On the Intrinsic Locality Properties of Web Reference Streams

There has been considerable work done in the study of Web reference streams: sequences of requests for Web objects. In particular, many studies have looked at the locality properties of such streams, because of the impact of locality on the design and performance of caching and prefetching systems. However, a general framework for understanding why reference streams exhibit given locality properties has not yet emerged. In this work we take a first step in this direction, based on viewing the Web as a set of reference streams that are transformed by Web components (clients, servers, and intermediaries). We propose a graph-based framework for describing this collection of streams and components. We identify three basic stream transformations that occur at nodes of the graph: aggregation, disaggregation and filtering, and we show how these transformations can be used to abstract the effects of different Web components on their associated reference streams. This view allows a structured approach to the analysis of why reference streams show given properties at different points in the Web. Applying this approach to the study of locality requires good metrics for locality. These metrics must meet three criteria: 1) they must accurately capture temporal locality; 2) they must be independent of trace artifacts such as trace length; and 3) they must not involve manual procedures or model-based assumptions. We describe two metrics meeting these criteria that each capture a different kind of temporal locality in reference streams. The popularity component of temporal locality is captured by entropy, while the correlation component is captured by interreference coefficient of variation. We argue that these metrics are more natural and more useful than previously proposed metrics for temporal locality. We use this framework to analyze a diverse set of Web reference traces. We find that this framework can shed light on how and why locality properties vary across different locations in the Web topology. For example, we find that filtering and aggregation have opposing effects on the popularity component of the temporal locality, which helps to explain why multilevel caching can be effective in the Web. Furthermore, we find that all transformations tend to diminish the correlation component of temporal locality, which has implications for the utility of different cache replacement policies at different points in the Web.National Science Foundation (ANI-9986397, ANI-0095988); CNPq-Brazi

Enhanced Forwarding Strategies in Information Centric Networking

Content Centric Networking (CCN), a Clean Slate architecture to Information Centric Networking (ICN) , uses new approaches to routing named content, achieving scalability, security and performance. This thesis proposes a design of an effective multi-path forwarding strategy and performs an evaluation of this strategy in a set of scenarios that consider large scale deployments. The evaluations show improved performance in terms of user application throughput, delays, adoptability and scalability against adverse conditions (such as differing background loads and mobility) compared to the originally proposed forwarding strategies. Secondly, this thesis proposes an analytical model based on Markov Modulated Rate Process (MMRP) to characterize multi-path data transfers in CCN. The results show a close resemblance in performance between the analytical model and the simulation model

mPart: Miss Ratio Curve Guided Partitioning in Key-Value Stores

Web applications employ key-value stores to cache the data that is most commonly accessed. The cache improves an web application’s performance by serving its requests from memory, avoiding fetching them from the backend database. Since the memory space is limited, maximizing the memory utilization is a key to delivering the best performance possible. This has lead to the use of multi-tenant systems, allowing applications to share cache space. In addition, application data access patterns change over time, so the system should be adaptive in its memory allocation. In this thesis, we address both multi-tenancy (where a single cache is used for mul- tiple applications) and dynamic workloads (changing access patterns) using a model that relates the cache size to the application miss ratio, known as a miss ratio curve. Intuitively, the larger the cache, the less likely the system will need to fetch the data from the database. Our efficient, online construction of the miss ratio curve allows us to determine a near optimal memory allocation given the available system memory, while adapting to changing data access patterns. We show that our model outper- forms an existing state-of-the-art sharing model, Memshare, in terms of cache hit ratio and does so at a lower time cost. We show that average hit ratio is consistently 1 percentage point greater and 99.9th percentile latency is reduced by as much as 2.9% under standard web application workloads containing millions of requests

Towards Optimized Traffic Provisioning and Adaptive Cache Management for Content Delivery

Content delivery networks (CDNs) deploy hundreds of thousands of servers around the world to cache and serve trillions of user requests every day for a diverse set of content such as web pages, videos, software downloads and images. In this dissertation, we propose algorithms to provision traffic across cache servers and manage the content they host to achieve performance objectives such as maximizing the cache hit rate, minimizing the bandwidth cost of the network and minimizing the energy consumption of the servers. Traffic provisioning is the process of determining the set of content domains hosted on the servers. We propose footprint descriptors that effectively capture the popularity characteristics and caching performance of different content classes. We also propose a footprint descriptor calculus that can be used to decide how content should be mixed or partitioned to efficiently provision traffic. To automate traffic provisioning, we propose optimization models to provision traffic such that the cache miss traffic from the network is minimized without overloading the servers. We find that such optimization models produce significant reductions in the cache miss traffic when compared with traffic provisioning algorithms in use today. Cache management is the process of deciding how content is cached in the servers of a CDN. We propose TTL-based caching algorithms that provably achieve performance targets specified by a CDN operator. We show that the proposed algorithms converge to the target hit rate and target cache size with low error. Finally, we propose cache management algorithms to make the servers energy-efficient using disk shutdown. We find that disk shutdown is well suited for CDN servers and provides energy savings without significantly impacting cache hit rates

Evaluation of background push content download services to mobile devices over DVB networks

© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper proposes a multicast content download service based on the use of residual network capacity to push multimedia content to available local storage in personal multimedia devices. The service under study is based on the FLUTE protocol. Specifically, FLUTE packets fill the spare capacity in the IP tunnels reserved for the primary streaming service (opportunistic insertion). The paper also evaluates the use of AL-FEC parity to overcome transmission errors,object multiplexing to send the most popular multimedia contents more frequently and cache management policies that consider user preferences in order to keep in storage the most useful items. The service has been evaluated through simulations and measurements performed with an application prototype based on the DVB-H standards. The results show that AL-FEC enables the use of residual capacity for background content download services. In turn, AL-FEC, as well as object multiplexing, improves the relation between the number of content items and the overall access time. Moreover, results show that high percentages of requests can be served from the local cache of the service, provided that it is possible to estimate the popularity of content items and the user preferences.This work was supported by the PAID-05-12 program of the UniversitatPolitecnica de Valencia.Fraile Gil, F.; De Fez Lava, I.; Guerri Cebollada, JC. (2014). Evaluation of background push content download services to mobile devices over DVB networks. IEEE Transactions on Broadcasting. 60(1):1-15. https://doi.org/10.1109/TBC.2013.2289639S11560

A MIDDLE-WARE LEVEL CLIENT CACHE FOR A HIGH PERFORMANCE COMPUTING I/O SIMULATOR

This thesis describes the design and run time analysis of the system level middle-ware cache for Hecios. Hecios is a high performance cluster I/O simulator. With Hecios, we provide a simulation environment that accurately captures the performance characteristics of all the components in a clusterwide parallel file system. Hecios was specifically modeled after PVFS2. It was designed to be extensible and to easily allow for various component modules to be easily replaced by those that model other system types. Built around the OMNeT++ simulation package, Hecios\u27 inner-cluster communication module is easily adaptable to any TCP/IP based protocol and all standard network interface cards, switches, hubs, and routers. We will examine the system cache component and describe a methodology for implementing other coherence and replacement techniques within Hecios. Similar to other cache simulation tools, we allow the size of the system cache to be varied independently of the replacement policy and caching technique used

High Throughput Push Based Storage Manager

The storage manager, as a key component of the database system, is responsible for organizing, reading, and delivering data to the execution engine for processing. According to the data serving mechanism, existing storage managers are either pull-based, incurring high latency, or push-based, leading to a high number of I/O requests when the CPU is busy. To improve these shortcomings, this thesis proposes a push-based prefetching strategy in a column-wise storage manager. The proposed strategy implements an efficient cache layer to store shared data among queries to reduce the number of I/O requests. The capacity of the cache is maintained by a time access-aware eviction mechanism. Our strategy enables the storage manager to coordinate multiple queries by merging their requests and dynamically generate an optimal read order that maximizes the overall I/O throughput. We evaluated our storage manager both over a disk-based redundant array of independent disks (RAID) and an NVM Express (NVMe) solid-state drive (SSD). With the high read performance of the SSD, we successfully minimized the total read time and number of I/O accesses

Modeling and scheduling heterogeneous multi-core architectures

Om de prestatie van toekomstige processors en processorarchitecturen te evalueren wordt vaak gebruik gemaakt van een simulator die het gedrag en de prestatie van de processor modelleert. De prestatie bepalen van de uitvoering van een computerprogramma op een gegeven processorarchitectuur m.b.v. een simulator duurt echter vele grootteordes langer dan de werkelijke uitvoeringstijd. Dit beperkt in belangrijke mate de hoeveelheid experimenten die gedaan kunnen worden. In dit doctoraatswerk werd het Multi-Program Performance Model (MPPM) ontwikkeld, een innovatief alternatief voor traditionele simulatie, dat het mogelijk maakt om tot 100.000x sneller een processorconfiguratie te evalueren. MPPM laat ons toe om nooit geziene exploraties te doen. Gebruik makend van dit raamwerk hebben we aangetoond dat de taakplanning cruciaal is om heterogene meerkernige processors optimaal te benutten. Vervolgens werd een nieuwe manier voorgesteld om op een schaalbare manier de taakplanning uit te voeren, namelijk Performance Impact Estimation (PIE). Tijdens de uitvoering van een draad op een gegeven processorkern schatten we de prestatie op een ander type kern op basis van eenvoudig op te meten prestatiemetrieken. Zo beschikken we op elk moment over alle nodige informatie om een efficiënte taakplanning te doen. Dit laat ons bovendien toe te optimaliseren voor verschillende criteria zoals uitvoeringstijd, doorvoersnelheid of fairness