The current deployment approach of the commercial Content Delivery Network (CDN) providers involves placing their Web server clusters in numerous geographical locations worldwide. However, the requirements for providing high quality service through global coverage might be an obstacle for new CDN providers, as well as affecting the commercial viability of existing ones. It is evident from the major consolidation of the CDN market, down to a handful of key players, which has occurred in recent years. Unfortunately, due to the proprietary nature, existing commercial CDN providers do not cooperate in delivering content to the end users in a scalable manner. In addition, content providers typically subscribe to one CDN provider and thus can not use multiple CDNs at the same time. Such a closed, noncooperative model results in disparate CDNs. Enabling coordinated and cooperative content delivery via internetworking among distinct CDNs could allow providers to rapidly “scale-out” to meet both flash crowds [2] and anticipated increases in demand, and remove the need for a given CDN to provision resources. CDN services are often priced out of reach for all but large enterprise customers. Further, commercial CDNs make specific commitments with their customers by signing Service Level Agreements (SLAs), which outline specific penalties if they fail to meet those commitments. Hence, if a particular CDN is unable to provide Quality of Service (QoS) to the end user requests, it may result in SLA violation and end up costing the CDN provider. Economies of scale, in terms of cost effectiveness and performance for both providers and end users, could be achieved by

Meta-schedulers map jobs to computational resources that are part of a Grid, such as clusters, that in turn have their own local job schedulers. Existing Grid meta-schedulers either target system-centric metrics, such as utilisation and throughput, or prioritise jobs based on utility metrics provided by the users. The system-centric approach gives less importance to users' individual utility, while the user-centric approach may have adverse effects such as poor system performance and unfair treatment of users. Therefore, this paper proposes a novel meta-scheduler, based on the well-known double auction mechanism that aims to satisfy users' service requirements as well as ensuring balanced utilisation of resources across a Grid. We have designed valuation metrics that commodify both the complex resource requirements of users and the capabilities of available computational resources. Through simulation using real traces, we compare our scheduling mechanism with other common mechanisms widely used by both existing market-based and traditional meta-schedulers. The results show that our meta-scheduling mechanism not only satisfies up to 15% more user requirements than others, but also improves system utilisation through load balancing.

The primary purpose of this book is to capture the state-of-the-art in Cloud Computing technologies and applications. The book will also aim to identify potential research directions and technologies that will facilitate creation a global market-place of cloud computing services supporting scientific, industrial, business, and consumer applications. We expect the book to serve as a reference for larger audience such as systems architects, practitioners, developers, new researchers and graduate level students. This area of research is relatively recent, and as such has no existing reference book that addresses it.This book will be a timely contribution to a field that is gaining considerable research interest, momentum, and is expected to be of increasing interest to commercial developers. The book is targeted for professional computer science developers and graduate students especially at Masters level. As Cloud Computing is recognized as one of the top five emerging technologies that will have a major impact on the quality of science and society over the next 20 years, its knowledge will help position our readers at the forefront of the field.

Virtual machines (VMs) have become capable enough to emulate full-featured physical machines in all aspects. Therefore, they have become the foundation not only for flexible data center infrastructure but also for commercial Infrastructureas-a-Service (IaaS) solutions. However, current providers of virtual infrastructure offer simple mechanisms through which users can ask for immediate allocation of VMs. More sophisticated economic and allocation mechanisms are required so that users can plan ahead and IaaS providers can improve their revenue. This paper introduces OpenPEX, a system that allows users to provision resources ahead of time through advance reservations. OpenPEX also incorporates a bilateral negotiation protocol that allows users and providers to come to an agreement by exchanging offers and counter-offers. These functions are made available to users through a web portal and a REST-based Web service interface.

A content delivery cloud, such as MetaCDN, is an integrated overlay that utilizes cloud computing to provide content delivery services to Internet end-users. While it ensures satisfactory user perceived performance, it also aims to improve the traffic activities in its world-wide distributed network and uplift the usefulness of its replicas. To realize this objective, in this paper, we measure the utility of content delivery via MetaCDN, capturing the system-specific perceived benefits. We use this utility measure to devise a request-redirection policy that ensures high performance content delivery. We also quantify a content provider's benefits from using MetaCDN based on its user perceived performance. We conduct a proof-of-concept testbed experiment for MetaCDN to demonstrate the performance of our approach and reveal our observations on the MetaCDN utility and content provider's benefits from using MetaCDN.

Many `Cloud Storage' providers have launched in the last two years, providing internet accessible data storage and delivery in several continents that is backed by rigourous Service Level Agreements (SLAs), guaranteeing specific performance and uptime targets. The facilities offered by these providers is leveraged by developers via provider-specific Web Service APIs. For content creators, these providers have emerged as a genuine alternative to dedicated Content Delivery Networks (CDNs) for global file storage and delivery, as they are significantly cheaper, have comparable performance and no ongoing contract obligations. As a result, the idea of utilising Storage Clouds as a `poor mans' CDN is very enticing. However, many of these `Cloud Storage' providers are merely basic storage services, and do not offer the capabilities of a fully-featured CDN such as intelligent replication, failover, load redirection and load balancing. Furthermore, they can be difficult to use for non-developers, as each service is best utilised via unique web services or programmer APIs. In this paper we describe the design, architecture, implementation and user-experience of MetaCDN, a system that integrates these `Cloud Storage' providers into an unified CDN service that provides high performance, low cost, geographically distributed content storage and delivery for content creators, and is managed by an easy to use web portal.

Simulation tools play an essential role in the evaluation of emerging peer-to-peer, computing, service and content delivery networks. Given the scale, complexity and operational costs of such networks, it is often impossible to analyse the low-level performance, or the effect of new scheduling, replication and organisational algorithms on actual test-beds. As such, practitioners turn to simulation tools to allow them to rapidly evaluate the efficiency, performance and reliability of new algorithms on large topologies before considering their implementation on test-beds and production systems.

With the significant advances in Information and Communications Technology (ICT) over the last half century, there is an increasingly perceived vision that computing will one day be the 5th utility (after water, electricity, gas, and telephony). This Computing utility, like all other four existing utilities. will provide the basic level of computing service that is considered essential to meet the everyday needs of the general Community. To deliver this vision, a number Of Computing paradigms have been proposed, of which the latest one is known as Cloud Computing. Hence, in this paper, we define Cloud computing and provide the architecture for creating Clouds with market-oriented resource allocation by leveraging technologies such as Virtual Machines (VMs). We also provide insights on market-based resource management strategies that encompass both customer-drive service management and computational risk management to sustain Service Level Agreement (SLA)-oriented resource allocation. In addition, we reveal our early thoughts on interconnecting Clouds for dynamically creating global Cloud exchanges and markets. Then, we present some representative Cloud platforms, especially those developed in industries, along with our Current work towards realizing market-oriented resource allocation of Clouds as realized in Aneka enterprise Cloud technology. Furthermore, we highlight the difference between High Performance Computing (HPC) workload and Internet-based services workload. We also describe a meta-negotiation infrastructure to establish global Cloud exchanges and markets, and illustrate a case Study of harnessing 'Storage Clouds' for high performance content delivery. Finally, we conclude with the need for convergence of competing IT paradigms to deliver our 21st century vision. (C) 2008 Elsevier B.V. All rights reserved.

Many existing works on multi-level time sharing policies have assumed infinitely small quanta, infinite levels or exponential service time distributions. In this paper, we investigate the performance of a multi-level time sharing policy under heavy-tailed workloads under finite levels when quanta are not infinitely small. Such a policy is consistent with those implemented on modern computer systems and these findings will enable system designers to better understand how various factors (e.g. system load, task size variability and number of levels) affect the overall performance of a given system. First, we obtain the performance metrics for a multi-level time sharing policy with finite number of levels. Second, for the case of 2 and 3 levels (queues), we show that optimal quantum multi-level time sharing policy (MLOQTP) can result in significant performance improvements over other policies under certain traffic and workload conditions. Finally, we investigate the impact of number of levels on the overall performance and propose a simple statistical regression model that can accurately estimate overall performance of a multi-level time sharing system.

Virtualization has become commonplace in modern data centers, often referred as “computing clouds”. The capability of virtual machine live migration brings benefits such as improved performance, manageability and fault tolerance, while allowing workload movement with a short service downtime. However, service levels of applications are likely to be negatively affected during a live migration. For this reason, a better understanding of its effects on system performance is desirable. In this paper, we evaluate the effects of live migration of virtual machines on the performance of applications running inside Xen VMs. Results show that, in most cases, migration overhead is acceptable but cannot be disregarded, especially in systems where availability and responsiveness are governed by strict Service Level Agreements. Despite that, there is a high potential for live migration applicability in data centers serving modern Internet applications. Our results are based on a workload covering the domain of multi-tier Web 2.0 applications.

Traditional resource management techniques (resource allocation, admission control and scheduling) have been found to be inadequate for many shared Grid and distributed systems, that consist of autonomous and dynamic distributed resources contributed by multiple organisations. They provide no incentive for users to request resources judiciously and appropriately, and do not accurately capture the true value, importance and deadline (the utility) of a user’s job. Furthermore, they provide no compensation for resource providers to contribute their computing resources to shared Grids, as traditional approaches have a user-centric focus on maximising throughput and minimising waiting time rather than maximising a providers own benefit. Consequently, researchers and practitioners have been examining the appropriateness of ‘market-inspired’ resource management techniques to address these limitations. Such techniques aim to smooth out access patterns and reduce the chance of transient overload, by providing a framework for users to be truthful about their resource requirements and job deadlines, and offering incentives for service providers to prioritise urgent, high utility jobs over low utility jobs. We examine the recent innovations in these systems (from 2000–2007), looking at the state-of-the-art in price setting and negotiation, Grid economy management and utility-driven scheduling and resource allocation, and identify the advantages and limitations of these systems. We then look to the future of these systems, examining the emerging ‘Catallaxy’ market paradigm. Finally we consider the future directions that need to be pursued to address the limitations of the current generation of market oriented Grids and Utility Computing systems.

Content Delivery Networks (CDNs) such as Akamai and Mirror Image place web server clusters in numerous geographical locations to improve the responsiveness and locality of the content it hosts for end-users. However, their services are priced out of reach for all but the largest enterprise customers. An alternative approach to content delivery could be achieved by harnessing existing infrastructure provided by ‘storage cloud’ providers, at a fraction of the cost. MetaCDN is a system that leverages several existing ‘storage clouds’, creating an integrated overlay network that provides a low cost, high performance content delivery network for content creators. MetaCDN intelligently places content onto one or many storage providers based on the quality of service, coverage and budget preferences of participants.

The proprietary nature of existing Content Delivery Networks (CDNs) means they are closed and do not naturally cooperate, resulting in "islands" of CDNs. Finding ways for distinct CDNs to coordinate and cooperate with other CDNs is necessary to achieve better overall service, as perceived by end-users, at lower cost. In this paper, we present an architecture to support peering arrangements among CDN providers, based on a Virtual Organization (VO) model. Our approach promotes peering among providers, reduces expenditure, while upholding user perceived performance. This is achieved through proper policy management of negotiated Service Level Agreements (SLAs) among peers. In addition, scalability and resource sharing among CDNs is improved through effective peering, thus evolving past the current landscape where "islands" of CDNs exist. We also show analytically that significant performance improvement can be achieved through the peering of CDNs.

Exponential distributions have traditionally been used to model the traffic (e.g. inter-arrival and service distributions) experienced in computer networks. They are attractive as they are amenable to analysis typically utilised in queueing models. However, modern traffic analysis has shown that many computing workloads are in fact 'heavy-tailed' and highly variable, and are better represented by general distributions such as Log-normal and Pareto. The use of General distributions can make an analytical analysis of some queueing metrics (e.g. waiting time, busy period, slowdown) difficult due to the fact that the Markovian properties of certain stochastic processes in queues are no longer in force. For such distributions Prony 's method can be utilised to fit a series of exponentials to the original General distribution, resulting in a Hyper-exponential distribution that represents the characteristics of the original workload, but is more amenable to analysis. Bounded representations of general distributions (such as Bounded Pareto) are frequently used, but by default, Prony's method is not ideally suited to fitting such distributions. We present two ways of addressing this issue: by normalising the Hyper-exponential resulting from Prony's method between the bounds of the workload distribution being approximated, and by re-evaluating Prony's method to fit directly to a Bounded Hyper-exponential.

{apathan, brobergj, raj}@csse.unimelb.edu.au ABSTRACT A Content Delivery Network (CDN) is expected to provide high performance Internet content delivery through global coverage, which might be an obstacle for new CDN providers, as well as affecting commercial viability of the existing ones. Peering of CDNs can be a way to allow cooperation between CDNs in a scalable manner and to achieve better overall service, as perceived by end-users. In this paper, we present a Quality of Service (QoS)-driven performance modeling approach for peering CDNs in order to predict the user performance. We also show that peering between CDNs upholds user perceived performance by satisfying the target QoS. The methodology presented in this paper provides CDNs a way to dynamically redirect user requests to other peering CDNs according to different request-redirection policies. The model-based approach helps an overloaded CDN to return to normal by offloading excess requests to the peers. It also assists in making concrete QoS guarantee for a CDN. Our approach endeavors to achieve scalability for a CDN in a user transparent manner.

In this paper we consider the problem of maximising utility in linked market-driven distributed and Grid systems. In such systems, users submit jobs through brokers who can virtualise and make available the resources of multiple service providers, achieving greater economies of scale, improving throughput and potentially reducing cost. Customers compete against each other by assigning a utility value or function to the successful processing of their jobs in an effort to have them prioritised in the face of contested and constrained resources. Brokers and service providers also attempt to maximise the utility they gain, choosing to process jobs that will earn them the highest profit with respect to the resources required. For this to be effective over many linked computing marketplaces highly distributed resource allocation is needed, where each participant can operate independently using only local information, and ideally reach a global state where all participants are satisfied. We model such a system by adapting the classical multi-commodity flow problem to the market-based, utility driven distributed systems, where all participants selfishly attempt to maximise their own gain. We then obtain a utility-aware distributed algorithm that generates increased utility for participants in such systems, especially under scenarios of high contention.

The proliferation of Content Delivery Networks (CDN) reveals that existing content networks are owned and operated by individual companies. As a consequence, closed delivery networks are evolved which do not cooperate with other CDNs and in practice, islands of CDNs are formed. Moreover, the logical separation between contents and services in this context results in two content networking domains. But present trends in content networks and content networking capabilities give rise to the interest in interconnecting content networks. Finding ways for distinct content networks to coordinate and cooperate with other content networks is necessary for better overall service. In addition to that, meeting the QoS requirements of users according to the negotiated Service Level Agreements between the user and the content network is a burning issue in this perspective. In this article, we present an open, scalable and Service-Oriented Architecture based system to assist the creation of open Content and Service Delivery Networks (CSDN) that scale and support sharing of resources with other CSDNs.