Concurrency and Computation: Practice & Experience

Yang, Zhe ; Ma, Kun; Zhang, Xiaoli; Cui, Lizhen; Yang, Bo

doi: 10.1002/cpe.5672pmid: N/A

In the mobile computing environment, how to make the data access more efficient is a challenge due to the narrow communication bandwidth, the frequent disconnections of network, and the limited resources. Therefore, it is necessary to cache data on the client side. Besides, a good cache consistency method is essential to ensure the correctness. In this article, a row‐based semantic cache with incremental versioning consistency (RSCVC) is proposed. In RSCVC, we designed a semantic cache algorithm, a query trimming and optimizing algorithm, and a version‐based consistency strategy. This RSCVC cache mainly has two advantages. On one hand, it can obviously improve the response time of query and the hit ratio of the cache. On the other hand, the version‐based consistency enhances the stability of the system especially in high‐concurrency situations. Experiments demonstrate the efficacy of our proposed method and its superiority to state‐of‐the‐art methods.

Debauche, Olivier; Mahmoudi, Sidi Ahmed; De Cock, Nicolas; Mahmoudi, Saïd; Manneback, Pierre; Lebeau, Frédéric

doi: 10.1002/cpe.5661pmid: N/A

Digital phenotyping is an emergent science mainly based on imagery techniques. The tremendous amount of data generated needs important cloud computing for their processing. The coupling of recent advance of distributed databases and cloud computing offers new possibilities of big data management and data sharing for the scientific research. In this paper, we present a solution combining a lambda architecture built around Apache Druid and a hosting platform leaning on Apache Mesos. Lambda architecture has already proved its performance and robustness. However, the capacity of ingesting and requesting of the database is essential and can constitute a bottleneck for the architecture, in particular, for in terms of availability and response time of data. We focused our experimentation on the response time of different databases to choose the most adapted for our phenotyping architecture. Apache Druid has shown its ability to respond to typical queries of phenotyping applications in times generally inferior to the second.

Calzarossa, Maria Carla; Massari, Luisa; Tessera, Daniele

doi: 10.1002/cpe.5667pmid: N/A

Cloud computing provides cost‐effective solutions for deploying services and applications. Although resources can be provisioned on demand, they need to adapt quickly and in a seamless way to the workload intensity and characteristics and satisfy at the same time the desired performance levels. In this paper, we evaluate the effects exercised by different incoming workload patterns on cloud autoscaling strategies. More specifically, we focus on workloads characterized by periodic, continuously growing, diurnal and unpredictable arrival patterns. To test these workloads, we simulate a realistic cloud infrastructure using customized extensions of the CloudSim simulation toolkit. The simulation experiments allow us to evaluate the cloud performance under different workload conditions and assess the benefits of autoscaling policies as well as the effects of their configuration settings.

Elkhoukhi, Hamza; NaitMalek, Youssef; Bakhouya, Mohamed; Berouine, Anass; Kharbouch, Abdelhak; Lachhab, Fadwa; Hanifi, Majdoulayne; El Ouadghiri, Driss; Essaaidi, Mohamed

doi: 10.1002/cpe.5651pmid: N/A

Context‐awareness in energy‐efficient buildings has been considered as a crucial fact for developing context‐driven control approaches in which sensing and actuation tasks are performed according to the contextual changes. This could be done by including the presence of occupants, number, actions, and behaviors in up‐to‐date context, taking into account the complex interlinked elements, situations, processes, and their dynamics. However, many studies have shown that occupancy information is a major leading source of uncertainty when developing control approaches. Comprehensive and real‐time fine‐grained occupancy information has to be, therefore, integrated in order to improve the performance of occupancy‐driven control approaches. The work presented in this paper is toward the development of a holistic platform that combines recent IoT and Big Data technologies for real‐time occupancy detection in smart building. The purpose of this work focuses mainly on the presence of occupants by comparing both static and dynamic machine learning techniques. An open‐access occupancy detection dataset was first used to assess the usefulness of the platform and the effectiveness of static machine learning strategies for data processing. This dataset is used for applications that follow the strategy aiming at storing data first and processing it later. However, many smart buildings' applications, such as HVAC and ventilation control, require online data streams processing. Therefore, a distributed real‐time machine learning framework was integrated into the platform and tested to show its effectiveness for this kind of applications. Experiments have been conducted for ventilation systems in energy‐efficient building laboratory (EEBLab) and preliminary results show the effectiveness of this platform in detecting on‐the‐fly presence of occupants, which is required to either make ON or OFF the system and then activate the corresponding embedded control technique (eg, ON/OFF, PID, state‐feedback).

Bakhouya, Mohamed; Zbakh, Mostapha; Essaaidi, Mohamed; Manneback, Pierre

doi: 10.1002/cpe.5896pmid: N/A

Haugerud, Hårek; Xue, Noha; Yazidi, Anis

doi: 10.1002/cpe.5662pmid: N/A

Hybrid cloud technology is becoming increasingly popular as it merges private and public clouds to bring the best of two worlds together. However, due to the heterogeneous cloud installation, facilitating a hybrid cloud setup is not simple. Despite some commercial solutions being available to build a hybrid cloud, an open‐source implementation is still unavailable. In this paper, we try to bridge the gap by providing an open‐source implementation using the power of Apache Mesos. We build a hybrid cloud on top of multiple cloud platforms, private and public. Through comprehensive experimental results, we show that our solution is able to ensure resource bursting by leveraging the power of the public cloud.

doi: 10.1002/cpe.5387pmid: N/A

Liu, Feiqiang; Chen, Lihui; Lu, Lu; Ahmad, Awais; Jeon, Gwanggil; Yang, Xiaomin

doi: 10.1002/cpe.5632pmid: N/A

Medical image fusion is a technology of combining multi‐modal images to generate a composite image, which is favorable to improve the capability of doctors in diagnosis and treatment of the disease. In order to achieve good performance, a fusion method by combining Laplacian pyramid (LP) and convolutional sparse representation (CSR) is proposed. In the proposed fusion method, LP transform is performed on each pair of pre‐registered computed tomography image and magnetic resonance image to obtain their detail layers and base layer. Then, the base layer is fused with a CSR‐based approach, whereas the detail layers are merged using the popular “max‐absolute” rule. Finally, the fused image is reconstructed by performing the inverse LP transform over the fused base layer and detail layers. The advantages of our method are that the texture detail information contained in source images can be fully extracted and the overall contrast of the final fused image will not be decreased. Experimental results demonstrate the superiority of the proposed method.

Casalicchio, Emiliano; Iannucci, Stefano

doi: 10.1002/cpe.5668pmid: N/A

Containerization is a lightweight virtualization technology enabling the deployment and execution of distributed applications on cloud, edge/fog, and Internet‐of‐Things platforms. Container technologies are evolving at the speed of light, and there are many open research challenges. In this paper, an extensive literature review is presented that identifies the challenges related to the adoption of container technologies in High Performance Computing, Big Data analytics, and geo‐distributed (Edge, Fog, Internet‐of‐Things) applications. From our study, it emerges that performance, orchestration, and cyber‐security are the main issues. For each challenge, the state‐of‐the‐art solutions are then analyzed. Performance is related to the assessment of the performance footprint of containers and comparison with the footprint of virtual machines and bare metal deployments, the monitoring, the performance prediction, the I/O throughput improvement. Orchestration is related to the selection, the deployment, and the dynamic control of the configuration of multi‐container packaged applications on distributed platforms. The focus of this work is on run‐time adaptation. Cyber‐security is about container isolation, confidentiality of containerized data, and network security. From the analysis of 97 papers, it came out that the state‐of‐the‐art is more mature in the area of performance evaluation and run‐time adaptation rather than in security solutions. However, the main unsolved challenges are I/O throughput optimization, performance prediction, multilayer monitoring, isolation, and data confidentiality (at rest and in transit).

Mahmoudi, Sidi Ahmed; Belarbi, Mohammed Amin; Mahmoudi, Saïd; Belalem, Ghalem; Manneback, Pierre

doi: 10.1002/cpe.5699pmid: N/A

The last few years have been marked by the presence of very large sets of images and videos in our everyday lives. These multimedia objects have a very fast frequency of creation and sharing since images and videos can come from different devices such as smartphones, satellites, cameras, or drones. They are generally used to illustrate objects in different situations (public areas, train stations, hospitals, political and sport events and competitions, etc). As consequence, image and video processing algorithms have got increasing importance for several computer vision applications that should be adapted for managing large‐scale volumes and exploiting high performance computing resources (local or cloud). In this work, we propose a cloud‐based toolbox (platform) for computer vision applications. This platform integrates a toolbox of image and video processing algorithms that can (i) exploit high performance computing cloud resources, (ii) execute applications in real time, and (iii) manage large‐scale database using Big Data technologies. The related libraries and hardware drivers are automatically integrated and configured in order to offer to users an access to the different applications without the need to download, install, and configure software or hardware. Experiments were conducted using three kinds of applications: (i) image and video processing applications, (ii) deep learning techniques for images classification and multiobject localization, and (iii) images indexation and retrieval. These experiments demonstrated the interest of our platform for sharing, in an efficient way, our scientific contributions and annotated databases in order to improve the quality and performance of computer vision applications.