Energy-aware task scheduling strategies with QoS constraint for green computing in cloud data centers

Liu, Xing; Liu, Panwen; Li, Hongjing; Li, Zheng; Zhou, Haiying; Yan, Xinping; Xia, Ruoshi

doi:10.1145/3264746.3264792

Cited by 5 publications

(7 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several works pursue ways to deal with RES uncertainties. However, they introduce, in many cases, grid (brown) connections or just one level of management (online or offline) [10], [11], [12], [13], [14], [15]. In work [14], the authors created an offline optimization framework using a model to capture the randomness of the RES.…”

Section: Related Workmentioning

confidence: 99%

“…The main objective is to maximize the jobs running when there is more solar irradiation, using the grid and batteries to deal with the intermittence. The authors in [13] describe two offline scheduling algorithms: a Genetic Algorithm (GA) and a Similar Mathematical Morphology (SMM). Both algorithms use Dynamic Voltage-Frequency Scaling (DVFS) technique to reduce the processor's frequency, using less energy.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Analyzing Power Decisions in Data Center Powered by Renewable Sources

Nardin

Stolf

Caux

2022

2022 IEEE 34th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

View full text Add to dashboard Cite

Both academics and industry have engaged their efforts in reducing greenhouse gas (GHG) emissions of Information and Communications Technology (ICT). Data centers are one of the most electricity-expensive ICT actors due to their uninterrupted service. Reducing the usage of brown energy or migrating to green energy using renewable sources (RES) is a way to reduce these emissions. However, this migration is not straightforward due to intermittence from these sources. This work is part of Datazero 2 ANR project. This project aims to design a data center powered only by RES production and storage elements. This architecture requires several decisions at different levels of management. This project divides the problem into two groups: offline and online. On the offline side, it uses renewable and workload predictions to prepare an offline plan with a power envelope (power delivered to the servers) and hints on how to manage the storage (batteries and hydrogen). Given the offline plan, the article's contribution is how to deal with real and dynamic power constraints online while keeping the planned storage level at the end. So, this article proposes policies to modify the plan according to the changes in predictions. We evaluate these policies in a homogeneous and heterogeneous data center. The results demonstrate that our policies could approach the storage level and improve Quality of Service (QoS) independently of data center infrastructures.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Analyzing Power Decisions in Data Center Powered by Renewable Sources

Nardin

Stolf

Caux

2022

2022 IEEE 34th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

View full text Add to dashboard Cite

show abstract

“…While reducing the power consumption, the algorithm ensures the fulfillment of targeted performance within their assigned deadlines by CPU frequency scaling and dynamic VM allocation. Similarly, the authors in [20], implemented a realtime energy optimization strategy to minimize the energy cost while satisfying the QoS and time constraint of DVFS capable CPU/GPU/FPGA heterogeneous cloud platform. However, majority of the existing works have focused on real-time energy efficient scheduling for general purpose processors (GPPs) but energy-aware real-time scheduling for FPGAs is still in its infancy.…”

Section: Related Workmentioning

confidence: 99%

“…SR(Service duration, Life-time)=SR 1 (8, 10), SR 2 (9, 10), SR 3 (6,20) We have two processors and need to schedule the given SRs. The LLF strategy is not sufficient to handle this type of scenario.…”

Section: Time-partitioned Based Approach Versus Greedy Approachmentioning

confidence: 99%

Energy-Aware Real-Time Tasks Processing for FPGA-Based Heterogeneous Cloud

Majumder

Saha

Chakrabarti

et al. 2022

IEEE Trans. Sustain. Comput.

View full text Add to dashboard Cite

Cloud computing is becoming a popular model of computing. Due to the increasing complexity of the cloud service request, it often exploits heterogeneous architecture. Moreover, some service requests (SRs)/tasks exhibit real-time features, which are required to be handled within a specified duration. Along with the stipulated temporal management, the strategy should also be energy efficient, as energy consumption in cloud computing is challenging. In this paper, we have proposed a strategy, called "Efficient Resource Allocation of Service Request" (ERASER) for energy efficient allocation and scheduling of periodic real-time SRs on cloud platform. The cloud platform is consists of Field Programmable Gate Arrays (FPGAs) as Processing Elements (PEs) along with the General Purpose Processors (GPP). We have further proposed, an SR migration technique to reduce the tasks rejection by serving maximum SRs. Simulation based experimental results demonstrate that the proposed methodology is capable to achieve upto 90% resource utilization with only 26% SR rejection rate over different experimental scenarios. Comparison results with other state-of-the-art techniques reveal that the proposed strategy outperforms the existing technique with 17% reduction in SR rejection rate and 21% reduction in energy consumption. Further, the simulation outcomes have been validated on real FPGA test-bed based on Xilinx Zynq SoC with standard benchmark tasks.

show abstract

“…Compared with traditional methods, the FPGA accelerated cloud system can save 60.32% of computing resources and increase the speed by 1.386 times. Liu et al [ 78 , 79 ] conducted energy optimization for cloud data centers under time constraints and Quality of service (QOS) constraints, respectively. Their software/hardware co‐scheduling optimization strategy is applied to the heterogeneous hardware architecture supporting Dynamic voltage and frequency scaling (DVFS), which can significantly reduce the energy cost of the cloud data center.…”

Section: Applicationmentioning

confidence: 99%

A Survey: FPGA‐Based Dynamic Scheduling of Hardware Tasks

Zhang

Guo

et al. 2021

Chinese J of Electronics

View full text Add to dashboard Cite

To meet the increasing computing needs of various application fields, Field programmable gate array (FPGA) has been widely deployed. In FPGA‐based processing, hardware tasks can be better accelerated by allocating appropriate computing resources. Therefore, FPGA‐based hardware task scheduling has become one of the mainstream research directions in academia and industry. However, the optimization objectives of existing FPGA‐based hardware task scheduling methods are relatively scattered. In this regard, this paper summarizes the research status of hardware task dynamic scheduling from the three essential elements of FPGA processing: time, resources, and power consumption. This paper analyzes, sorts out, categorizes the ideas and implementations of various scheduling methods and analyzes and evaluates optimization effects of various scheduling methods from multiple dimensions. Then, the shortcomings of the existing methods are summarized and some practical applications are introduced. Finally, the research direction of task scheduling based on FPGA is prospected and summarized.

show abstract

Energy-aware task scheduling strategies with QoS constraint for green computing in cloud data centers

Cited by 5 publications

References 14 publications

Analyzing Power Decisions in Data Center Powered by Renewable Sources

Analyzing Power Decisions in Data Center Powered by Renewable Sources

Energy-Aware Real-Time Tasks Processing for FPGA-Based Heterogeneous Cloud

A Survey: FPGA‐Based Dynamic Scheduling of Hardware Tasks

Contact Info

Product

Resources

About