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One of the fundamental problems in network virtualization is Virtual Network Embedding (VNE). The VNE problem deals with finding an effective mapping of the virtual nodes & links onto the substrate network. The recent advances in network…
Network hypervisors provide the network virtualization layer for Software Defined Networking (SDN). They enable virtual network (VN) tenants to bring their SDN controllers to program their logical networks individually according to their…
This paper introduces LLMServingSim2.0, a system simulator designed for exploring heterogeneous hardware in large-scale LLM serving systems. LLMServingSim2.0 addresses two key limitations of its predecessor: (1) integrating hardware models…
Quantum machine learning (QML) is an emerging field of research that leverages quantum computing to improve the classical machine learning approach to solve complex real world problems. QML has the potential to address cybersecurity related…
The unprecedented growth in data demand from emerging applications has turned virtual memory (VM) into a major performance bottleneck. Researchers explore new hardware/OS co-designs to optimize VM across diverse applications and systems. To…
Most existing studies on performance prediction for virtual machines (VMs) in multi-tenant clouds are at system level and generally require access to performance counters in Hypervisors. In this work, we propose uPredict, a user-level…
One of the main issues in the OS security is providing trusted code execution in an untrusted environment. During executing, kernel-mode drivers dynamically allocate memory to store and process their data: Windows core kernel structures,…
The emerging trend of Federated Cloud models enlist virtualization as a significant concept to offer a large scale distributed Infrastructure as a Service collaborative paradigm to end users. Virtualization leverage Virtual Machines (VM)…
Support Vector Machines (SVMs) are powerful learners that have led to state-of-the-art results in various computer vision problems. SVMs suffer from various drawbacks in terms of selecting the right kernel, which depends on the image…
Virtualization has become commonplace in modern data centers, often referred as "computing clouds". The capability of virtual machine live migration brings benefits such as improved performance, manageability and fault tolerance, while…
Machine learning (ML) is an important part of modern data science applications. Data scientists today have to manage the end-to-end ML life cycle that includes both model training and model serving, the latter of which is essential, as it…
Service virtualization is an approach that uses virtualized environments to automatically test enterprise services in production-like conditions. Many techniques have been proposed to provide such a realistic environment for enterprise…
Virtualization has gained astonishing popularity in recent decades. It is applied in several application domains, including mainframes, personal computers, data centers, and embedded systems. While the benefits of virtualization are no…
CaMeL (Capabilities for Machine Learning) introduces a capability-based sandbox to mitigate prompt injection attacks in large language model (LLM) agents. While effective, CaMeL assumes a trusted user prompt, omits side-channel concerns,…
The sustained popularity of the cloud and cloud-related services accelerate the evolution of virtualization-enabling technologies. Modern off-the-shelf computers are already equipped with specialized hardware that enables a hypervisor to…
Cloud computing has transformed the way organizations manage and scale their IT infrastructure by offering flexible, scalable, and cost-effective solutions. However, the Infrastructure as a Service (IaaS) model faces performance challenges…
Hardware virtualization technologies play a significant role in cyber security. On the one hand these technologies enhance security levels, by designing a trusted operating system. On the other hand these technologies can be taken up into…
Shared virtual memory (SVM) is key in heterogeneous systems on chip (SoCs), which combine a general-purpose host processor with a many-core accelerator, both for programmability and to avoid data duplication. However, SVM can bring a…
A large body of research has employed Machine Learning (ML) models to develop learned operating systems (OSes) and kernels. The latter dynamically adapts to the job load and dynamically adjusts resources (CPU, IO, memory, network bandwidth)…
Many applications have service requirements that are not easily met by existing operating systems. Real-time and security-critical tasks, for example, often require custom OSes to meet their needs. However, development of special purpose…