Cooperative announcement-based caching for video-on-demand streaming

Recently, video-on-demand (VoD) streaming services like Netflix and Hulu have gained a lot of popularity. This has led to a strong increase in bandwidth capacity requirements in the network. To reduce this network load, the design of appropriate caching strategies is of utmost importance. Based on the fact that, typically, a video stream is temporally segmented into smaller chunks that can be accessed and decoded independently, cache replacement strategies have been developed that take advantage of this temporal structure in the video. In this paper, two caching strategies are proposed that additionally take advantage of the phenomenon of binge watching, where users stream multiple consecutive episodes of the same series, reported by recent user behavior studies to become the everyday behavior. Taking into account this information allows us to predict future segment requests, even before the video playout has started. Two strategies are proposed, both with a different level of coordination between the caches in the network. Using a VoD request trace based on binge watching user characteristics, the presented algorithms have been thoroughly evaluated in multiple network topologies with different characteristics, showing their general applicability. It was shown that in a realistic scenario, the proposed election-based caching strategy can outperform the state-of-the-art by 20% in terms of cache hit ratio while using 4% less network bandwidth

Design of competitive paging algorithms with good behaviour in practice

Paging is one of the most prominent problems in the field of online algorithms. We have to serve a sequence of page requests using a cache that can hold up to k pages. If the currently requested page is in cache we have a cache hit, otherwise we say that a cache miss occurs, and the requested page needs to be loaded into the cache. The goal is to minimize the number of cache misses by providing a good page-replacement strategy. This problem is part of memory-management when data is stored in a two-level memory hierarchy, more precisely a small and fast memory (cache) and a slow but large memory (disk). The most important application area is the virtual memory management of operating systems. Accessed pages are either already in the RAM or need to be loaded from the hard disk into the RAM using expensive I/O. The time needed to access the RAM is insignificant compared to an I/O operation which takes several milliseconds. The traditional evaluation framework for online algorithms is competitive analysis where the online algorithm is compared to the optimal offline solution. A shortcoming of competitive analysis consists of its too pessimistic worst-case guarantees. For example LRU has a theoretical competitive ratio of k but in practice this ratio rarely exceeds the value 4. Reducing the gap between theory and practice has been a hot research issue during the last years. More recent evaluation models have been used to prove that LRU is an optimal online algorithm or part of a class of optimal algorithms respectively, which was motivated by the assumption that LRU is one of the best algorithms in practice. Most of the newer models make LRU-friendly assumptions regarding the input, thus not leaving much room for new algorithms. Only few works in the field of online paging have introduced new algorithms which can compete with LRU as regards the small number of cache misses. In the first part of this thesis we study strongly competitive randomized paging algorithms, i.e. algorithms with optimal competitive guarantees. Although the tight bound for the competitive ratio has been known for decades, current algorithms matching this bound are complex and have high running times and memory requirements. We propose the algorithm OnlineMin which processes a page request in O(log k/log log k) time in the worst case. The best previously known solution requires O(k^2) time. Usually the memory requirement of a paging algorithm is measured by the maximum number of pages that the algorithm keeps track of. Any algorithm stores information about the k pages in the cache. In addition it can also store information about pages not in cache, denoted bookmarks. We answer the open question of Bein et al. '07 whether strongly competitive randomized paging algorithms using only o(k) bookmarks exist or not. To do so we modify the Partition algorithm of McGeoch and Sleator '85 which has an unbounded bookmark complexity, and obtain Partition2 which uses O(k/log k) bookmarks. In the second part we extract ideas from theoretical analysis of randomized paging algorithms in order to design deterministic algorithms that perform well in practice. We refine competitive analysis by introducing the attack rate parameter r, which ranges between 1 and k. We show that r is a tight bound on the competitive ratio of deterministic algorithms. We give empirical evidence that r is usually much smaller than k and thus r-competitive algorithms have a reasonable performance on real-world traces. By introducing the r-competitive priority-based algorithm class OnOPT we obtain a collection of promising algorithms to beat the LRU-standard. We single out the new algorithm RDM and show that it outperforms LRU and some of its variants on a wide range of real-world traces. Since RDM is more complex than LRU one may think at first sight that the gain in terms of lowering the number of cache misses is ruined by high runtime for processing pages. We engineer a fast implementation of RDM, and compare it to LRU and the very fast FIFO algorithm in an overall evaluation scheme, where we measure the runtime of the algorithms and add penalties for each cache miss. Experimental results show that for realistic penalties RDM still outperforms these two algorithms even if we grant the competitors an idealistic runtime of 0

Cache Equalizer: A Cache Pressure Aware Block Placement Scheme for Large-Scale Chip Multiprocessors

This paper describes Cache Equalizer (CE), a novel distributed cache management scheme for large scale chip multiprocessors (CMPs). Our work is motivated by large asymmetry in cache sets usages. CE decouples the physical locations of cache blocks from their addresses for the sake of reducing misses caused by destructive interferences. Temporal pressure at the on-chip last-level cache, is continuously collected at a group (comprised of cache sets) granularity, and periodically recorded at the memory controller to guide the placement process. An incoming block is consequently placed at a cache group that exhibits the minimum pressure. CE provides Quality of Service (QoS) by robustly offering better performance than the baseline shared NUCA cache. Simulation results using a full-system simulator demonstrate that CE outperforms shared NUCA caches by an average of 15.5% and by as much as 28.5% for the benchmark programs we examined. Furthermore, evaluations manifested the outperformance of CE versus related CMP cache designs

Performance evaluation of caching techniques for video on demand workload in named data network

The rapid growing use of the Internet in the contemporary context is mainly for content distribution. This is derived primarily due to the emergence of Information-Centric Networking (ICN) in the wider domains of academia and industry. Named Data Network (NDN) is one of ICN architectures. In addition, the NDN has been emphasized as the video traffic architecture that ensures smooth communication between the request and receiver of online video. The concise research problem of the current study is the issue of congestion in Video on Demand (VoD) workload caused by frequent storing of signed content object in the local repositories, which leads to buffering problems and data packet loss. The study will assess the NDN cache techniques to select the preferable cache replacement technique suitable for dealing with the congestion issues, and evaluate its performance. To do that, the current study adopts a research process based on the Design Research Methodology (DRM) and VoD approach in order to explain the main activities that produced an increase in the expected findings at the end of the activities or research. Datasets, as well as Internet2 network topology and the statistics of video views were gathered from the PPTV platform. Actually, a total of 221 servers is connected to the network from the same access points as in the real deployment of PPTV. In addition, an NS3 analysis the performance metrics of caching replacement technique (LRU, LFU, and FIFO) for VoD in Named Data Network (NDN) in terms of cache hit ratio, throughput, and server load results in reasonable outcomes that appears to serve as a potential replacement with the current implementation of the Internet2 topology, where nodes are distributed randomly. Based on the results, LFU technique gives the preferable result for congestion from among the presented techniques. Finally, the research finds that the performance metrics of cache hit ratio, throughput, and server load for the LFU that produces the lowest congestion rate which is sufficient. Therefore, the researcher concluded that the efficiency of the different replacement techniques needs to be well investigated in order to provide the insights necessary to implement these techniques in certain context. However, this result enriches the current understanding of replacement techniques in handling different cache sizes. After having addressed the different replacement techniques and examined their performances, the performance characteristics along with their expected performance were also found to stimulate a cache model for providing a relatively fast running time of across a broad range of embedded applications

Switching Between Page Replacement Algorithms Based on Work Load During Runtime in Linux Kernel

Today’s computers are equipped with multiple processor cores to execute multiple programs effectively at a single point of time. This increase in the number of cores needs to be equipped with a huge amount of physical memory to keep multiple applications in memory at a time and to effectively switch between them, without getting affected by the low speed disk memory. The physical memory of today’s world has become so cheap such that all the computer systems are always equipped with sufficient amount of physical memory required effectively to run most of the applications. Along with the memory, the sizes of applications have also became huge. This again arises the problem of memory contention in most of the heavily loaded servers. So the physical memory has to be handled very effectively to achieve high performance. Many page replacement algorithms were developed by researchers and everything has its own advantages and disadvantages. All the algorithms have one goal of minimizing the page faults with less background work done [1]. Effectiveness of an algorithm depends on the work load as well. This paper discusses the various page replacement algorithms, their benefits, their failures and analyses them in certain workloads. Finally it presents an idea of changing the page replacement algorithms depending on the performance of each algorithm in that particular workload during runtime using system variables