An Introduction to MultiAgent Systems.

1. Health care is a human right. 2. The care of the individual is at the center of health care, but the whole system needs to work to improve the health of populations. 3. The health care system must treat illness, alleviate suffering and disability, and promote health. 4. Cooperation with each other, those served, and those in other sectors is essential for all who work in health care. 5. All who provide health care must work to improve it. 6. Do no harm.

https://www.journalacs.org/article/S1072-7515(00)00768-7/pdf

Is this a practical approach

The emerging edge computing paradigm promises to deliver superior user experience and enable a wide range of Internet of Things (IoT) applications. In this paper, we propose a new market-based framework for efficiently allocating resources of heterogeneous capacity-limited edge nodes (EN) to multiple competing services at the network edge. By properly pricing the geographically distributed ENs, the proposed framework generates a market equilibrium (ME) solution that not only maximizes the edge computing resource utilization but also allocates optimal resource bundles to the services given their budget constraints. When the utility of a service is defined as the maximum revenue that the service can achieve from its resource allotment, the equilibrium can be computed centrally by solving the Eisenberg-Gale (EG) convex program. We further show that the equilibrium allocation is Pareto-optimal and satisfies desired fairness properties including sharing incentive, proportionality, and envy-freeness. Also, two distributed algorithms, which efficiently converge to an ME, are introduced. When each service aims to maximize its net profit (i.e., revenue minus cost) instead of the revenue, we derive a novel convex optimization problem and rigorously prove that its solution is exactly an ME. Extensive numerical results are presented to validate the effectiveness of the proposed techniques.

Price-Based Resource Allocation for Edge Computing: A Market Equilibrium Approach

Government organisations have been shifting to cloud-based services in order to reduce their total investments in IT infrastructures and resources (e.g. data centers), as well as capitalise on cloud computing’s numerous rewards. However, just like any other technology investments there are also concerns over the potential risks of implementing cloud-based technologies. Such concerns and the paucity of scholarly literature focusing on cloud computing from a governmental context confirm the need for exploratory research and to draw lessons for government authorities and others in order to ensure a reduction in costly mistakes. This paper therefore investigates the implementation of cloud computing in both a practical setting and from an organisational user perspective via three UK local government authorities. Through the qualitative case study enquiries, the authors are able to extrapolate perceived rewards and risks factors which are mapped against the literature so that emergent factors can be identified. All three cloud deployments resulted in varying outcomes which included key rewards such as improved information management, flexibility of work practices and also posed risks such as loss of control and lack of data ownership to the organisations. These findings derived from the aggregated organisational user perspectives will be of benefit to both academics and practitioners engaged in cloud computing research and its strategic implementation in the public sector.

/pdf/risks-and-rewards-of-cloud-computing-in-the-uk-public-sector-193fxmyu4u.pdf

Risks and rewards of cloud computing in the UK public sector: A reflection on three Organisational case studies

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Enterprise Soa Service Oriented Architecture Best Practices

As the rate of cloud computing adoption grows, so does the need for consumption assistance Enterprises that are looking to migrate their IT systems to the cloud, would like to quickly identify providers that offer resources with the most appropriate pricing and performance levels to match their specific business needs However, no two vendors offer the same resource configurations, pricing and provisioning models, making the task of selecting appropriate computing resources complex, time-consuming and expensive In this paper, we present Smart CloudBench - a platform that automates the performance benchmarking of cloud infrastructure, helping potential consumers quickly identify the cloud providers that can deliver the most appropriate price/performance levels to meet their specific requirements Users can estimate the actual performance of the different cloud platforms by testing representative benchmark applications under representative load conditions Experimentation using the prototype implementation shows that higher price does not necessarily translate to better or more consistent performance, and benchmarking results can provide more information to help enterprises make better informed decisions

Smart CloudBench -- Automated Performance Benchmarking of the Cloud

The increasingly growing supply and demand for infrastructure as a service (IaaS) makes cloud trading possible in an open exchange (OCX) marketplace. The mechanisms based on economic principles show promise in addressing the problem of efficient cloud resource provisioning in such a marketplace, including resources allocation and pricing. Therefore, this article proposes effective and efficient economics-inspired double-sided mechanisms, focussing on attaining the allocative efficiency and truthfulness. Given non-deterministic polynomial time (NP) complexity of the considered problem and the computational tractability requirement for the practical solutions, we design and evaluate the approximation mechanisms for such markets. We propose a combinatorial greedy allocation mechanism to determine the distribution of cloud resources based on the sorted order of allocation candidates. We design the pricing mechanisms that derive the buyer prices based on critical-value, and the seller payments are determined via a mixed surplus-distribution rule that relies on direct and proportional-value payment. The theoretical analysis of the economic properties proves that the proposed mechanisms maintain Budget-Balance (BB), Individual Rationality (IR), Computational Tractability (CT), and achieve Truthfulness (T) for single-minded buyers. The experimental investigation of the approximation quality and the seller strategic manipulation reveal near-optimal allocation performance and near-truthful strategic incentive in our pricing mechanisms.

Towards Efficient and Truthful Market Mechanisms for Double-Sided Cloud Markets

Cloud migration allows organizations to benefit from reduced operational costs, improved flexibility, and greater scalability, and enables them to focus on core business goals. However, it also has the flip side of reduced visibility. Enterprises considering migration of their IT systems to the cloud only have a black box view of the offered infrastructure. While information about server pricing and specification is publicly available, there is limited information about cloud infrastructure performance. Comparison of alternative cloud infrastructure offerings based only on price and server specification is difficult because cloud vendors use heterogeneous hardware resources, offer different server configurations, apply different pricing models and use different virtualization techniques to provision them. Benchmarking the performance of software systems deployed on the top of the black box cloud infrastructure offers one way to evaluate the performance of available cloud server alternatives. However, this process can be complex, time-consuming and expensive, and cloud consumers can greatly benefit from tools that can automate it. Smart CloudBench is a generic framework and system that offers automated, on-demand, real-time and customized benchmarking of software systems deployed on cloud infrastructure. It provides greater visibility and insight into the run-time behavior of cloud infrastructure, helping consumers to compare and contrast available offerings during the initial cloud selection phase, and monitor performance for service quality assurance during the subsequent cloud consumption phase. In this paper, we first discuss the rationale behind our approach for benchmarking the black box cloud infrastructure. Then, we propose a generic architecture for benchmarking representative applications on the heterogeneous cloud infrastructure and describe the Smart CloudBench benchmarking workflow. We also present simple use case scenarios that highlight the need for tools such as Smart CloudBench.

Smart CloudBench--A framework for evaluating cloud infrastructure performance

With the rapidly growing demand for the cloud services, a need for efficient methods to trade computing resources increases. Commonly used fixed-price model is not always the best approach for trading cloud resources, because of its inflexible and static nature. Market-based trading shows promise for more efficient resource allocation and pricing in the cloud. However, most of the existing mechanisms ignore the seller's costs of providing the resources. In order to address it, we design a single-sided market mechanism for trading virtual machine instances in the cloud, where the cloud provider can express the reservation prices for traded services. We prove that the proposed mechanism is truthful, i.e. the buyers do not have an incentive to lie about their true valuation of the services. We perform extensive experiments in order to investigate the impact of the reserve price on the market outcome. Our experiments show that the proposed mechanism yields near-optimal allocations and has a low execution time.

Truthful Market-Based Trading of Cloud Services with Reservation Price

As the rate of cloud computing adoption grows, so does the need for consumption assistance. Enterprises looking to migrate their IT systems to the cloud require assistance in identifying providers that offer resources with the most appropriate pricing and performance levels to match their specific business needs. In this paper, we present Smart Cloud Broker - a suite of software tools that allows cloud infrastructure consumers to evaluate and compare the performance of different Infrastructure as a Service (IaaS) offerings from competing cloud service providers, and consequently supports selection of the cloud configuration and provider with the specifications that best meet the user's requirements. Using Smart Cloud Broker, prospective cloud users can estimate the performance of the different cloud platforms by running live tests against representative benchmark applications under representative load conditions.

Sergei Chichin

Papers

Smart CloudBench -- Automated Performance Benchmarking of the Cloud

Towards Efficient and Truthful Market Mechanisms for Double-Sided Cloud Markets

Smart CloudBench--A framework for evaluating cloud infrastructure performance

Truthful Market-Based Trading of Cloud Services with Reservation Price

Smart cloud broker: finding your home in the clouds