To adapt to volatile edge environments this paper envisages distributed systems built from small units of coordi-nation called microcompositions. Microcompositions decompose a single goal into subgoals and declaratively express compo-sition and coordination constraints. These microcompositions are managed by agents that self-organise themselves into a distributed management overlay structure - a Goal Realisation Tree (GRT) - based on goal realisation (means-ends) links. Both microcompositions and GRTs serve as runtime models, allowing agents to dynamically restructure them in response to changes in goals, service provision and context. Results from our evaluation demonstrate the effectiveness and scalability of our approach.

Resilience and antifragility are highly desirable properties for systems operating in dynamic, contested environments. Recent work has proposed an approach for achieving antifragility through three new self-* properties, one of which is adversarial self-exploration. In that approach, a single agent is responsible for defending a (distributed) managed system against an adversary. However, a major limitation is that the agent requires full observability of the managed system and its environment, which is not practical in many scenarios - including when a system operates in contested environments. We address this limitation by extending the approach to multi-agent self-exploration, where agents with partial observability and control of the managed system coordinate with other agents to compute the most resilient responses in the presence of adversaries. We demonstrate the feasibility and scalability of the proposed approach through extensive experimentation.

Online advertisements and third-party web tracking has gained much attention in recent years. Advertisers gather as much data and information about the users to provide targeted advertisement. Though this leads to a better user experience, it comes at the cost of privacy intrusive tracking. To this end, ad-blocking lists (or filter-lists, blacklists) have been introduced which prevent third-party tracking. Ad-blocking lists operate in a crowd-sourced manner, where new tracking domains (or rules) are continuously added by privacy activists and the redundant domains are discarded from the filter-list. Over time, the number of rules added outgrow the number of rules omitted, making it hard to manage the filter-lists. We empirically observe that the filter-lists mostly detect different ad and tracking domains. The filter-lists also use less than 1% of their rules on Alexa top 5,000 websites. This suggests the need to curate optimized filter-lists that provide high coverage and require less time to scan for a given domain on mobile devices. We develop an aggregated and filtered blacklist that is more than 150 times less bulky, and provides the same coverage as the union of the blacklists on Alexa top 5,000 websites. We also develop an update mechanism to incorporate new ad and tracking domains in the aggregated and filtered blacklist in a resource efficient manner.