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We study general techniques for implementing distributed data structures on top of future many-core architectures with non cache-coherent or partially cache-coherent memory. With the goal of contributing towards what might become, in the…
The rapid deployment of machine learning across platforms from milliwatt-class TinyML devices to large language models has made energy efficiency a primary constraint for sustainable AI. Across these scales, performance and energy are…
Technology use has grown rapidly in recent years. It is infused in virtually every aspect of organizational and individual life. This technology runs on servers, typically in data centers. As workloads grow, more serves are required. Each…
The environmental impact of video streaming services has been discussed as part of the strategies towards sustainable information and communication technologies. A first step towards that is the energy profiling and assessment of energy…
Locating data efficiently is a key process in every distributed data storage solution and particularly those deployed in multi-site environments, such as found in Cloud and Fog computing. Nevertheless, the existing protocols dedicated to…
In recent studies, it could be shown that the energy demand of Versatile Video Coding (VVC) decoders can be twice as high as comparable High Efficiency Video Coding (HEVC) decoders. A significant part of this increase in complexity is…
The accelerating expansion of AI workloads is colliding with an energy landscape increasingly dominated by intermittent renewable generation. While vast quantities of zero-carbon energy are routinely curtailed, today's centralized…
We study the problem of optimizing data storage and access costs on the cloud while ensuring that the desired performance or latency is unaffected. We first propose an optimizer that optimizes the data placement tier (on the cloud) and the…
With the ubiquitous use of modern large language models (LLMs) across industries, the inference serving for these models is ever expanding. Given the high compute and memory requirements of modern LLMs, more and more top-of-the-line GPUs…
We study energy management policies for the compression and transmission of source data collected by an energy-harvesting sensor node with a finite energy buffer (e.g., rechargeable battery) and a finite data buffer (memory) between source…
Energy efficiency has become a growing concern in software development, leading to the need for tools designed to measure energy consumption. While several energy measurement tools are available as open-source projects, their…
With the current rate of data growth, processing needs are becoming difficult to fulfill due to CPU power and energy limitations. Data serving systems and especially persistent key-value stores have become a substantial part of data…
The relative ease of collaborative data science and analysis has led to a proliferation of many thousands or millions of $versions$ of the same datasets in many scientific and commercial domains, acquired or constructed at various stages of…
Recently, there are significant advances in the areas of networking, caching and computing. Nevertheless, these three important areas have traditionally been addressed separately in the existing research. In this paper, we present a novel…
In applications of distributed storage systems to modern key-value stores, the stored data is highly dynamic due to frequent updates. The multi-version coding problem was formulated to study the cost of storing dynamic data in distributed…
We consider the tradeoff between resource efficiency and performance isolation that emerges when multiplexing the resource demands of Network Slices (NSs). On the one hand, multiplexing allows the use of idle resources, which increases…
Energy efficiency has become an important measurement of scheduling algorithms in virtualized data centers. One of the challenges of energy-efficient scheduling algorithms, however, is the trade-off between minimizing energy consumption and…
Storing tabular data to balance storage and query efficiency is a long-standing research question in the database community. In this work, we argue and show that a novel DeepMapping abstraction, which relies on the impressive memorization…
Consider a source and multiple users who observe the independent and identically distributed (i.i.d.) copies of correlated Gaussian random variables. The source wishes to compress its observations and store the result in a public database…
Energy systems generate vast amounts of data in extremely short time intervals, creating challenges for efficient data management. Traditional data management methods often struggle with scalability and accessibility, limiting their…