Surrounded by the Clouds

Corneo, Lorenzo; Eder, Maximilian; Mohan, Nitinder; Zavodovski, Aleksandr; Bayhan, Suzan; Wong, W. K.; Gunningberg, Per; Kangasharju, Jussi; Ott, Jörg

doi:10.1145/3442381.3449854

Cited by 33 publications

(5 citation statements)

References 24 publications

(26 reference statements)

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“…The reason could be the lack of business or hosting model, slowness of standardization, or the lack of "killer" applications that would benefit from the lower latency com- VOLUME 4, 2016 pared to the cloud. In fact, it has been demonstrated that the latency from the client devices to the cloud in many parts of the world is already sufficient for most applications [52].…”

Section: Answer To Rq3: What Are the Pros And Cons Of These Methods?mentioning

confidence: 99%

Systematic Literature Review on Cost-Efficient Deep Learning

Klemetti,

Raatikainen,

Myllyaho

et al. 2023

IEEE Access

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Cloud computing and deep learning, the recent big trends in the software industry, have enabled small companies to scale their business up rapidly. However, this growth is not without a costdeep learning models are related to the heaviest workloads in cloud data centers. When the business grows, the monetary cost of deep learning in the cloud grows fast as well. Deep learning practitioners should be prepared and equipped to limit the growing cost. We performed a systematic literature review on the methods to control the monetary cost of deep learning. Our library search resulted in 16066 papers from three article databases, IEEE Xplore, ACM Digital Library, and Scopus. We narrowed them down to 112 papers that we categorized and summarized. We found that: 1) Optimizing inference has raised more interest than optimizing training. Popular deep learning libraries already support some of the inference optimization methods such as quantization, pruning, and teacher-student. 2) The research has been centered around image inputs, and there seems to be a research gap for other types of inputs. 3) The research has been hardwareoriented, and the most typical approach to control the cost of deep learning is based on algorithm-hardware co-design. 4) Offloading some of the processing to client devices is gaining interest and has the potential to reduce the monetary cost of deep learning.

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Section: Answer To Rq3: What Are the Pros And Cons Of These Methods?mentioning

confidence: 99%

Systematic Literature Review on Cost-Efficient Deep Learning

Klemetti,

Raatikainen,

Myllyaho

et al. 2023

IEEE Access

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“…The WF-CCRA method is elaborated in Algorithm 2. The first step is to initialize the vectors of parameters and variables used in (1) (or in (2)). Following that, two empty sets, R ′ and Ω, are established.…”

Section: B Wf-ccramentioning

confidence: 99%

“…In these circumstances, a distributed cloud architecture is essential [2], which could be implemented via a solid cloudnetwork integrated infrastructure built of distinct domains in Beyond 5G (B5G) [3]. These domains can be distinguished by the technology employed, including radio access, transport, and core networks, as well as edge, access, aggregation, regional, and central clouds.…”

Section: Introductionmentioning

confidence: 99%

Double Deep Q-Learning-Based Path Selection and Service Placement for Latency-Sensitive Beyond 5G Applications

Shokrnezhad

Taleb

Dazzi

2024

IEEE Trans. on Mobile Comput.

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Nowadays, as the need for capacity continues to grow, entirely novel services are emerging. A solid cloudnetwork integrated infrastructure is necessary to supply these services in a real-time responsive, and scalable way. Due to their diverse characteristics and limited capacity, communication and computing resources must be collaboratively managed to unleash their full potential. Although several innovative methods have been proposed to orchestrate the resources, most ignored network resources or relaxed the network as a simple graph, focusing only on cloud resources. This paper fills the gap by studying the joint problem of communication and computing resource allocation, dubbed CCRA, including function placement and assignment, traffic prioritization, and path selection considering capacity constraints and quality requirements, to minimize total cost. We formulate the problem as a non-linear programming model and propose two approaches, dubbed B&B-CCRA and WF-CCRA, based on the Branch & Bound and Water-Filling algorithms to solve it when the system is fully known. Then, for partially known systems, a Double Deep Q-Learning (DDQL) architecture is designed. Numerical simulations show that B&B-CCRA optimally solves the problem, whereas WF-CCRA delivers near-optimal solutions in a substantially shorter time. Furthermore, it is demonstrated that DDQL-CCRA obtains near-optimal solutions in the absence of request-specific information.

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“…The main reason is the fact that computing devices at the disposal of the general public are exactly where they are needed the most -at the edge of the network. In the best possible case, clients would have a direct connection to them, avoiding the so-called last mile, often responsible for the significant fraction of network delay as recent measurement studies show [9,23]. Also, in areas with the dense deployment of cloud datacenters, in-network edge would give mostly minor improvements [10].…”

Section: Background and Motivationmentioning

confidence: 99%

Decentralizing Computation with Edge Computing

Zavodovski

Corneo

Johnsson

et al. 2021

Proceedings of the Interdisciplinary Workshop on (De) Centralization in the Internet

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Edge computing promises to bring computation close to the endusers to support emergent applications such as virtual reality. However, the computational capacity at the edge of the network is currently limited. To become a pervasive paradigm, edge computing needs highly dispersed decentralized deployments, that, contrary to cloud, cannot benefit from economies of scale. In this situation, crowdsourcing appears attractive -there are plenty of computing devices at the disposal of the general public, and these devices are located exactly where computing power is needed the most -at the edge of the network. Crowdsourcing has been a success maker for scientific computing projects, e.g., SETI@home, or distributed ledger systems empowering decentralized finance. However, as of now, there is no crowdsourced system that addresses the needs of edge computing. In this position paper, we aim to identify the causes of this shortcoming, analyze the potential ways to overcome it, and outline future directions. CCS CONCEPTS• Networks → Cloud computing; • Information systems → Crowdsourcing.

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Surrounded by the Clouds

Cited by 33 publications

References 24 publications

Systematic Literature Review on Cost-Efficient Deep Learning

Systematic Literature Review on Cost-Efficient Deep Learning

Double Deep Q-Learning-Based Path Selection and Service Placement for Latency-Sensitive Beyond 5G Applications

Decentralizing Computation with Edge Computing

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