An Algorithm Incarnating Deep Integration of Hardware-Software Energy Regulation Principles for Heterogeneous Green Scheduling

Abstract

Heterogeneous green scheduling in virtual cloud is an urgent need of human sustainable developments. However, on the one hand, there is still considerable space beyond reach of the hardware energy regulation mode; on the other hand, as the core of green software methods, meta-heuristics algorithms are still underperforming in heterogeneous scheduling, although with many achievements in homogeneous scheduling. In this paper, an efficient new meta-heuristics algorithm is presented (i.e., GHSA_di), including the co-evolutionary dynamics equation emphasizing on and taking advantage of the hardware energy-regulation principles. The experimental results show that compared with the other three meta-heuristic scheduling algorithms, GHSA_di algorithm has obvious advantages in overall performance, energy saving and scalability, for both data intensive and computing intensive instances.

A Greener Meta-Heuristics Scheduling Algorithm With Energized Optimization Dynamics by Deeper Intelligence Fusion

“…In fact, the intelligent decision-making of the scheduling middleware is key, where green scheduling aims for the computing evolution from high performance to high efficiency. For green scheduling as the high-dimensional multi-objective optimization problems under the strong restriction in the real complex super-system, metaheuristics algorithms like genetic algorithms and artificial immune algorithms, have been used [2] [3] . Although with many achievements in homogeneous scheduling, metaheuristics algorithms are underperforming in the nonlinear heterogeneous green scheduling, with the balance conflict between convergence and distribution [4][5][6] .…”

“…In [31], the algorithm incarnating deep integration of hardware-software energy regulation principles for heterogeneous scheduling, i.e., GHSA_di [31] , was proposed.…”

“…IN CA R _FI(HS) OVER THAT IN GHSA_DI [31] ① The parameters involved and shown in Table Ⅱ in Section 3.A in this paper, are easier to obtain, more representative and as few as possible, which is more suitable for the real-time, dynamic and particularity of heterogeneous scheduling deployment stage, and is obtained by means of exploring the explicit and implicit relationships of nonlinear circuit characteristics.…”

“…In [31], the algorithm incarnating deep integration of hardware-software energy regulation principles for heterogeneous scheduling, i.e., GHSA_di [31] , was proposed. In a word, literature [31] belongs to the preliminary work of this paper; and this paper is its further expansion and extension, no matter the radiation breadth, theoretical depth, difficulty in tackling key problems or innovation height, which are listed in Table Ⅰ.…”

“…Furthermore, because different definitions of evolution simulation in algorithms (CA r _FI(HS) and GHSA_di [31] ), there is the lower space complexity in CA r _FI(HS).…”