3,557 research outputs found

    Adaptive TTL-Based Caching for Content Delivery

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    Content Delivery Networks (CDNs) deliver a majority of the user-requested content on the Internet, including web pages, videos, and software downloads. A CDN server caches and serves the content requested by users. Designing caching algorithms that automatically adapt to the heterogeneity, burstiness, and non-stationary nature of real-world content requests is a major challenge and is the focus of our work. While there is much work on caching algorithms for stationary request traffic, the work on non-stationary request traffic is very limited. Consequently, most prior models are inaccurate for production CDN traffic that is non-stationary. We propose two TTL-based caching algorithms and provide provable guarantees for content request traffic that is bursty and non-stationary. The first algorithm called d-TTL dynamically adapts a TTL parameter using a stochastic approximation approach. Given a feasible target hit rate, we show that the hit rate of d-TTL converges to its target value for a general class of bursty traffic that allows Markov dependence over time and non-stationary arrivals. The second algorithm called f-TTL uses two caches, each with its own TTL. The first-level cache adaptively filters out non-stationary traffic, while the second-level cache stores frequently-accessed stationary traffic. Given feasible targets for both the hit rate and the expected cache size, f-TTL asymptotically achieves both targets. We implement d-TTL and f-TTL and evaluate both algorithms using an extensive nine-day trace consisting of 500 million requests from a production CDN server. We show that both d-TTL and f-TTL converge to their hit rate targets with an error of about 1.3%. But, f-TTL requires a significantly smaller cache size than d-TTL to achieve the same hit rate, since it effectively filters out the non-stationary traffic for rarely-accessed objects

    Distributed Selfish Coaching

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    Although cooperation generally increases the amount of resources available to a community of nodes, thus improving individual and collective performance, it also allows for the appearance of potential mistreatment problems through the exposition of one node's resources to others. We study such concerns by considering a group of independent, rational, self-aware nodes that cooperate using on-line caching algorithms, where the exposed resource is the storage at each node. Motivated by content networking applications -- including web caching, CDNs, and P2P -- this paper extends our previous work on the on-line version of the problem, which was conducted under a game-theoretic framework, and limited to object replication. We identify and investigate two causes of mistreatment: (1) cache state interactions (due to the cooperative servicing of requests) and (2) the adoption of a common scheme for cache management policies. Using analytic models, numerical solutions of these models, as well as simulation experiments, we show that on-line cooperation schemes using caching are fairly robust to mistreatment caused by state interactions. To appear in a substantial manner, the interaction through the exchange of miss-streams has to be very intense, making it feasible for the mistreated nodes to detect and react to exploitation. This robustness ceases to exist when nodes fetch and store objects in response to remote requests, i.e., when they operate as Level-2 caches (or proxies) for other nodes. Regarding mistreatment due to a common scheme, we show that this can easily take place when the "outlier" characteristics of some of the nodes get overlooked. This finding underscores the importance of allowing cooperative caching nodes the flexibility of choosing from a diverse set of schemes to fit the peculiarities of individual nodes. To that end, we outline an emulation-based framework for the development of mistreatment-resilient distributed selfish caching schemes. Our framework utilizes a simple control-theoretic approach to dynamically parameterize the cache management scheme. We show performance evaluation results that quantify the benefits from instantiating such a framework, which could be substantial under skewed demand profiles.National Science Foundation (CNS Cybertrust 0524477, CNS NeTS 0520166, CNS ITR 0205294, EIA RI 0202067); EU IST (CASCADAS and E-NEXT); Marie Curie Outgoing International Fellowship of the EU (MOIF-CT-2005-007230

    Cache policies for cloud-based systems: To keep or not to keep

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    In this paper, we study cache policies for cloud-based caching. Cloud-based caching uses cloud storage services such as Amazon S3 as a cache for data items that would have been recomputed otherwise. Cloud-based caching departs from classical caching: cloud resources are potentially infinite and only paid when used, while classical caching relies on a fixed storage capacity and its main monetary cost comes from the initial investment. To deal with this new context, we design and evaluate a new caching policy that minimizes the overall cost of a cloud-based system. The policy takes into account the frequency of consumption of an item and the cloud cost model. We show that this policy is easier to operate, that it scales with the demand and that it outperforms classical policies managing a fixed capacity.Comment: Proceedings of IEEE International Conference on Cloud Computing 2014 (CLOUD 14

    Improving the Performance and Energy Efficiency of GPGPU Computing through Adaptive Cache and Memory Management Techniques

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    Department of Computer Science and EngineeringAs the performance and energy efficiency requirement of GPGPUs have risen, memory management techniques of GPGPUs have improved to meet the requirements by employing hardware caches and utilizing heterogeneous memory. These techniques can improve GPGPUs by providing lower latency and higher bandwidth of the memory. However, these methods do not always guarantee improved performance and energy efficiency due to the small cache size and heterogeneity of the memory nodes. While prior works have proposed various techniques to address this issue, relatively little work has been done to investigate holistic support for memory management techniques. In this dissertation, we analyze performance pathologies and propose various techniques to improve memory management techniques. First, we investigate the effectiveness of advanced cache indexing (ACI) for high-performance and energy-efficient GPGPU computing. Specifically, we discuss the designs of various static and adaptive cache indexing schemes and present implementation for GPGPUs. We then quantify and analyze the effectiveness of the ACI schemes based on a cycle-accurate GPGPU simulator. Our quantitative evaluation shows that ACI schemes achieve significant performance and energy-efficiency gains over baseline conventional indexing scheme. We also analyze the performance sensitivity of ACI to key architectural parameters (i.e., capacity, associativity, and ICN bandwidth) and the cache indexing latency. We also demonstrate that ACI continues to achieve high performance in various settings. Second, we propose IACM, integrated adaptive cache management for high-performance and energy-efficient GPGPU computing. Based on the performance pathology analysis of GPGPUs, we integrate state-of-the-art adaptive cache management techniques (i.e., cache indexing, bypassing, and warp limiting) in a unified architectural framework to eliminate performance pathologies. Our quantitative evaluation demonstrates that IACM significantly improves the performance and energy efficiency of various GPGPU workloads over the baseline architecture (i.e., 98.1% and 61.9% on average, respectively) and achieves considerably higher performance than the state-of-the-art technique (i.e., 361.4% at maximum and 7.7% on average). Furthermore, IACM delivers significant performance and energy efficiency gains over the baseline GPGPU architecture even when enhanced with advanced architectural technologies (e.g., higher capacity, associativity). Third, we propose bandwidth- and latency-aware page placement (BLPP) for GPGPUs with heterogeneous memory. BLPP analyzes the characteristics of a application and determines the optimal page allocation ratio between the GPU and CPU memory. Based on the optimal page allocation ratio, BLPP dynamically allocate pages across the heterogeneous memory nodes. Our experimental results show that BLPP considerably outperforms the baseline and state-of-the-art technique (i.e., 13.4% and 16.7%) and performs similar to the static-best version (i.e., 1.2% difference), which requires extensive offline profiling.clos

    Report on the Information Retrieval Festival (IRFest2017)

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    The Information Retrieval Festival took place in April 2017 in Glasgow. The focus of the workshop was to bring together IR researchers from the various Scottish universities and beyond in order to facilitate more awareness, increased interaction and reflection on the status of the field and its future. The program included an industry session, research talks, demos and posters as well as two keynotes. The first keynote was delivered by Prof. Jaana Kekalenien, who provided a historical, critical reflection of realism in Interactive Information Retrieval Experimentation, while the second keynote was delivered by Prof. Maarten de Rijke, who argued for more Artificial Intelligence usage in IR solutions and deployments. The workshop was followed by a "Tour de Scotland" where delegates were taken from Glasgow to Aberdeen for the European Conference in Information Retrieval (ECIR 2017
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