Self-adjusting advertisement of cache indicators with bandwidth constraints

Cache advertisements reduce the access cost by allowing users to skip the cache when it does not contain their datum. Such advertisements are used in multiple networked domains such as 5G networks, wide area networks, and information-centric networking. The selection of an advertisement strategy exposes a trade-off between the access cost and bandwidth consumption. Still, existing works mostly apply a trial-and-error approach for selecting the best strategy, as the rigorous foundations required for optimizing such decisions is lacking.Our work shows that the desired advertisement policy depends on numerous parameters such as the cache policy, the workload, the cache size, and the available bandwidth. In particular, we show that there is no ideal single configuration. Therefore, we design an adaptive, self-adjusting algorithm that periodically selects an advertisement policy. Our algorithm does not require any prior information about the cache policy, cache size, or work-load, and does not require any apriori configuration. Through extensive simulations, using several state-of-the-art cache policies, and real workloads, we show that our approach attains a similar cost to that of the best static configuration (which is only identified in retrospect) in each case

Access Strategies for Network Caching

Having multiple data stores that can potentially serve content is common in modern networked applications. Data stores often publish approximate summaries of their content to enable effective utilization. Since these summaries are not entirely accurate, forming an efficient access strategy to multiple data stores becomes a complex risk management problem.This paper formally models this problem, and introduces practical algorithms with guaranteed approximation ratios, and in particular we show that our algorithms are optimal in a variety of settings. We also perform an extensive simulation study based on real data, and show that our algorithms are more robust than existing heuristics. That is, they exhibit near optimal performance in various settings whereas the efficiency of existing approaches depends upon system parameters that may change over time, or be otherwise unknown

SALSA: Self-Adjusting Lean Streaming Analytics

Counters are the fundamental building block of many data sketching schemes, which hash items to a small number of counters and account for collisions to provide good approximations for frequencies and other measures. Most existing methods rely on fixed-size counters, which may be wasteful in terms of space, as counters must be large enough to eliminate any risk of overflow. Instead, some solutions use small, fixed-size counters that may overflow into secondary structures.This paper takes a different approach. We propose a simple and general method called SALSA for dynamic re-sizing of counters, and show its effectiveness. SALSA starts with small counters, and overflowing counters simply merge with their neighbors. SALSA can thereby allow more counters for a given space, expanding them as necessary to represent large numbers. Our evaluation demonstrates that, at the cost of a small overhead for its merging logic, SALSA significantly improves the accuracy of popular schemes (such as Count-Min Sketch and Count Sketch) over a variety of tasks. Our code is released as open source

Parallel VM Deployment with Provable Guarantees

Network Function Virtualization (NFV) carries the potential for on-demand deployment of network algorithms in virtual machines (VMs). In large clouds, however, VM resource allocation incurs delays that hinder the dynamic scaling of such NFV deployment. Parallel resource management is a promising direction for boosting performance, but it may significantly increase the communication overhead and the decline ratio of deployment attempts. Our work analyzes the performance of various placement algorithms and provides empirical evidence that state of the art parallel resource management dramatically increases the decline ratio of deterministic algorithms, but hardly affects randomized algorithms. We therefore introduce APSR - an efficient parallel random resource management algorithm that requires information only from a small number of hosts and dynamically adjusts the degree of parallelism to provide provable decline ratio guarantees. We formally analyze APSR, evaluate it on real workloads, and integrate it into the popular OpenStack cloud management platform. Our evaluation shows that APSR matches the throughput provided by other parallel schedulers, while achieving up to 13x lower decline ratio and a reduction of over 85% in communication overheads

Poster abstract: Parallel VM placement with provable guarantees

Efficient on-demand deployment of VMs is at the core of cloud infrastructure but the existing resource management approaches are too slow to fulfill this promise. Parallel resource management is a promising direction for boosting performance, but when applied naïvely, it significantly increases the communication overhead and the decline ratio of deployment attempts. We propose a new dynamic and randomized algorithm, APSR, for parallel assignment of VMs to hosts in a cloud environment. APSR is guaranteed to satisfy an SLA containing decline ratio constraints, and communication overheads constraints. Furthermore, via extensive simulations, we show that APSR obtains a higher throughput than other commonly employed policies (including those used in OpenStack) while achieving a reduction of up to 13x in decline ratio and a reduction of over 85% in communication overheads

Path Integral Approach to Strongly Nonlinear Composite

We study strongly nonlinear disordered media using a functional method. We solve exactly the problem of a nonlinear impurity in a linear host and we obtain a Bruggeman-like formula for the effective nonlinear susceptibility. This formula reduces to the usual Bruggeman effective medium approximation in the linear case and has the following features: (i) It reproduces the weak contrast expansion to the second order and (ii) the effective medium exponent near the percolation threshold are $s=1$, $t=1+\kappa$, where $\kappa$ is the nonlinearity exponent. Finally, we give analytical expressions for previously numerically calculated quantities.Comment: 4 pages, 1 figure, to appear in Phys. Rev.

Electromagnetic Wave Theory and Applications

Contains table of contents for Section 3 and reports on seven research projects.Joint Services Electronics Program Contract DAAL03-89-C-0001National Science Foundation Contract ECS 86-20029Schlumberger- Doll ResearchU.S. Army Research Office Contract DAAL03 88-K-0057National Aeronautics and Space Administration Contract NAGW-1617U.S. Navy - Office of Naval Research Contract N00014-89-J-1107National Aeronautics and Space Administration Contract NAGW-1272National Aeronautics and Space Administration Contract 958461Simulation Technologies Contract DAAH01-87-C-0679U.S. Army Corp of Engineers Contract DACA39-87-K-0022WaveTracer, Inc.U.S. Navy - Office of Naval Research Contract N00014-89-J-1019U.S. Air Force Systems - Electronic Systems Division Contract F19628-88-K-0013Digital Equipment CorporationInternational Business Machines CorporationU.S. Department of Transportation Contract DTRS-57-88-C-0007

Cumulative damage fraction design approach for LMFBR metallic fuel elements

The cumulative damage fraction (CDF) analytical technique is currently being used to analyze the performance of metallic fuel elements for proliferation-resistant LMFBRs. In this technique, the fraction of the total time to rupture of the cladding is calculated as a function of the thermal, stress, and neutronic history. Cladding breach or rupture is implied by CDF = 1. Cladding wastage, caused by interactions with both the fuel and sodium coolant, is assumed to uniformly thin the cladding wall. The irradiation experience of the EBR-II Mark-II driver fuel with solution-annealed Type 316 stainless steel cladding provides an excellent data base for testing the applicability of the CDF technique to metallic fuel. The advanced metal fuels being considered for use in LMFBRs are U-15-Pu-10Zr, Th-20Pu and Th-2OU (compositions are given in weight percent). The two cladding alloys being considered are Type 316 stainless steel and a titanium-stabilized Type 316 stainless steel. Both are in the cold-worked condition. The CDF technique was applied to these fuels and claddings under the assumed steady-state operating conditions