Load shifting and peak clipping for reducing energy consumption in an indian university campus

This paper analyzes the intelligent use of time-varying electrical load via developing efficient energy utilization patterns using demand-side management (DSM) strategies. This approach helps distribution utilities decrease maximum demand and electrical energy billing costs. A case study of DSM implementation of electric energy utility for an educational building Alagappa Chettiar Government College of Engineering and Technology (ACGCET) campus was simulated. The new optimum energy load model was established for peak and off-peak periods from the system's existing load profile using peak clipping and load shifting DSM techniques. The result reflects a significant reduction in maximum demand from 189 kW to 170 kW and a reduction in annual electricity billing cost from $11,340 to$10,200 (approximately 10%) in the upgraded system. This work highlights the importance of time of day (TOD) tariff structure consumers that aid reduction in their distribution system's maximum demand and demand charges. Copyright

LDM: Lineage-Aware Data Management in Multi-tier Storage Systems

We design and develop LDM, a novel data management solution to cater the needs of applications exhibiting the lineage property, i.e. in which the current writes are future reads. In such a class of applications, slow writes significantly hurt the over-all performance of jobs, i.e. current writes determine the fate of next reads. We believe that in a large scale shared production cluster, the issues associated due to data management can be mitigated at a way higher layer in the hierarchy of the I/O path, even before requests to data access are made. Contrary to the current solutions to data management which are mostly reactive and/or based on heuristics, LDM is both deterministic and pro-active. We develop block-graphs, which enable LDM to capture the complete time-based data-task dependency associations, therefore use it to perform life-cycle management through tiering of data blocks. LDM amalgamates the information from the entire data center ecosystem, right from the application code, to file system mappings, the compute and storage devices topology, etc. to make oracle-like deterministic data management decisions. With trace-driven experiments, LDM is able to achieve 29–52% reduction in over-all data center workload execution time. Moreover, by deploying LDM with extensive pre-processing creates efficient data consumption pipelines, which also reduces write and read delays significantly

A Modified Three Leg Watkins Johnson Bridge Type DC to DC Converter Simulation and Experimental Verification

In this work, a novel, high boost, DC-to-DC converter topology is proposed and validated. The proposed topology is an extension of the existing two leg bridge type Watkins Johnson DC to DC converter. In the proposed system, an additional leg is included along with an additional inductor and an additional capacitor. The inclusion of the third leg to the existing, two-leg version of Watkins Johnson DC–to-DC bridge type converter helps to boost further the voltage gain. This paper presents the proposed topology and a detailed analysis of the topology using the circuit model in the MATLAB SIMULINK simulation environment. An experimental prototype was also developed to validate the proposed idea. The results obtained from the simulations and the experimental prototype are also presented herein. The studies were carried out with open loop configuration

Active storage

In brief, Active Storage refers to an architectural hardware and software paradigm, based on collocation storage and compute units. Ideally, it will allow to execute application-defined data ... within the physical data storage. Thus Active Storage seeks to minimize expensive data movement, improving performance, scalability, and resource efficiency. The effective use of Active Storage mandates new architectures, algorithms, interfaces, and development toolchains

Interconnects for DNA, Quantum, In-Memory and Optical Computing: Insights from a Panel Discussion

The computing world is witnessing a proverbial Cambrian explosion of emerging paradigms propelled by applications, such as artificial intelligence, big data, and cybersecurity. The recent advances in technology to store digital data inside a deoxyribonucleic acid (DNA) strand, manipulate quantum bits (qubits), perform logical operations with photons, and perform computations inside memory systems are ushering in the era of emerging paradigms of DNA computing, quantum computing, optical computing, and in-memory computing. In an orthogonal direction, research on interconnect design using advanced electro-optic, wireless, and microfluidic technologies has shown promising solutions to the architectural limitations of traditional von-Neumann computers. In this article, experts present their comments on the role of interconnects in the emerging computing paradigms, and discuss the potential use of chiplet-based architectures for the heterogeneous integration of such technologies.Electronic