A super-peer is a node in a peer-to-peer network that operates both as a server to a set of clients, and as an equal in a network of super-peers. Super-peer networks strike a balance between the efficiency of centralized search, and the autonomy, load balancing and robustness to attacks provided by distributed search. Furthermore, they take advantage of the heterogeneity of capabilities (e.g., bandwidth, processing power) across peers, which recent studies have shown to be enormous. Hence, new and old P2P systems like KaZaA and Gnutella are adopting super-peers in their design. Despite their growing popularity, the behavior of super-peer networks is not well understood. For example, what are the potential drawbacks of super-peer networks? How can super-peers be made more reliable? How many clients should a super-peer take on to maximize efficiency? we examine super-peer networks in detail, gaming an understanding of their fundamental characteristics and performance tradeoffs. We also present practical guidelines and a general procedure for the design of an efficient super-peer network.

Designing an Super-Peer Network

Advances in Cloud computing opens up many new possibilities for Internet applications developers. Previously, a main concern of Internet applications developers was deployment and hosting of applications, because it required acquisition of a server with a fixed capacity able to handle the expected application peak demand and the installation and maintenance of the whole software infrastructure of the platform supporting the application. Furthermore, server was underutilized because peak traffic happens only at specific times. With the advent of the Cloud, deployment and hosting became cheaper and easier with the use of pay-peruse flexible elastic infrastructure services offered by Cloud providers. Because several Cloud providers are available, each one offering different pricing models and located in different geographic regions, a new concern of application developers is selecting providers and data center locations for applications. However, there is a lack of tools that enable developers to evaluate requirements of large-scale Cloud applications in terms of geographic distribution of both computing servers and user workloads. To fill this gap in tools for evaluation and modeling of Cloud environments and applications, we propose CloudAnalyst. It was developed to simulate large-scale Cloud applications with the purpose of studying the behavior of such applications under various deployment configurations. CloudAnalyst helps developers with insights in how to distribute applications among Cloud infrastructures and value added services such as optimization of applications performance and providers incoming with the use of Service Brokers.

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CloudAnalyst: A CloudSim-Based Visual Modeller for Analysing Cloud Computing Environments and Applications

– The purpose of this paper is to contribute to a growing body of research on cloud computing, by studying the small to medium‐sized enterprise (SME) adoption process. If SMEs have access to scalable technologies they could potentially deliver products and services that in the past only large enterprises could deliver, flattening the competitive arena., – By adopting the Technological, Organisational and Environmental (TOE) framework as a theoretical base, this qualitative exploratory study used semi‐structured interviews to collect data in 15 different SMEs and service providers in the north east of England. The north east of England was selected as it is a region that aspires to become home to innovative digital firms and most of the companies in the region are SMEs., – The main factors that were identified as playing a significant role in SME adoption of cloud services were: relative advantage, uncertainty, geo‐restriction, compatibility, trialability, size, top management support, prior experience, innovativeness, industry, market scope, supplier efforts and external computing support. In contrast, this study did not find enough evidence that competitive pressure was a significant determinant of cloud computing adoption., – These findings have important implications and great value to the research community, managers and information and communication technologies (ICT) providers, in terms of formulating better strategies for cloud computing adoption. For service providers, using the research model in this study can assist in increasing their understanding of why some SMEs choose to adopt cloud computing services, while seemingly similar ones facing similar market conditions do not. Also, cloud computing providers may need to improve their interaction with SMEs which are involved in the cloud computing experience, in an effort to create a healthy environment for cloud computing adoption, and to remove any vagueness surrounding this type of technology., – This study is an attempt to explore and develop an SME cloud computing adoption model that was theoretically grounded in the TOE framework. By adopting the TOE framework this study has shown that the three contexts of this framework (technological, organisational, and environmental) are connected to each other.

Cloud computing adoption by SMEs in the north east of England: A multi‐perspective framework

A goal in cloud computing is to allocate (and thus pay for) only those cloud resources that are truly needed. To date, cloud practitioners have pursued schedule-based (e.g., time-of-day) and rule-based mechanisms to attempt to automate this matching between computing requirements and computing resources. However, most of these "auto-scaling" mechanisms only support simple resource utilization indicators and do not specifically consider both user performance requirements and budget concerns. In this paper, we present an approach whereby the basic computing elements are virtual machines (VMs) of various sizes/costs, jobs are specified as workflows, users specify performance requirements by assigning (soft) deadlines to jobs, and the goal is to ensure all jobs are finished within their deadlines at minimum financial cost. We accomplish our goal by dynamically allocating/deallocating VMs and scheduling tasks on the most cost-efficient instances. We evaluate our approach in four representative cloud workload patterns and show cost savings from 9.8% to 40.4% compared to other approaches.

Auto-scaling to minimize cost and meet application deadlines in cloud workflows

Cloud Computing is emerging today as a commercial infrastructure that eliminates the need for maintaining expensive computing hardware. Through the use of virtualization, clouds promise to address with the same shared set of physical resources a large user base with different needs. Thus, clouds promise to be for scientists an alternative to clusters, grids, and supercomputers. However, virtualization may induce significant performance penalties for the demanding scientific computing workloads. In this work we present an evaluation of the usefulness of the current cloud computing services for scientific computing. We analyze the performance of the Amazon EC2 platform using micro-benchmarks and kernels. While clouds are still changing, our results indicate that the current cloud services need an order of magnitude in performance improvement to be useful to the scientific community.

A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing

In this paper, we investigate the benefits that organisations can reap by using "Cloud Computing" providers to augment the computing capacity of their local infrastructure. We evaluate the cost of six scheduling strategies used by an organisation that operates a cluster managed by virtual machine technology and seeks to utilise resources from a remote Infrastructure as a Service (IaaS) provider to reduce the response time of its user requests. Requests for virtual machines are submitted to the organisation's cluster, but additional virtual machines are instantiated in the remote provider and added to the local cluster when there are insufficient resources to serve the users' requests. Naive scheduling strategies can have a great impact on the amount paid by the organisation for using the remote resources, potentially increasing the overall cost with the use of IaaS. Therefore, in this work we investigate six scheduling strategies that consider the use of resources from the "Cloud", to understand how these strategies achieve a balance between performance and usage cost, and how much they improve the requests' response times.

Evaluating the cost-benefit of using cloud computing to extend the capacity of clusters

In this paper, we investigate the benefits that organisations can reap by using "Cloud Computing" providers to augment the computing capacity of their local infrastructure. We evaluate the cost of seven scheduling strategies used by an organisation that operates a cluster managed by virtual machine technology and seeks to utilise resources from a remote Infrastructure as a Service (IaaS) provider to reduce the response time of its user requests. Requests for virtual machines are submitted to the organisation's cluster, but additional virtual machines are instantiated in the remote provider and added to the local cluster when there are insufficient resources to serve the users' requests. Naive scheduling strategies can have a great impact on the amount paid by the organisation for using the remote resources, potentially increasing the overall cost with the use of IaaS. Therefore, in this work we investigate seven scheduling strategies that consider the use of resources from the "Cloud", to understand how these strategies achieve a balance between performance and usage cost, and how much they improve the requests' response times.

A cost-benefit analysis of using cloud computing to extend the capacity of clusters

Programming for the Grid raises new challenge for parallel and distributed programming. Mainly characterized by resource heterogeneousness, location dispersity, and high volatility (among others); the Grid requires the adoption of new programming paradigms that address these issues. Based on our research, in this article we propose an approach for Grid programming using the ProActive middleware. ProActive was originally created as an implementation of Active Object programming model [11], and has developed into a multifeatured middleware for programming and deploying distributed applications on the Grid. ProActive is still evolving; and currently represents the manifestation of our studies.

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ProActive: an integrated platform for programming and running applications on Grids and P2P systems

This paper presents a Peer-to-Peer (P2P) infrastructure that supports a large scale grid. The P2P infrastructure is implemented in Java and federates Java Virtual Machines (JVMs) for computation. The management of shared JVMs is decentralized, self-organized, and configurable. The P2P infrastructure was deployed as a permanent desktop grid, with which we have achieved a computation record by solving the NQueens problem for 25 queens. Thereafter, we have mixed this desktop grid with five highly heterogeneous clusters from the Grid'5000 platform. We analyze the behavior of this thousand CPU grid with two communicating applications: NQueens and Flow-Shop.

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Peer-to-peer for computational grids: mixing clusters and desktop machines

https://hal.archives-ouvertes.fr/hal-00128505/document

Alexandre Di Costanzo

Papers

Evaluating the cost-benefit of using cloud computing to extend the capacity of clusters

A cost-benefit analysis of using cloud computing to extend the capacity of clusters

ProActive: an integrated platform for programming and running applications on Grids and P2P systems

Peer-to-peer for computational grids: mixing clusters and desktop machines

Peer-to-Peer and fault-tolerance: Towards deployment-based technical services