Efficiency of Modern Data Centers (EMDC)

A special issue of IoT (ISSN 2624-831X).

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 3811

Special Issue Editors

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Guest Editor
Department of Computer Science and Systems, School of Engineering and Technology (SET), University of Washington Tacoma, 1900 Commerce Street, Tacoma, WA 98402, USA
Interests: services computing; web of things; edge computing; distributed sensing
Special Issues, Collections and Topics in MDPI journals
Microsoft, One Microsoft Way Redmond, WA 98052, USA
Interests: Artificial Intelligence; Data Centers; Internet of Things; Sustainable Computing

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Guest Editor
Facebook, 1 Hacker Way Menlo Park, CA 94025, USA
Interests: Data Centers; Power/Energy Management; Storage Systems; Data Analytics

Special Issue Information

Dear Colleagues, 

Major research challenges in the operations of data centers include performance, power efficiency, availability, scalability, security among many others. As the number of Internet of Things (IoT) devices proliferates, data center capabilities will transcend basic management operations. That is, traditional management capabilities for CPU, memory and input/output operations need to be replaced with more advanced IoT-based management capabilities to include items such as temperature sensors, fan speed sensors, power sensors, moisture sensors among many others. Many modern data centers today continuously collect and aggregate a wide range of telemetry data in order to avoiding critical downtimes. For example, as heat load of modern data centers increases, the ability to monitor and manage ambient temperature becomes more and more vital for the availability, reliability, serviceability, safety, manageability and scalability of these mission-critical assets. However, such management capabilities also contribute to the consumption of network bandwidth, computational processing power and data storage. Therefore, we need more rigorous architectures and design methods for the efficient modern data centers, more sophisticated design and simulation tools, reliable equipment and software systems benchmarks, accurate performance evaluation methods, among many others. 

The First International Workshop on the Efficiency of Modern Data Centers (EMDC 2020, https://sites.google.com/view/emdc/) will provide researchers and practitioners a venue to discuss the efficiency of modern data centers. The workshop’s ambition is to help in shaping a community of interest on the existing research opportunities and challenges associated with the engineering design and management of modern data centers. In this context, we believe having a dedicated workshop that brings researchers and practitioners together will help investigate innovative ideas or approaches to this new research challenge with main focus on the efficiency of modern data centers, foster collaborations and exchange points of view. 

Dr. Eyhab Al-Masri
Dr. Di Wang
Dr. Iyswarya Narayanan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. IoT is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • IoT-Based Management for Ambient Devices
  • Reliability and Performance Methods
  • Design Methodologies for Data Centers
  • IT Equipment and Software Systems Benchmarks
  • Risk Management and Implementation Methods
  • Disaster Recovery Planning Methods
  • Server Metrics and Dashboards
  • Data Centers’ Design and Simulation Tools
  • Data Center Standards and Certifications
  • Data Centers’ Capacity Planning Methods
  • Data Centers’ Big Data Analytics
  • Data Centers’ Trends and Research Challenges
  • Data Center’s Architecture Design
  • Data Centers’ Environmental Conditions and Energy Efficiency
  • Data Center’s Power Provisioning and Management
  • Data Center’s Cooling System
  • Data Center’s (Renewable) Energy Sources and Management
  • Data Center’s Workload Management
  • Data Center’s Hardware Design and Optimization
  • Data Center’s Availability and Reliability
  • Data Center’s Infrastructure Optimization
  • Data Center’s Network Provisioning, Design and Optimization
  • Data Center’s Compute
  • Provisioning, Design and Optimization
  • Data Center’s Storage Provisioning, Design and Optimization
  • Data Center’s Operation Optimization
  • Data Center’s Emerging Energy Sources
  • Data Center’s Hardware/Software Stack Co-Optimization
  • Data Center’s Performance Monitoring, Accounting, and Optimization
  • Data Center’s Service Pricing Design and OptimizationMulti-tenant datacenter design and optimization
  • Virtualization in datacenters
  • Software-defined compute, network and storage
  • Distributed training of deep learning models in datacenters
  • Deep learning inference optimization in datacenters
  • Green Datacenters
  • Micro datacenter Management for Fog Computing
  • Resource Allocation and Management for Fog Computing

Published Papers (1 paper)

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19 pages, 602 KiB  
A Deep Learning Model for Demand-Driven, Proactive Tasks Management in Pervasive Computing
by Kostas Kolomvatsos and Christos Anagnostopoulos
IoT 2020, 1(2), 240-258; https://doi.org/10.3390/iot1020015 - 14 Oct 2020
Cited by 7 | Viewed by 2831
Pervasive computing applications deal with the intelligence surrounding users that can facilitate their activities. This intelligence is provided in the form of software components incorporated in embedded systems or devices in close distance with end users. One example of infrastructure that can host [...] Read more.
Pervasive computing applications deal with the intelligence surrounding users that can facilitate their activities. This intelligence is provided in the form of software components incorporated in embedded systems or devices in close distance with end users. One example of infrastructure that can host intelligent pervasive services is the Edge Computing (EC) ecosystem. EC nodes can execute a number of tasks for data collected by devices present in the Internet of Things (IoT). In this paper, we propose an intelligent, proactive tasks management model based on demand. Demand depicts the number of users or applications interested in using the available tasks in EC nodes, thus characterizing their popularity. We rely on a Deep Machine Learning (DML) model and more specifically on a Long Short Term Memory (LSTM) network to learn the distribution of demand indicators for each task and estimate the future interest in them. This information is combined with historical observations of and support for a decision making scheme to conclude which tasks that are offloaded due to limited interest in them. We have to recognise that, in our decision making, we also take into consideration the load that every task may add to the processing node where it will be allocated. The description of our model is accompanied by a large set of experimental simulations for evaluating the proposed mechanism. We provide numerical results and reveal that the proposed scheme is capable of deciding on the fly, while concluding the most efficient decisions. Full article
(This article belongs to the Special Issue Efficiency of Modern Data Centers (EMDC))
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