Session details: Special Issue on the 2016 Greenmetrics Workshop
Bibliographic record
Abstract
The seventh annual GreenMetrics Workshop was held on June 14, 2016 in Antibes Juan-les-Pins, France, in conjunction with the ACM SIGMETRICS/IFIP Performance 2016 conference. For the past five years the workshop has been expanded from topics on the energy and ecological impact of Information and Communication Technology (ICT) systems, to include emerging work on the Smart Grid. Topics of interest fall broadly into three main areas: designing sustainable ICT, ICT for sustainability, and building a smarter, more sustainable electricity grid. The workshop brought together researchers from the traditional SIGMETRICS and Performance communities with researchers and practitioners in the three areas above, to exchange technical ideas and experiences on issues related to sustainability and ICT. The workshop program included three 45-min keynote talks, and nine 20-min presentations of technical papers. All papers are included in this special issue and we briefly summarize the keynote talks here. In the first keynote "The New Sharing Economy for the Grid2050", Kameshwar Poolla from UC Berkeley discussed three sharing economy opportunities in the electricity sector- sharing storage, sharing PV generation, and sharing recruited demand flexibility. He also discussed regulatory and technical challenges to these opportunities. In addition, he presented a micro-economic analysis of decisions by firms, and quantify the benefits of sharing to various participants. Xue (Steve) Liu from McGill University presented the second keynote talk, titled "When Bits Meet Joules: A View from Data Center Operations' Perspective". He used data centers as an example to illustrate the importance of the codesign of information technologies and new energy technologies. Specifically, he focused on how to design cost-saving power management strategies for Internet data center operations. Our third keynote talk was by Florian Dörfler from ETH Zürich, titled "Virtual Inertia Emulation and Placement in Power Grids". He presented a comprehensive analysis to address the optimal inertia placement problem, in particular, by providing a set of closed-form global optimality results for particular problem instances as well as a computational approach resulting in locally optimal solutions. He illustrated the results with a three-region power grid case study. The best student paper award was given to "Opportunities for Price Manipulation by Aggregators in Electricity Markets" by Ruhi et al. The award was determined by a committee of the invited speakers, chaired by Catherine Rosenberg, after considering both the papers and the presentations of the candidates. The authors quantified the profit an aggregator can obtain through strategic curtailment of generation in an electricity market. Efficient algorithms were shown to exist when the topology of the network is radial (acyclic). Further, significant increases in profit can be obtained through strategic curtailment in practical settings. Demand response is discussed in the following two papers. In "Optimizing the Level of Commitment in Demand Response", Comden et al. proposed a generalized demand response framework called Flexible Commitment Demand Response (FCDR) to allow for explicit choices of the level of commitment. Numerical simulations were conducted to demonstrate that FCDR brings in significant (around 50%) social cost reductions and benefits both the LSE and customers simultaneously. In "An Emergency Demand Response Mechanism for Cloud Computing", Zhou et al. proposed an online auction for dynamic cloud resource provisioning under the emergency demand response program, which runs in polynomial time, achieves truthfulness and close-to-optimal social welfare for the cloud ecosystem. Geographical load balancing was examined by Neglia et al. in "Geographical Load Balancing Across Green Datacenters: a Mean Field Analysis". They modeled via a Markov Chain the problem of scheduling jobs by prioritizing datacenters where renewable energy is currently available. Mean field techniques were employed to derive an asymptotic approximate model and to investigate relationships and tradeoffs among the various system parameters. In "Emergence of Shared Behaviour in Distributed Scheduling Systems for Domestic Appliances", Facchini et al. showed social interaction can increase the flexibility of users and lower the peak power, resulting in a more smooth usage of energy throughout the day. Rossi et al. examined public lighting in "AURORA: an Energy Efficient Public Lighting IoT System for Smart Cities" by proposing Aurora: a low-budget, easy-to-deploy IoT control system. Aurora was deployed in a mid-size Italian municipality and its performance over 4 months was evaluated to quantify both the power and the economic saving. Wireless and wired network power consumption was studied in the following three papers. In "Radio Resource Management for Improving Energy Self-Sufficiency of Green Mobile Networks", Dalmasso et al. designed Resource on Demand strategies to reduce the base station cluster energy consumption and to adapt it to energy availability. Fan etal. also examined base stations in "Boosting Service Availability for Base Stations of Cellular Networks by Event-Driven Battery Profiling" by conducting a systematical analysis on a real world dataset and proposing an event-driven battery profiling approach to precisely extract the features that cause the working condition degradation of the battery group. Last but not least, in "Toward Power-Efficient Backbone Routers", Lu et al. studied how InTerFaces can distribute traffic flows to the Processing Engines (PEs) so that the offered loads on all active PEs are near-perfectly balanced over time, and kept close to a target load, so that the number of active PEs can be minimized. The papers presented at the workshop reflected a current concern of energy consumption associated with proliferating data centers, and other fundamental issues in green computing. The workshop incited interesting discussions and exchange among participants from North America, Europe, and Asia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".