Energy-Optimization Scheduling of Task Dependent Graph on DVS-Enabled Cluster System

Abstract

At present, power management in High-Performance Computing (HPC) environment is becoming a hot topic owning to its high operation cost, low reliability and environmental impact. In this paper, we investigate energy minimization scheduling algorithm of data dependent tasks in DVS-Enabled cluster system. Considering the data-intensive characteristics, the proposed EOTD (Energy Optimization scheduling for Task Dependent graph) algorithm adopts task clustering to reduce data transmission time and communication energy consumption. In order to decrease dynamic power of processing elements, it uses one of the power-saving techniques in system levelDynamic Voltage Scaling while not violating the deadline users specify. Moreover, on the premise that application execution is predictive and exclusive for processing elements, we employ Dynamic Power Management and Binary Search technique to reduce the static power of idle processing elements and last find the optimal number of processing elements. EOTD algorithm not only optimizes the energy consumption of task dependent graph, but also satisfies the QoS requirements service level agreement gives. Compared with VM and LJ-VM algorithm, experimental results demonstrate that EOTD algorithm can achieve larger energy optimization in less optimizing time.

Energy efficient rescheduling algorithm for High Performance Computing

“…In [1], Y. Ma et al explain how energy consumption can be reduced by efficient task clustering with task duplication. The paper [12] discusses how task aggregation can contribute to energy savings.…”

“…The operating cost, reliability and environmental impact are the prime concern in operation of HPC systems [1]. In advent of multi core technology, advancement in chip miniaturization and the component based hardware abstraction has pave the way for researchers to exploit these abstraction to have better control on execution environment.…”

Adaptive Weight-Based Energy-Efficient Scheduling Algorithm for heterogeneous computing systems

“…DVS (dynamic voltage scaling) among these has been proven to be a very promising technique with its demonstrated capability for energy savings (e.g., [4], [5], and [6]). DVS enables processors to dynamically adjust voltage supply levels aiming to reduce power consumption; however, this reduction is achieved at the expense of sacrificing clock frequencies.…”

“…In [4] several different scheduling algorithms using the concept of slack sharing among DVS-enabled processors were proposed. Ma Yan et al in [5] developed an algorithm based on integer linear programming (ILP) that chooses frequency and voltage level for executing parallel tasks. In [6] an adaptive threshold-based task duplication strategy was presented, it can meet the makespan requirement while reducing energy consumption.…”