System Stability Under Adversarial Injection of Dependent Tasks

Technological changes (NFV, Osmotic Computing, Cyber-physical Systems) are making very important devising techniques to efficiently run a flow of jobs formed by dependent tasks in a set of servers. These problem can be seen as generalizations of the dynamic job-shop scheduling problem, with very rich dependency patterns and arrival assumptions. In this work, we consider a computational model of a distributed system formed by a set of servers in which jobs, that are continuously arriving, have to be executed. Every job is formed by a set of dependent tasks (i. e., each task may have to wait for others to be completed before it can be started), each of which has to be executed in one of the servers. The arrival of jobs and their properties is assumed to be controlled by a bounded adversary, whose only restriction is that it cannot overload any server. This model is a non-trivial generalization of the Adversarial Queuing Theory model of Borodin et al., and, like that model, focuses on the stability of the system: whether the number of jobs pending to be completed is bounded at all times. We show multiple results of stability and instability for this adversarial model under different combinations of the scheduling policy used at the servers, the arrival rate, and the dependence between tasks in the jobs

Ensuring Uniformity in Random Peer Sampling Services

The peer sampling service is a core building block for gossip protocols in peer-to-peer networks. Ideally, a peer sampling service continuously provides each peer with a sample of peers picked uniformly at random in the network. While empirical studies have shown that uniformity was achieved, analysis proposed so far assume strong restrictions on the topology of the overlay network it continuously generates. In this work, we analyze a Generic Random Peer Sampling Service (GRPS) that satisfies the desirable properties for any peer sampling service –small views, uniform sample, load balancing, and independence– and relieve strong degree connections in the nodes assumed in previous works. The main result we prove is: starting from any simple (without loops and parallel edges) directed graph with out-degree equal to c for all nodes, and recursively applying GRPS, eventually results in a random simple directed graph with out-degree equal to c for all nodes. We test empirically convergence time and independence time for GRPS. We use this empirical evaluation to show that GRPS performs better than previously presented peer sampling services. We also present a variant of GRPS that ensures that the in and out-degrees of nodes in the initial network are maintained in the resulting graph. Finally, we discuss on how to deal with new nodes in both settings

An Early-stopping Protocol for Computing Aggregate Functions in Sensor Networks

International audienceIn this paper, we study algebraic aggregate com- putations in Sensor Networks. The main contribution is the presentation of an early-stopping protocol that computes the average function under a harsh model of the conditions under which sensor nodes operate. This protocol is shown to be time-optimal in presence of unfrequent failures. The approach followed saves time and energy by relying the computation on a small network of delegate nodes that can be rebuilt fast in case of node failures and communicate using a collision- free schedule. Delegate nodes run simultaneously two protocols, namely, a collection/dissemination tree-based algorithm, which is shown to be optimal, and a mass-distribution algorithm. Both algorithms are analyzed under a model where the frequency of failures is a parameter. Other aggregate computation algo- rithms can be easily derived from this protocol. To the best of our knowledge, this is the first optimal early-stopping algorithm for aggregate computations in Sensor Networks

Deterministic Recurrent Communication and Synchronization in Restricted Sensor Networks

Monitoring physical phenomena in Sensor Networks requires guaranteeing permanent communication between nodes. Moreover, in an eective implementation of such infrastructure, the delay between any two consecutive communications should be minimized. The problem is challenging because, in a restricted Sensor Network, the communication is carried out through a single and shared radio channel without collision detection. Dealing with collisions is crucial to ensure eective communication between nodes. Additionally, minimizing them yields energy consumption minimization, given that sensing and computational costs in terms of energy are negligible with respect to radio communication. In this work, we present a deterministic recurrent-communication protocol for Sensor Networks. After an initial negotiation phase of the access pattern to the channel, each node running this protocol reaches a steady state, which is asymptotically optimal in terms of energy and time effciency. As a by-product, a protocol for the synchronization of a Sensor Network is also proposed. Furthermore, the protocols are resilient to an arbitrary node power-up schedule and a general node failure model