On the Stability of Isolated and Interconnected Input-Queued Switches under Multiclass Traffic

In this correspondence, we discuss the stability of scheduling algorithms for input-queueing (IQ) and combined input/output queueing (CIOQ) packet switches. First, we show that a wide class of IQ schedulers operating on multiple traffic classes can achieve 100 % throughput. Then, we address the problem of the maximum throughput achievable in a network of interconnected IQ switches and CIOQ switches loaded by multiclass traffic, and we devise some simple scheduling policies that guarantee 100 % throughput. Both the Lyapunov function methodology and the fluid modeling approach are used to obtain our results

Video upload from public transport vehicles using multihomed systems

Abstract: We consider a surveillance system for public transport vehicles, which is based on the collection of on-board videos, and the upload via mobile network to a central security system of video segments corresponding to those cameras and time intervals involved in an accident. We assume that vehicles are connected to several wireless interfaces, provided by different Mobile Network Operators (MNOs), each charging a different cost. Both the cost and the upload rate for each network interface change over time, according to the network load and the position of the vehicle. When a video must be uploaded to the central security, the system has to complete the upload within a deadline, deciding i) which interface(s) to use, ii) when to upload from that interface(s) and iii) at which rate to upload. The goal is to minimize the total cost of the upload, which we assume to be proportional to the data volume being transmitted and to the cost of using a given interface. We formalize the optimization problem and discuss greedy heuristics to solve it. Then, we discuss scientific and technical challenges to solve the system

Delay tolerant video upload from public vehicles

In this paper we study a surveillance system for public transport vehicles, which is based on the collection of on-board videos, and the upload via wireless transmission to a central security system of video segments corresponding to those cameras and time intervals involved in an accident. We assume that vehicles are connected to several wireless interfaces, provided by different Mobile Network Operators (MNOs), each charging a different cost. Both the cost and the upload rate for each network interface change over time, according to the network load and the position of the vehicle. When a video must be uploaded to the central security, the system has to complete the upload within a deadline, deciding i) which interface(s) to use, ii) when to upload from that interface(s) and iii) at which rate to upload. The goal is to minimize the total cost of the upload, which we assume to be proportional to the data volume being transmitted and to the cost of using a given interface. We formalize the optimization problem and propose greedy heuristics. Results are generated, using real wireless bandwidth traces, showing that one of the proposed greedy heuristics comes very close to the optimal solution

On the intertwining between capacity scaling and TCP congestion control

Recent works advocate the possibility of improving energy efficiency of network devices by modulating switching and transmission capacity according to traffic load. However, addressing the trade-off between energy saving and Quality of Service (QoS) under these approaches is not a trivial task, specially because most of the traffic in the Internet of today is carried by TCP, and is hence adaptive to the available resources. In this paper we present a preliminary investigation of the possible intertwining between capacity scaling approaches and TCP congestion control, and we show how this interaction can affect performance in terms of both energy saving and QoS

Adaptive schedulers for deadline-constrained content upload from mobile multihomed vehicles

We consider the practical problem of video surveillance in public transport systems, where security videos are stored onboard, and a central operator occasionally needs to access portions of the recordings. When this happens, the selected video must be uploaded within a deadline, possibly using multiple parallel wireless interfaces. Interfaces have different associated costs, related to tariffs charged by Mobile Network Operators (MNOs), energy consumption, data quotas, system load. Our goal is to choose which interfaces to use, and when, so as to minimize the cost of the upload while meeting the deadline, despite the unknown short-term variations in throughput. To achieve this goal, we first collect real traces of mobile uploads from vehicles for different MNOs. Examination of these traces confirms the unpredictability of the short-term throughput of wireless connections, and motivates the adoption of adaptive schedulers with limited a-priori knowledge of the system status. To effectively solve our problem, we devised a family of adaptive algorithms, that we thoroughly evaluated using a trace-driven approach. Results show that our adaptive approach can effectively leverage the fundamental tradeoff between the total cost and the delivery time of content upload, despite unknown short-term variations in throughput

The Importance of Worker Reputation Information in Microtask-Based Crowd Work Systems

This paper presents the first systematic investigation of the potential performance gains for crowd work systems, deriving from available information at the requester about individual worker reputation. In particular, we first formalize the optimal task assignment problem when workers’ reputation estimates are available, as the maximization of a monotone (sub-modular) function subject to Matroid constraints. Then, being the optimal problem NP-hard, we propose a simple but efficient greedy heuristic task allocation algorithm. We also propose a simple “maximum a-posteriori” decision rule and a decision algorithm based on message passing. Finally, we test and compare different solutions, showing that system performance can greatly benefit from information about workers’ reputation. Our main findings are that: i) even largely inaccurate estimates of workers’ reputation can be effectively exploited in the task assignment to greatly improve system performance; ii) the performance of the maximum a-posteriori decision rule quickly degrades as worker reputation estimates become inaccurate; iii) when workers’ reputation estimates are significantly inaccurate, the best performance can be obtained by combining our proposed task assignment algorithm with the message-passing decision algorithm