The use of containers has become standard in cloud environments. However, many parallel applications in containers will not present gains proportional to the extra available hardware. This inefficient use of hardware naturally leads to energy consumption waste. With that in mind, we proposeTT-Autoscaling. It works at two different levels: a) in the container, by automatically and transparently tuning the number of threads at runtime of the application, in a way to optimize the trade-off between energy and performance; b) in the cloud infrastructure, by smartly transferring the released resources to other containers that may run in parallel, making better use of the available resources. We compareTT-Autoscalingto the default execution of containers (serial execution with the maximum number of threads), showing 55.8% of performance improvements, 53.6% of energy reductions, and 79.5% of EDP improvements. We also show thatTT-Autoscalingoutperforms strategies that apply vertical autoscalers proposed by orchestrator tools.

Although advances in modern GPUs have accelerated the execution of heavy data processing applications, speeding up graph processing on these systems is not a trivial task: graph applications are characterized by their high volume of irregular memory access that varies with the graph structure so that they do not reach their peak performance when executing on GPUs in many times. In these cases, the CPU execution is more suitable. Given that graph structures can be identified through high-level metrics (e.g., diameter and average clustering coefficient), they may assist the designer in deciding where to execute a given input graph (GPU or CPU). Based on that, in this work, we propose GraCo: a graph processing framework to help the decision-making on where to process a batch of graph applications. Whenever a new batch is submitted to the target HPC system, GraCo decides the best machine to execute each application based only on the available high-level features, precluding any additional applications' execution. Our experimental results comparing GraCo with three other strategies executed on an HPC system comprised of 4 CPUs and 3 GPUs showed that GraCo outperforms the other strategies by at least 34.94×, 13.59×, and 492.31× in total execution time, energy, and energy-delay product.

Summary The growing need for extracting information from large graphs has been pushing the development of parallel graph algorithms. However, the highly irregular structure of the real‐world graphs limits the performance and energy improvements of graph applications. In this paper, we show that, in most cases, using all the available cores of the multiprocessor is not the best option in terms of the aforementioned non‐functional requirements. Based on that, we proposeGraphKat, a framework that enables the simultaneous processing of several algorithms/graphs instead of executing them serially (i.e., one after another), increasing efficiency in terms of performance and energy.GraphKatworks in two steps: (i) it characterizes the graph applications with a specific number of threads based on their efficiency levels; and (ii) it defines the execution order of all graph applications in the target system. Experimental results on three multicore processors (Intel and AMD) show thatGraphKatimproves the overall system's efficiency related to performance (up to ) and energy‐saving (up to 245.21), and reduces the graph applications' execution time (up to ) and energy consumption (up to 6.64) compared to the default execution of parallel applications on HPC systems.

This paper proposes PredG, a Machine Learning framework to enhance the graph processing performance by finding the ideal thread and data mapping on NUMA systems. PredG is agnostic to the input graph: it uses the available graphs' features to train an ANN to perform predictions as new graphs arrive - without any application execution after being trained. When evaluating PredG over representative graphs and algorithms on three NUMA systems, its solutions are up to 41% faster than the Linux OS Default and the Best Static - on average 2% far from the Oracle -, and it presents lower energy consumption.

The execution of large real-world graphs, such as web searches and social networks, has been boosting by modern HPC systems. However, their irregular communication patterns and poor data locality impose many challenges, mainly when executed on NUMA systems. As we show in this paper, there is no one-fits-all configuration for threads/data mapping, and the best combination will vary according to the NUMA system, graph algorithm, and input graph at hand. Based on that, we propose Graphith: a framework that automatically enhances graph processing performance by adapting its execution considering the variables mentioned above. Graphith also goes one step further and improves the existing policies: it uses a Genetic Algorithm to fine-tune the thread-to-core allocation combined with data mapping policies. With that, Graphith improves in 21%, on average, the default execution, and is, on average, 7% better than the best possible combination of standard policies.

Efficiently exploiting thread-level parallelism has been challenging. Many parallel applications are not sufficiently balanced or CPU-bound to take advantage of the increasing number of cores and the highest possible operating frequency. Moreover, many variables may change according to the system (input set, microarchitecture, and number of cores) or during execution, influencing each parallel region in different ways. Therefore, the task of rightly choosing the ideal configuration (number of threads and DVFS) for each parallel region to deliver the best Energy-Delay Product (EDP) is not straightforward. While the significant number of variables prevents the use of exhaustive search methods, the changing nature of the problem precludes offline strategies. Few solutions are online and synergistically consider thread throttling and DVFS. However, they lack transparency (demand changes in the original code) and/or adaptability (do not automatically adjust to applications at run-time). Our proposed Hoder covers all the characteristics above, optimizing at run-time any dynamically linked OpenMP application, without requiring any code transformation or recompilation. We show Hoder's efficiency by comparing it to two exhaustive offline and two online search approaches, three state-of-the-art techniques, and regular OpenMP execution, considering different setups (Intel 44-, 16- and 12-core; AMD 8- and 12-core).

Situation awareness in surveillance systems benefits from high‐quality video streaming service. This is even more important considering military systems, in which delays in image transmission may have a significant impact on the decision‐making process. However, in order to deliver high‐quality video streaming service, the required network infrastructure may be prohibitively complex, or even completely impossible to deploy, if mobile data providers are considered. Moreover, the demand for high network throughput poses extra requirements on the network. Considering this context, this paper addresses the problem of highly mobile networks composed of unmanned aerial vehicles (UAVs) as data providers of a military surveillance system. The proposed approach to tackle the problem is based on a Software Defined Networking (SDN) approach aiming at providing the best routes to deliver the data, enhancing the end‐user quality of experience. An extensive experimental campaign was performed by means of simulations and the acquired results provide solid evidence of the usefulness of this proposal.

Video streaming is an important service provided by surveillance systems to enhance situation awareness. However, in military systems, data acquisition heavily depends on the network infrastructure. In this application domain, units are spread and the distance between the sources of data and the decision makers may be very large. In the case of video streaming, the demand for high network throughput poses some extra requirements on the network. Considering the mobility patterns of the military units and the diversity of the new generations of sensors, especially those used by Unmanned Aerial Vehicles (UAV), the configuration and the management of the network must be so dynamic and so sensitive to data flow parameters that manual configuration is not acceptable. For this reason, the capability of the network to configure itself to offer the necessary Quality of Service is a must. Using principles of Software Defined Networks (SDN), this paper presents an analysis of video streaming for military surveillance in which multiple UAVs are employed as data providers through an SDN-enabled network, with promising results.