HMDS: A Makespan Minimizing DAG Scheduler for Heterogeneous Distributed Systems

The problem of scheduling Directed Acyclic Graphs in order to minimize makespan ( schedule length ), is known to be a challenging and computationally hard problem. Therefore, researchers have endeavored towards the design of various heuristic solution generation techniques both for homogeneous as well as heterogeneous computing platforms. This work first presents HMDS-Bl , a list-based heuristic makespan minimization algorithm for task graphs on fully connected heterogeneous platforms. Subsequently, HMDS-Bl has been enhanced by empowering it with a low-overhead depth-first branch and bound based search approach, resulting in a new algorithm called HMDS . HMDS has been equipped with a set of novel tunable pruning mechanisms, which allow the designer to obtain a judicious balance between performance ( makespan ) and solution generation times, depending on the specific scenario at hand. Experimental analyses using randomly generated DAGs as well as benchmark task graphs, have shown that HMDS is able to comprehensively outperform state-of-the-art algorithms such as HEFT , PEFT , PPTS , etc., in terms of archived makespans while incurring bounded additional computation time overhead.

An Optimized Nature-Inspired Metaheuristic Algorithm for Application Mapping in 2D-NoC

Mapping application task graphs on intellectual property (IP) cores into network-on-chip (NoC) is a non-deterministic polynomial-time hard problem. The evolution of network performance mainly depends on an effective and efficient mapping technique and the optimization of performance and cost metrics. These metrics mainly include power, reliability, area, thermal distribution and delay. A state-of-the-art mapping technique for NoC is introduced with the name of sailfish optimization algorithm (SFOA). The proposed algorithm minimizes the power dissipation of NoC via an empirical base applying a shared k-nearest neighbor clustering approach, and it gives quicker mapping over six considered standard benchmarks. The experimental results indicate that the proposed techniques outperform other existing nature-inspired metaheuristic approaches, especially in large application task graphs.