SocialEdge: Enabling Trusted Data Processing Workflow in Smart Communities In typical commercial cloud and edge providers data collection and processing nodes might be owned by a single trusted entity. In contrast, in a voluntary infrastructure, ownership is distributed. In such a setup, while individuals contributing data might demand constraints on how their data is accessed and used, volunteers of compute resources might want assurance that their resources are used only for intended purposes. These significant differences motivate SocialEdge, an edge orchestration framework that 1) leverages online social network for bootstrapping and permissioned block chain for information assurance, 2) exploits recursive containerization for enabling secure data access, ensuring only intended workloads are run and enforcing finegrained resource control, and 3) leverages components from Kubernetes to instantiate and manage an ephemeral cluster composed of volunteered resources. In this paper we describe the design and prototype implementation of SocialEdge and model the problem of orchestrating the ondemand data processing infrastructure under data access constraints as a variant of the multicommodity flow problem. We propose and evaluate mixed integer programming and greedy heuristic approaches, and characterize recursive containerized environment experimentally using benchmarks.

Design and Evaluation of Decentralized Scaling Mechanisms for Stream Processing Over the last decade, stream processing (SP) gained momentum as an efficient paradigm dealing with the realtime processing of large streams of data. As mature as they are, mainstream stream processing engines are not ready to be used over more distributed platforms that are today emerging such as Fog platforms. Streams being produced at the edge of the platform, and edges being composed of less powerful compute nodes, a decentralized approach of Stream Processing is needed. This paper moves a step towards decentralized SP by proposing a decentralized autoscaling mechanism for SP applications. Autoscaling refers to the ability for a system to automatically scale in and out as the load varies. As a stream processing application is generally composed of a set of operators in a pipeline, our protocol lets each operatoru0027s replica take scaling decisions and enforce them independently. Assuming a fully decentralized setting, we consider each replica has only a partial view of the graph of operators. Then, without care, concurrent distributed scaling could lead to inconsistencies in replicasu0027 views and to data loss. To address this issue, we devise a maintenance protocol ensuring the consistency of replicasu0027 views, no data loss and no downtime during reconfigurations. Finally, the paper describes the early experimentations conducted with a software prototype of a decentralized SP engine over a computing cluster, showing the usability of the mechanism.