Robust Workload and Energy Management for Sustainable Data Centers

2016 ◽  
Vol 34 (3) ◽  
pp. 651-664 ◽  
Author(s):  
Tianyi Chen ◽  
Yu Zhang ◽  
Xin Wang ◽  
Georgios B. Giannakis
Keyword(s):  
Energy Management ◽  
Data Centers

Preemption-Aware Energy Management in Virtualized Data Centers

2012 ◽  
Author(s):  
Mohsen Amini Salehi ◽  
P. Radha Krishna ◽  
Krishnamurty Sai Deepak ◽  
Rajkumar Buyya
Keyword(s):  
Energy Management ◽  
Data Centers ◽  
Virtualized Data Centers

scholarly journals Managing Energy Plus Performance in Data Centers and Battery-Based Devices Using an Online Non-Clairvoyant Speed-Bounded Multiprocessor Scheduling

2020 ◽  
Vol 10 (7) ◽  
pp. 2459 ◽  
Author(s):  
Pawan Singh ◽  
Baseem Khan ◽  
Om Prakash Mahela ◽  
Hassan Haes Alhelou ◽  
Ghassan Hayek
Keyword(s):  
Energy Management ◽  
Completion Time ◽  
Processing Time ◽  
Data Centers ◽  
Function Analysis ◽  
Multiprocessor Scheduling ◽  
Flow Time ◽  
Service Cost ◽  
Energy Plus ◽  
Time Required

An efficient scheduling reduces the time required to process the jobs, and energy management decreases the service cost as well as increases the lifetime of a battery. A balanced trade-off between the energy consumed and processing time gives an ideal objective for scheduling jobs in data centers and battery based devices. An online multiprocessor scheduling multiprocessor with bounded speed (MBS) is proposed in this paper. The objective of MBS is to minimize the importance-based flow time plus energy (IbFt+E), wherein the jobs arrive over time and the job’s sizes are known only at completion time. Every processor can execute at a different speed, to reduce the energy consumption. MBS is using the tradition power function and bounded speed model. The functioning of MBS is evaluated by utilizing potential function analysis against an offline adversary. For processors m ≥ 2, MBS is O(1)-competitive. The working of a set of jobs is simulated to compare MBS with the best known non-clairvoyant scheduling. The comparative analysis shows that the MBS outperforms other algorithms. The competitiveness of MBS is the least to date.

Blockchain-based decentralized workload and energy management of geo-distributed data centers

2020 ◽  
pp. 100461 ◽  
Author(s):  
Sara Sajid ◽  
Muhammad Jawad ◽  
Kanza Hamid ◽  
Muhammad U.S. Khan ◽  
Sahibzada M. Ali ◽  
...  
Keyword(s):  
Energy Management ◽  
Data Centers ◽  
Distributed Data
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