Credibility-Based Binary Feedback Model for Grid Resource Planning

In commercial grids, Grid Service Providers (GSPs) can improve their profitability by maintaining the lowest possible amount of resources to meet client demand. Their goal is to maximize profits by optimizing resource planning. In order to achieve this goal, they require an estimate of the demand for their service, but collecting demand data is costly and difficult. In this paper we develop an approach to building a proxy for demand, which we call a value profile. To construct a value profile, we use binary feedback from a collection of heterogeneous clients. We show that this can be used as a proxy for a demand function that represents a client’s willingness-to-pay for grid resources. As with all binary feedback systems, clients may require incentives to provide feedback and deterrents to selfish behavior, such as misrepresenting their true preferences to obtain superior services at lower costs. We use credibility mechanisms to detect untruthful feedback and penalize insincere or biased clients. Finally, we use game theory to study how cooperation can emerge in this community of clients and GSPs

REPUTATION MANAGEMENT ALGORITHMS IN DISTRIBUTED APPLICATIONS

Nowadays, several distributed systems and applications rely on interactions between unknown agents that cooperate in order to exchange resources and services. The distributed nature of these systems, and the consequent lack of a single centralized point of control, let agents to adopt selfish and malicious behaviors in order to maximize their own utility. To address such issue, many applications rely on Reputation Management Systems (RMSs) to estimate the future behavior of unknown agents before establishing actual interactions. The relevance of these systems is even greater if the malicious or selfish behavior exhibited by a few agents may reduce the utility perceived by cooperative agents, leading to a damage to the whole community. RMSs allow to estimate the expected outcome of a given interaction, thus providing relevant information that can be exploited to take decisions about the convenience of interacting with a certain agent. Agents and their behavior are constantly evolving and becoming even more complex, so it is increasingly difficult to successfully develop the RMS, able to resist the threats presented. A possible solution to this problem is the use of agent-based simulation software designed to support researchers in evaluating distributed reputation management systems since the design phase. This dissertation presents the design and the development of a distributed simulation platform based on HPC technologies called DRESS. This solution allows researchers to assess the performance of a generic reputation management system and provides a comprehensive assessment of its ability to withstand security attacks. In the scientific literature, a tool that allows the comparison of distinct RMS and different design choices through a set of defined metrics, also supporting large-scale simulations, is still missing. The effectiveness of the proposed approach is demonstrated by the application scenario of user energy sharing systems within smart-grids and by considering user preferences differently from other work. The platform has proved to be useful for the development of an energy sharing system among users, which with the aim of maximizing the amount of energy transferred has exploited the reputation of users once learned their preferences

Cloud provider capacity augmentation through automated resource bartering

© 2017 Elsevier B.V. Growing interest in Cloud Computing places a heavy workload on cloud providers which is becoming increasingly difficult for them to manage with their primary data centre infrastructures. Resource scarcity can make providers vulnerable to significant reputational damage and it often forces customers to select services from the larger, more established companies, sometimes at a higher price. Funding limitations, however, commonly prevent emerging and even established providers from making a continual investment in hardware speculatively assuming a certain level of growth in demand. As an alternative, they may opt to use the current inter-cloud resource sharing systems which mainly rely on monetary payments and thus put pressure on already stretched cash flows. To address such issues, a new multi-agent based Cloud Resource Bartering System (CRBS) is implemented in this work that fosters the management and bartering of pooled resources without requiring costly financial transactions between IAAS cloud providers. Agents in CRBS collaborate to facilitate bartering among providers which not only strengthens their trading relationships but also enables them to handle surges in demand with their primary setup. Unlike existing systems, CRBS assigns resources by considering resource urgency which comparatively improves customers’ satisfaction and the resource utilization rate by more than 50%. The evaluation results verify that our system assists providers to timely acquire the additional resources and to maintain sustainable service delivery. We conclude that the existence of such a system is economically beneficial for cloud providers and enables them to adapt to fluctuating workloads

Peer-to-Peer Networks and Computation: Current Trends and Future Perspectives

This research papers examines the state-of-the-art in the area of P2P networks/computation. It attempts to identify the challenges that confront the community of P2P researchers and developers, which need to be addressed before the potential of P2P-based systems, can be effectively realized beyond content distribution and file-sharing applications to build real-world, intelligent and commercial software systems. Future perspectives and some thoughts on the evolution of P2P-based systems are also provided

Towards sustainable energy-efficient communities based on a scheduling algorithm

The Internet of Things (IoT) and Demand Response (DR) combined have transformed the way Information and Communication Technologies (ICT) contribute to saving energy and reducing costs, while also giving consumers more control over their energy footprint. Unlike current price and incentive based DR strategies, we propose a DR model that promotes consumers reaching coordinated behaviour towards more sustainable (and green) communities. A cooperative DR system is designed not only to bolster energy efficiency management at both home and district levels, but also to integrate the renewable energy resource information into the community's energy management. Initially conceived in a centralised way, a data collector called the "aggregator" will handle the operation scheduling requirements given the consumers' time preferences and the available electricity supply from renewables. Evaluation on the algorithm implementation shows feasible computational cost (CC) in different scenarios of households, communities and consumer behaviour. Number of appliances and timeframe flexibility have the greatest impact on the reallocation cost. A discussion on the communication, security and hardware platforms is included prior to future pilot deployment.Comunidad de Madri

Credibility-based Binary Feedback Model for Grid Resource Planning

Grid service providers (GSPs), in commercial grids, improve their profitability by maintaining the least possible set of resources to meet client demand. Their goal is to maximize profits by optimizing resource planning. In order to achieve such goal, they require feedback from clients to estimate demand for their service. The objective of this research is to develop an approach to build a useful value profile for a collection of heterogeneous grid clients. For developing the approach, we use binary feedback as the theoretical framework to build the value profile, which can be used as a proxy for a demand function that represents client's willingness-to-pay for grid resources. However, clients may require incentives to provide feedback and deterrents from selfish behavior, such as misrepresenting their true preferences to obtain superior services at lower costs. To address this concern, we use credibility mechanisms to detect untruthful feedback and penalize insincere or biased clients. We also use game theory to study how the cooperation can emerge.In this dissertation, we propose the use of credibility-based binary feedback to build value profiles, which GSPs can use to plan their resources economically. The use of value profiles aims to benefit both GSPs and clients, and helps to accelerate an adoption of commercial grids