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With the growth of data-driven services and expansion of mobile application usage, traditional methods of capacity and resource planning methods may not be efficient and often fall short in meeting rapid changes in the business landscape.…
Robotic applications nowadays are widely adopted to enhance operational automation and performance of real-world Cyber-Physical Systems (CPSs) including Industry 4.0, agriculture, healthcare, and disaster management. These applications are…
Conventional theoretical machine learning studies generally assume explicitly or implicitly that there are enough or even infinitely supplied computational resources. In real practice, however, computational resources are usually limited,…
Due to the ever-increasing popularity of resource-hungry and delay-constrained mobile applications, the computation and storage capabilities of remote cloud has partially migrated towards the mobile edge, giving rise to the concept known as…
We investigate the problem of computation offloading in a mobile edge computing architecture, where multiple energy-constrained users compete to offload their computational tasks to multiple servers through a shared wireless medium. We…
Fog computing emerged as a crucial platform for the deployment of IoT applications. The complexity of such applications requires methods that handle the resource diversity and network structure of Fog devices while maximizing the service…
Model Predictive Control (MPC) is a powerful control strategy; however, its reliance on online optimization poses significant challenges for implementation on systems with limited computational resources. One possible approach to address…
The evolution of high-performance computing is associated with the growth of energy consumption. Performance of cluster computes (is increased via rising in performance and the number of used processors, GPUs, and coprocessors. An increment…
Machine learning is playing an increasingly significant role in emerging mobile application domains such as AR/VR, ADAS, etc. Accordingly, hardware architects have designed customized hardware for machine learning algorithms, especially…
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…
Computational Science on large high performance computing resources is hampered by the complexity of these systems. Much of this complexity is due to low-level details on these resources that are exposed to the application and the end user.…
One of the critical challenges in infrastructural constructions is designing and planning operations and their related resources. The complex interlinked composition of different factors and variables affecting resource productivity has…
A smart city improves operational efficiency and comfort of living by harnessing techniques such as the Internet of Things (IoT) to collect and process data for decision making. To better support smart cities, data collected by IoT should…
The objective of this paper is to design novel multi-layer neural network architectures for multiscale simulations of flows taking into account the observed data and physical modeling concepts. Our approaches use deep learning concepts…
Simulation is a fundamental research tool in the computer architecture field. These kinds of tools enable the exploration and evaluation of architectural proposals capturing the most relevant aspects of the highly complex systems under…
Modern applications process massive data volumes that overwhelm the storage and retrieval capabilities of memory systems, making memory the primary performance and energy-efficiency bottleneck of computing systems. Although many…
Recent trends of technology have explored a numerous applications of cloud services, which require a significant amount of energy. In the present scenario, most of the energy sources are limited and have a greenhouse effect on the…
Due to their highly parallel multi-cores architecture, GPUs are being increasingly used in a wide range of computationally intensive applications. Compared to CPUs, GPUs can achieve higher performances at accelerating the programs'…
This paper presents refinements to the execution-cache-memory performance model and a previously published power model for multicore processors. The combination of both enables a very accurate prediction of performance and energy…
This paper presents a robust adaptive learning Model Predictive Control (MPC) framework for linear systems with parametric uncertainties and additive disturbances performing iterative tasks. The approach refines the parameter estimates…