Related papers: Distributed Data Stream Processing and Edge Comput…
The emergence of cloud computing has made dynamic provisioning of elastic capacity to applications on-demand. Cloud data centers contain thousands of physical servers hosting orders of magnitude more virtual machines that can be allocated…
Mobile edge computing is beneficial to reduce service response time and core network traffic by pushing cloud functionalities to network edge. Equipped with storage and computation capacities, edge nodes can cache services of…
As computational infrastructure extends to the edge, it will increasingly offer the same fine-grained resource provisioning mechanisms used in large-scale cloud datacenters, and advances in low-latency, wireless networking technology will…
With the growth of real-time applications and IoT devices, computation is moving from cloud-based services to the low latency edge, creating a computing continuum. This continuum includes diverse cloud, edge, and endpoint devices, posing…
More widespread adoption requires swarms of robots to be more flexible for real-world applications. Multiple challenges remain in complex scenarios where a large amount of data needs to be processed in real-time and high degrees of…
Contemporary Distributed Computing Systems (DCS) such as Cloud Data Centres are large scale, complex, heterogeneous, and distributed across multiple networks and geographical boundaries. On the other hand, the Internet of Things…
The recent advance of edge computing technology enables significant sensing performance improvement of Internet of Things (IoT) networks. In particular, an edge server (ES) is responsible for gathering sensing data from distributed sensing…
The growing deployment of sensors as part of Internet of Things (IoT) is generating thousands of event streams. Complex Event Processing (CEP) queries offer a useful paradigm for rapid decision-making over such data sources. While often…
The surging development of Artificial Intelligence-Generated Content (AIGC) marks a transformative era of the content creation and production. Edge servers promise attractive benefits, e.g., reduced service delay and backhaul traffic load,…
With rapid growth in the amount of unstructured data produced by memory-intensive applications, large scale data analytics has recently attracted increasing interest. Processing, managing and analyzing this huge amount of data poses several…
Big data processing applications are becoming more and more complex. They are no more monolithic in nature but instead they are composed of decoupled analytical processes in the form of a workflow. One type of such workflow applications is…
Edge computing addresses critical limitations of cloud computing such as high latency and network congestion by decentralizing processing from cloud to the edge. However, the need for software replication across heterogeneous edge devices…
The radical advances in mobile computing, the IoT technological evolution along with cyberphysical components (e.g., sensors, actuators, control centers) have led to the development of smart city applications that generate raw or…
Edge computing is an emerging technology which places computing at the edge of the network to provide an ultra-low latency. Computation offloading, a paradigm that migrates computing from mobile devices to remote servers, can now use the…
Edge Computing emerges as a promising alternative of Cloud Computing, with scalable compute resources and services deployed in the path between IoT devices and Cloud. Since virtualization techniques can be applied on Edge compute nodes,…
By acquiring cloud-like capacities at the edge of a network, edge computing is expected to significantly improve user experience. In this paper, we formulate a hybrid edge-cloud computing system where an edge device with limited local…
With weather becoming more extreme both in terms of longer dry periods and more severe rain events, municipal water networks are increasingly under pressure. The effects include damages to the pipes, flash floods on the streets and combined…
Stream clustering is a fundamental problem in many streaming data analysis applications. Comparing to classical batch-mode clustering, there are two key challenges in stream clustering: (i) Given that input data are changing continuously,…
Emerging distributed cloud architectures, e.g., fog and mobile edge computing, are playing an increasingly important role in the efficient delivery of real-time stream-processing applications (also referred to as augmented information…
In the edge-cloud continuum, datacenters provide microservices (MSs) to mobile users, with each MS having specific latency constraints and computational requirements. Deploying such a variety of MSs matching their requirements with the…