Energy Reduction Through Memory Aware Real-Time Scheduling on Virtual Machine in Multi-Cores Server

Abstract

Not only weighty energy usage pose issues for the environment, but it also raises server maintenance costs in data centers. The massive task with the various power control functions in computer components was made to minimize energy consumption. Increasing consumption of energy in data server environments means that data centers will have high maintenance costs. Various geo-distributed data centers are starting to grow in an age of data proliferation and information growth. Energy management for servers is now demanded for technological, environmental, and economic reasons. In this environment, the main memory is a major energy consumer, not less than the processor. At the same time, an energy-efficient task scheduling strategy is a viable way to meet these goals. Unfortunately, mapping Virtual Machine (VM) resources to the Main Memory (MM) demands to achieve good performance by minimizing the energy consumption within a certain limit is a huge challenge. This paper simulates energy-efficient task scheduling algorithms in a heterogeneous virtualized environment using real-time virtual machine scheduling to resolve the issue of energy consumption. Using a simulator Real-Time system SIMulator (RTSIM), several hardwarebased scheduling algorithms are implemented to observe VM memory scheduling efficiency to save memory energy. The simulation results show that, compared to current energy-efficient scheduling methods Rate Monotonic (RM), Earliest-Deadline-First (EDF), and Least-Laxity-First (LLF), helps to reduce energy consumption and improve performance. It is also observed that memory-aware energy management architecture reduces energy and memory consumption efficiently by using EDF scheduling algorithms. In particular, EDF saves approximately 58.3 percent of memory energy than conventional systems that cannot benefit from memory-aware energy management algorithms. The energy efficiency of the algorithms continues to improve as the level of server consolidation rises. We also implemented the EDF scheduling algorithm in Xen's Credit scheduler to see if the simulation outcomes can be simulated on physical systems. Results of simulation and deployment are equated, and comparable outcomes are achieved. We also identified that shared memory between virtual machines deliberately affects memory's energy consumption based on the implementation.

Indirect Vector Control of Linear Induction Motors Using Space Vector Pulse Width Modulation

“…4 and 5 q-and d-axis flux linkages for the rotor are represented mathematically in Eqs. ( 9)- (11): (9) λ qr = L lr i qr + L m i qs + i qr = L r i qr + L m i qs (10) Total flux linkages of rotor side = (λ dr ) 2 + λ qr 2 (11) where, R r and L r are rotor resistance and inductance, respectively, d represents the primary length in meters and v represents the primary linear speed in m/s. From Figs.…”

“…The working principle of a linear motor is simple: the magnetic flux of the moving part is synchronized with the stationary section, which converts electromagnetic energy to linear motion, and the load is directly fixed with the mover. The invention of linear machines eliminated many tools such as ball and screw, belt and pulley, and other turning arrangements which were used for the conversion of rotary motion to linear motion, as well as losses involved in this conversion process [8][9][10][11]. Additionally, linear machines provide speed precision with improved efficiency and are easy to construct as the idea is simple: cut the rotary motor radially and set it flat.…”