The Weakest Failure Detector for Genuine Atomic Multicast

Atomic broadcast is a group communication primitive to order messages across a set of distributed processes. Atomic multicast is its natural generalization where each message m is addressed to dst(m), a subset of the processes called its destination group. A solution to atomic multicast is genuine when a process takes steps only if a message is addressed to it. Genuine solutions are the ones used in practice because they have better performance. Let ? be all the destination groups and ? be the cyclic families in it, that is the subsets of ? whose intersection graph is hamiltonian. This paper establishes that the weakest failure detector to solve genuine atomic multicast is ? = (?_{g,h ? ?} ?_{g ? h}) ? (?_{g ? ?} ?_g) ? ?, where ?_P and ?_P are the quorum and leader failure detectors restricted to the processes in P, and ? is a new failure detector that informs the processes in a cyclic family f ? ? when f is faulty. We also study two classical variations of atomic multicast. The first variation requires that message delivery follows the real-time order. In this case, ? must be strengthened with 1^{g ? h}, the indicator failure detector that informs each process in g ? h when g ? h is faulty. The second variation requires a message to be delivered when the destination group runs in isolation. We prove that its weakest failure detector is at least ? ? (?_{g, h ? ?} ?_{g ? h}). This value is attained when ? = ?

SGX-Aware Container Orchestration for Heterogeneous Clusters

Containers are becoming the de facto standard to package and deploy applications and micro-services in the cloud. Several cloud providers (e.g., Amazon, Google, Microsoft) begin to offer native support on their infrastructure by integrating container orchestration tools within their cloud offering. At the same time, the security guarantees that containers offer to applications remain questionable. Customers still need to trust their cloud provider with respect to data and code integrity. The recent introduction by Intel of Software Guard Extensions (SGX) into the mass market offers an alternative to developers, who can now execute their code in a hardware-secured environment without trusting the cloud provider. This paper provides insights regarding the support of SGX inside Kubernetes, an industry-standard container orchestrator. We present our contributions across the whole stack supporting execution of SGX-enabled containers. We provide details regarding the architecture of the scheduler and its monitoring framework, the underlying operating system support and the required kernel driver extensions. We evaluate our complete implementation on a private cluster using the real-world Google Borg traces. Our experiments highlight the performance trade-offs that will be encountered when deploying SGX-enabled micro-services in the cloud.Comment: Presented in the 38th IEEE International Conference on Distributed Computing Systems (ICDCS 2018

Resilience-Building Technologies: State of Knowledge -- ReSIST NoE Deliverable D12

This document is the first product of work package WP2, "Resilience-building and -scaling technologies", in the programme of jointly executed research (JER) of the ReSIST Network of Excellenc

Congestion adaptive traffic light control and notification architecture using Google maps APIs

Mishra, S., Bhattacharya, D., & Gupta, A. (2018). Congestion Adaptive Traffic Light Control and Notification Architecture Using Google Maps APIs. Data, 3(4), [67]. DOI: 10.3390/data3040067Traffic jams can be avoided by controlling traffic signals according to quickly building congestion with steep gradients on short temporal and small spatial scales. With the rising standards of computational technology, single-board computers, software packages, platforms, and APIs (Application Program Interfaces), it has become relatively easy for developers to create systems for controlling signals and informative systems. Hence, for enhancing the power of Intelligent Transport Systems in automotive telematics, in this study, we used crowdsourced traffic congestion data from Google to adjust traffic light cycle times with a system that is adaptable to congestion. One aim of the system proposed here is to inform drivers about the status of the upcoming traffic light on their route. Since crowdsourced data are used, the system does not entail the high infrastructure cost associated with sensing networks. A full system module-level analysis is presented for implementation. The system proposed is fail-safe against temporal communication failure. Along with a case study for examining congestion levels, generic information processing for the cycle time decision and status delivery system was tested and confirmed to be viable and quick for a restricted prototype model. The information required was delivered correctly over sustained trials, with an average time delay of 1.5 s and a maximum of 3 s.publishersversionpublishe