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Although the concept of elasticity can introduce significant QoS and cost benefits, its implementation in real systems is full of challenges. Indeed, nowadays systems are mainly distributed, built upon several layers of abstraction, and with centralized control mechanisms. In such a complex environment, controlling elasticity in a centralized manner might strongly penalize scalability. To overcome this issue, we can conveniently split the system in autonomous subsystems that implement elasticity mechanisms and run control policies in a decentralized manner. To efficiently and effectively cooperate with each other, the subsystems need to communicate among themselves to determine elasticity decisions that collectively improve the overall system performance. This new architecture calls for the development of new mechanisms and efficient policies. In this chapter, we focus on elasticity in IoT and Cloud-based systems, which can be geo-distributed also at the edge of the networks, and discuss its engineering perspectives along with various coordination mechanisms. We focus on the design choices that may affect the elasticity properties and provide an overview of some decentralized design patterns related to the coordination of elasticity decisions.<\/jats:p>","DOI":"10.1007\/978-3-319-90415-3_8","type":"book-chapter","created":{"date-parts":[[2018,5,24]],"date-time":"2018-05-24T17:12:54Z","timestamp":1527181974000},"page":"182-211","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["QoS-Based Elasticity for Service Chains in Distributed Edge Cloud Environments"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6870-7083","authenticated-orcid":false,"given":"Valeria","family":"Cardellini","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4351-4082","authenticated-orcid":false,"given":"Tihana","family":"Galinac Grbac","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9519-9387","authenticated-orcid":false,"given":"Matteo","family":"Nardelli","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3936-1244","authenticated-orcid":false,"given":"Nikola","family":"Tankovi\u0107","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1465-9722","authenticated-orcid":false,"given":"Hong-Linh","family":"Truong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,5,25]]},"reference":[{"key":"8_CR1","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., et al.: TensorFlow: a system for large-scale machine learning. 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