A Survey on Energy Efficiency in Information Systems

Vitali, Monica; Pernici, Barbara

doi:10.1142/s0218843014500014

Cited by 16 publications

(6 citation statements)

References 45 publications

(38 reference statements)

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“…Orgerie et al [57] presented a survey on improving the energy-efficiency of large scale distributed systems. A broad review of energy efficiency in information systems at the hardware and at the software level is offered by Vitali and Pernici [58]. Shuja et al [71] reviewed the techniques and architectures for designing energyefficient datacenters and presented an overview of server cooling and storage component.…”

Section: Related Surveys and Our Workmentioning

confidence: 99%

A Taxonomy and Future Directions for Sustainable Cloud Computing

2018

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The cloud computing paradigm offers on-demand services over the Internet and supports a wide variety of applications. With the recent growth of Internet of Things (IoT) based applications the usage of cloud services is increasing exponentially. The next generation of cloud computing must be energy-efficient and sustainable to fulfil the end-user requirements which are changing dynamically. Presently, cloud providers are facing challenges to ensure the energy efficiency and sustainability of their services. The usage of large number of cloud datacenters increases cost as well as carbon footprints, which further effects the sustainability of cloud services. In this paper, we propose a comprehensive taxonomy of sustainable cloud computing. The taxonomy is used to investigate the existing techniques for sustainability that need careful attention and investigation as proposed by several academic and industry groups. Further, the current research on sustainable cloud computing is organized into several categories: application design, sustainability metrics, capacity planning, energy management, virtualization, thermal-aware scheduling, cooling management, renewable energy and waste heat utilization. The existing techniques have been compared and categorized based on the common characteristics and properties. A conceptual model for sustainable cloud computing has been proposed along with discussion on future research directions.

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Section: Related Surveys and Our Workmentioning

confidence: 99%

A Taxonomy and Future Directions for Sustainable Cloud Computing

2018

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“…The surveys of [4][5][6] describe different approaches focusing on multiple aspects of energy consumption, which are not limited to the IT infrastructure, but also take into account the physical infrastructure and cooling.…”

Section: Related Workmentioning

confidence: 99%

“…In general, this can lead to a less performant system compared to round robin, but this modification allows the models presented in Figures 3 to 5 to become separable networks and be analyzed in linear time O(n) using the mean value analysis technique [30]. Figure 21 shows the results for different values of α ∈ {0, 25%, 50%, 75%, 100%}, with the number of VMs in the range [4,24]. We have avoided the cases with n < 4, because we wanted n slow and n f ast to be different integers for all of the considered values of α.…”

Section: Execution In Heterogeneous Environmentsmentioning

confidence: 99%

“…We will present results where newer machines in the round robin cycle are selected first (we also have tested the case in which priority is given to slower machines and obtained basically the same behavior). Figure 22 shows the results for α ∈ {0, 25%, 50%, 75%, 100%} and the number of VMs in the range [4,24]. As a general trend, we see that there is always a noticeable performance increase, both in terms of response time and energy per job reduction, whenever the number of VMs allows terminating the N jobs in one less cycle: with N = 50 jobs, this occurs with the number of VMs n ∈ {1-10, 13, 17, 25, 50}.…”

Section: Execution In Heterogeneous Environmentsmentioning

confidence: 99%

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Characterizing Energy per Job in Cloud Applications

Gribaudo

Pernici

2016

Electronics

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Energy efficiency is a major research focus in sustainable development and is becoming even more critical in information technology (IT) with the introduction of new technologies, such as cloud computing and big data, that attract more business users and generate more data to be processed. While many proposals have been presented to optimize power consumption at a system level, the increasing heterogeneity of current workloads requires a finer analysis in the application level to enable adaptive behaviors and in order to reduce the global energy usage. In this work, we focus on batch applications running on virtual machines in the context of data centers. We analyze the application characteristics, model their energy consumption and quantify the energy per job. The analysis focuses on evaluating the efficiency of applications in terms of performance and energy consumed per job, in particular when shared resources are used and the hosts on which the virtual machines are running are heterogeneous in terms of energy profiles, with the aim of identifying the best combinations in the use of resources.

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“…While such a ubiquitous WoT environment offers the capability of integrating information from both the physical world and the virtual one leading to tremendous business and social opportunities (e.g., efficient supply chains, independent living of elderly persons, and improved environmental monitoring), it also presents significant challenges [Ferscha 2012;Baresi et al 2015;Vitali and Pernici 2014;Sheng et al 2008]. With many things connected and interacted over the Web, there is an urgent need to efficiently index, organize, and manage these things for object search, recommendation, and mash-up, and effectively reveal interesting patterns from things.…”

Section: Introductionmentioning

confidence: 99%

Unveiling Correlations via Mining Human-Thing Interactions in the Web of Things

Yao

Sheng

Ngu

et al. 2017

ACM Trans. Intell. Syst. Technol.

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With recent advances in radio-frequency identification (RFID), wireless sensor networks, and Web services, physical things are becoming an integral part of the emerging ubiquitous Web. Finding correlations among ubiquitous things is a crucial prerequisite for many important applications such as things search, discovery, classification, recommendation, and composition. This article presents DisCor-T, a novel graph-based approach for discovering underlying connections of things via mining the rich content embodied in the humanthing interactions in terms of user, temporal and spatial information. We model these various information using two graphs, namely a spatio-temporal graph and a social graph. Then, random walk with restart (RWR) is applied to find proximities among things, and a relational graph of things (RGT) indicating implicit correlations of things is learned. The correlation analysis lays a solid foundation contributing to improved effectiveness in things management and analytics. To demonstrate the utility of the proposed approach, we develop a flexible feature-based classification framework on top of RGT and perform a systematic case study. Our evaluation exhibits the strength and feasibility of the proposed approach.

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A Survey on Energy Efficiency in Information Systems

Cited by 16 publications

References 45 publications

A Taxonomy and Future Directions for Sustainable Cloud Computing

A Taxonomy and Future Directions for Sustainable Cloud Computing

Characterizing Energy per Job in Cloud Applications

Unveiling Correlations via Mining Human-Thing Interactions in the Web of Things

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