Related papers: Performance Provisioning and Energy Efficiency in …
Machine intelligence, especially using convolutional neural networks (CNNs), has become a large area of research over the past years. Increasingly sophisticated hardware accelerators are proposed that exploit e.g. the sparsity in…
Current approaches to designing energy-efficient applications typically rely on measuring individual components using readily available local metrics, like CPU utilization. However, these metrics fall short when applied to cloud-native…
High performance grid computing is a key enabler of large scale collaborative computational science. With the promise of exascale computing, high performance grid systems are expected to incur electricity bills that grow super-linearly over…
We propose in this paper to study the energy-, thermal- and performance-aware resource management in heterogeneous datacenters. Witnessing the continuous development of heterogeneity in datacenters, we are confronted with their different…
We consider the problem of online dynamic power management that provides hard real-time guarantees. In this problem, each of the given jobs is associated with an arrival time, a deadline, and an execution time, and the objective is to…
Cloud Computing is a paradigm of both parallel processing and distributed computing. It offers computing facilities as a utility service in pay as par use manner. Virtualization, self service provisioning, elasticity and pay per use are the…
The increase in computation and storage has led to a significant growth in the scale of systems powering applications and services, raising concerns about sustainability and operational costs. In this paper, we explore power-saving…
This note discusses an essentially decentralized interior point method, which is well suited for optimization problems arising in energy networks. Advantages of the proposed method are guaranteed and fast local convergence also for problems…
Efficient virtual machine load balancing scheduling is crucial in cloud computing to optimize resource utilization and system performance. To address this issue, several load balancing scheduling algorithms have been proposed, including…
Distributed Stream Processing (DSP) focuses on the near real-time processing of large streams of unbounded data. To increase processing capacities, DSP systems are able to dynamically scale across a cluster of commodity nodes, ensuring a…
We consider a hierarchical edge-cloud architecture in which services are provided to mobile users as chains of virtual network functions. Each service has specific computation requirements and target delay performance, which require placing…
High Performance and Energy Efficiency are critical requirements for Internet of Things (IoT) end-nodes. Exploiting tightly-coupled clusters of programmable processors (CMPs) has recently emerged as a suitable solution to address this…
We consider energy minimization for data-intensive applications run on large number of servers, for given performance guarantees. We consider a system, where each incoming application is sent to a set of servers, and is considered to be…
With the rapid development of software defined networking and network function virtualization, researchers have proposed a new cloud networking model called Network-as-a-Service (NaaS) which enables both in-network packet processing and…
In order to reduce the energy cost of data centers, recent studies suggest distributing computation workload among multiple geographically dispersed data centers, by exploiting the electricity price difference. However, the impact of data…
Production high-performance computing systems continue to grow in complexity and size. As applications struggle to make use of increasingly heterogeneous compute nodes, maintaining high efficiency (performance per watt) for the whole…
We propose a disruptive paradigm to actively place and schedule TWhrs of parallel AI jobs strategically on the grid, at distributed, grid-aware high performance compute data centers (HPC) capable of using their massive power and energy load…
Cloud resource allocation has emerged as a major challenge in modern computing environments, with organizations struggling to manage complex, dynamic workloads while optimizing performance and cost efficiency. Traditional heuristic…
With growing deployment of Internet of Things (IoT) and machine learning (ML) applications, which need to leverage computation on edge and cloud resources, it is important to develop algorithms and tools to place these distributed…
The rapid development of renewable energy in the energy Internet is expected to alleviate the increasingly severe power problem in data centers, such as the huge power costs and pollution. This paper focuses on the eco-friendly power cost…