Manuel Malatyali

On Competitive Algorithms for Approximations of Top-k-Position Monitoring of Distributed Streams

Consider the continuous distributed monitoring model in which n distributed nodes, receiving individual data streams, are connected to a designated server. The server is asked to continuously monitor a function defined over the values observed across all streams while minimizing the communication. We study a variant in which the server is equipped with a broadcast channel and is supposed to keep track of an approximation of the set of nodes currently observing the k largest values. Such an approximate set is exact except for some imprecision in an -neighborhood of the k-th largest value. This approximation of the Top-k-Position Monitoring Problem is of interest in cases where marginal changes (e.g. due to noise) in observed values can be ignored so that monitoring an approximation is sufficient and can reduce communication. This paper extends our results from [6], where we have developed a filter-based online algorithm for the (exact) Top-k-Position Monitoring Problem. There we have presented a competitive analysis of our algorithm against an offline adversary that also is restricted to filter-based algorithms. Our new algorithms as well as their analyses use new methods. We analyze their competitiveness against adversaries that use both exact and approximate filter-based algorithms, and observe severe differences between the respective powers of these adversaries.

Non-preemptive Scheduling on Machines with Setup Times

Consider the problem in which n jobs that are classified into k types are to be scheduled on m identical machines without preemption. A machine requires a proper setup taking s time units before processing jobs of a given type. The objective is to minimize the makespan of the resulting schedule. We design and analyze an approximation algorithm that runs in time polynomial in n, m and k and computes a solution with an approximation factor that can be made arbitrarily close to \({^3 /_2}\).

On Two-Party Communication through Dynamic Networks

We study two-party communication in the context of directed dynamic networks that are controlled by an adaptive adversary. This adversary is able to change all edges as long as the networks stay strongly-connected in each round. In this work, we establish a relation between counting the total number of nodes in the network and the problem of exchanging tokens between two communication partners which communicate through a dynamic network. We show that the communication problem for a constant fraction of n tokens in a dynamic network with n nodes is at most as hard as counting the number of nodes in a dynamic network with at most 4n + 3 nodes. For the proof, we construct a family of directed dynamic networks and apply a lower bound from two-party communication complexity.

Monitoring of Domain-Related Problems in Distributed Data Streams

Consider a network in which n distributed nodes are connected to a single server. Each node continuously observes a data stream consisting of one value per discrete time step. The server has to continuously monitor a given parameter defined over all information available at the distributed nodes. That is, in any time step t, it has to compute an output based on all values currently observed across all streams. To do so, nodes can send messages to the server and the server can broadcast messages to the nodes. The objective is the minimisation of communication while allowing the server to compute the desired output.

Online Top-k-Position Monitoring of Distributed Data Streams

Consider n nodes connected to a single coordinator. Each node receives an individual online data stream of numbers and, at any point in time, the coordinator has to know the k nodes currently observing the largest values, for a given k between 1 and n. We design and analyze an algorithm that solves this problem while bounding the amount of messages exchanged between the nodes and the coordinator. Our algorithm employs the idea of using filters which, intuitively speaking, leads to few messages to be sent, if the new input is “similar” to the previous ones. The algorithm uses a number of messages that is on expectation by a factor of O ((log Δ + k) · log n) larger than that of an offline algorithm that sets filters in an optimal way, where Δ is upper bounded by the largest value observed by any node.

Token Dissemination in Geometric Dynamic Networks

We consider the \(k\)-token dissemination problem, where \(k\) initially arbitrarily distributed tokens have to be disseminated to all nodes in a dynamic network (as introduced by Kuhn et al. STOC 2010). In contrast to general dynamic networks, our dynamic networks are unit disk graphs, i.e., nodes are embedded into the Euclidean plane and two nodes are connected if and only if their distance is at most \(R\). Our worst-case adversary is allowed to move the nodes on the plane, but the maximum velocity \(v_{\max }\) of each node is limited and the graph must be connected in each round. For this model, we provide almost tight lower and upper bounds for \(k\)-token dissemination if nodes are restricted to send only one token per round. It turns out that the maximum velocity \(v_{\max }\) is a meaningful parameter to characterize dynamics in our model.

A Communication-Efficient Distributed Data Structure for Top- k and k -Select Queries

We consider the scenario of

Non-clairvoyant Scheduling to Minimize Max Flow Time on a Machine with Setup Times

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Cost-Efficient Scheduling on Machines from the Cloud

sensor nodes observing streams of data. The nodes are connected to a central server whose task it is to compute some function over all data items observed by the nodes. In our case, there exists a total order on the data items observed by the nodes. Our goal is to compute the

Cost-efficient scheduling on machines from the cloud

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