (MOOP) is formulated and it is solved in three stages. At first, by employing the $\epsilon$-constraint method, the MOOP is converted to an EE-SE trade-off optimization problem. Then, by leveraging iterative function evaluation based power control and generalized 3D-resource matching, the EE-SE trade-off optimization problem is solved and a novel resource allocation algorithm is proposed to obtain near-optimal Pareto-front for the proposed MOOP. To reduce the complexity of obtaining near-optimal Pareto-front, a sub-optimal resource allocation algorithm is proposed as well. Finally, a low-complexity algorithm is devised to select a suitable operating EE-SE pair from the obtained Pareto-front. The conducted simulations demonstrate that the proposed resource allocation schemes achieve substantial improvement of system EE and SE over the benchmark schemes. </div>

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