A Methodology based on Computational Patterns for Offloading of Big Data Applications on CloudEdge Platforms Internet of Things (IoT) is becoming a widespread reality, as interconnected smart devices and sensors have overtaken the IT market and invaded every aspect of the human life. This kind of development, while already foreseen by IT experts, implies additional stress to already congested networks, and may require further investments in computational power when considering centralized and Cloud based solutions. That is why a common trend is to rely on local resources, provided by smart devices themselves or by aggregators, to deal with part of the required computations: this is the base concept behind Fog Computing, which is becoming increasingly adopted as a distributed calculation solution. In this paper a methodology, initially developed within the TOREADOR European project for the distribution of Big Data computations over Cloud platforms, will be described and applied to an algorithm for the prediction of energy consumption on the basis of data coming from home sensors, already employed within the CoSSMic European Project. The objective is to demonstrate that, by applying such a methodology, it is possible to improve the calculation performances and reduce communication with centralized resources.

Efficient resource management and workload allocation in fogxe2x80x93cloud computing paradigm in IoT using learning classifier systems Abstract   With the rapid growth in networkconnected computing devices, the Internet of Things (IoT) has progressed in terms of size and speed. Subsequently, the amount of produced data and computation loads has increased dramatically. A solution to handle this huge volume of workloads is cloud computing in which a considerable delay exists in the processing load and this has remained a concern in the field of distributed computing networks. Processing workloads at the edge of the network can reduce the response time while at the same time imposing energy constraints by bringing the task of load processing from data centers, which are supplied by electrical energy sources, to the network edges which are only supported by limited energies of batteries. Therefore, workloads need to be distributed evenly between the clouds and the edges of the network. In this paper, two methods based on XCS learning classifier systems (LCS), namely, XCS and BCMXCS, are proposed to balance the power consumption at the edge of the network and to reduce delays in the processing of workloads. The results of our experiments are indicative of the superiority of BCMXCS over the basic XCSbased method. The proposed methods distribute the workloads in a way that the delay in their processing and the communication delay between the cloud and fog nodes are both minimized. In addition to considerable advantages in controlling the fluctuations of the processing delay, the proposed methods can simultaneously reduce the processing delay by 42% by using a moderate power consumption at the edge of the network. The proposed methods can also recharge the renewable batteries used at the edge of the network about 18 percent more than the best stateoftheart method.