Related papers: K8S Power Irrigation: Deep Reinforcement Learning …
Typically, a memory request from a processor may need to go through many intermediate interconnect routers, directory node, owner node, etc before it is finally serviced. Current multiprocessors do not give preference to any particular…
Learning effective configurations in computer systems without hand-crafting models for every parameter is a long-standing problem. This paper investigates the use of deep reinforcement learning for runtime parameters of cloud databases…
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…
Cyber-physical systems (CPS) integrate sensing, computing, communication and actuation capabilities to monitor and control operations in the physical environment. A key requirement of such systems is the need to provide predictable…
In particular, large-scale deep learning and artificial intelligence model training uses a lot of computational power and energy, so it poses serious sustainability issues. The fast rise in model complexity has resulted in exponential…
The disparity between the computational demands of deep learning and the capabilities of compute hardware is expanding drastically. Although deep learning achieves remarkable performance in countless tasks, its escalating requirements for…
Memory controller scheduling is crucial in multicore processors, where DRAM bandwidth is shared. Since increased number of requests from multiple cores of processors becomes a source of bottleneck, scheduling the requests efficiently is…
Modern Infrastructure-as-a-Service Clouds operate in a competitive environment that caters to any user's requirements for computing resources. The sharing of the various types of resources by diverse applications poses a series of…
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…
Serverless computing has revolutionized cloud architectures by enabling developers to deploy event-driven applications via lightweight, self-contained virtualized containers. However, serverless frameworks face critical cold-start…
The year of 2020 has witnessed the unprecedented development of 5G networks, along with the widespread deployment of 5G base stations (BSs). Nevertheless, the enormous energy consumption of BSs and the incurred huge energy cost have become…
Edge computing enables latency-critical applications to process data close to end devices, yet task heterogeneity and limited resources pose significant challenges to efficient orchestration. This paper presents a measurement-driven,…
Wireless networks used for Internet of Things (IoT) are expected to largely involve cloud-based computing and processing. Softwarised and centralised signal processing and network switching in the cloud enables flexible network control and…
Microservice applications are created as loosely coupled application components and they leverage cloud elasticity to reduce costs and increase development speed. However, microservice applications exhibit complex interactions among…
The increasing demand for scalable, efficient resource management in hybrid cloud environments has led to the exploration of AI-driven approaches for dynamic resource allocation. This paper presents an AI-driven framework for resource…
In a cloud-native era, the Kubernetes-based workflow engine enables workflow containerized execution through the inherent abilities of Kubernetes. However, when encountering continuous workflow requests and unexpected resource request…
Fine-grained power monitoring of software activities becomes unavoidable to maximize the power usage efficiency of data centers. In particular, achieving an optimal scheduling of containers requires the deployment of software-defined…
Embodied intelligence is a key step towards Artificial General Intelligence (AGI), yet its development faces multiple challenges including data, frameworks, infrastructure, and evaluation systems. To address these issues, we have, for the…
Hardware data prefetcher engines have been extensively used to reduce the impact of memory latency. However, microprocessors' hardware prefetcher engines do not include any automatic hardware control able to dynamically tune their…
Efficient mobility management and load balancing are critical to sustaining Quality of Service (QoS) in dense, highly dynamic 5G radio access networks. We present a deep reinforcement learning framework based on Proximal Policy Optimization…