On Effective Slack Reclamation in Task Scheduling for Energy Reduction

Abstract

Abstract:Power consumed by modern computer systems, particularly servers in data centers has almost reached an unacceptable level. However, their energy consumption is often not justifiable when their utilization is considered; that is, they tend to consume more energy than needed for their computing related jobs. Task scheduling in distributed computing systems (DCSs) can play a crucial role in increasing utilization; this will lead to the reduction in energy consumption. In this paper, we address the problem of scheduling precedence-constrained parallel applications in DCSs, and present two energy-conscious scheduling algorithms. Our scheduling algorithms adopt dynamic voltage and frequency scaling (DVFS) to minimize energy consumption. DVFS, as an efficient power management technology, has been increasingly integrated into many recent commodity processors. DVFS enables these processors to operate with different voltage supply levels at the expense of sacrificing clock frequencies. In the context of scheduling, this multiple voltage facility implies that there is a trade-off between the quality of schedules and energy consumption. Our algorithms effectively balance these two performance goals using a novel objective function and its variant, which take into account both goals; this claim is verified by the results obtained from our extensive comparative evaluation study.

Energy-Efficient Scheduling for Tasks with Deadline in Virtualized Environments

“…A number of new green scheduling algorithms for saving energy and resource have been proposed, such as [11,12]. In [13], researchers developed energyefficient algorithms by incorporating DVS and frequency scaling technology to minimize energy consumption. For the tasks with or without precedence, scheduling algorithms proposed in [14] adopted shared slack reclamation on variable voltage/speed processors to minimize energy consumption.…”

Energy-Efficient Task Scheduling Algorithms for Cloud Data Centers

“…In [17], the need for scheduling algorithms in order to minimize the wasted server energy is considered. Energy-conscious scheduling algorithms are used to minimize energy consumption in servers of data centers [18]. Power consumption problem is considered in [19] and is optimized through priority-based preemptive scheduling algorithm.…”

Skyline-Based Aggregator Node Selection in Wireless Sensor Networks

“…In this procedure, the aggregator node that has the best combination of attributes, calculated in Algorithm 1, is selected as a root node (lines 2 and 3). Then, by checking each child nodes, the algorithm recurs down the left or right subtree and builds a tree of aggregator nodes (lines [3][4][5][6][7][8][9][10][11][12][13][14][15][16]. Once BuildTree function builds a tree of aggregator nodes, the algorithm calls aggregator node traversal function, which traverses a tree in a postorder manner.…”

“…Communication is a dominant source of energy consumption in the WSNs [6,7]. Thus, the general approach is to jointly process the sensor data, generated by the different sensor nodes while transmitting it to the base station.…”