QoS-Aware Service Continuity in the Virtualized Edge

Abdah, Hadeel; Barraca, João Paulo; Aguiar, Rui L.

doi:10.1109/access.2019.2907457

Cited by 27 publications

(13 citation statements)

References 87 publications

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“…Thus, it needs to consider multi-metrics when SP is operated. To respond to changing workloads, there have been previous approaches for placement schemes [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] with various objectives.…”

Section: Related Workmentioning

confidence: 99%

“…In previous work, for various user requirements, VM placement schemes have been proposed considering different metrics such as resource utilization, response time, etc. [3,4,9], according to the objective.…”

Section: Placement Actionmentioning

confidence: 99%

“…In addition, most of the approaches focus on minimization of the placement time. Time minimization reduces the execution time, downtime, and waiting time [1][2][3][4][5][6][7]. Time minimization is an important metric due to the affection of the performance of VMs.…”

Section: Performancementioning

confidence: 99%

“…The limited coverage of a single server to support mobile users will result in service-level agreement (SLA) violation, which further leads to a decrease in performance and quality of service (QoS). Therefore, it is difficult to ensure seamless service [4,5].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing

Son

Huh

2019

Applied Sciences

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With the rapid increase in the development of the cloud data centers, it is expected that massive data will be generated, which will decrease service response time for the cloud data centers. To improve the service response time, distributed cloud computing has been designed and researched for placement and migration from mobile devices close to edge servers that have secure resource computing. However, most of the related studies did not provide sufficient service efficiency for multi-objective factors such as energy efficiency, resource efficiency, and performance improvement. In addition, most of the existing approaches did not consider various metrics. Thus, to maximize energy efficiency, maximize performance, and reduce costs, we consider multi-metric factors by combining decision methods, according to user requirements. In order to satisfy the user’s requirements based on service, we propose an efficient service placement system named fuzzy- analytical hierarchical process and then analyze the metric that enables the decision and selection of a machine in the distributed cloud environment. Lastly, using different placement schemes, we demonstrate the performance of the proposed scheme.

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Section: Related Workmentioning

confidence: 99%

Section: Placement Actionmentioning

confidence: 99%

Section: Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing

Son

Huh

2019

Applied Sciences

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“…Containers provide a light-weight cloud-native framework for implementing services that can be separated into several micro-services [2]- [4]. The micro-services can be scaled to demand and migrated between nodes according to application needs [5]- [10].…”

Section: Introduction a Motivation And Backgroundmentioning

confidence: 99%

Hardware Acceleration for Container Migration on Resource-Constrained Platforms

et al. 2020

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The computing capabilities of client devices are continuously increasing; at the same time, demands for ultra-low latency (ULL) services are increasing. These ULL services can be provided by migrating some micro-service container computations from the cloud and multi-access edge computing (MEC) to the client devices. The migration of a container image requires compression and decompression, which are computationally demanding. We quantitatively examine the hardware acceleration of container image compression and decompression on a client device. Specifically, we compare the Intel ® Quick Assist Technology (QAT) hardware acceleration with software compression/decompression. For scenarios with a local container image registry (i.e., without network bandwidth constraints), we find that QAT speeds up compression by a factor of over 7 compared to the single-core GZIP software and reduces the CPU core utilization by over 15% for large container images. These QAT benefits come at the expense of Input/Output (IO) memory access bitrates of up to 900 Mbyte/s (while the software compression/decompression does not require IO memory access). For scenarios with a remote container image registry, we find that the container push (compression) time savings increase with the network bandwidth, while the container pull (decompression) time savings level out for moderately high network bandwidths and slightly decrease for a very high network bandwidth. Furthermore, QAT acceleration achieves substantial power consumption reductions for container push compression for low to moderately high network bandwidths. Our evaluation results give reference performance benchmarks of the achievable latencies for container image instantiation and migration with and without hardware acceleration of the compression and decompression of container images.

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