Communication-Aware Container Placement and Reassignment in Large-Scale Internet Data Centers

Lv, Liang; Zhang, Yuchao; Li, Yusen; Xu, Ke; Wang, Dan; Wang, Wendong; Li, Minghui; Xuan, Chuang; Liang, Qingqing

doi:10.1109/jsac.2019.2895473

Cited by 71 publications

(16 citation statements)

References 69 publications

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“…Moreover, the performance per computing core on the public cloud could be comparable to modern traditional supercomputing systems. Ly et al [34] proposed a communication-aware worst-fit decreasing heuristic algorithm for the container placement problem, but MPI-based applications were not considered. Reddy and Lastovetsky [35] formulated a bi-objective optimization problem for performance and energy for data-parallel applications on homogeneous clusters.…”

Section: Introductionmentioning

confidence: 99%

Performance of Communication- and Computation-Intensive SaaS on the OpenStack Cloud

2021

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The pervasive use of cloud computing has led to many concerns, such as performance challenges in communication- and computation-intensive services on virtual cloud resources. Most evaluations of the infrastructural overhead are based on standard benchmarks. Therefore, the impact of communication issues and infrastructure services on the performance of parallel MPI-based computations remains unclear. This paper presents the performance analysis of communication- and computation-intensive software based on the discrete element method, which is deployed as a service (SaaS) on the OpenStack cloud. The performance measured on KVM-based virtual machines and Docker containers of the OpenStack cloud is compared with that obtained by using native hardware. The improved mapping of computations to multicore resources reduced the internode MPI communication by 34.4% and increased the parallel efficiency from 0.67 to 0.78, which shows the importance of communication issues. Increasing the number of parallel processes, the overhead of the cloud infrastructure increased to 13.7% and 11.2% of the software execution time on native hardware in the case of the Docker containers and KVM-based virtual machines of the OpenStack cloud, respectively. The observed overhead was mainly caused by OpenStack service processes that increased the load imbalance of parallel MPI-based SaaS.

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Section: Introductionmentioning

confidence: 99%

Performance of Communication- and Computation-Intensive SaaS on the OpenStack Cloud

2021

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“…Effective data analysis methods need to be used in such scenarios, among which machine learning and deep learning are particularly important [3]. Among the commonly used machine learning methods, support vector machine (SVM) model has significant advantages in performance and robustness, so it has a wide range of applications [4].…”

Section: Introductionmentioning

confidence: 99%

A machine learning model based on heterogeneous data

Narbayeva

Tapeeva

Turarbek

et al. 2022

JMMCS

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Big data is widely used in many areas of business. The information between organizations is systematically reproduced and processed by data, and the collected data differs significantly in attributes. By composing heterogeneous data sets, they complement each other, therefore, data exchange between organizations is necessary. In a machine learning collaborative learning process based on heterogeneous data, the current schema has many challenges, including efficiency, security, and availability in real-world situations. In this paper, we propose a secure SVM learning mechanism based on the consortium blockchain and a threshold homomorphic encryption algorithm. By implementing the consortium's blockchain, it is possible to build a decentralized data exchange platform, and also to develop a secure algorithm for the support-vector machine classifier based on threshold homomorphic encryption.

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“…However, these frequent migrations result in high computation cost (i.e., the hardware resources consumed to host the migrated applications) and high bandwidth consumption (i.e., the network bandwidth used for the state transfers). The existing service migration optimization studies, e.g., [18]- [21], have considered VM or container migration schemes. To the best of our knowledge, the optimization of MEC state transfer has not been examined in detail to date.…”

Section: Introductionmentioning

confidence: 99%

FAST: Flexible and Low-Latency State Transfer in Mobile Edge Computing

et al. 2021

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Mobile Edge Computing (MEC) brings the benefits of cloud computing, such as computation, networking, and storage resources, close to end users, thus reducing end-to-end latency and enabling various novel use cases, such as vehicle platooning, autonomous driving, and the tactile internet. However, frequent user mobility makes it challenging for the MEC to guarantee the close proximity to the users. To tackle this challenge, the underlying network has to be capable of seamlessly migrating applications between multiple MEC sites. This application migration requires the quick and flexible migration of the application states without service interruption, while minimizing the state transfer cost. In this article, we first study the state transfer optimization problem in the MEC. To solve this problem, we propose a metaheuristic algorithm based on Tabu search. We then propose Flexible and Low-Latency State Transfer in Mobile Edge Computing (FAST), the first programmable state forwarding framework. FAST flexibly and directly forwards states between source instance and destination instance based on Software-Defined Networking (SDN). Both simulation results and practical testbed results demonstrate the favorable performance of the proposed Tabu search algorithm and the FAST framework compared to the state-of-the-art schemes.INDEX TERMS Application state transfer, multi-access edge computing (MEC), network function virtualization (NFV), software-defined networking (SDN).

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Communication-Aware Container Placement and Reassignment in Large-Scale Internet Data Centers

Cited by 71 publications

References 69 publications

Performance of Communication- and Computation-Intensive SaaS on the OpenStack Cloud

Performance of Communication- and Computation-Intensive SaaS on the OpenStack Cloud

A machine learning model based on heterogeneous data

FAST: Flexible and Low-Latency State Transfer in Mobile Edge Computing

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