The rapid growth of IoT and edge computing has led to a surging demand for intelligent services at the network edge. Deploying and orchestrating such services in edge-cloud environments face challenges due to edge devices’ heterogeneity, dynamicity, and resource constraints. We propose a lightweight cloud service composition and orchestration framework for edge intelligence applications, leveraging container technologies for efficient deployment of data-intensive services across heterogeneous edge nodes and cloud servers. A data-driven container scheduling algorithm, DICS-OPT, is introduced, utilizing multi-criteria decision-making and weight factor optimization to optimize task completion performance, resource utilization, and cloud cost savings. Extensive experiments demonstrate the effectiveness of the proposed framework in handling diverse edge intelligence workloads. DICS-OPT achieves significant improvements, with up to 80% reduction in task completion time, 90% increase in resource utilization, and 70% savings in cloud costs. The framework's scalability limitations are discussed and future research directions are proposed. This work enables developing advanced edge-cloud systems for seamless integration of edge and cloud resources in edge intelligence applications.
LiuHEldarratFAlqahtaniH, et al.Mobile edge cloud system: architectures, challenges, and approaches. IEEE Syst J2018; 12: 2495–2508.
2.
WangZZhaoZMinG, et al.User mobility aware task assignment for mobile edge computing. Future Gener Comput Syst2018; 85: 1–8.
3.
FedakGHeHCappelloF. Bitdew: a data management and distribution service with multi-protocol file transfer and metadata abstraction. J Network Comput Appl2009; 32: 961–975.
4.
HeHFedakGTangB,et al.BLAST Application with data-aware desktop grid middleware. In: 2006 Ninth International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT’06), 2006, pp.284–291.
5.
BalHKaashoekM. Object distribution in orca using compile-time and run-time techniques. In: Proceedings of the Eighth Annual Conference on Object-Oriented Programming Systems, Languages, and Applications, 1993, pp.162–177.
6.
MaoYYouCZhangJ, et al.A survey on mobile edge computing: the communication perspective. IEEE Commun Surv Tutor2017; 19: 2322–2358.
7.
WangXHanYWangC, et al.In-edge AI: intelligentizing mobile edge computing, caching and communication by federated learning. IEEE Netw2019; 33: 156–165.
8.
RenJGuoHXuC,et al.Serving at the edge: a scalable IoT architecture based on transparent computing. IEEE Netw2017; 31: 96–105.
9.
CaoWLiuZWangP, et al.Distributed transfer learning in edge computing: concept, methods and frameworks. In: 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS), 2018, pp.892–899.
10.
WangXLiXLeungVCM. Federated deep reinforcement learning for internet of things with decentralized cooperative edge caching. IEEE Internet Things J2020; 7: 9441–9455.
11.
RenJZhangDHeS, et al.A survey on end-edge-cloud orchestrated network computing paradigms: transparent computing, mobile edge computing, fog computing, and cloudlet. ACM Comput Surv (CSUR)2019; 52: 1–36.
12.
ChunBCullerDRoscoeT, et al.Planetlab: an overlay testbed for broad-coverage services. SIGCOMM Comput Commun Rev2003; 33: 3–12.
13.
HaKChenZHuW, et al.Towards wearable cognitive assistance. In: Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services, 2014, pp.68–81.
14.
BittencourtLFDiaz-MontesJBuyyaR, et al.Mobility-aware application scheduling in fog computing. IEEE Cloud Comput2017; 4: 26–35.
15.
MengXWangWZhangZ. Delay-constrained hybrid computation offloading with cloud and fog computing. IEEE Access2017; 5: 21355–21367.
16.
PuthalDRanjanRNepalS,et al.Iot and big data: an architecture with data flow and security issues. In: Cloud infrastructures, services, and IoT systems for smart cities. Cham: Springer International Publishing, 2017, pp. 243–252.
17.
MaXGaoHXuH,et al.An IoT-based task scheduling optimization scheme considering the deadline and cost-aware scientific workflow for cloud computing. EURASIP J Wireless Commun Netw2019; 2019: 249.
18.
XiaoWBaoWZhuX,et al.Cost-aware big data processing across geo-distributed datacenters. IEEE Trans Parallel Distrib Syst2017; 28: 3114–3127.
19.
PayaAMarinescuDC. Energy-aware load balancing and application scaling for the cloud ecosystem. IEEE Trans Cloud Comput2017; 5: 15–27.
20.
TalebTSamdanisKMadaB, et al.On multi-access edge computing: a survey of the emerging 5G network edge cloud architecture and orchestration. IEEE Commun Surv Tutor2017; 19: 1657–1681.
21.
ChenXJiaoLLiW,et al.Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Trans Netw2016; 24: 2795–2808.
22.
EliasARGolubovicNKrintzC,et al.Where’s the bear?-automating wildlife image processing using IoT and edge cloud systems. In: 2017 IEEE/ACM Second International Conference on Internet-of-Things Design and Implementation (IoTDI), 2017, pp.247–258.
23.
WangJPanJEspositoF, et al.Edge cloud offloading algorithms: issues, methods, and perspectives. ACM Comput Surv2019; 52: 1–23.
24.
SahniYCaoJZhangS,et al.Edge mesh: a new paradigm to enable distributed intelligence in internet of things. IEEE Access2017; 5: 16441–16458.
25.
HongZChenWHuangH, et al.Multi-Hop cooperative computation offloading for industrial IoT-edge-cloud computing environments. IEEE Trans Parallel Distrib Syst2019; 30: 2759–2774.
26.
BonomiFMilitoRZhuJ,et al.Fog computing and its role in the internet of things. In: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, 2012, pp.13–16.
27.
BaktirACOzgovdeAErsoyC. How can edge computing benefit from software-defined networking: a survey, use cases, and future directions. IEEE Commun Surv Tutor2017; 19: 2359–2391.
28.
ChengZLiPWangJ,et al.Just-in-time code offloading for wearable computing. IEEE Trans Emerging Top Comput2015; 3: 74–83.
29.
YiSQinZLiQ. Security and privacy issues of fog computing: a survey. In: Wireless algorithms, systems, and applications. Cham: Springer International Publishing, 2015, pp. 685–695.
30.
SkarlatONardelliMSchulteS, et al.Optimized IoT service placement in the fog. Serv Oriented Comput Appl2017; 11: 427–443.
31.
ChenLXuJ. Socially trusted collaborative edge computing in ultra dense networks. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, 2017, pp.1–11.
32.
GrassiGJamiesonKBahlP,et al.Parkmaster: an in-vehicle, edge-based video analytics service for detecting open parking spaces in urban environments. In: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, 2017, pp.1–14.
33.
BellavistaPBerrocalJCorradiA, et al.A survey on fog computing for the internet of things. Pervasive Mob Comput2019; 52: 71–99.
34.
ShiWSunHCaoJ, et al.Edge computing-an emerging computing model for the internet of everything era. J Comput Res Dev2017; 54: 907–916.
35.
HwangCYoonK. Multiple attribute decision making: methods and applications a state-of-the-art survey. Berlin, Heidelberg: Springer Berlin Heidelberg, 1981.
36.
ZhangQChengLBoutabaR. Cloud computing: state-of-the-art and research challenges. J Internet Serv Appl2010; 1: 7–18.
37.
DebKJainH. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Trans Evol Comput2014; 18: 577–601.
38.
XiaoYKrunzM. Dynamic network slicing for scalable fog computing systems with energy harvesting. IEEE J Sel Areas Commun2018; 36: 2640–2654.
39.
YousefpourAFungCNguyenT, et al.All one needs to know about fog computing and related edge computing paradigms: a complete survey. J Syst Arch2019; 98: 289–330.
40.
VargheeseSSriramSSVinayakumarK,et al.A comprehensive tutorial and survey of applications on deep learning for cyber security. In: Advances in big data and cloud computing. Singapore: Springer Singapore, 2019, pp. 359–378.
41.
DengSZhaoHFangW, et al.Edge intelligence: the confluence of edge computing and artificial intelligence. IEEE Internet Things J2020; 7: 7457–7469.
42.
SatyanarayananMSimoensPXiaoY, et al.Edge analytics in the internet of things. IEEE Pervasive Comput2015; 14: 24–31.
43.
KangKWangJXiongJ, et al.Edge-Fog computing for intelligent IoT applications: a survey. In: 2020 IEEE 15th Conference on Industrial Electronics and Applications (ICIEA), 2020, pp.1692–1700.
44.
TranTXHajisamiAPandeyP,et al.Collaborative mobile edge computing in 5G networks: new paradigms, scenarios, and challenges. IEEE Commun Mag2017; 55: 54–61.
