9 research outputs found
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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
Adaptive TTL-Based Caching for Content Delivery
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
Carbon Responder: Coordinating Demand Response for the Datacenter Fleet
The increasing integration of renewable energy sources results in
fluctuations in carbon intensity throughout the day. To mitigate their carbon
footprint, datacenters can implement demand response (DR) by adjusting their
load based on grid signals. However, this presents challenges for private
datacenters with diverse workloads and services. One of the key challenges is
efficiently and fairly allocating power curtailment across different workloads.
In response to these challenges, we propose the Carbon Responder framework.
The Carbon Responder framework aims to reduce the carbon footprint of
heterogeneous workloads in datacenters by modulating their power usage. Unlike
previous studies, Carbon Responder considers both online and batch workloads
with different service level objectives and develops accurate performance
models to achieve performance-aware power allocation. The framework supports
three alternative policies: Efficient DR, Fair and Centralized DR, and Fair and
Decentralized DR. We evaluate Carbon Responder polices using production
workload traces from a private hyperscale datacenter. Our experimental results
demonstrate that the efficient Carbon Responder policy reduces the carbon
footprint by around 2x as much compared to baseline approaches adapted from
existing methods. The fair Carbon Responder policies distribute the performance
penalties and carbon reduction responsibility fairly among workloads
Fast Rerouting for IP Multicast Under Single Node Failures
Abstract—In this paper, we propose multicast protection trees that provide instantaneous failure recovery from single node failures. For a given node v, the multicast protection tree spans all the neighbors v and does not include v. Thus, when node v fails, its neighbors are connected through the multicast protection tree instead of node v, and the neighbors of node v forward the traffic over this tree. The multicast protection trees are constructed a priori, without the knowledge of the multicast traffic in the network. This facilitates protocol independent single node failure recovery in multicast networks. These trees are used when a new multicast tree is being formed after a node failure has occurred. We analyze the effectiveness of the proposed fast rerouting technique using three practical networks. I
A novel fiber Bragg grating system for eye tracking
Eye movement evaluation is vital for diagnosis of various ophthalmological and neurological disorders. The present study proposes a novel, noninvasive, wearable device to acquire the eye movement based on a Fiber Bragg Grating (FBG) Sensor. The proposed Fiber Bragg Grating Eye Tracker (FBGET) can capture the displacement of the eyeball during its movements in the form of strain variations on a cantilever. The muscular displacement generated by the eyeball over the lower eyelid, by its swiveling action while moving the gaze on a target object, is converted into strain variations on a cantilever. The developed FBGET is investigated for dynamic tracking of the eye-gaze movement for various actions of the eye such as fixations, saccades and main sequence. This approach was validated by recording the eye movement using the developed FBGET as well as conventional camera-based eye tracker methodology simultaneously. The experimental results demonstrate the feasibility and the real-time applicability of the proposed FBGET as an eye tracking device. In conclusion, the present study illustrates a novel methodology involving displacement of lower eyelid for eye tracking application along with the employment of FBG sensors to carry out the same. The proposed FBGET can be utilized in both clinical and hospital environment for diagnostic purposes owing to its advantages of wear-ability and ease of implementation making it a point of care device. Keywords: Fiber Bragg grating sensor, Eye tracker, Eye muscular movement detectio
Exome sequence analysis of rare frequency variants in Late-Onset Alzheimer Disease
Alzheimer disease (AD) is a leading cause of dementia in elderly patients who continue to live between 3 and 11Â years of diagnosis. A steep rise in AD incidents is observed in the elderly population in East-Asian countries. The disease progresses through several changes, including memory loss, behavioural issues, and cognitive impairment. The etiology of AD is hard to determine because of its complex nature. The whole exome sequences of late-onset AD (LOAD) patients of Korean origin are investigated to identify rare genetic variants that may influence the complex disorder. Computational annotation was performed to assess the function of candidate variants in LOAD. The in silico pathogenicity prediction tools such as SIFT, Polyphen-2, Mutation Taster, CADD, LRT, PROVEAN, DANN, VEST3, fathmm-MKL, GERP + + , SiPhy, phastCons, and phyloP identified around 17 genes harbouring deleterious variants. The variants in the ALDH3A2 and RAD54B genes were pathogenic, while in 15 other genes were predicted to be variants of unknown significance. These variants can be potential risk candidates contributing to AD. In silico computational techniques such as molecular docking, molecular dynamic simulation and steered molecular dynamics were carried out to understand the structural insights of RAD54B with ATP. The simulation of mutant (T459N) RAD54B with ATP revealed reduced binding strength of ATP at its binding site. In addition, lower binding free energy was observed when compared to the wild-type RAD54B. Our study shows that the identified uncommon variants are linked to AD and could be probable predisposing genetic factors of LOAD