Energy Efficiency Support Through Intra-layer Cloud Stack Adaptation

Djemame, Karim; Kavanagh, Richard; Armstrong, Django; Lordan, Francesc; Ejarque, Jorge; Macías, Mario; Sirvent, Raül; Guitart, Jordi; Badía, Rosa M.

doi:10.1007/978-3-319-61920-0_10

Cited by 5 publications

(5 citation statements)

References 8 publications

(10 reference statements)

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“…Edge computing aims to reduce the load in the Cloud, which in turn reduces the latency and produces faster response times because it reduces the movement of data from the end device to the core of the Cloud [14]. Moreover, according to recent studies [23], edge computing could reduce the energy consumption of the Cloud by up to 40%, a significant benefit in light of current concerns about energy consumption [24]. Also, edge computing provides a vast amount of resources to IoT devices, enabling such devices to become smarter by processing complex tasks in a short time.…”

Section: Edge Computingmentioning

confidence: 99%

Resource Management and Task Offloading Issues in the Edge–Cloud Environment

Almutairi¹,

Aldossary²

2021

Intelligent Automation &Amp; Soft Computing

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With the increasing number of Internet of Things (IoT) devices connected to the internet, a platform is required to support the enormous amount of data they generate. Since cloud computing is far away from the connected IoT devices, applications that require low-latency, real-time interaction and high quality of service (QoS) may suffer network delay in using the Cloud. Consequently, the concept of edge computing has appeared to complement cloud services, working as an intermediate layer with computation capabilities between the Cloud and IoT devices, to overcome these limitations. Although edge computing is a promising enabler for issues related to latency sensitivity, its deployment produces new challenges. Therefore, this paper presents and discusses the main factors of service latency for IoT applications, considering the impact of application characteristics (e.g., computation and communication demands), as well as resource utilization and heterogeneity at the edge level. Thus, a number of simulation experiments were set up to evaluate the influence of these factors. The outcomes of this research can be used to understand the complex interactions between many factors that affect the overall service time for latency-sensitive applications. Additionally, several open challenges are highlighted, serving as potential directions for future research.

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Section: Edge Computingmentioning

confidence: 99%

Resource Management and Task Offloading Issues in the Edge–Cloud Environment

Almutairi¹,

Aldossary²

2021

Intelligent Automation &Amp; Soft Computing

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“…Harmony is a heterogeneity-aware framework that dynamically adjusts the number of hosts to strike a balance between energy savings and scheduling delays (SLA's) while considering the host's reconfiguration cost. Djemame et al [33] also studied three different VM allocation policies (energy-aware, cost-aware and consolidation) for energy-performance-cost evaluation. The energy-aware policy predicts the VM energy use and places it on a host that will consume less energy.…”

Section: Related Workmentioning

confidence: 99%

Managing energy, performance and cost in large scale heterogeneous datacenters using migrations

Zakarya

Gillam

2019

Future Generation Computer Systems

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“…This can include CPU load prediction in models such as LiRCUP, which is aimed at assisting in the maintenance of Service Level Agreements (SLAs) and others that search for workload patterns. Workload prediction has enjoyed a lot of attention with a particular focus on the cloud property of the scaling of resources and the maintenance of Quality of Service (QoS) parameters . Workload prediction in Clouds has also been seen as a means to plan future workloads so that physical hosts may be switched off when not required but may also be used as a basis of the prediction of future power consumption.…”

Section: Related Workmentioning

confidence: 99%

“…Workload prediction has enjoyed a lot of attention with a particular focus on the cloud property of the scaling of resources and the maintenance of Quality of Service (QoS) parameters. [30][31][32][33] Workload prediction in Clouds has also been seen as a means to plan future workloads so that physical hosts may be switched off when not required 34 but may also be used as a basis of the prediction of future power consumption.…”

Section: Related Workmentioning

confidence: 99%

Rapid and accurate energy models through calibration with IPMI and RAPL

Kavanagh

Djemame

2019

Concurrency and Computation

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Summary Energy consumption in Cloud and High Performance Computing platforms is a significant issue and affects aspects such as the cost of energy and the cooling of the data center. Host level monitoring and prediction provides the groundwork for improving energy efficiency through the placement of workloads. Monitoring must be fast and efficient without unnecessary overhead, to enable scalability. This precludes the use of Watt meters attached per host, requiring alternative approaches such as integrated measurements and models. IPMI and RAPL are subject to error and partial measurement, which may be mitigated. Models allow for prediction and more responsive measures of power consumption, but require calibrating. The causes of calibration error are discussed, along with mitigation strategies, without overly complicating the underlying model. An outcome is a Watt meter emulator that provides hosts level power measurement along with estimated power consumption for a given workload, with an average error of 0.20W.

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Energy Efficiency Support Through Intra-layer Cloud Stack Adaptation

Cited by 5 publications

References 8 publications

Resource Management and Task Offloading Issues in the Edge–Cloud Environment

Resource Management and Task Offloading Issues in the Edge–Cloud Environment

Managing energy, performance and cost in large scale heterogeneous datacenters using migrations

Rapid and accurate energy models through calibration with IPMI and RAPL

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