An integration of autonomic computing with multicore systems for performance optimization in Industrial Internet of Things

Abstract

The goal of this work is to investigate how the self‐awareness characteristic of autonomic computing, paired with existing performance optimization rules, may be used in applications to minimise multi‐core processor performance concerns. The suggested self‐awareness technique can assist applications in self‐execution while also assisting other applications in executing in the system with optimal resource usage and reducing conflicts in a collaborative manner. It means self‐awareness is created in the application to get resources, schedules itself, running autonomically with respect to runtime variations in the applications and system parameters. Further, self‐aware applications would help collaboratively resolving the performance issues like; contention, bandwidth bottleneck, efficient task to core mapping, and performance monitoring through analysis in a dynamic collaborative multi‐core environment. To show the usefulness of this research, “Autonomic Computing Interface” (ACI) for is proposed Multicore systems. The proposed firmware as an interface between applications and the multi‐core system can dynamically handle performance issues with coordination of applications in an autonomic way. Also, in a highly dynamic execution environment the proposed runtime parameter tuning, existing performance improvement policies and self‐awareness approach in totality, for monitoring, analysis, and possibility for performance improvement would be effective as compared to the existing approaches which work in isolation.

Many-objective African vulture optimization algorithm: A novel approach for many-objective problems

“…Table 14 provides the input data for a series-parallel system where r i , α i and β i are uniformly generated from the ranges [0.95,1.0], [ 6 , 10 ], [ 1 , 5 ], and [ 11 , 20 ] respectively.…”

“…Most real-world applications may have more than four conflicting objective functions and are mathematically being modelled as MaOPs. Some of these applications include automotive engineering, aerospace engineering, many-objective simplified nurse scheduling problem, the five-objective water resource management problem, the ten-objective general aviation aircraft design problem, the many-objective space trajectory design problem, many-objective software refactoring, the hybrid car controller optimization problem with six objectives, optimization of three centrifugal design problems having six to nine objectives, the many-objective 0/1 knapsack problem, Heuristic Learning, Travelling Salesman Problem (TSP), Job shop scheduling, flight control system, supersonic wing design, six-objective design of a factory-shed truss [ 2 ], Big data applications which need sophisticated architectures with inherent capabilities to be scaled and optimized [ 3 ], NP-hard workflow allocation problems in cloud systems [ 4 ], Multicore computers are transforming the embedded computing market [ 5 ], and recently Internet of Everything (IoE) [ 6 ]. The difficulty of the MaOPs returns to the increase in the problem scale; as the number of objectives grows, the number of nondominated solutions grows exponentially [ 1 ].…”

“…MOEAs are not scalable enough and have problems addressing the MaOPs. These problems are summarized by [11] as follows: (1) As the number of objective functions grows, the obtained results become non-dominated; (2) As the size of the objective space grows, the conflict between diversity and convergence grows; (3) For computational efficiency, the population size can be small; (4) Computational complexity grows exponentially as the number of objectives grows (for example, hypervolume calculation); (5) Balancing diversity and convergence becomes more complicated; and (6) Due to the vast dimensions, visualizing the Pareto-optimal front is difficult. Due to these challenging issues, MaOPs are more complex and need to be handled using more effective and scalable evolutionary algorithms.…”