Related papers: Partitioning, Indexing and Querying Spatial Data o…
Cloud computing provides scientists a platform that can deploy computation and data intensive applications without infrastructure investment. With excessive cloud resources and a decision support system, large generated data sets can be…
With the ever-increasing range of applications of Internet in Things (IoT) and sensor networks, challenges are emerging in various categories of classification tasks. Applications such as vehicular networking, UAV swarm coordination and…
The rapid growth of big spatial data urged the research community to develop several big spatial data systems. Regardless of their architecture, one of the fundamental requirements of all these systems is to spatially partition the data…
In every form of digital store-and-forward communication, intermediate forwarding nodes are computers, with attendant memory and processing resources. This has inevitably stimulated efforts to create a wide-area infrastructure that goes…
When data stores and users are distributed geographically, it is essential to organize distributed data cache points at ideal locations to minimize data transfers. To answer this, we are developing an adaptive distributed data caching…
Clustering analysis has received considerable attention in spatial data mining for several years. With the rapid development of the geospatial information technologies, the size of spatial information data is growing exponentially which…
The dynamic nature of resource allocation and runtime conditions on Cloud can result in high variability in a job's runtime across multiple iterations, leading to a poor experience. Identifying the sources of such variation and being able…
As one of the most promising hotspots in the 6G era, space remote sensing information networks play a key and irreplaceable role in areas such as emergency response and scientific research, and are expected to foster remote sensing data…
Cloud workloads today are typically managed in a distributed environment and processed across geographically distributed data centers. Cloud service providers have been distributing data centers globally to reduce operating costs while also…
Spatially-coupled (SC) codes, known for their threshold saturation phenomenon and low-latency windowed decoding algorithms, are ideal for streaming applications. They also find application in various data storage systems because of their…
Cloud computing has motivated renewed interest in resource allocation problems with new consumption models. A common goal is to share a resource, such as CPU or I/O bandwidth, among distinct users with different demand patterns as well as…
Data centers are facilities housing computing infrastructure for processing and storing digital information. The rapid expansion of artificial intelligence is driving unprecedented growth in data center capacity, with global electricity…
Internet-scale services rely on data partitioning and replication to provide scalable performance and high availability. Moreover, to reduce user-perceived response times and tolerate disasters (i.e., the failure of a whole datacenter),…
Autoscaling system can reconfigure cloud-based services and applications, through various configurations of cloud software and provisions of hardware resources, to adapt to the changing environment at runtime. Such a behavior offers the…
Cyber-physical systems increasingly rely on distributed computing platforms where sensing, computing, actuation, and communication resources are shared by a multitude of applications. Such `cyber-physical cloud computing platforms' present…
With the proliferation of the Internet of Things (IoT) and the wide penetration of wireless networks, the surging demand for data communications and computing calls for the emerging edge computing paradigm. By moving the services and…
Modern Internet services are increasingly leveraging on cloud computing for flexible, elastic and on-demand provision. Typically, Quality of Service (QoS) of cloud-based services can be tuned using different underlying cloud configurations…
In several environmental applications data are functions of time, essentially con- tinuous, observed and recorded discretely, and spatially correlated. Most of the methods for analyzing such data are extensions of spatial statistical tools…
Mobile devices are becoming the primary platforms for many users who always roam around when accessing the cloud computing services. From this, the cloud computing is integrated into the mobile environment by introducing a new paradigm,…
As machine learning techniques are applied to a widening range of applications, high throughput machine learning (ML) inference servers have become critical for online service applications. Such ML inference servers pose two challenges:…