A CMOS Q-Enhancement Bandpass-Filter For Use In Paging Receivers

Paging receivers often have to work in a dense\ud signal environment. This poses high demands on the preselection\ud filter. One of the most difficult aspects is the large\ud image rejection demand, which only can be satisfied by use\ud of a narrow-band or high-Q filter. The practical restrictions\ud for possible filter implementations are the low cost, low\ud power and the small size of the pager. By use of positive feedback\ud around a cheap off-chip low-Q inductor we obtain an\ud enhanced quality factor. We are therefore able to construct\ud selective filters using cheap small-size inductors. The price\ud paid for Q-enhancement is a larger noise and higher sensitivity\ud to component variations. The higher noise influence\ud is eliminated using a high gain in the preceding LNA-stage,\ud which is considered a part of the filter. Simulated results\ud are: Q enhanced from 30 to 100, Image-rejection = 48dB,\ud f0 = 280MHz, Voltage-gain = 20dB, Noise- figure = 2.4dB,\ud IMFDR = 66dB, IDD = 1mA, VDD = 2V. The original contribution\ud of this work is the application of the enhancement\ud principle to off-chip components, which benefits the minimization\ud of size and cost

Online Coded Caching

We consider a basic content distribution scenario consisting of a single origin server connected through a shared bottleneck link to a number of users each equipped with a cache of finite memory. The users issue a sequence of content requests from a set of popular files, and the goal is to operate the caches as well as the server such that these requests are satisfied with the minimum number of bits sent over the shared link. Assuming a basic Markov model for renewing the set of popular files, we characterize approximately the optimal long-term average rate of the shared link. We further prove that the optimal online scheme has approximately the same performance as the optimal offline scheme, in which the cache contents can be updated based on the entire set of popular files before each new request. To support these theoretical results, we propose an online coded caching scheme termed coded least-recently sent (LRS) and simulate it for a demand time series derived from the dataset made available by Netflix for the Netflix Prize. For this time series, we show that the proposed coded LRS algorithm significantly outperforms the popular least-recently used (LRU) caching algorithm.Comment: 15 page

Control theory for principled heap sizing

We propose a new, principled approach to adaptive heap sizing based on control theory. We review current state-of-the-art heap sizing mechanisms, as deployed in Jikes RVM and HotSpot. We then formulate heap sizing as a control problem, apply and tune a standard controller algorithm, and evaluate its performance on a set of well-known benchmarks. We find our controller adapts the heap size more responsively than existing mechanisms. This responsiveness allows tighter virtual machine memory footprints while preserving target application throughput, which is ideal for both embedded and utility computing domains. In short, we argue that formal, systematic approaches to memory management should be replacing ad-hoc heuristics as the discipline matures. Control-theoretic heap sizing is one such systematic approach

The economics of garbage collection

This paper argues that economic theory can improve our understanding of memory management. We introduce the allocation curve, as an analogue of the demand curve from microeconomics. An allocation curve for a program characterises how the amount of garbage collection activity required during its execution varies in relation to the heap size associated with that program. The standard treatment of microeconomic demand curves (shifts and elasticity) can be applied directly and intuitively to our new allocation curves. As an application of this new theory, we show how allocation elasticity can be used to control the heap growth rate for variable sized heaps in Jikes RVM

First-Come-First-Served for Online Slot Allocation and Huffman Coding

Can one choose a good Huffman code on the fly, without knowing the underlying distribution? Online Slot Allocation (OSA) models this and similar problems: There are n slots, each with a known cost. There are n items. Requests for items are drawn i.i.d. from a fixed but hidden probability distribution p. After each request, if the item, i, was not previously requested, then the algorithm (knowing the slot costs and the requests so far, but not p) must place the item in some vacant slot j(i). The goal is to minimize the sum, over the items, of the probability of the item times the cost of its assigned slot. The optimal offline algorithm is trivial: put the most probable item in the cheapest slot, the second most probable item in the second cheapest slot, etc. The optimal online algorithm is First Come First Served (FCFS): put the first requested item in the cheapest slot, the second (distinct) requested item in the second cheapest slot, etc. The optimal competitive ratios for any online algorithm are 1+H(n-1) ~ ln n for general costs and 2 for concave costs. For logarithmic costs, the ratio is, asymptotically, 1: FCFS gives cost opt + O(log opt). For Huffman coding, FCFS yields an online algorithm (one that allocates codewords on demand, without knowing the underlying probability distribution) that guarantees asymptotically optimal cost: at most opt + 2 log(1+opt) + 2.Comment: ACM-SIAM Symposium on Discrete Algorithms (SODA) 201

Practical Fine-grained Privilege Separation in Multithreaded Applications

An inherent security limitation with the classic multithreaded programming model is that all the threads share the same address space and, therefore, are implicitly assumed to be mutually trusted. This assumption, however, does not take into consideration of many modern multithreaded applications that involve multiple principals which do not fully trust each other. It remains challenging to retrofit the classic multithreaded programming model so that the security and privilege separation in multi-principal applications can be resolved. This paper proposes ARBITER, a run-time system and a set of security primitives, aimed at fine-grained and data-centric privilege separation in multithreaded applications. While enforcing effective isolation among principals, ARBITER still allows flexible sharing and communication between threads so that the multithreaded programming paradigm can be preserved. To realize controlled sharing in a fine-grained manner, we created a novel abstraction named ARBITER Secure Memory Segment (ASMS) and corresponding OS support. Programmers express security policies by labeling data and principals via ARBITER's API following a unified model. We ported a widely-used, in-memory database application (memcached) to ARBITER system, changing only around 100 LOC. Experiments indicate that only an average runtime overhead of 5.6% is induced to this security enhanced version of application