Handling service allocation in combined Fog-cloud scenarios

Abstract

The recent technological advances related to computing, storage, cloud, networking and the unstoppable deployment of end-user devices, are all coining the so-called Internet of Things (IoT). IoT embraces a wide set of heterogeneous services in highly impacting societal sectors, such as Healthcare, Smart Transportation or Media\ud delivery, all of them posing a diverse set of requirements, including real time response, low latency, or high capacity. In order to properly address such diverse set of requirements, the combined use of Cloud and Fog computing turns up as an emerging trend. Indeed, Fog provides low delay for services demanding real time response, constrained to support low capacity queries, whereas Cloud provides high capacity at the cost of a higher latency. It is with no doubt that a\ud new strategy is required to ease the combined operation of cloud and fog infrastructures in IoT scenarios, also referred to as Combined Fog-Cloud (CFC), in terms of service execution performance metrics. To that end, in this paper, we introduce and formulate the QoS-aware service allocation problem for CFC architectures as an integer optimization problem, whose solution minimizes the latency experienced by the services while guaranteeing the fulfillment of the\ud capacity requirements.Peer ReviewedPostprint (published version

How to place your apps in the fog: State of the art and open challenges

“…Mahmud et al also considered a general resource value, but they additionally defined the service demand and device capacity in terms of the expected and offered processing times. On the contrary, this model was sometimes simplified to a scalar value that represents a general capacity unit or with general resources slots . Finally, some other papers defined the hardware resources of the fog computing nodes, but they did not include this constraint in the optimization process to simplify it …”

“…On the contrary, link reliability is seldom considered in the analyzed research works. Various works solely considered latency among the metrics related with the network, 26,42,49,56,57,59 often but not always along with bandwidth. 24,[28][29][30][31][32]43,58 Bandwidth was also considered by Taneja and Davy 36 and Arkian et al 38 along with hardware constraints.…”

“…On the contrary, this model was sometimes simplified to a scalar value that represents a general capacity unit 27,35,[51][52][53] or with general resources slots. 42,59 Finally, some other papers defined the hardware resources of the fog computing nodes, but they did not include this constraint in the optimization process to simplify it. 34 In other papers, the hardware is considered variables of a fitness function.…”

“…,42 Souza et al42 reduced the execution time of F I G U R E 5 Taxonomy of fog application placement problem optimization metrics [Colour figure can be viewed at wileyonlinelibrary.com]…”

A cost- and performance-effective approach for task scheduling based on collaboration between cloud and fog computing

“…Based on the collaboration between cloud and fog computing, Alsaffar et al 23 present an architecture of IoT service delegation and resource allocation, in which user requests can be efficiently managed and delegated to appropriate cloud or fog based on linearized decision tree which considers tree conditions (service size, completion time, and VM capacity). Souza and collegues 24,25 address QoS-aware service allocation problem in a combined cloud-fog architecture consisting a duallayer fog aiming to diminish the cloud access delay in IoT scenarios. Meanwhile, Deng et al 7 investigate the tradeoff between power consumption and delay in a cloud-fog computing system.…”