Paper #2

Enhanced Database Security using Hybrid GA-PSO for Parallel Elliptic Curve Cryptographic Scheduling with Offline Optimisation

Safaa Salam Hatem and Fahad Naim Nife

Abstract: Modern database systems increasingly employ cryptographic primitives; however, the significant computational overhead of encryption and decryption represents a major performance bottleneck in high-throughput environments. In particular, Elliptic Curve Cryptography (ECC) is of interest because it provides strong security guarantees with compact key sizes and relatively efficient arithmetic, thus making ECC suitable for resource-constrained platforms and latency-sensitive services. Realistic database workloads require the concurrent execution of many ECC scalar multiplications, such as batch encryption pipelines, parallel client authentication, and transaction-intensive workloads, hence inducing a non-trivial scheduling problem on modern multi-core and heterogeneous architectures. This work introduces a hybrid Genetic Algorithm-Particle Swarm Optimisation (GA-PSO) framework that acts as an offline configuration mechanism for optimizing parallel ECC operation execution on heterogeneous resources, which runs in a pre-computation phase: the optimisation routine is executed only once during system initialization, or when the database is deployed on new hardware, and the scheduling parameters are cached and reused for the system's operational lifetime. In this way, the optimisation cost is amortized over millions of subsequent cryptographic operations, reconciling the apparent trade-off between sophisticated scheduling strategies and the stringent performance constraints of real-time database workloads; Moreover, the new scheduler has a four-dimensional decision space: ECC window size, core assignment, run ordering, and memory placement, and it utilizes a five-dimensional score function which considers runtime, memory footprint, load balancing, energy consumption, and schedule predictability. Experimental evaluation on the widely deployed secp256r1 curve shows that the Offline GA-PSO scheduler improves the execution time by 4.2% compared to the dynamic Work-Stealing baseline, by 9.8% compared to Greedy SJF and by 24.5% compared to Round-Robin, and given an offline optimisation cost of 0.85 s and a per-batch saving of 9.5 ms relative to the strongest runtime baseline, this overhead is amortised after approximately 4,500 scalar multiplications. Overall, our results indicate that offline meta-heuristic scheduling is a viable and efficient building block for smart, crypto-aware resource management in secure high-performance database systems, and moreover, the experimental evaluation shows that on the target multi-core platform the proposed offline hybrid GA-PSO scheduler converges most frequently to an ECC window size of 8, as this choice offers the best trade-off between precomputation overhead and scalar multiplication speed.

Keywords: Cryptographic scheduling; Database security; Elliptic Curve Cryptography (ECC); Genetic Algorithm (GA); Multi-core optimisation; Offline optimisation; Particle Swarm Optimisation (PSO); Resource management.