Robustness-oriented k Edge Server Placement

Cui, Guangming; He, Qiang; Xia, Xiaoyu; Chen, Feifei; Jin, Hai; Yang, Yun

doi:10.1109/ccgrid49817.2020.00-85

Cited by 40 publications

(31 citation statements)

References 26 publications

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“…To improve the robustness of edge server placement, [14]- [17] studied how to account for server failures in their server placement formulations. However, workload uncertainty is inherently different from server failures, and may happen much more frequently in real time.…”

Section: Related Workmentioning

confidence: 99%

“…This presolving step is critical to ensure low workload rejection rate. Remark on Algorithmic Complexity: Although server on-off scheduling is only calculated once everyday, directly solving (14), (15) and ( 19) is still computationally prohibitive. To reduce complexity, we can adopt the same relaxing and rounding approach proposed in Section V-C.…”

Section: B Considering Switching Cost For Server Schedulingmentioning

confidence: 99%

“…When the real-time workload patterns deviate from the predictions, the performance guarantee offered by these proposals become questionable. Some recent works [14]- [17] studied how to design edge server placement policies that are robust to server failures. However, workload uncertainty is inherently different from server failures, and thus may need completely new handling mechanisms.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design of Robust and Efficient Edge Server Placement and Server Scheduling Policies: Extended Version

Zhao¹,

Xiao²,

Cao³

et al. 2021

Preprint

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We study how to design edge server placement and server scheduling policies under workload uncertainty for 5G networks. We introduce a new metric called resource pooling factor to handle unexpected workload bursts. Maximizing this metric offers a strong enhancement on top of robust optimization against workload uncertainty. Using both real traces and synthetic traces, we show that the proposed server placement and server scheduling policies not only demonstrate better robustness against workload uncertainty than existing approaches, but also significantly reduce the cost of service providers. Specifically, in order to achieve close-to-zero workload rejection rate, the proposed server placement policy reduces the number of required edge servers by about 25% compared with the state-of-theart approach; the proposed server scheduling policy reduces the energy consumption of edge servers by about 13% without causing much impact on the service quality.

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Section: Related Workmentioning

confidence: 99%

Section: B Considering Switching Cost For Server Schedulingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design of Robust and Efficient Edge Server Placement and Server Scheduling Policies: Extended Version

Zhao¹,

Xiao²,

Cao³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…29 Mocrii et al 29 have surveyed many OSs about the sensing devices and gateways. Unfortunately, some important OSs applicable to the smart gateways are not surveyed and reviewed, such as OpenWrt/LEDE 7,8 , Raspbian 9 , Windows CE (latterly called Windows Embedded Compact) 10 , and Google Fuchsia 11 .…”

Section: Operating Systemsmentioning

confidence: 99%

“…The cloud computing system is not a unique optimal scheme, which must combine with the fog and edge computing systems and comprehensively consider the computing performance, security, delay, data caching, dynamic service placement, and so on. [5][6][7][8][9][10] Although a lot of companies and researchers are attempting various smart gateway technologies and methods from different angles, ranging from hardware to system structure and software, from functions to services and applications, or, from cloud to fog and edge computing, including QoE, intelligent agent, software-defined network (SDN), network function virtualization (NFV), and network slicing, [11][12][13] the foresaid problems still expect to be solved perfectly. Moreover, actually, there is a long way to go to deal with those problems and achieve those goals.…”

Section: Introductionmentioning

confidence: 99%

Survey on recent smart gateways for smart home: Systems, technologies, and challenges

Wang

et al. 2020

Trans Emerging Tel Tech

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A smart gateway, which plays a crucial role in a smart home, generally bridges the inside and outside home networks, as well as collaboratively manages the intelligent Internet of things (IoT) devices equipped in the smart home, due to it can convert different communication protocols, gather generated data from surrounding devices, and even conduct some local data processing tasks. In this survey, we first review the whole smart home research area with a focus on a framework based on edge computing and multiple intelligent agents. Then, relying on the time‐line and different metrics including quality of experience (QoE), artificial intelligence support, software‐defined network, and so on, we divide the evolution of smart gateways into three generations. Considering recent smart gateways mainly belong to the second generation, we further investigate and classify them into two categories from another perspective of user‐awareness and data‐awareness. Afterward, we discussed the key enabling technologies and components of smart gateways, such as operating systems, wireless communication protocols, and security. At last, we explore the challenges and trends of smart gateways and point out that maximizing QoE as well as data‐awareness is the key feature of future gateways. This survey has practical significance for building a smart home system, broadly speaking, even for constructing the IoT and smart space.

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Edge server placement for offloading real‐time statistics tasks of bus passenger

Guo

Shang

et al. 2022

Trans Emerging Tel Tech

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With the continuous development of smart bus systems, higher requirements are expected for the accuracy and timeliness of the real‐time statistics of bus passengers. Although the statistical method of image and video processing based on deep learning has higher accuracy, it has higher requirements on the computing power and hardware equipment of the computer. A traditional solution is cloud computing, but cloud computing cannot meet real‐time requirements due to long‐distance transmission. In order to meet the real‐time demand, it can be offloaded to the edge of the network and processed by edge servers. In edge computing, the location of the edge server will have a great impact on the access delay and the traffic load in the edge network. Currently, few people optimize the traffic load in the edge network during the placement process. In view of this, an edge server placement algorithm for task offloading, named ESPTO, is designed to balance the average delay and traffic load under the control of each edge server while minimizing the average delay and traffic load in the edge network. First, a decomposition‐based multi‐objective evolutionary algorithm (MOEA/D) is used to find a better set of placement strategies, and then the optimal placement strategy is obtained through TOPSIS. Experimental results based on the Hangzhou bus station dataset prove the effectiveness of ESPTO.

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Robustness-oriented k Edge Server Placement

Cited by 40 publications

References 26 publications

Design of Robust and Efficient Edge Server Placement and Server Scheduling Policies: Extended Version

Design of Robust and Efficient Edge Server Placement and Server Scheduling Policies: Extended Version

Survey on recent smart gateways for smart home: Systems, technologies, and challenges

Edge server placement for offloading real‐time statistics tasks of bus passenger

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