By Giuseppe Di Fatta, Giancarlo Fortino, Wenfeng Li, Mukaddim Pathan, Frederic Stahl, Antonio Guerrieri
This e-book constitutes the refereed court cases of the eighth foreign convention on net and allotted Computing structures, IDCS 2015, held in Windsor, united kingdom, in September 2015. the nineteen revised complete and six revised brief papers provided have been conscientiously reviewed and chosen from forty two submissions. the chosen contributions lined state-of-the-art points of Cloud Computing and web of items, sensor networks, parallel and dispensed computing, complicated networking, clever towns and shrewdpermanent structures, monstrous info and social networks.
Read or Download Internet and Distributed Computing Systems: 8th International Conference, IDCS 2015, Windsor, UK, September 2-4, 2015. Proceedings PDF
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Additional info for Internet and Distributed Computing Systems: 8th International Conference, IDCS 2015, Windsor, UK, September 2-4, 2015. Proceedings
Example text
When the applications are being executed in the testbed (IoT-lab), the average execution time increases since the code must be uploaded to the server and after that to IoT-lab. The server will then compile the code and generate the Web2Compile-CoT: A Web IDE for the Cloud of Things 27 Fig. 4. Average execution time sensor’s image. This image will be uploaded to the testbed. In the testbed, the application will be executed and the result returned to Web2Compile-CoT. All this process increases the execution delay.
We are using total 4 linguistic variables including VM selection as output. We have used trapezoidal membership function. Range of these membership functions is chosen from the simulation from Real cloud data of PlanetLab. In order to do so, we have run the simulation and collected data of all these variables and proportioned to decide the range. Membership functions of the linguistic variables are given below: • • • • RAM: T(RAM) = {Low, Medium, High} Correlation: T(Correlation) = {Low, Medium, High} Standard Deviation: T(Stdev) = {Low, Medium, High} VM selection: T(Vmselection) = {Low, Medium, High, Very High} (5) Fuzzy Inference Rule: Fuzzy inference rules are generated from the given linguistic variables.
VM Consolidation problem is divided into sub-problems and algorithms for each sub-problem had been designed. The sub-problems are: (i) Under load detection, (ii) Overload detection, (iii) VM selection and, (iv) VM placement. Heuristic based algorithms are designed for each sub-problems and designed in such a way that they can adapt and keep their threshold changing based on different scenario in different time so that they can still provide the functionality and consolidation decision in changed environment.
