IEEE Transactions on Sustainable Computing

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From the July-September 2017 issue

Non-Stationary Bayesian Learning for Global Sustainability

By Kartikeya Bhardwaj and Radu Marculescu

Featured article thumbnail image An increasingly warming planet calls for widespread use of sustainable energy sources like solar energy. To meet the rising energy demand, the focus of state-of-the-art solar energy models on local predictions is no longer sufficient as it only leads to local optimization of solar resources. Hence, a new class of models is needed that can provide a global response towards sustainability. In this paper, therefore, we propose a new approach that models cloud movement as a multilayer network and then performs parameter learning on it to generate short-term predictions of cloud fraction/solar irradiance simultaneously at a large number of locations. These learned parameters capture the spatio-temporal interdependencies of solar energy which can allow power-grid operators and policy-makers at different locations to know who impacts the solar energy of whom. Our results indicate a Root Mean Square Error (RMSE) of 8-18% in one-hour cloud fraction prediction. Finally, using our network approach, we show that the cloud movement likely follows a power law distribution, an important domain knowledge discovery that may be useful for future models. A major consequence of our approach is that it can enable power-grid operators/policy-makers to see beyond the local boundaries of their respective geographical locations.

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Editorials and Announcements

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.

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Call for Papers

Special Issue on Energy Efficiency of New Architectures and Systems

Submission deadline: December 31, 2017. View PDF.

Over the last decade, worldwide server energy consumptions have been increasing exponentially. With this continuing energy consumption in Big Data and cloud computing era, it is necessary to store and process data more energy efficiently by utilizing enormous computing power that is available in the form of new architectures and systems, ranging from low power ARM64 servers to light weighted containerization systems. By taking advantage of these new architectures and systems, system designers can incorporate novel energy-efficient policies for existing installations and applications. However, these new architectures and systems are posing many challenges in exploiting their potentials in energy reductions, such as hardware and software co-design and coordination, application specific automated energy efficiency characterization and profiling, novel programming models, etc. These challenges are becoming the bottlenecks for implementation, deployment and commercial adoptions of new architectures and systems. These new architectures and systems also come up with algorithmic and engineering issues such as performance aspects not yet eminent but expected to grow with their scaling of large installation systems, and the dynamics of its energy management. These new challenges may comprise, sometimes even deteriorate the performance, efficiency, and scalability of the dedicated applications.

This special issue focuses on the energy efficiency challenges imposed by new architectures and systems, and on the state-of-the-art designs and solutions proposed to overcome these challenges. In this special issue we solicit the contributions from the sustainable and green computing community and the system architecture community on the modeling, evaluation, and implementation, of new architectures and systems.

Special Issue on Beyond the Energy & Performance Scaling Boundaries of Modern Computer Architectures

Submission deadline: February 21, 2018. View PDF.

State-of-the-art microprocessor and memory chips are conservatively manufactured and operate at pessimistic levels of supply voltage, clock frequency and DRAM refresh rate for addressing the worsening static and dynamic variability in nanometer technologies. Typically, the worst chips being in the weakest corners of the manufacturing process determine the nominal values of the operating conditions although it is widely known that a very large number of chips can safely operate at reduced voltage levels, increased frequencies and relaxed DRAM refresh rates. The margins between the nominal values of the parameters and the actual safe values of individual chips can potentially lead to large savings in energy/power consumption or to large improvements of the performance. Fine-grained tuning of the voltage, frequency and refresh rate settings can also harness the inherent variability among the cores of the same multicore chip or the different DRAM DIMMs of a machine. Different accelerators architectures such as GPUs, FPGAs or other custom accelerator chips already used with state-of-art embedded and high performance systems can be similarly tuned to improve energy and performance efficiency.

An accurate identification and characterization of the voltage, frequency and refresh rate margins and their variability under various workload and environmental conditions can effectively drive software layers solutions for improved energy efficiency or performance, while the correctness/reliability of operation and the required quality-of-service level is preserved. To this aim, diligent communication between the hardware components of a computing system and the system software components that will take workload allocation and system operation integrity decisions are essential. Intelligent margins and variability aware software stack design can maximize the gains, while ensuring robust system operation.

This Special Issue of IEEE Transactions on Sustainable Computing will be covering this cross-layer research domain seeking three broad types of contributions: (a) novel methods and flows for the characterization and measurement of the pessimistic design margins and their variability across memory and processor chips and cores, (b) effective prediction and communication/reporting mechanisms to expose hardware margins and variability as well device behavior under non-nominal operating conditions to the software layers and (c) error-resilient software paradigms to harness the exposed margins and variability for improved energy-efficiency and/or performance.

Special Issue on Sustainable Information Security and Forensic Computing

Submission deadline: May 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 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

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