The Redundant Energy Consumption Laxity Based Algorithm to Perform Computation Processes for IoT Services Abstract   There are various kinds of Internet of Things (IoT) services like home management services. In the IoT services, a large volume of data is gathered from various types of smart devices and is manipulated in scalable, high performance, and faulttolerant computing systems like cloud computing systems. Reliable and available IoT services can be provided by replicating each application process on multiple virtual machines in a server cluster system. On the other hand, a server cluster system with process replicas consumes a larger amount of electric energy than nonreplication approaches. Hence, not only faulttolerant but also energyefficient server cluster systems by using virtual machine technologies are required to realize IoT services. In this paper, a redundant energy consumption laxity based (RECLB) algorithm to redundantly and energyefficiently perform each application process on multiple virtual machines is newly proposed. The RECLB algorithm is evaluated in terms of the total electric energy consumption of a server cluster and the average computation time of each process in both homogeneous and heterogeneous server clusters.

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.