Bounded Delay Scheduling with Packet Dependencies

A common situation occurring when dealing with multimedia traffic is having large data frames fragmented into smaller IP packets, and having these packets sent independently through the network. For real-time multimedia traffic, dropping even few packets of a frame may render the entire frame useless. Such traffic is usually modeled as having {\em inter-packet dependencies}. We study the problem of scheduling traffic with such dependencies, where each packet has a deadline by which it should arrive at its destination. Such deadlines are common for real-time multimedia applications, and are derived from stringent delay constraints posed by the application. The figure of merit in such environments is maximizing the system's {\em goodput}, namely, the number of frames successfully delivered. We study online algorithms for the problem of maximizing goodput of delay-bounded traffic with inter-packet dependencies, and use competitive analysis to evaluate their performance. We present competitive algorithms for the problem, as well as matching lower bounds that are tight up to a constant factor. We further present the results of a simulation study which further validates our algorithmic approach and shows that insights arising from our analysis are indeed manifested in practice

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. 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 a simulation study that provides further insights as to our algorithmic design principles in face of limited knowledge

On the Power of False Negative Awareness in Indicator-based Caching Systems

Distributed caching systems such as content distribution networks often advertise their content via lightweight approximate indicators (e.g., Bloom filters) to efficiently inform clients where each datum is likely cached. While false-positive indications are necessary and well understood, most existing works assume no false-negative indications. Our work illustrates practical scenarios where false-negatives are unavoidable and ignoring them has a significant impact on system performance. Specifically, we focus on false-negatives induced by indicator staleness, which arises whenever the system advertises the indicator only periodically, rather than immediately reporting every change in the cache. Such scenarios naturally occur, e.g., in bandwidth-constraint environments or when latency impedes the ability of each client to obtain an updated indicator. Our work introduces novel false-negative aware access policies that continuously estimate the false-negative ratio and sometimes access caches despite negative indications. We present optimal policies for homogeneous settings and provide approximation guarantees for our algorithms in heterogeneous environments. We further perform an extensive simulation study with multiple real system traces. We show that our false-negative aware algorithms incur a significantly lower access cost than existing approaches or match the cost of these approaches while requiring an order of magnitude fewer resources (e.g., caching capacity or bandwidth)

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 as a cost minimization problem, while taking into account both access costs, the inaccuracy of the approximate summaries, as well as the penalties incurred by failed requests. We introduce practical algorithms with guaranteed approximation ratios and further show that they are optimal in various 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

Jitter Regulation for Multiple Streams (Extended Abstract)

Abstract. For widely-used interactive communication, it is essential that traffic is kept as smooth as possible; the smoothness of a traffic is typically captured by its delay jitter, i.e., the difference between the maximal and minimal end-to-end delays. The task of minimizing the jitter is done by jitter regulators that use a limited-size buffer in order to shape the traffic. In many real-life situations regulators must handle multiple streams simultaneously and provide low jitter on each of them separately. This paper investigates the problem of minimizing jitter in such an environment, using a fixed-size buffer. We show that the offline version of the problem can be solved in polynomial time, by introducing an efficient offline algorithm that finds a release schedule with optimal jitter. When regulating M streams in the online setting, we take a competitive analysis point of view and note that previous results in [1] can be extended to an online algorithm that uses a buffer of size 2MB and obtains the optimal jitter possible with a buffer of size B. The question arises whether such a resource augmentation is essential. We answer this question in the affirmative, by proving a lower bound that is tight up to a factor of 2, thus showing that jitter regulation does not scale well as the number of streams increases unless the buffer is sized-up proportionally.

Reducing Service Deployment Cost Through VNF Sharing

Thanks to its computational and forwarding capabilities, the mobile network infrastructure can support several third-party (“vertical”) services, each composed of a graph of virtual (network) functions (VNFs). Importantly, one or more VNFs are often common to multiple services, thus the services deployment cost could be reduced by letting the services share the same VNF instance instead of devoting a separate instance to each service. By doing that, however, it is critical that the target KPI (key performance indicators) of all services are met. To this end, we study the VNF sharing problem and make decisions on 1) when sharing VNFs among multiple services is possible, 2) how to adapt the virtual machines running the shared VNFs to the combined load of the assigned services, and 3) how to prioritize the services traffic within shared VNFs. All decisions aim to minimize the cost for the mobile operator, subject to requirements on end-to-end service performance, e.g., total delay. Notably, we show that the aforementioned priorities should be managed dynamically and vary across VNFs. We then propose the FlexShare algorithm to provide near-optimal VNF-sharing and priority assignment decisions in polynomial time. We prove that FlexShare is within a constant factor from the optimum and, using real-world VNF graphs, we show that it consistently outperforms baseline solutions.This work was supported by the EU Commission through the 5GROWTH project (grant agreement no. 856709). The work of G. Scalosub has been supported by the Israel Science Foundation (grant No. 1036/14) and the Neptune Consortium, administered by the Israeli Ministry of Economy and Industry