Researcher:
Güler, Hüseyin

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PhD Student

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Hüseyin

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Güler

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Güler, Hüseyin

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2013 - 20153

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Researcher Of Publications: search.filters.isAuthorOfPublication.130d8a5c-25ff-4c8d-9d8f-b3bc0ea0dc68

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Now showing 1 - 3 of 3
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    Task allocation in volunteer computing networks under monetary budget constraints
    (Springer, 2015) Barla Cambazoglu, B.; Department of Computer Engineering; Department of Computer Engineering; Güler, Hüseyin; Özkasap, Öznur; PhD Student; Faculty Member; Department of Computer Engineering; College of Engineering; College of Engineering; N/A; 113507
    In volunteer computing networks, the peers contribute to the solution of a computationally intensive problem by freely providing their computational resources, i.e., without seeking any immediate financial benefit. In such networks, although the peers can set certain bounds on how much their resources can be exploited by the network, the monetary cost that the network brings to the peers is unclear. In this work, we propose a volunteer computing network where the peers can set monetary budgets, limiting the financial burden incurred on them due the usage of their computational resources. Under the assumption that the price of the electricity consumed by the peers has temporal variation, we show that our approach leads to an interesting task allocation problem, where the goal is to maximize the amount of work done by the peers without violating the monetary budget constraints set by them. We propose various heuristics as solution to the problem, which is NP-hard. Our extensive simulations using realistic data traces and real-life electricity prices demonstrate that the proposed techniques considerably increase the amount of useful work done by the peers, compared to a baseline technique.
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    Online client assignment in dynamic real-time distributed interactive applications
    (Ieee, 2013) N/A; N/A; Department of Computer Engineering; Uçar, Seyhan; Güler, Hüseyin; Özkasap, Öznur; PhD Student; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 113507
    Qulaity of user experience in Distributed Interactive Applications (DIAs) highly depends on the network latencies during the system execution. In DIAs, each user is assigned to a server and communication with any other client is performed throught its assigned server. Hence, latency measured between two clients, called interaction time, consists of two components. One is the latency between the client and its assigned server, and the other is the inter-server latency, that is the latency between servers that the clients are assigned. In this paper, we investigate a real-time client to server assignment scheme in a DIA where the objective is to minimize the interaction time among clients. The client assignment problem is known to be NP-complete and heuristics play an important role in finding near optimal solutions. We propose two distributed heuristic algorithms to the online client assignment problem in a dynamic DIA system. We utilized real-time Internet latency data on PlanetLab platform and performed extensive 3 experiments using geographically distributed PlanetLab nodes where nodes can arbitrarily join/leave the system. The experimental results demonstrate that our proposed algorthims can reduce the maximum interaction time among clients up to 45% compared to an exiting baseline technique.
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    Cutting down the energy cost of geographically distributed cloud data centers
    (Springer-Verlag Berlin, 2013) Cambazoğlu, Berkant Barla; N/A; Department of Computer Engineering; Güler, Hüseyin; Özkasap, Öznur; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 113507
    The energy costs constitute a significant portion of the total cost of cloud providers. The major cloud data centers are often geographically distributed, and this brings an opportunity to minimize their energy cost. In this work, we model a geographically distributed data center network that is specialized to run batch jobs. Taking into account the spatio-temporal variation in the electricity prices and the outside weather temperature, we model the problem of minimizing the energy cost as a linear programming problem. We propose various heuristic solutions for the problem. Our simulations using real-life workload traces and electricity prices demonstrate that the proposed heuristics can considerably decrease the total energy cost of geographically distributed cloud data centers, compared to a baseline technique.