New caching algorithms performance evaluation

Zaidenberg, Nezer Jacob; Gavish, Limor; Meir, Yuval

doi:10.1109/spects.2015.7285291

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…In this direction, there is work that (1) searches for the best way to optimise memory allocation (Radhakrishnan, 2004;Ghandeharizadeh et al, 2015); (2) improves admission or replacement algorithms (Qin et al, 2014;Venketesh and Venkatesan, 2009;Ali Ahmed and Shamsuddin, 2011;Yang and Zhang, 2003;Saemundsson et al, 2014;Santhanakrishnan et al, 2006;Ali et al, 2012); (3) provides adaptive caching policies (Alici et al, 2012;Subramanian et al, 2006;Megiddo and Modha, 2004); or (4) automatically manages the caching at the server side (Ports et al, 2010). In addition, there are approaches (Xu et al, 2014;Zaidenberg et al, 2015;Hwang and Wood, 2013) that focus on addressing issues of a specific cache component, namely Memcached. Given that caching opportunities are often located at the application boundaries, there are approaches that focus on specific locations to cache.…”

Section: Application-level Cachingmentioning

confidence: 99%

A comparative study of application-level caching recommendations at the method level

Meloca

Nunes

2022

Empir Software Eng

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Performance and scalability requirements have a fundamental role in most large-scale software applications. To satisfy such requirements, caching is often used at various levels and infrastructure layers. Application-level cachingor memoization-is an increasingly used form of caching within the application boundaries, which consists of storing the results of computations in memory to avoid re-computing them. This is typically manually done by developers, who identify caching opportunities in the code and write additional code to manage the cache content. The task of identifying caching opportunities is a challenge because it requires the analysis of workloads and code locations where it is feasible and beneficial to cache objects. To aid developers in this task, there are approaches that automatically identify cacheable methods. Although such approaches have been individually evaluated, their effectiveness has not been compared. We thus in this paper present an empirical evaluation to compare the method recommendations made by the two existing application-level caching approaches at the method level, namely APLCache and MemoizeIt, using seven open-source web applications. We analyse the recommendations made by each approach as well as the hits, misses and throughput achieved with their valid caching recommendations. Our results show that the effectiveness of both approaches largely depends on the specific application, the presence of invalid recommendations and additional configurations, such as the time-to-live. By inspecting the obtained results, we observed in which cases the recommendations of each approach fail and succeed, which allowed us to derive a set of seven lessons learned that give directions for future approaches to support developers in the adoption of this type of caching.

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Section: Application-level Cachingmentioning

confidence: 99%

A comparative study of application-level caching recommendations at the method level

Meloca

Nunes

2022

Empir Software Eng

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“…Blott et al [ 16 ] proposed a hybrid solution by combining DRAMs and serial-attached flash memory. Zaidenberg et al [ 17 ] proposed five new algorithms in place of LRU for Memcached. Memcached can work with single and clustered nodes; Bakar et al [ 18 ] discussed the difference in Memcached servers’ behavior in both scenarios.…”

Section: Related Workmentioning

confidence: 99%

Memcached: An Experimental Study of DDoS Attacks for the Wellbeing of IoT Applications

Mishra

Pandya

Patel³

et al. 2021

Sensors

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Distributed denial-of-service (DDoS) attacks are significant threats to the cyber world because of their potential to quickly bring down victims. Memcached vulnerabilities have been targeted by attackers using DDoS amplification attacks. GitHub and Arbor Networks were the victims of Memcached DDoS attacks with 1.3 Tbps and 1.8 Tbps attack strengths, respectively. The bandwidth amplification factor of nearly 50,000 makes Memcached the deadliest DDoS attack vector to date. In recent times, fellow researchers have made specific efforts to analyze and evaluate Memcached vulnerabilities; however, the solutions provided for security are based on best practices by users and service providers. This study is the first attempt at modifying the architecture of Memcached servers in the context of improving security against DDoS attacks. This study discusses the Memcached protocol, the vulnerabilities associated with it, the future challenges for different IoT applications associated with caches, and the solutions for detecting Memcached DDoS attacks. The proposed solution is a novel identification-pattern mechanism using a threshold scheme for detecting volume-based DDoS attacks. In the undertaken study, the solution acts as a pre-emptive measure for detecting DDoS attacks while maintaining low latency and high throughput.

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“…In a separate study about general caching, Zaidenberg, Gavish, and Meir [17] discussed novel caching algorithms that could potentially be applied to a Lastly, Anand and Barua [1] discussed the use of locking mechanisms in instruction cache in order to improve system performance, specifically that found in embedded systems. By locking particular lines of cache, they could assure certain elements remain in the cache for the duration of some system activity or event.…”

Section: Related Workmentioning

confidence: 99%

“…LFU involves keeping track of the number of times a particular UEC element in the cache is accessed. In the event that eviction needs to be performed when the cache is full and space is needed for new elements, the element with the lowest number of accesses will be evicted [17].…”

Section: Baseline Comparisonsmentioning

confidence: 99%

Cache Management Schemes for User Equipment Contexts in 5th Generation Cloud Radio Access Networks

Kaur

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By Gurpreet Kaur Advances in cellular network technology continue to develop to address increasing demands from the growing number of devices resulting from the Internet of Things, or IoT. IoT has brought forth countless new equipment competing for service on cellular networks. The latest in cellular technology is 5th Generation Cloud Radio Access Networks, or 5G C-RAN, which consists of an architectural design created specifically to meet novel and necessary requirements for better performance, reduced latency of service, and scalability. As part of this design is the inclusion of a virtual cache, there is a necessity for useful cache management schemes and protocols, which ultimately will provide users better performance on the cellular network. This paper explores a few different cache management schemes, and analyzes their performance in comparison to each other. They include a probability based scoring scheme for cache elements; a hierarchical, or tiered, approach aimed at separating the cache into different levels or sections; and enhancements to previously existing approaches including reverse random marking as well as a scheme based on an exponential decay model. These schemes aim to offer better hit ratios, reduced latency of request service, preferential treatment based on users' service levels and mobility, and a reduction in network traffic compared to other traditional and classic caching mechanisms. v TABLE OF CONTENTS LIST OF TABLES vii LIST OF FIGURES viii

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New caching algorithms performance evaluation

Cited by 4 publications

References 7 publications

A comparative study of application-level caching recommendations at the method level

A comparative study of application-level caching recommendations at the method level

Memcached: An Experimental Study of DDoS Attacks for the Wellbeing of IoT Applications

Cache Management Schemes for User Equipment Contexts in 5th Generation Cloud Radio Access Networks

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