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Dynamic spectrum access is a new paradigm of secondary spectrum utilization and sharing. It allows unlicensed secondary users (SUs) to exploit opportunistically the under-utilized licensed spectrum. Market mechanism is a widely-used…
This paper addresses the challenges of rapid resource variation and highly uncertain task loads in cloud computing environments. It proposes an optimization method for elastic cloud resource scaling based on a multi-agent system. The method…
Integrating land use, travel demand, and traffic models represents a gold standard for regional planning, but is rarely achieved in a meaningful way, especially at the scale of disaggregate data. In this paper, we present a new architecture…
Distributed dataflow systems enable the use of clusters for scalable data analytics. However, selecting appropriate cluster resources for a processing job is often not straightforward. Performance models trained on historical executions of…
Cloud computing is an increasingly popular computing paradigm, now proving a necessity for utility computing services. Each provider offers a unique service portfolio with a range of resource configurations. Resource provisioning for cloud…
In this tutorial paper, we will firstly review some basic simulation concepts and then introduce the parallel and distributed simulation techniques in view of some new challenges of today and tomorrow. More in particular, in the last years…
We propose a game theoretic framework for task allocation in mobile cloud computing that corresponds to offloading of compute tasks to a group of nearby mobile devices. Specifically, in our framework, a distributor node holds a…
On-demand resource provisioning in cloud computing provides tailor-made resource packages (typically in the form of VMs) to meet users' demands. Public clouds nowadays provide more and more elaborated types of VMs, but have yet to offer the…
The conventional model of resource allocation in HPC systems is static. Thus, a job cannot leverage newly available resources in the system or release underutilized resources during the execution. In this paper, we present Kub, a…
Securing necessary resources for edge computing processes via effective resource trading becomes a critical technique in supporting computation-intensive mobile applications. Conventional onsite spot trading could facilitate this paradigm…
Cloud-native applications are increasingly becoming popular in modern software design. Employing a microservice-based architecture into these applications is a prevalent strategy that enhances system availability and flexibility. However,…
Emerging real-time multi-model ML (RTMM) workloads such as AR/VR and drone control involve dynamic behaviors in various granularity; task, model, and layers within a model. Such dynamic behaviors introduce new challenges to the system…
Research in Simultaneous Localization and Mapping (SLAM) has made outstanding progress over the past years. SLAM systems are nowadays transitioning from academic to real world applications. However, this transition has posed new demanding…
Cloud computing has recently emerged as a major trend in distributed computing. We proposed a platform for selecting and configuring automatically an appropriate cloud environment that meets a set of consumer and provider requirements. It…
Distributed cloud environments hosting data-intensive applications often experience slowdowns due to network congestion, asymmetric bandwidth, and inter-node data shuffling. These factors are typically not captured by traditional host-level…
This paper considers the scheduling of jobs on distributed, heterogeneous High Performance Computing (HPC) clusters. Market-based approaches are known to be efficient for allocating limited resources to those that are most prepared to pay.…
Companies are rushing to deliver their services and solutions through the cloud. The scheduling process is very critical in reducing delays. Scheduling also has a role in accessing resources without excessive waiting time. All this in…
For large-scale scientific simulations, it is expensive to store raw simulation results to perform post-analysis. To minimize expensive I/O, "in-situ" analysis is often used, where analysis applications are tightly coupled with scientific…
Infrastructure as a service clouds hide the complexity of maintaining the physical infrastructure with a slight disadvantage: they also hide their internal working details. Should users need knowledge about these details e.g., to increase…
This paper proposes an architectural framework for the efficient orchestration of containers in cloud environments. It centres around resource scheduling and rescheduling policies as well as autoscaling algorithms that enable the creation…