Flexible Computation Offloading at the Edge for Autonomous Drones with Uncertain Flight Times

An ever increasing number of applications can employ aerial unmanned vehicles, or so-called drones, to perform different sensing and possibly also actuation tasks from the air. In some cases, the data that is captured at a given point has to be processed before moving to the next one. Drones can exploit nearby edge servers to offload the computation instead of performing it locally. However, doing this in a naive way can be suboptimal if servers have limited computing resources and drones have limited energy resources. In this paper, we propose a protocol and resource reservation scheme for each drone and edge server to decide, in a dynamic and fully decentralized way, whether to offload the computation and respectively whether to accept such an offloading requests, with the objective to evenly reduce the drones' mission times. We evaluate our approach through extensive simulation experiments, showing that it can significantly reduce the mission times compared to a no-offloading scenario by up to 26.2%, while outperforming an offloading schedule that has been computed offline by up to 7.4% as well as a purely opportunistic approach by up to 23.9%.Comment: 9 pages, 3 figures, preprint accepted in the 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT

RF Jamming Classification Using Relative Speed Estimation in Vehicular Wireless Networks

Wireless communications are vulnerable against radio frequency (RF) interference which might be caused either intentionally or unintentionally. A particular subset of wireless networks, Vehicular Ad-hoc NETworks (VANET), which incorporate a series of safety-critical applications, may be a potential target of RF jamming with detrimental safety effects. To ensure secure communications between entities and in order to make the network robust against this type of attacks, an accurate detection scheme must be adopted. In this paper, we introduce a detection scheme that is based on supervised learning. e k-nearest neighbors (KNN) and random forest (RaFo) methods are used, including features, among which one is the metric of the variations of relative speed (VRS) between the jammer and the receiver. VRS is estimated from the combined value of the useful and the jamming signal at the receiver. e KNN-VRS and RaFo-VRS classification algorithms are able to detect various cases of denial-of-service (DoS) RF jamming attacks and differentiate those attacks from cases of interference with very high accuracy

Towards Efficient Decentralized Federated Learning

We focus on the problem of efficiently deploying a federated learning training task in a decentralized setting with multiple aggregators. To that end, we introduce a number of improvements and modifications to the recently proposed IPLS protocol. In particular, we relax its assumption for direct communication across participants, using instead indirect communication over a decentralized storage system, effectively turning it into a partially asynchronous protocol. Moreover, we secure it against malicious aggregators (that drop or alter data) by relying on homomorphic cryptographic commitments for efficient verification of aggregation. We implement the modified IPLS protocol and report on its performance and potential bottlenecks. Finally, we identify important next steps for this line of research

A Design Pattern and Programming Framework for Interactive Metacomputing

Abstract. This paper presents ADIOS, a system for the development of interactively controlled metacomputations. In ADIOS, coarse grain distributed computations, involving several different program components, are structured according to a distributed Model-View-Controller architecture. Design support is offered via interfaces and a base class, through which the fundamental behavior of program components is established within a concrete framework. In addition, appropriate communication mechanisms are provided to back up the data exchange between the various components. Consisting of only few Java classes, ADIOS is lightweight and promotes disciplined prototyping of metacomputations while allowing for the integration of legacy code.

High-throughput sockets over RDMA for the Intel Xeon Phi coprocessor

In this paper we describe the design, implementation and performance of Trans4SCIF, a user-level socket-like transport library for the Intel Xeon Phi coprocessor. Trans4SCIF library is primarily intended for high-throughput applications. It uses RDMA transfers over the native SCIF support, in a way that is transparent for the application, which has the illusion of using conventional stream sockets. We also discuss the integration of Trans4SCIF with the ZeroMQ messaging library, used extensively by several applications running at CERN. We show that this can lead to a substantial, up to 3x, increase of application throughput compared to the default TCP/IP transport option

Fast Dynamic Binary Rewriting for Flexible Thread Migration on Shared-ISA Heterogeneous MPSoCs

Heterogeneous MPSoCs where different types of cores share a baseline ISA but implement different operational accelerators combine programmability with flexible customization. They hold promise for high performance under power and area limitations. However, transparent binary execution and dynamic scheduling is hard on those platforms. The state-of-the-art approach for transparent accelerated execution is fault-and-migrate (FAM): when a thread executes an accelerating instruction unavailable on the host core, it is forcibly migrated to an accelerating core which implements the instruction natively. Unfortunately, this approach prohibits dynamic scheduling through flexible thread migration, which is essential to any asymmetric platform for efficient utilization of heterogeneous resources. © 2014 IEEE

An open market-based architecture for distributed computing

Abstract. One of the challenges in large scale distributed computing is to utilize the thousands of idle personal computers. In this paper, we present a system that enables users to e ortlessly and safely export their machines in a global market of processing capacity. E cient resource allocation is performed based on statistical machine pro les and leases are used to promote dynamic task placement. The basic programming primitives of the system can be extended to develop class hierarchies which support di erent distributed computing paradigms. Due to the objectoriented structuring of code, developing a distributed computation can be as simple as implementing a few methods.