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

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

Queueing in the mist: Buffering and scheduling with limited knowledge

Scheduling and managing queues with bounded buffers are among the most fundamental problems in computer networking. Traditionally, it is often assumed that all the properties of each packet are known immediately upon arrival. However, as traffic becomes increasingly heterogeneous and complex, such assumptions are in many cases invalid. In particular, in various scenarios information about packet characteristics becomes available only after the packet has undergone some initial processing. In this work, we study the problem of managing queues with limited knowledge. We start by showing lower bounds on the competitive ratio of any algorithm in such settings. The techniques used in our proofs, which make use of a carefully crafted Markov process, may be of independent interest, and can potentially be used in other similar settings as well. Next, we use the insight obtained from these bounds to identify several algorithmic concepts appropriate for the problem, and use these guidelines to design a concrete algorithmic framework. We analyze the performance of our proposed algorithm, and further show how it can be implemented in various settings, which differ by the type and nature of the unknown information. We further validate our results and algorithmic approach by an extensive simulation study that provides further insights as to our algorithmic design principles in face of limited knowledge