A performance model of speculative prefetching in distributed information systems

Previous studies in speculative prefetching focus on building and evaluating access models for the purpose of access prediction. This paper investigates a complementary area which has been largely ignored, that of performance modelling. We use improvement in access time as the performance metric, for which we derive a formula in terms of resource parameters (time available and time required for prefetching) and speculative parameters (probabilities for next access). The performance maximization problem is expressed as a stretch knapsack problem. We develop an algorithm to maximize the improvement in access time by solving the stretch knapsack problem, using theoretically proven apparatus to reduce the search space. Integration between speculative prefetching and caching is also investigated, albeit under the assumption of equal item sizes

A Low-Complexity Approach to Distributed Cooperative Caching with Geographic Constraints

We consider caching in cellular networks in which each base station is equipped with a cache that can store a limited number of files. The popularity of the files is known and the goal is to place files in the caches such that the probability that a user at an arbitrary location in the plane will find the file that she requires in one of the covering caches is maximized. We develop distributed asynchronous algorithms for deciding which contents to store in which cache. Such cooperative algorithms require communication only between caches with overlapping coverage areas and can operate in asynchronous manner. The development of the algorithms is principally based on an observation that the problem can be viewed as a potential game. Our basic algorithm is derived from the best response dynamics. We demonstrate that the complexity of each best response step is independent of the number of files, linear in the cache capacity and linear in the maximum number of base stations that cover a certain area. Then, we show that the overall algorithm complexity for a discrete cache placement is polynomial in both network size and catalog size. In practical examples, the algorithm converges in just a few iterations. Also, in most cases of interest, the basic algorithm finds the best Nash equilibrium corresponding to the global optimum. We provide two extensions of our basic algorithm based on stochastic and deterministic simulated annealing which find the global optimum. Finally, we demonstrate the hit probability evolution on real and synthetic networks numerically and show that our distributed caching algorithm performs significantly better than storing the most popular content, probabilistic content placement policy and Multi-LRU caching policies.Comment: 24 pages, 9 figures, presented at SIGMETRICS'1

V-Cache: Towards Flexible Resource Provisioning for Multi-tier Applications in IaaS Clouds

Abstract—Although the resource elasticity offered by Infrastructure-as-a-Service (IaaS) clouds opens up opportunities for elastic application performance, it also poses challenges to application management. Cluster applications, such as multi-tier websites, further complicates the management requiring not only accurate capacity planning but also proper partitioning of the resources into a number of virtual machines. Instead of burdening cloud users with complex management, we move the task of determining the optimal resource configuration for cluster applications to cloud providers. We find that a structural reorganization of multi-tier websites, by adding a caching tier which runs on resources debited from the original resource budget, significantly boosts application performance and reduces resource usage. We propose V-Cache, a machine learning based approach to flexible provisioning of resources for multi-tier applications in clouds. V-Cache transparently places a caching proxy in front of the application. It uses a genetic algorithm to identify the incoming requests that benefit most from caching and dynamically resizes the cache space to accommodate these requests. We develop a reinforcement learning algorithm to optimally allocate the remaining capacity to other tiers. We have implemented V-Cache on a VMware-based cloud testbed. Exper-iment results with the RUBiS and WikiBench benchmarks show that V-Cache outperforms a representative capacity management scheme and a cloud-cache based resource provisioning approach by at least 15 % in performance, and achieves at least 11 % and 21 % savings on CPU and memory resources, respectively. I

Dataplane Specialization for High-performance OpenFlow Software Switching

OpenFlow is an amazingly expressive dataplane program- ming language, but this expressiveness comes at a severe performance price as switches must do excessive packet clas- sification in the fast path. The prevalent OpenFlow software switch architecture is therefore built on flow caching, but this imposes intricate limitations on the workloads that can be supported efficiently and may even open the door to mali- cious cache overflow attacks. In this paper we argue that in- stead of enforcing the same universal flow cache semantics to all OpenFlow applications and optimize for the common case, a switch should rather automatically specialize its dat- aplane piecemeal with respect to the configured workload. We introduce ES WITCH , a novel switch architecture that uses on-the-fly template-based code generation to compile any OpenFlow pipeline into efficient machine code, which can then be readily used as fast path. We present a proof- of-concept prototype and we demonstrate on illustrative use cases that ES WITCH yields a simpler architecture, superior packet processing speed, improved latency and CPU scala- bility, and predictable performance. Our prototype can eas- ily scale beyond 100 Gbps on a single Intel blade even with complex OpenFlow pipelines

I Know Why You Went to the Clinic: Risks and Realization of HTTPS Traffic Analysis

Revelations of large scale electronic surveillance and data mining by governments and corporations have fueled increased adoption of HTTPS. We present a traffic analysis attack against over 6000 webpages spanning the HTTPS deployments of 10 widely used, industry-leading websites in areas such as healthcare, finance, legal services and streaming video. Our attack identifies individual pages in the same website with 89% accuracy, exposing personal details including medical conditions, financial and legal affairs and sexual orientation. We examine evaluation methodology and reveal accuracy variations as large as 18% caused by assumptions affecting caching and cookies. We present a novel defense reducing attack accuracy to 27% with a 9% traffic increase, and demonstrate significantly increased effectiveness of prior defenses in our evaluation context, inclusive of enabled caching, user-specific cookies and pages within the same website