Mobile Cloudization Storytelling: Current Issues From an Optimization Perspective

Dao, Nhu‐Ngoc; Na, Woongsoo; Cho, Sungrae; Deelman, Ewa

doi:10.1109/mic.2020.2965485

Cited by 15 publications

(7 citation statements)

References 7 publications

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“…Cost of computing is lower as you go up from C to A, while the latency is higher as you go up from C to A. In this scenario, our partitioning scheme will first consider C as the edge and B as the cloud for equations ( 1), ( 2) and (3). Once this partition is determined, certain microservices will be scheduled to run on C (edge), while others will be scheduled to run on B (cloud).…”

Section: Runtime For Edge-cloudmentioning

confidence: 99%

“…The new applications demand a different kind of computing fabric, one that is distributed and built to support low-latency and high-bandwidth service delivery capability, which centralized cloud implementations with 100+ milliseconds (ms) network latencies are not well-suited for [3]. For such applications, consideration of a few human-related benchmarks is useful.…”

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confidence: 99%

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ECO: Edge-Cloud Optimization of 5G applications

Rao

Coviello

Hsiung

et al. 2021

2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid)

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Centralized cloud computing with 100+ milliseconds network latencies cannot meet the tens of milliseconds to submillisecond response times required for emerging 5G applications like autonomous driving, smart manufacturing, tactile internet, and augmented or virtual reality. We describe a new, dynamic runtime that enables such applications to make effective use of a 5G network, computing at the edge of this network, and resources in the centralized cloud, at all times. Our runtime continuously monitors the interaction among the microservices, estimates the data produced and exchanged among the microservices, and uses a novel graph min-cut algorithm to dynamically map the microservices to the edge or the cloud to satisfy applicationspecific response times. Our runtime also handles temporary network partitions, and maintains data consistency across the distributed fabric by using microservice proxies to reduce WAN bandwidth by an order of magnitude, all in an applicationspecific manner by leveraging knowledge about the application's functions, latency-critical pipelines and intermediate data. We illustrate the use of our runtime by successfully mapping two complex, representative real-world video analytics applications to the AWS/Verizon Wavelength edge-cloud architecture, and improving application response times by 2x when compared with a static edge-cloud implementation.Index Terms-edge-cloud optimization, microservices, runtime, AWS Wavelength, 5G applications I. INTRODUCTIONCloud services are everywhere. From individual users watching over-the-top video content to enterprises deploying software-as-a-service, cloud services are increasingly how the world consumes content and data. Although centralized cloud computing is ubiquitous, and economically efficient, an exponential growth in internet-connected machines and devices is resulting in emerging new applications, services, and workloads for which the centralized cloud quickly becomes computationally inefficient [1]. New, emerging applications like autonomous driving, smart manufacturing, tactile internet, remote surgeries, real-time closed-loop control as in Industry 4.0, augmented or virtual reality require tens of milliseconds to sub-millisecond response times [2]. For these applications, processing all data in the cloud and returning the results to the end user is not an option because it takes too long, uses excessive power, creates privacy and security vulnerabilities, and causes scalability problems.

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Section: Runtime For Edge-cloudmentioning

confidence: 99%

mentioning

confidence: 99%

ECO: Edge-Cloud Optimization of 5G applications

Rao

Coviello

Hsiung

et al. 2021

2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid)

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“…It is seen that 3V 3 if ∆[t] = 0, ∀t, i.e., the CPU frequency is equal to the average virtual workload arrival rate at all unit timeslot t.…”

Section: B Lightweight Lag Optimizationmentioning

confidence: 99%

“…Standardized by the European telecommunications standards institute (ETSI), MEC aims to reduce latency, ensures highly efficient network operation and service delivery, and offers an improved user experience by proposing virtualized computing platforms (a.k.a. MEC servers) implemented at the mobile access nodes, which are well known to be within the fog radio access network (F-RAN) [3].…”

Section: Introductionmentioning

confidence: 99%

Self-Calibrated Edge Computation for Unmodeled Time-Sensitive IoT Offloading Traffic

et al. 2020

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With the characterizing benefits of ultra-low latency, contextual computing, and mobile scalability, mobile edge computing (MEC) is considered as a key enabler for realizing a tremendous boom in heterogeneously time-sensitive Internet-of-Things (IoT) services in the fifth-generation (5G) ecosystems. However, achieving low-latency comes at a cost of energy-efficiency reduction. To address and balance this tradeoff, this paper proposes a joint optimization of energy consumption and latency satisfaction in MEC servers, called latency-aware green (LAG) computing algorithm. To fully consider the heterogeneity of IoT services offloaded to the MEC servers, offloading traffic at the MEC servers is assumed to be unmodeled and unpredictable. Using the proposed LAG algorithm, each MEC server autonomously and dynamically calibrates its own computing frequency based on the current status of the workload buffer size and computational workload arrival rate. This dynamic calibration provides minimum energy consumption for the workload computation while maintaining the computational latency stabilized under a desired threshold. Evaluation results show that the proposed algorithm maintains stable MEC servers in an energyefficient manner. INDEX TERMS edge computing, Internet of Things, mobile offloading, unmodeled traffic VOLUME 8, 2020 This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.

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“…The associate editor coordinating the review of this manuscript and approving it for publication was Chakchai So-In . the use of unnecessary bandwidth, and provide users with higher quality of service (QoS) [3], [4]. These fog-computing concepts can be combined with intelligent transportation systems (ITSs) to increase the benefits significantly.…”

Section: Introductionmentioning

confidence: 99%

Trustful Resource Management for Service Allocation in Fog-Enabled Intelligent Transportation Systems

et al. 2020

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A fog-enabled intelligent transportation system is constructed using Vehicle-to-Everything (V2X) communications towards the Internet of Vehicles (IoV). In this system, the road side unit (RSU) and vehicle correspond to the fog server and fog device, respectively. In practice, the RSU offers instant infotainment services (e.g., video streaming and traffic assistance) to vehicles. However, the vehicles are considered untrustworthy and could possibly be exploited to attack the system, thereby compromising the service stability and data integrity. To address this problem, this article proposes truthful service allocation using a Vickrey-Clarke-Groves (VCG) auction mechanism. The RSU leads the proposed auction as a seller who sells their computing resources, and the vehicles participate as buyers who purchase these computing resources for the offered services. Consequently, the RSU generates a transaction whenever a service allocation is assigned. To secure the service transactions, we utilize a distributed blockchain system that implements Hyperledger Fabric framework among RSUs for transaction verification. The simulation results demonstrate that the proposed system provides service stability while ensuring service trustfulness.INDEX TERMS Blockchain, Internet of Vehicles (IoV), fog computing, VCG-auction.

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Mobile Cloudization Storytelling: Current Issues From an Optimization Perspective

Cited by 15 publications

References 7 publications

ECO: Edge-Cloud Optimization of 5G applications

ECO: Edge-Cloud Optimization of 5G applications

Self-Calibrated Edge Computation for Unmodeled Time-Sensitive IoT Offloading Traffic

Trustful Resource Management for Service Allocation in Fog-Enabled Intelligent Transportation Systems

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