Abstract-In this paper we present Kingfisher, a cost-aware system that provides efficient support for elasticity in the cloud by (i) leveraging multiple mechanisms to reduce the time to transition to new configurations, and (ii) optimizing the selection of a virtual server configuration that minimizes the cost. We have implemented a prototype of Kingfisher and have evaluated its efficacy on a laboratory cloud platform. Our experiments with varying application workloads demonstrate that Kingfisher is able to (i) decrease the cost of virtual server resources by as much as 24% compared to the current costunaware approach, (ii) reduce by an order of magnitude the time to transition to a new configuration through multiple elasticity mechanisms in the cloud, and (iii), illustrate the opportunity for design alternatives which trade-off the cost of server resources with the time required to scale the application.

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The high cost of provisioning resources to meet peak application demands has led to the widespread adoption of pay-as-you-go cloud computing services to handle workload fluctuations. Some enterprises with existing IT infrastructure employ a hybrid cloud model where the enterprise uses its own private resources for the majority of its computing, but then “bursts” into the cloud when local resources are insufficient. However, current commercial tools rely heavily on the system administrator’s knowledge to answer key questions such as when a cloud burst is needed and which applications must be moved to the cloud. In this article, we describe Seagull, a system designed to facilitate cloud bursting by determining which applications should be transitioned into the cloud and automating the movement process at the proper time. Seagull optimizes the bursting of applications using an optimization algorithm as well as a more efficient but approximate greedy heuristic. Seagull also optimizes the overhead of deploying applications into the cloud using an intelligent precopying mechanism that proactively replicates virtualized applications, lowering the bursting time from hours to minutes. Our evaluation shows over 100% improvement compared to naïve solutions but produces more expensive solutions compared to ILP. However, the scalability of our greedy algorithm is dramatically better as the number of VMs increase. Our evaluation illustrates scenarios where our prototype can reduce cloud costs by more than 45% when bursting to the cloud, and that the incremental cost added by precopying applications is offset by a burst time reduction of nearly 95%.

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Internet flash crowds (a.k.a. hot spots) are a phenomenon that result from a sudden, unpredicted increase in an on-line object's popularity. Currently, there is no efficient means within the Internet to scalably deliver web objects under hot spot conditions to all clients that desire the object. We present PROOFS: a simple, lightweight, peer-to-peer (P2P) approach that uses randomized overlay construction and randomized, scoped searches to efficiently locate and deliver objects under heavy demand to all users that desire them. We evaluate PROOFS' robustness in environments in which clients join and leave the P2P network as well as in environments in which clients are not always fully cooperative. Through a mix of analytical modeling, simulation, and prototype experimentation in the Internet, we show that randomized approaches like PROOFS should effectively relieve flash crowd symptoms in dynamic, limited-participation environments.

As more business applications have become web enabled, the web server architecture has evolved to provide performance isolation, service differentiation, and QoS guarantees. Various server mechanisms that provide QoS extensions, however, rely on external administrators to set the right parameter values for their desirable performance. Due to the complexity of handling varying workloads and bursty traffic, configuring such parameters optimally becomes a challenge. In this paper, we describe an observation-based approach for selfmanaging web servers that can adapt to changing workloads while maintaining the QoS requirements of different classes. In this approach, the system state is monitored continuously and parameter values of various system resources-primarily the accept queue and the CPU-are adjusted to maintain the system-wide QoS goals. We implement our techniques using the Apache web server and the Linux operating system. We first demonstrate the need to manage different resources in the system depending on the workload characteristics. We then experimentally demonstrate that our observation-based system monitors such as workload changes and adjusts the resource parameters of the accept queue and CPU schedulers in order to maintain the QoS requirements of the different classes. q

In this paper, we investigate the suitability of embedding Internet hosts into a Euclidean space given their pairwise distances (as measured by round-trip time). Using the classical scaling and matrix perturbation theories, we first establish the (sum of the) magnitude of negative eigenvalues of the (doubly-centered, squared) distance matrix as a measure of suitability of Euclidean embedding. We then show that the distance matrix among Internet hosts contains negative eigenvalues of large magnitude, implying that embedding the Internet hosts in a Euclidean space would incur relatively large errors. Motivated by earlier studies, we demonstrate that the inaccuracy of Euclidean embedding is caused by a large degree of triangle inequality violation (TIV) in the Internet distances, which leads to negative eigenvalues of large magnitude. Moreover, we show that the TIVs are likely to occur locally, hence, the distances among these close-by hosts cannot be estimated accurately using a global Euclidean embedding, in addition, increasing the dimension of embedding does not reduce the embedding errors. Based on these insights, we propose a new hybrid model for embedding the network nodes using only a 2-dimensional Euclidean coordinate system and small error adjustment terms. We show that the accuracy of the proposed embedding technique is as good as, if not better, than that of a 7-dimensional Euclidean embedding.

Internet flash crowds (a.k.a. hot spots) are a phenomenon that result from a sudden, unpredicted increase in an on-line object's popularity. Currently, there is no efficient means within the Internet to scalably deliver web objects under hot spot conditions to all clients that desire the object. We present PROOFS: a simple, lightweight, peer-to-peer (P2P) approach that uses randomized overlay construction and randomized, scoped searches to efficiently locate and deliver objects under heavy demand to all users that desire them. We evaluate PROOFS' robustness in environments in which clients join and leave the P2P network as well as in environments in which clients are not always fully cooperative. Through a mix of analytical modeling, simulation, and prototype experimentation in the Internet, we show that randomized approaches like PROOFS should effectively relieve flash crowd symptoms in dynamic, limited-participation environments.