相关论文: Elastic Processing of Analytical Query Workloads o…

Over the past ten years, many different approaches have been proposed for different aspects of the problem of resources management for long running, dynamic and diverse workloads such as processing query streams or distributed deep…

We study an edge demand response problem where, based on historical edge workload demands, an edge provider needs to dispatch moving computing units, e.g. truck-carried modular data centers, in response to emerging hotspots within service…

Cloud computing has reached significant maturity from a systems perspective, but currently deployed solutions rely on rather basic economics mechanisms that yield suboptimal allocation of the costly hardware resources. In this paper we…

In this work, we detail the design and structure of a Synopses Data Engine (SDE) which combines the virtues of parallel processing and stream summarization towards delivering interactive analytics at extreme scale. Our SDE is built on top…

Cloud data lakes provide a modern solution for managing large volumes of data. The fundamental principle behind these systems is the separation of compute and storage layers. In this architecture, inexpensive cloud storage is utilized for…

Elasticity in the cloud is often achieved by on-demand autoscaling. In such context, the goal is to optimize the Quality of Service (QoS) and cost objectives for the cloud-based services. However, the difficulty lies in the facts that these…

Large Language Models (LLMs) enable intelligent multi-robot collaboration but face fundamental trade-offs: open-loop methods that compile tasks into formal representations for external executors produce sound plans but lack adaptability in…

Resource management for cloud-native microservices has attracted a lot of recent attention. Previous work has shown that machine learning (ML)-driven approaches outperform traditional techniques, such as autoscaling, in terms of both SLA…

Analyzing big data in a highly dynamic environment becomes more and more critical because of the increasingly need for end-to-end processing of this data. Modern data flows are quite complex and there are not efficient, cost-based,…

Scalability is an important characteristic of cloud computing. With scalability, cost is minimized by provisioning and releasing resources according to demand. Most of current Infrastructure as a Service (IaaS) providers deliver…

An Edge-Cloud Continuum integrates edge and cloud resources to provide a flexible and scalable infrastructure. This paradigm can minimize latency by processing data closer to the source at the edge while leveraging the vast computational…

The demand for stringent interactive quality-of-service has intensified in both mobile edge computing (MEC) and cloud systems, driven by the imperative to improve user experiences. As a result, the processing of computation-intensive tasks…

Serverless query processing has become increasingly popular due to its auto-scaling, high elasticity, and pay-as-you-go pricing. It allows cloud data warehouse (or lakehouse) users to focus on data analysis without the burden of managing…

AI deployment increasingly resembles a pipeline of data transformation, fine-tuning, and agent interactions rather than a monolithic LLM job; recent examples include RLHF/RLAIF training and agentic workflows. To cope with this shift, we…

Since emerging edge applications such as Internet of Things (IoT) analytics and augmented reality have tight latency constraints, hardware AI accelerators have been recently proposed to speed up deep neural network (DNN) inference run by…

Edge computing hosts applications close to the end users and enables low-latency real-time applications. Modern applications inturn have adopted the microservices architecture which composes applications as loosely coupled smaller…

Serverless is an increasingly popular choice for service architects because it can provide elasticity and load-based billing with minimal developer effort. A common and important use case is to compose serverless functions and cloud storage…

In today's Web and social network environments, query workloads include ad hoc and OLAP queries, as well as iterative algorithms that analyze data relationships (e.g., link analysis, clustering, learning). Modern DBMSs support ad hoc and…

Edge computing has become a popular paradigm where services and applications are deployed at the network edge closer to the data sources. It provides applications with outstanding benefits, including reduced response latency and enhanced…

For many algorithmic problems, traditional algorithms that optimise on the number of instructions executed prove expensive on I/Os. Novel and very different design techniques, when applied to these problems, can produce algorithms that are…