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With technological advancements and constant changes of Internet, cloud computing has been today's trend. With the lower cost and convenience of cloud computing services, users have increasingly put their Web resources and information in…

LLM serving is increasingly multi-tenant: the same deployment must handle latency-critical interactive requests and more relaxed background workloads under a fixed GPU budget. This creates a tiered-SLO setting where maximizing overall…

In this study, we investigate how the updating of weights during forward operation and the computation of gradients during backpropagation impact the optimization process, training procedure, and overall performance of the neural network,…

Large language models (LLMs) are increasingly deployed in AI infrastructure, driving the need for high throughput, resource efficient serving systems. Disaggregated LLM serving, which separates prompt prefill from auto-regressive decode,…

Transformer-based language models spread FLOPs uniformly across input sequences. In this work we demonstrate that transformers can instead learn to dynamically allocate FLOPs (or compute) to specific positions in a sequence, optimising the…

Large Language Models (LLMs) are increasingly deployed in both latency-sensitive online services and cost-sensitive offline workloads. Co-locating these workloads on shared serving instances can improve resource utilization, but directly…

Serverless computing systems are becoming very popular. Large corporations such as Netflix, Airbnb, and Coca-Cola use such systems for running their websites and IT systems. The advantages of such systems include superior support for…

In this paper, we propose an efficient algorithm for the network slicing problem which attempts to map multiple customized virtual network requests (also called services) to a common shared network infrastructure and allocate network…

Training Long-Context Large Language Models (LLMs) is challenging, as hybrid training with long-context and short-context data often leads to workload imbalances. Existing works mainly use data packing to alleviate this issue, but fail to…

In-Memory Computing (IMC) represents a paradigm shift in deep learning acceleration by mitigating data movement bottlenecks and leveraging the inherent parallelism of memory-based computations. The efficient deployment of Convolutional…

In video generation models, particularly world models, training large-scale video diffusion Transformers (such as DiT and MMDiT) poses significant computational challenges due to the extreme variance in sequence lengths within mixed-mode…

In this paper, we consider a multiuser mobile edge computing (MEC) system, where a mixed-integer offloading strategy is used to assist the resource assignment for task offloading. Although the conventional branch and bound (BnB) approach…

Code offloading refers to partitioning software and migrating the mobile codes to other computational entities for processing. Often when a large number of mobile codes need to be distributed to many heterogenous hosts, this can easily lead…

There has been a significant societal push towards sustainable practices, including in computing. Modern interactive workloads such as geo-distributed web-services exhibit various spatiotemporal and performance flexibility, enabling the…

In recent years, to sustain the resource-intensive computational needs for training deep neural networks (DNNs), it is widely accepted that exploiting the parallelism in large-scale computing clusters is critical for the efficient…

Index structures are fundamental for efficient query processing on large-scale datasets. Learned indexes model the indexing process as a prediction problem to overcome the inherent trade-offs of traditional indexes. However, most existing…

Parallel iterative applications often suffer from load imbalance, one of the most critical performance degradation factors. Hence, load balancing techniques are used to distribute the workload evenly to maximize performance. A key challenge…

This work proposes a novel learning driven bandwidth optimization framework called DRASTIC (Dynamic Resource Allocation for Slicing in Task aware Closed loop tactile Internet applications). The proposed framework dynamically allocates…

In the operation of networked control systems, where multiple processes share a resource-limited and time-varying cost-sensitive network, communication delay is inevitable and primarily influenced by, first, the control systems deploying…

This study introduces the LPBSA, an advanced optimization algorithm that combines Learner Performance-based Behavior (LPB) and Simulated Annealing (SA) in a hybrid approach. Emphasizing metaheuristics, the LPBSA addresses and mitigates the…