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    Autonomic cloud resource sharing for intercloud federations
    (Elsevier, 2013) Erdil, D. Cenk
    Recent advances in information technology make remote collaboration and resource sharing easier for next generation distributed systems, such as grids and clouds. One common model of study is the convergence of these systems, along with interclouds to a unified global computing resource. Despite similarities between grids and clouds, there are a number of fundamental differences that make this convergence process harder. For example, clouds have inherent administrative boundaries, something which the grid computing paradigm avoided from the early stages of research. Such administrative boundaries primarily affect capabilities of clouds to be interoperable. Moreover, they also negatively affect the possibility of a seamless intercloud federation on the path to convergence. Resource sharing in general and related communication methodologies, such as information dissemination and matchmaking are also integral elements in this convergence process. To help improve the success of distributed cloud resource schedulers, we propose proxies that disseminate information as agents of dissemination sources. Such proxies can then make information about resource states available at 'distant' clouds, where there may be no direct, or even no indirect control. Moreover, they can make this resource state available more efficiently than where no proxies are used. In particular, with proxies, dissemination overhead is reduced by up to 65% under different scenarios, where existing solutions may not even produce efficient protocols. In addition, proxies help reduce dissemination overhead by 19% on average. Our results also show that randomly selecting proxy nodes perform comparably to other strategies that may select proxies based on particular criteria. (c) 2012 Elsevier B.V. All rights reserved.
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    Dynamic grid load sharing with adaptive dissemination protocols
    (Springer, 2012) Erdil, D. Cenk; Lewis, Michael J.
    Scheduling in large scale dynamic grids comprising eclectic collections of resources is increasingly difficult. Autonomous resource neighborhoods may wish to determine the level of grid offered load that they can or will accept; different sites may wish to attract different amounts of load, to satisfy some desired property within a grid economy. This changes the traditional notion of load sharing, which generally assumes that the desired equilibrium should be an equal distribution of load across all participating machines, because they are under the jurisdiction of a single site, and therefore more likely to implement one common policy. In large-scale grids, nodes and neighborhoods should instead get a portion of the load that best matches their local policies for supporting and admitting grid jobs. This article describes information dissemination protocols that can distribute load in this way, without using load rebalancing through job migration, which is more difficult and costly in large-scale heterogeneous grids. Essentially, nodes adjust their advertising rates and aggressiveness to influence where jobs get scheduled. We report experimental results with example resource configurations in which each resource neighborhood determines its ideal grid load and disseminates accordingly. In turn, each neighborhood attracts the requisite amount of resource requests from the grid. Moreover, performance does not degrade: overall query satisfaction rates are within 9% of both adaptive dissemination protocols that use static adaptation policies, and static dissemination protocols that may be custom-tailored to specific resource and load distributions.
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    Simulating peer-to-peer cloud resource scheduling
    (Springer, 2012) Erdil, D. Cenk
    Resource scheduling in large-scale distributed systems, such as grids and clouds, is difficult due to the size, dynamism, and volatility of resources. These resources are eclectic and autonomous, and may exhibit different usage policies, levels of participation, capabilities, local load, and reliability. Moreover, applications are likely to exhibit various patterns and levels, and distributed resources may organize into various different overlay topologies for information and query dissemination. Researchers have proposed a wide variety of approaches and policies for mapping offered load onto resources and for solving the various component parts of the scheduling problem. However, production clouds and grids may be underutilized, and may not exhibit the load to effectively characterize all of the scheduling system inputs. The composition of large-scale systems is also changing, potentially to include more individual and peer-to-peer resources. These factors will influence the effectiveness of proposed scheduling solutions. Therefore, a simulation environment is necessary to study different approaches under different scenarios, especially those that are expected, but that are not currently characteristic of existing systems. This article describes a general-purpose peer-to-peer simulation environment that allows a wide variety of parameters, protocols, strategies and policies to be varied and studied. To provide a proof of concept, utilization of the simulation environment is presented in a large-scale distributed system problem that includes a core model and related mechanisms. In particular, this article presents a definition and possible peer-to-peer solutions for the large-scale scheduling problem. Moreover, this article describes a general simulation model, some policies that can be varied, an implementation, and some sample results.