Caching is extensively used in various networking environments to optimize performance by reducing latency, bandwidth, and energy consumption. To optimize performance, caches often advertise their content using indicators, which are data structures that trade space efficiency for accuracy. However, this tradeoff introduces the risk of false indications. Existing solutions for cache content advertisement and cache selection often lead to inefficiencies, failing to adapt to dynamic network conditions. This paper introduces SALSA2, a Scalable Adaptive and Learning-based Selection and Advertisement Algorithm, which addresses these limitations through a dynamic and adaptive approach. SALSA2 accurately estimates mis-indication probabilities by considering inter-cache dependencies and dynamically adjusts the size and frequency of indicator advertisements to minimize transmission overhead while maintaining high accuracy. Our extensive simulation study, conducted using a variety of real-world cache traces, demonstrates that SALSA2 achieves up to 84% bandwidth savings compared to the state-of-the-art solution and close-to-optimal service cost in most scenarios. These results highlight SALSA2’s effectiveness in enhancing cache management, making it a robust and versatile solution for modern networking challenges.

We present a lightweight, self-adjusting algorithm for cache-content advertisement and cache selection. Our algorithm increases the hit ratio and mitigates wasteful, unnecessary cache accesses and cachecontent advertisements.

Placement of VMs in the cloud is one of the most fundamental problems in systems research. Traditionally, placement algorithms assume that the schedulers have complete information about the currently available resources at each host. However, this assumption is in many cases unrealistic, as gathering fresh status information from each of the thousands of hosts in a large data center incurs excessive communication overhead, which results in long queueing delays. Efforts to resolve this problem by employing several parallel schedulers typically exhibit collisions when several schedulers are simultaneously trying to place VMs on the same host. Our work analyzes the performance of various placement algorithms and provides empirical evidence that using multiple randomized schedulers obtains high throughput, while significantly decreasing both the communication overhead, and the number of collisions between schedulers. We, therefore, introduce Adaptive Partial State Random (APSR) – an efficient parallel random resource management algorithm that samples only from a small number of hosts and dynamically adjusts the degree of parallelism to provide provable guarantees on the probability of collisions between distinct schedulers. 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.

We consider a hierarchical edge-cloud architecture in which services are provided to mobile users as chains of virtual network functions. Each service has specific computation requirements and target delay performance, which require placing the corresponding chain properly and allocating a suitable amount of computing resources. Furthermore, chain migration may be necessary to meet the services’ target delay. We model and formalize the problem of finding a feasible chain placement and resource allocation, while minimizing the migration, bandwidth, and computation costs. We tackle this problem by partitioning it into a (i) CPU allocation problem, and a (ii) placement problem. For the CPU allocation problem, we find an optimal solution. For the placement problem, we show that even finding a feasible solution is NP-hard, and envision an algorithm that is guaranteed to find a feasible solution while leveraging a bounded amount of resource augmentation. Our algorithms are incorporated into a solution framework that aims to minimize both the cost and the required resource augmentation. The results, obtained through trace-driven, large-scale simulations, show that our framework can provide a close-to-optimal solution while running several orders of magnitude faster than an ILP solver.

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 significantly impacts 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 each client’s ability 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 service 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).

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 each client's ability 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).

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.

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.

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.

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.

Chip multiprocessors (CMPs) combine increasingly many general-purpose processor cores on a single chip. These cores run several tasks with unpredictable communication needs, resulting in uncertain and often-changing traffic patterns. This unpredictability leads network-on-chip (NoC) designers to plan for the worst case traffic patterns, and significantly overprovision link capacities. In this paper, we provide NoC designers with an alternative statistical approach. We first present the traffic-load distribution plots (T-Plots), illustrating how much capacity overprovisioning is needed to service 90, 99, or 100 percent of all traffic patterns. We prove that in the general case, plotting T-Plots is #P-complete, and therefore extremely complex. We then show how to determine the exact mean and variance of the traffic load on any edge, and use these to provide Gaussian-based models for the T-Plots, as well as guaranteed performance bounds. We also explain how to practically approximate T-Plots using random-walk-based methods. Finally, we use T-Plots to reduce the network power consumption by providing an efficient capacity allocation algorithm with predictable performance guarantees.