Towards Joint Optimization Over ICT and Cooling Systems in Data Centre: A Survey

Zhang, Weiwen; Wen, Yonggang; Wong, Y. W.; Toh, K.C.; Chen, Chiu-Hao

doi:10.1109/comst.2016.2545109

Cited by 79 publications

(34 citation statements)

References 117 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(15) can be regarded as a general assignment problem (GAP). GAP is very common for modeling distributed systems and applications, such as video placement for social media [38,39], virtual machine placement in data center networks [40], warehouse selection and fleet routing [41], task scheduling in distributed systems [42,43], and resource management in cloud systems [44][45][46]. The assignment problems are usually solved by standard solvers or heuristic algorithms, which can be applied to DIMA optimization.…”

Section: Generic Problem Formulationmentioning

confidence: 99%

A Survey of Optimal Hardware and Software Mapping for Distributed Integrated Modular Avionics Systems

et al. 2020

Self Cite

View full text Add to dashboard Cite

With an increasing number of aircraft systems, a fully manual developmental approach is impractical for finding optimal hardware and software mapping from overwhelming configurations for Distributed Integrated Modular Avionics (DIMA) systems. The automation of finding such optimized mapping should be available and thoroughly understood. This paper is an investigation on the foundations of optimal hardware and software mapping for DIMA. We begin by reviewing the DIMA system architecture. Following that, we present the problem statement of hardware and software mapping and its ensuring mathematical optimization models. A set of primary architectural quality metrics (e.g., reliability and scalability) and aircraft constraints (e.g., segregation and resource constraints) are identified, which can be used to compose an objective function or compare and trade alternatives. Based on the quality metrics and aircraft constraints, we synthesize an encompassing formulation by means of multi-objective optimization. Various optimization approaches for hardware and software mapping are then reviewed and compared. Case studies of DIMA optimization are presented for avionics systems, in which running time is reported for different optimization problems with different objectives and constraints. In addition, we present and discuss open issues and future trends, from which future developments may draw upon.

show abstract

Section: Generic Problem Formulationmentioning

confidence: 99%

A Survey of Optimal Hardware and Software Mapping for Distributed Integrated Modular Avionics Systems

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Later plans have racks of computers cooled by fluids that are pumped through the racks, servers and even chips. Yet, these proposed solutions are costly to implement and give a limited reduction in energy consumption [5][6][7].…”

Section: Review Of Research Efforts In Cloud Computing Data Center Enmentioning

confidence: 99%

An Adaptive Autonomic Framework for Optimizing Energy Consumption in the Cloud Data Centers

Diouani¹,

Medromi²

2019

IJIES

View full text Add to dashboard Cite

Given the development of the Cloud Computing recently, clients and customers using the Cloud for both individual and business needs have expanded to an uncommon scale. This has normally prompted the expanded deployments of Cloud data centers over the globe. As a result, Cloud data centers are seen to be monstrous energy consumers and natural polluters. They require an extraordinary measure of regular energy which has made an effect on the energy supply and natural conditions of the environment. This is the reason why the vulnerability of persistent energy supply, later on, is being referred to. In this way, there is a need of an energy-aware cloud-based system which automatically and efficiently manages and optimize cloud computing data center resources by considering energy consumption as an essential Quality of Service (QoS) parameter. This paper, focus on the energy utilization of the data centers and how this can be limited so as to make the cloud computing greener. Thus, a new autonomic resource optimization manager has been proposed to avail the most optimum level of resources with reduced server energy consumption. The proposed framework has been verified theoretically and tested experimentally. The experimental analysis has demonstrated that the effectiveness of the proposed solution is greater than the state-ofthe-art methods in terms of the achieved results related to reducing energy consumption and response time in cloud computing data centers.

show abstract

“…Shutdown policies' impacts also extend to temperature management in data centers, and consequently, cooling systems, as they abruptly clear or add heat sources (ie, servers). 17 At a data center level, it translates into power budgeting, where the total power budget is partitioned among the cooling and computing units, and where the cooling power has to be sufficient to extract the heat of the computing power. 10 Given the computing power budget, Zhan and Reda proposed an optimal computing budgeting technique based on knapsack-solving algorithms to determine the power caps for the individual servers.…”

Section: Shutdown Policiesmentioning

confidence: 99%

Quantifying the impact of shutdown techniques for energy‐efficient data centers

Raïs

Orgerie

Quinson

et al. 2018

Concurrency and Computation

View full text Add to dashboard Cite

Summary Current large‐scale systems, like datacenters and supercomputers, are facing an increasing electricity consumption. These infrastructures are often dimensioned according to the workload peak. However, as their consumption is not power‐proportional when the workload is low, the power consumption is still high. Shutdown techniques have been developed to adapt the number of switched‐on servers to the actual workload. However, datacenter operators are reluctant to adopt such approaches because of their potential impact on reactivity and hardware failures, and their energy gain is often largely misjudged. In this article, we evaluate the potential gain of shutdown techniques by taking into account shutdown and boot up costs in time and energy. This evaluation is made on recent server architectures and future energy‐aware architectures. Our simulations exploit real traces collected on production infrastructures under various machine configurations with several shutdown policies, with and without workload prediction. We study the impact of future's knowledge for saving energy with such policies. Finally, we examine the energy benefits brought by suspend‐to‐disk and suspend‐to‐RAM techniques, and we study the impact of shutdown techniques on the energy consumption of prospective hardware with heterogeneous processors (big‐medium‐little paradigm).

show abstract

Towards Joint Optimization Over ICT and Cooling Systems in Data Centre: A Survey

Cited by 79 publications

References 117 publications

A Survey of Optimal Hardware and Software Mapping for Distributed Integrated Modular Avionics Systems

A Survey of Optimal Hardware and Software Mapping for Distributed Integrated Modular Avionics Systems

An Adaptive Autonomic Framework for Optimizing Energy Consumption in the Cloud Data Centers

Quantifying the impact of shutdown techniques for energy‐efficient data centers

Contact Info

Product

Resources

About