IEEE Transactions on Sustainable Computing
Expand your horizons with Colloquium, a monthly survey of abstracts from all CS transactions!
From the January-March 2018 issue
Energy Theft Detection in Multi-Tenant Data Centers with Digital Protective Relay Deployment
By Yuchen Zhou, Yang Liu, and Shiyan Hu
High performance data centers serve as the backbone of the prevailing cloud computing paradigm. Among data centers with different operational structures, multi-tenant data centers (MTDCs) are increasingly popular among various internet service providers for the ease of deployment. Despite the offered benefits, MTDCs are vulnerable to various cyberattacks. An important cyberattack is energy theft which can be launched by malicious tenants to reduce monetary cost of the electricity consumption. It can be achieved through attacking smart meters in the data center to undercount the energy usage of the attacker. Since the attackers could consume an excessive amount of energy without incurring elevated utility cost, energy theft discourages frugality in terms of energy consumption, which is highly undesirable in the era of sustainable computing. Despite fruitful research results on MTDCs, none of existing works address energy theft. When energy theft occurs, it might be necessary for the data center operator to examine smart meter of all tenants to find the compromised ones which could induce excessive labor cost. Localization of energy theft detection is an effective way to limit the labor cost in detecting energy thefts in MTDCs. It can be facilitated through deploying Digital Protective Relays (DPR) in the data center where a DPR is a device for fault detection and event logging in the power system. In this paper, an anomaly rate range based dynamic programming algorithm is proposed for inserting minimal DPRs into the data center, where the anomaly rate range is computed using Minimum Covariance Determinant (MCD) algorithm. To the best of our knowledge, this is the first work addressing the energy theft issue in multi-tenant data centers. The simulation results demonstrate that our algorithm inserts 19.2 percent less DPRs into the data center compared to a natural baseline algorithm. Meanwhile, in an attempt to identify all energy theft cases, our DPR insertion solution requires 12.8 percent less tenants to be checked compared with the baseline algorithm. More importantly, we demonstrate that using MCD alone cannot achieve accurate detection while using DPR alone cannot handle collusive energy theft. In contrast, integrating DPR with MCD can achieve a high detection accuracy (of 97.6 percent) for collusive energy theft.
Editorials and Announcements
- Congratulations to Albert Zomaya on his appointment as 2016-2018 Editor-in-Chief of IEEE Transactions on Sustainable Computing. Dr. Zomaya is currently the Chair Professor of High Performance Computing & Networking and Director of the Centre for Distributed and High Performance Computing in the School of Information Technologies, The University of Sydney.
- Advances in Orchestrating Sustainable Smart Cities (Part 2) (Jan-March 2018)
- Advances in Orchestrating Sustainable Smart Cities (Part 1) (Oct-Dec 2017)
- Special Issue on Algorithms and Computational Models for Sustainable Computing in Cloud and Data Centers (April-June 2017)
Call for Papers
Special Issue on Sustainable Information Security and Forensic Computing
Extended submission deadline: June 1, 2018. View PDF.
Modern societies are becoming increasingly reliance on inter-connected digital systems, where commercial activities and government services are delivered. Despite the benefits, it is impossible to overstate the importance of information security and forensics in a highly inter-connected system. To address security threats to network infrastructure devices and sensitive data, many different solutions capable of providing a suitable degree of security and forensic capability have been proposed. However, such solutions have not been properly designed to address important aspects such as computational costs, scalability, energy efficiency and resource usage. This special issue thus focuses on practical aspects of information security and forensics in sustainable computing. We solicit original contributions on novel threats, defences and security, information, tools, and digital forensics applications in sustainable computing. We also seek contributions motivated by taking real-world security and forensic problems and theoretical works that have clear intention for practical applications.
Special Issue on Sustainability of Fog/Edge Computing Systems
Submission deadline: May 31, 2018. View PDF.
Fog/Edge Computing is an emerging architectural as well as technical approach aimed at addressing various shortcomings in traditional cloud computing paradigms and responding to today’s constantly increasing data-demanding services such as Internet-of-Things, 5G embedded artificial intelligence and smart cities. In Fog/Edge Computing, nodes at the edge of a network are equipped with processing, storage, networking, etc. capabilities to take over several tasks that were used to be sent to cloud services. Pre-filtering and aggregation of data as well as online processing and actuation are sample procedures envisaged/dedicated to fog/edge nodes.
Although slightly different in the way they are implemented, fog and edge paradigms are designed in direct response to various challenges in operating smooth IoT and 5G services including –but not limited to: stringent latency requirements from sensing to actuation, network bandwidth limitation for large-sized aggregated data, limited resources for edge devices to perform tasks, and security requirements for all data flows and operations. Satisfying all aforementioned concerns becomes even more challenging when considering the rapid constant grow of edge devices/sensors. For example, the current number of IoT devices will rapidly increase from 15 billion to 50 billion by 2020 (according to CISCO), while the number of sensors will increase to as high as 1 trillion by 2030 (according to HP Labs). As a consequence, sustainability of such systems becomes a necessity rather than a luxury.
To address several major issues regarding sustainability of future fog/edge systems, this special issue aims at highlighting challenges, state-of-the-art, and solutions to a set of currently unresolved key questions including –but not limited to—performance, modelling, optimization, reliability, security, privacy and techno-economic aspects of fog/edge architectures. Through addressing these concerns while understanding their impacts and limitations, technological advancements will be channelled toward more sustainable/efficient platforms for tomorrow’s ever-connected systems.
Special Issue on Intelligent Data Analysis for Sustainable Computing
Submission deadline: September 1, 2018. View PDF.
Recent years have witnessed a deluge of new and big spatio-temporal data streams that contain a wealth of information relevant to sustainable development goals. The analysis of such data streams poses tremendous challenges in the current computing systems, due to its strong correlations between the temporal and spatial domain of the data, and the emerging needs of real-time decision support in some real-world problems.
To obtain this valuable information, there is an urgent demand for high-level computational intelligence based on emerging analytical techniques, such as big data analytics, Web analytics, and network analytics, employing software tools from advanced analytics disciplines, such as machine learning, data mining, and predictive analytics. This results in modern data analysis techniques having the potential to yield accurate, inexpensive, and high scalable models for providing intelligent and real-time decision support in creating effective computing systems. This will also result in addressing sustainability problems in computing and information processing environments at different levels of computational intelligence paradigms. Computational intelligent data analysis is playing an ever-increasingly important and critical role in achieving sustainable ICT (Information and Communication Technology) in new computing paradigms of the current data-driven era.
This special issue is devoted to the most recent developments and research outcomes addressing the related theoretical and practical aspects of computational intelligence solutions in sustainable computing and aims at presenting latest innovative ideas targeted at the corresponding key challenges, either from a methodological or from an application perspective.
Special Issue on Intersection of Computing and Communication Technologies with Energy Systems
Submission deadline: 15 Dec. 2018. View PDF.
Computing and communication technologies impact energy systems in two distinct ways. The exponential growth of these technologies has made them large energy consumers. Therefore, new architectures, technologies and systems are being developed and deployed to make computing and networked system more energy efficient. Additionally, these technologies will play a central role in the on-going transformation of our energy systems. They help measure, monitor and control energy resources, inform and shape human demand, and determine how utilities, generators, regulators, and consumers interact. Recently, there have been vibrant developments in the research community at the intersection of computing and communication technologies with energy systems. Diverse applications of computing and networked systems have made legacy systems more energy-efficient, as well as improved the design, analysis, and development of innovative new energy systems.
This special issue calls for novel ideas for shaping the future of this area. We seek high-quality papers at the intersection of computing and communication technologies with energy systems. We welcome submissions describing conceptual advances, as well as advances in system design, implementation and experimentation.
General Call for Papers
Access Recently Published TSUSC Articles
Sign up for e-mail notifications through IEEE Xplore Content Alerts
TSUSC is financially cosponsored by:
TSUSC is published in cooperation with: