Coordinate Channel-Aware Page Mapping Policy and Memory Scheduling for Reducing Memory Interference Among Multimedia Applications

"© 2017 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works."[EN] In a modern multicore system, memory is shared among more and more concurrently running multimedia applications. Therefore, memory contention and interference are more andmore serious, inducing system performance degradation significantly, the performance degradation of each thread differently, unfairness in resource sharing, and priority inversion, even starvation. In this paper, we propose an approach of coordinating channel-aware page mapping policy and memory scheduling (CCPS) to reduce intermultimedia application interference in a memory system. The idea is to map the data of different threads to different channels, together with memory scheduling. The key principles of the policies of page mapping and memory scheduling are: 1) the memory address space, the thread priority, and the load balance; and 2) prioritizing a low-memory request thread, a row-buffer hit access, and an older request. We evaluate the CCPS on a variety of mixed single-thread and multithread benchmarks and system configurations, and we compare them with four previously proposed state-of-the-art interference-reducing policies. Experimental results demonstrate that the CCPS improves the performance while reducing the energy consumption significantly; moreover, the CCPS incurs a much lower hardware overhead than the current existing policies.This work was supported in part by the Qing Lan Project; by the National Science Foundation of China under Grant 61003077, Grant 61100193, and Grant 61401147; and by the Zhejiang Provincial Natural Science Foundation under Grant LQ14F020011.Jia, G.; Han, G.; Li, A.; Lloret, J. (2017). Coordinate Channel-Aware Page Mapping Policy and Memory Scheduling for Reducing Memory Interference Among Multimedia Applications. IEEE Systems Journal. 11(4):2839-2851. https://doi.org/10.1109/JSYST.2015.2430522S2839285111

A multiqueue interlacing peak scheduling method based on tasks’ classification in cloud computing

In cloud computing, resources are dynamic, and the demands placed on the resources allocated to a particular task are diverse. These factors could lead to load imbalances, which affect scheduling efficiency and resource utilization. A scheduling method called interlacing peak is proposed. First, the resource load information, such as CPU, I/O, and memory usage, is periodically collected and updated, and the task information regarding CPU, I/O, and memory is collected. Second, resources are sorted into three queues according to the loads of the CPU, I/O, and memory: CPU intensive, I/O intensive, and memory intensive, according to their demands for resources. Finally, once the tasks have been scheduled, they need to interlace the resource load peak. Some types of tasks need to be matched with the resources whose loads correspond to a lighter types of tasks. In other words, CPU-intensive tasks should be matched with resources with low CPU utilization; I/O-intensive tasks should be matched with resources with shorter I/O wait times; and memory-intensive tasks should be matched with resources that have low memory usage. The effectiveness of this method is proved from the theoretical point of view. It has also been proven to be less complex in regard to time and place. Four experiments were designed to verify the performance of this method. Experiments leverage four metrics: 1) average response time; 2) load balancing; 3) deadline violation rates; and 4) resource utilization. The experimental results show that this method can balance loads and improve the effects of resource allocation and utilization effectively. This is especially true when resources are limited. In this way, many tasks will compete for the same resources. However, this method shows advantage over other similar standard algorithms

User behavior prediction via heterogeneous information preserving network embedding

© 2018 Elsevier B.V. User behavior prediction with low-dimensional vectors generated by user network embedding models has been verified to be efficient and reliable in real applications. However, most user network embedding models utilize homogeneous properties to represent users, such as attributes or user network structure. Though some works try to combine two kinds of properties, the existing works are still not enough to leverage the rich semantics of users. In this paper, we propose a novel heterogeneous information preserving user network embedding model, which is named HINE, for user behavior classification in user network. HINE applies attributes, user network connection, user network structure, and user behavior label information for user representation in user network embedding. The embedded vectors considering these multi-type properties of users contribute to better user behavior classification performances. Experiments verified the superior performances of the proposed approach on real-world complex user network dataset

Socialized healthcare service recommendation using deep learning

© 2018, The Natural Computing Applications Forum. Socialized recommender system recommends reliable healthcare services for users. Ratings are predicted on the healthcare services by merging recommendations given by users who has social relations with the active users. However, existing works did not consider the influence of distrust between users. They recommend items only based on the trust relations between users. We therefore propose a novel deep learning-based socialized healthcare service recommender model, which recommends healthcare services with recommendations given by recommenders with both trust relations and distrust relations with the active users. The influences of recommenders, considering both the node information and the structure information, are merged via the deep learning model. Experimental results show that the proposed model outperforms the existing works on prediction accuracy and prediction coverage simultaneously, even for cold start users or users with very sparse trust relations. It is also computational less expensive

Recent advances in green industrial networking

[EN] The articles in this special section focus on green industrial networking. Due to the explosive increase in energy usage, it is essential that governmental and industrial institutions address this problem. Therefore, the development of green and low-carbon economy has recently become a hot issue in the industry. It is envisioned in such cases to meet the growing demands for industrial networking with limited resources. This is considered one of the challenges that needs to be addressed. Addressing such key problems will hopefully allow us to reach the realization of sustainable development. We believe that network technologies will be critical and will greatly contribute to achieving large-scale energy savings in all areas of industrial production. In addition, the role of green industrial networking technologies includes not only emission reduction and energy savings in products and services, but also enabling low-carbon emissions in other industries. To meet the requirement of low-carbon economic development, it is necessary to reduce the energy consumption of industrial networking. The need for green industrial networking technologies has been recognized as a challenge during the last few years by our research communities. However, many issues still remain to be addressedHan, G.; Guizani, M.; Lloret, J.; Wu, H.; Chan, S.; Rayes, A. (2016). Recent advances in green industrial networking. IEEE Communications Magazine. 54(10):14-15. https://doi.org/10.1109/MCOM.2016.75882231415541

An efficient approach of secure group association management in densely deployed heterogeneous distributed sensor network

A heterogeneous distributed sensor network (HDSN) is a type of distributed sensor network where sensors with different deployment groups and different functional types participate at the same time. In other words, the sensors are divided into different deployment groups according to different types of data transmissions, but they cooperate with each other within and out of their respective groups. However, in traditional heterogeneous sensor networks, the classification is based on transmission range, energy level, computation ability, and sensing range. Taking this model into account, we propose a secure group association authentication mechanism using one-way accumulator which ensures that: before collaborating for a particular task, any pair of nodes in the same deployment group can verify the legitimacy of group association of each other. Secure addition and deletion of sensors are also supported in this approach. In addition, a policy-based sensor addition procedure is also suggested. For secure handling of disconnected nodes of a group, we use an efficient pairwise key derivation scheme to resist any adversary’s attempt. Along with proposing our mechanism, we also discuss the characteristics of HDSN, its scopes, applicability, future, and challenges. The efficiency of our security management approach is also demonstrated with performance evaluation and analysis

Emerging Trends, Issues, and Challenges in Big Data and Its Implementation toward Future Smart Cities

(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other worksHan, G.; Guizani, M.; Lloret, J.; Chan, S.; Wan, L.; Guibene, W. (2017). Emerging Trends, Issues, and Challenges in Big Data and Its Implementation toward Future Smart Cities. IEEE Communications Magazine. 55(12):16-17. https://doi.org/10.1109/MCOM.2017.8198795S1617551

Coordinate Memory Deduplication and Partition for Improving Performance in Cloud Computing

[EN] Both limited main memory size and memory interference are considered as the major bottlenecks in virtualization environments. Memory deduplication, detecting pages with same content and being shared into one single copy, reduces memory requirements; memory partition, allocating unique colors for each virtual machine according to page color, reduces memory interference among virtual machines to improve performance. In this paper, we propose a coordinate memory deduplication and partition approach named CMDP to reduce memory requirement and interference simultaneously for improving performance in virtualization. Moreover, CMDP adopts a lightweight page behavior-based memory deduplication approach named BMD to reduce futile page comparison overhead meanwhile to detect page sharing opportunities efficiently. And a virtual machine based memory partition called VMMP is added into CMDP to reduce interference among virtual machines. According to page color, VMMP allocates unique page colors to applications, virtual machines and hypervisor. The experimental results show that CMDP can efficiently improve performance (by about 15.8 percent) meanwhile accommodate more virtual machines concurrently.This work was supported by "Qing Lan Project", "the National Natural Science Foundation of China under Grants 61572172, 61401147, and 61572164", " the Natural Science Foundation of Jiangsu Province of China, Nos. BK20131137 and BK20140248", "Zhejiang provincial Natural Science Foundation Nos. LQ14F020011 and LQ12F02003", by Instituto de Telecomunicacoes, Next Generation Networks and Applications Group (NetGNA), Covilha Delegation, Portugal and by National Funding from the FCT Fundacao para a Ciencia e a Tecnologia through the UID/EEA/500008/2013 Project. Guangjie Han is the corresponding author.Jia, G.; Han, G.; Rodrigues, JJPC.; Lloret, J.; Li, W. (2019). Coordinate Memory Deduplication and Partition for Improving Performance in Cloud Computing. IEEE Transactions on Cloud Computing. 7(2):357-368. https://doi.org/10.1109/TCC.2015.25117383573687