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The problem of distributed election majority-winner monitoring for checkpoint-based protocols has been of interest for some time now, and the approach that most of the major algorithms take on this is to employ a center-based pull from all voting sites in conjunction with a count tracker, to continually monitor the information about the incoming voter stream. This paper presents an alternative solution to this problem, using site-based voter information push to the center, with reduced communication complexity, using a unique sampling technique. This technique is utilized to come up with a winner or an approximate winner with very high probability for Approval-based and Scoring-based rules. The site-push algorithm introduced here has been validated with correctness results, and has also been analyzed for different scenarios to see the improvement in the number of messages initiated, in comparison to the center-based pull algorithm considered for a checkpoint-based protocol. The effect of this algorithm is to reduce the communication complexity by a factor of $(1+\frac{\log {k}}{\log {\frac{n}{k}}})$ in comparison to the center-pull algorithm (where $k$ is the number of voting sites, and $n$ the number of voters), which leads to a large reduction in communication complexity in democratic election scenarios. Also, to analyze other scenarios, simulation results are presented to show how the algorithm fares under randomized voter assignment, with variations in the value of total voters, total sites and the approximation parameter $\epsilon$ .

Virtualization of resources on the cloud offers a scalable means of consuming services beyond the capabilities of small systems. In a cloud that offers infrastructure such as processor, memory, hard disk, etc., a coalition of virtual machines formed by grouping two or more may be needed. Economical management of cloud resources needs allocation strategies with minimum wastage, while configuring services ahead of actual requests. We propose a resource allocation mechanism for machines on the cloud, based on the principles of coalition formation and the uncertainty principle of game theory. We compare the results of applying this mechanism with existing resource allocation methods that have been deployed on the cloud. We also show that this method of resource allocation by coalition-formation of the machines on the cloud leads not only to better resource utilization but also higher request satisfaction.

As military, academic, and commercial computing systems evolve from autonomous entities that deliver computing products into network centric enterprise systems that deliver computing as a service, opportunities emerge to consolidate computing resources, software, and information through cloud computing. Along with these opportunities come challenges, particularly to service providers and operations centers that struggle to monitor and manage quality of service (QoS) for these services in order to meet customer service commitments. Traditional approaches fall short in addressing these challenges because they examine QoS from a limited perspective rather than from a system-of-systems (SoS) perspective applicable to a net-centric enterprise system in which any user from any location can share computing resources at any time. This paper presents a SoS approach to enable QoS monitoring, management, and response for enterprise systems that deliver computing as a service through a cloud computing environment. A concrete example is provided for application of this new SoS approach to a real-world scenario (viz., distributed denial of service). Simulated results confirm the efficacy of the approach.

The air cargo transportation system is a large and complex service system, in which demand forecasting is a key element in the master planning process. Demand forecasting is essential for analyzing existing cargo flight schedules and identifying future facility requirements of air cargo companies. We use the Potluck Problem approach to propose a multiproducer/multiconsumer solution for predicting the cargo demand of a specific airline in a given route, and the cargo load factor for a given flight schedule on that route. This solution considers each airline as a producer and the users of air cargo services as consumers, with a producer having no explicit communication with other producers/airlines. The model analyzes the existing cargo capacity plan, highlights drawbacks, and proposes a new capacity plan to demonstrate the effectiveness of using the solution. Examples are provided to illustrate the efficacy of the approach.

Solar irradiation tends to vary greatly throughout the day. The difference is very large especially between the early hours of sunshine, and during the peak hours of the day. Yet, it is desirable to supply a nearly constant amount of power to loads or power grids for high reliability. Hence, we propose a technique aimed at reducing the variation in solar power generation. Solar irradiation data is considered as a time-series data and the approach involves conversion of this data to the frequency domain through Fourier analysis. A more balanced supply by a set of plants is devised by interconnecting the plants, which also requires finding the optimum number of plants to connect using the above analysis. The effectiveness of the procedure is demonstrated by applying a suitable supply prediction algorithm over the individual plants and the effective data for the plants, using real data from Nevada, Texas, and California.

A framework is proposed for an electrical power microgrid, such as for a colony or small township of homes that generate electrical power from solar energy and use it directly when possible, and via stored battery power at other times. The situation is described as a demand and supply problem in a multi-agent system with many consumers and suppliers and no explicit communication or coordination among the agents. Such a demand and supply problem is modeled as a Potluck Problem, a generalization of the Santa Fe Bar Problem. Power produced by PV panels and batteries may be used in the local market, in addition to being consumed locally. The proposed microgrid system model is able to determine the optimum operation of a solar-powered microgrid with respect to load demand, environmental requirements, PV panel and battery capacities. The results indicate the effect of various such parameters on the performance of these micro-grids. This paper also analyzes and proposes, based on auction theory, the most efficient and competing pricing mechanism in the proposed microgrid system model. Two important market bidding techniques, single bidding and discriminatory bidding, are considered. The microgrid is made to participate in the bidding process to serve the consumers at a reduced price and to provide better revenues. The viability of the model proposed is illustrated with analyses using realistic assumptions and published historical data.