Architecture of Computing Systems (Lecture Notes in Computer ...

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182 T. Abdullah et al. (except when d = n) and cannot search the resources of all participating nodes. Figure 3a and 3b compare the number of messages required to find a match in the varying the degree of neighborhood approach with centralized and multiple adaptive matchmakers approach, respectively, in the ad hoc grid. Although the distribution of ad hoc grid nodes among two or more matchmakers vary according to the workload of the matchmakers [12], we assume ni = n/m for the case of multiple adaptive matchmakers, while comparing the message complexity for varying the degree of neighborhood in completely decentralized approach with other two approaches in Figure 3a and 3b. 1MM represents one matchmaker of the centralized approach, whereas 2MM, ..., 5MM represent two or multiple matchmakers of the multiple adaptive matchmakers approach in Figure 3b. The number of messages exchanged in different resource discovery approaches are summarized in Table 1. 6 Experimental Setup and Results We developed our ad hoc grid experimental platform on top of Pastry [15]. Although we used Pastry, in principle any other structured overlay network can be used. Pastry is a self-organizing and adaptive overlay network. Pastry is used for node arrival/departure, node failure handling, and for message routing in this work. Node join/leave and Pastry message routing is explained in [15]. The experiments are executed on PlanetLab [16]. PlanetLab is a global, community-based effort and is used mostly for network related experiments. The PlanetLab nodes are connected through the Internet. Research institutions/ organizations contribute a minimum of 2 computing machines. The researchers of the corresponding institute/organization are granted access to a pool of more than 1000 PlanetLab nodes. The experiments are executed to answer the questions discussed in Section 1. The first set of experiments are executed to analyze the effect of varying the degree of neighborhood. In the second set of experiments, the experimental results with varying the degree of neighborhood are compared with total centralized approach and with multiple adaptive matchmakers approach for resource discovery in an ad hoc grid. The number of participating nodes varies from 15 to 650. The number of matchmakers varies from 1 to 5 in the experiments with multiple adaptive matchmakers. TTL of the request/offer messages is set to 10000 milliseconds in order to cater the delays observed in PlanetLab. In this work, we have only considered computational power (CPU cycle) as a resource. However, other resources like memory, bandwidth and disk storage can also be incorporated in this model. The job execution time, job deadline, budget, and request/offer computational resource amount are randomly generated from a predefined range. The request/offer resource quantity varies for each request/offer message. Data presented is obtained after the system reaches a steady state, when 1/4th of the experiment time is elapsed.
Effect of the Degree of Neighborhood on Resource Discovery 183 Matchmaking efficiency, response time and the message complexity are analyzed in these experiments. Message complexity analysis is explained in Section 5. The matchmaking efficiency in time interval T =[Tstart,Tend] is defined as: ( Tend � Tstart Tend � Matched Message/ Tstart T otal Message)∗100 Where Tstart and Tend represent the start and end time of the time interval T =[Tstart, Tend]. The response time denotes the time interval, starting from the time a message is received, and ends at the moment when a match is found for the received message. The response time is calculated as: RT = Tmatch−Treceive, where RT represents the response time, Tmatch is the time when the matchmaker agent found a matching offer/request for the received request/offer message and Treceive is the receiving time of the received request/offer message. All the experiments are executed in different network conditions, including task intensive (tasks >> resources), balanced (tasks ≈ resources) andresource intensive (tasks 1 in Figure 4a). The completely centralized approach shows higher matchmaking efficiency for small workloads. However, one matchmaker cannot maintain its matchmaking efficiency with the increasing work load. The matchmaking efficiency keeps on decreasing with increasing work load of the matchmaker. This phenomenon can be understood with the following explanation. With the increasing workload, the matchmaker has to process more messages, so it takes more time to find matched pairs. This results in an increased response time of the matchmaker. Since each request/offer message has a validity period (TTL), therefore the TTL of the request/offer messages start expiring with increased processing time of the matchmaker and consequently the matchmaking efficiency of the matchmaker decreases with increasing workload of the matchmaker. The work load threshold for one matchmaker system, decreasing matchmaking efficiency with increasing workload of a matchmaker are explained in our earlier work [13]. The matchmaking efficiency of multiple adaptive matchmakers approach is not affected by the increasing workload. The adaptive mechanism introduces
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Lecture Notes in Computer Science 5
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Volume Editors Christian Müller-Sc
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General Chair Organization Christia
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Organization IX Hartmut Schmeck Kar
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Keynote Table of Contents HyVM - Hy
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Table of Contents XIII JetBench:AnO
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130 M. Kayaalp et al. INSTRUCTION 1
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Abdullah, Tariq 174 Alima, Luc Onan
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