MIFaaS: A Mobile-IoT-Federation-as-a-Service Model for dynamic cooperation of IoT Cloud Providers

Abstract

In the Internet of Things (IoT) arena, a constant evolution is observed towards the deployment of integrated environments, wherein heterogeneous devices pool their capacities to match wide-ranging user requirements. Solutions for efficient and synergistic cooperation among objects are, therefore, required. This paper suggests a novel paradigm to support dynamic cooperation among private/public local clouds of IoT devices. Differently from . device-oriented approaches typical of Mobile Cloud Computing, the proposed paradigm envisages an . IoT Cloud Provider (ICP)-oriented cooperation, which allows all devices belonging to the same private/public owner to participate in the federation process. Expected result from dynamic federations among ICPs is a remarkable increase in the amount of service requests being satisfied. Different from the Fog Computing vision, the network edge provides only management support and supervision to the proposed Mobile-IoT-Federation-as-a-Service (MIFaaS), thus reducing the deployment cost of peripheral micro data centers. The paper proposes a coalition formation game to account for the interest of rational cooperative ICPs in their own payoff. A proof-of-concept performance evaluation confirms that obtained coalition structures not only guarantee the satisfaction of the players' requirements according to their utility function, but also these introduce significant benefits for the cooperating ICPs in terms of number of tasks being successfully assigned

ENORM: A Framework For Edge NOde Resource Management

“…This model has been employed in video transcribing and face recognition use-cases [22]. The obvious disadvantage of this model is that it can only cover devices in a small region such as a shopping mall [23] or buses/trains [24] and is known for unstable computing due to the mobility of devices [25].…”

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“…Nesta direção, a resposta para esta questão é ilustrada na Fig. 4, que apresenta um gráfico de frequência, representando as ferramenta utilizadas Algoritmos [35] Complex Network Teory [45] Edge Balancing [46] Modified Best Fit Decreasing (MBFD) [46] Maximum Density Consolidation (MDC) [47] Hungarian Algorithm [48] Teoria dos Jogos [49] RR [49] NPS [49] Random Number Generator (RNG) [50], [23], [26], [43] ILP [22], [37] GA [51], [52] Alternating Direction Method of Multipliers (ADMM) [9] Edge-ward Placement [39] MPSO-CO [36] Adaptive Algorithm [38], [24] Heurística Gulosa [38] Vineyard Algorithms [53] MINLP [22] First-Fit [28] Lyapunov Optimization para validação dos experimentos gerenciamento de recursos em Fog Computing.…”

“…Ferramenta de Simulação [25], [56], [45] Cloudsim [50], [36], [38], [53] iFogSim [46], [22], [24], [9], [57] IBM CEPLEX [47], [48] Matlab [58], [52], [37], [28] Não Informa plataforma utilizadas em experimentos e na implementação da Fog Computing. [60], [61], [62], [63], [64] ARM [65] Intel Edison 4.4.…”

“…Trabalhos Domínio [25], [56], [51], [9], [38] Multimídia [35], [22] Industria 4.0 [23], [50], [45], [24], [36], [37], [62], [63], [28], [65], [52], [48] Genérico [59] Smart Home [60], [47], [26], [69] Smart Cities [46] Smart Grid [61] Smart Transportation System [9], [66] Healthcare [58] Internet of Vehicles (IoV)…”

Traffic Management for Cloud Federation

“…Atzori et al [64,65] examined IoT systems in a survey. They identified many application scenarios, and classified them into five application domains: transportation and logistics, healthcare, smart environments (home, office, plant), personal, social and futuristic domains.…”