There is a growing demand for live, on-the-fly processing of increasingly large amounts of data. In order to ensure the timely and reliable processing of streaming data, a variety of distributed stream processing architectures and platforms have been developed, which handle the fundamental tasks of (dynamically) assigning processing tasks to the currently available physical resources and routing streaming data between these resources. However, while there are plenty of platforms offering such functionality, the theory behind it is not well understood. In particular, it is unclear how to best allocate the processing tasks to the given resources.In this paper, we establish a theoretical foundation by formally defining a task allocation problem for distributed stream processing, which we prove to be NP-hard. Furthermore, we propose an approximation algorithm for the class of seriesparallel decomposable graphs, which captures a broad range of common stream processing applications. The algorithm achieves a constant-factor approximation under the assumptions that the number of resources scales at least logarithmically with the number of computational tasks and the computational cost of the tasks dominates the cost of communication.
This paper studies the question of how to overcome inefficiencies due to hidden actions in a rational milieu, such as a grid computing system with open clientele. We consider the so-called principal-agent model known from economic theory, where the members (or agents) of a distributed system collaborate in complex ways. We adopt the perspective of the principal and investigate auditing mechanisms that incentivize participants to contribute more to a common project. As conducting audits might be costly, the principal must balance the tradeoff between low auditing costs and the level of incentives offered to the participants to exert high effort. We present optimal solutions for this optimization problem in scenarios, where the project success either depends on all, on any or on the majority of the participants succeeding in their subtask. In the first case, we further find that with an increasing principal valuation, there is exactly one transition point where the optimal choices for achieving the maximal principal utility switch. Compared to a combinatorial agency without the leverage of audits, this transition occurs earlier.
We consider the question of how a conspiring subgroup of peers in a p2p network can find each other and communicate without provoking suspicion among regular peers or an authority that monitors the network. In particular, we look at the problem of how a conspirer can broadcast a message secretly to all fellow conspirers. As a subproblem of independent interest, we study the problem of how a conspirer can safely determine a connected peer's type, i.e., learning whether the connected peer is a conspirer or a regular peer without giving away its own type in the latter case. For several levels of monitoring, we propose distributed and efficient algorithms that transmit hidden information by varying the block request sequence meaningfully. We find that a p2p protocol offers several steganographic channels through which hidden information can be transmitted, and p2p networks are susceptible to hidden communication even if they are completely monitored.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.