In-memory caches play a pivotal role in optimizing distributed systems by significantly reducing query response times. Correctly sizing these caches is critical, especially considering that prominent organizations use terabytes and even petabytes of DRAM for these caches. The Miss Ratio Curve (MRC) and Working Set Size (WSS) are the most widely used tools for sizing these caches.

Graph Neural Networks (GNNs) have revolutionized many Machine Learning (ML) applications, such as social network analysis, bioinformatics, etc. GNN inference can be accelerated by exploiting data sparsity in the input graph, vertex features, and intermediate data in GNN computations. For dynamic sparsity exploitation, we leverage the heterogeneous computing capabilities of AMD Versal ACAP architecture to accelerate GNN inference. We develop a custom hardware module that executes the sparse primitives of the computation kernel on the Programmable Logic (PL) and efficiently computes the dense primitives using the AI Engine (AIE). To exploit data sparsity during inference, we devise a runtime kernel mapping strategy that dynamically assigns computation tasks to the PL and AIE based on data sparsity. Our implementation on the VCK5000 ACAP platform leads to superior performance compared with the state-of-the-art implementations on CPU, GPU, ACAP, and other custom GNN accelerators. Compared with these implementations, we achieve significant average runtime speedup across various models and datasets of 162.42x, 17.01×, 9.90×, and 27.23×, respectively. Furthermore, for Graph Convolutional Network (GCN) inference, our approach leads to a speedup of 3.9-96.7× compared to designs using PL only on the same ACAP device.

One of Lehman's laws of software evolution is that the functionality of programs has to increase over time to maintain user satisfaction. In the domain of mobile apps, though, too much functionality can easily impact usability, resource consumption, and maintenance effort. Hence, does the law of continuous growth apply there? This paper shows that in mobile apps, deletion of functionality is actually common, challenging Lehman's law. We analyzed user driven requests for deletions which were found in 213,866 commits from 1,519 open source Android mobile apps from a total of 14,238 releases. We applied hybrid (open and closed) card sorting and created taxonomies for nature and causes of deletions. We found that functionality deletions are mostly motivated by unneeded functionality, poor user experience, and compatibility issues. We also performed a survey with 106 mobile app developers. We found that 78.3% of developers consider deletion of functionality to be equally or more important than the addition of new functionality. Developers confirmed that they plan for deletions. This implies the need to re-think the process of planning for the next release, overcoming the simplistic assumptions to exclusively look at adding functionality to maximize the value of upcoming releases. Our work is the first to study the phenomenon of functionality deletion and opens the door to a wider perspective on software evolution.

A data center is defined as a set of computing resources that is owned by an organization and shared among multiple applications from different client organizations. Applications often have non-functional run time requirements for different classes of users. These are referred to as service level objectives (SLO) and are considered part of a service level agreement (SLA). Allocating resources to an application should be dynamic and based on specific run-time conditions. It should be possible to change these conditions without recoding. This paper describes a framework, a prototype based on this framework and an application of the prototype that dynamically allocates resources and allows the application environment to adapt in the case of having insufficient resources.