Kalika Suksomboon

PopCache: Cache more or less based on content popularity for information-centric networking

Due to a mismatch between downloading and caching content, the network may not gain significant benefit from the sophisticated in-network caching of information-centric networking (ICN) architectures by using a basic caching mechanism. This paper aims to seek an effective caching decision policy to improve the content dissemination in ICN. We propose PopCache-a caching decision policy with respect to the content popularity-that allows an individual ICN router to cache content more or less in accordance with the popularity characteristic of the content. We propose an analytical model to evaluate the performance of different caching decision policies in terms of the server-hit rate and expected round-trip time. The analysis confirmed by simulation results shows that PopCache yields the lowest expected round-trip time compared with three benchmark caching decision policies, i.e., the always, fixed probability and path-capacity-based probability, and PopCache provides the server-hit rate comparable to the lowest ones.

Performance of probabilistic caching and cache replacement policies for Content-Centric Networks

The Content-Centric Networking (CCN) architecture exploits a universal caching strategy whose inefficiency has been confirmed by research communities. Various caching schemes have been proposed to overcome some drawbacks of the universal caching strategy but they come with additional complexity and overheads. Besides those sophisticated caching schemes, there is a probabilistic caching scheme that is more efficient than the universal caching strategy and adds a modest complexity to a network. The probabilistic caching scheme was treated as a benchmark and the insights into its behavior have never been studied despite its promising performance and feasibility in practical use. In this paper we study the probabilistic caching scheme by means of computer simulation to explore the behavior of the probabilistic caching scheme when it works with various cache replacement policies. The simulation results show the different behavioral characteristics of the probabilistic caching scheme as a function of the cache replacement policy.

Cooperative routing protocol for Content-Centric Networking

A typical Forwarding Information Based (FIB) construction in the Content Centric Networking (CCN) architecture relies on the name prefix dissemination following the shortest path manner. However, routing based on the shortest path may not fully exploit the benefits of forwarding and data planes of the CCN architecture since different content requester routers may use disjoint paths to forward their interest packets, even though these packets aim at the same content. To exploit this opportunity, we propose a cooperative routing protocol for CCN, which focuses on a FIB reconstruction based on the content retrieval statistics to improve the in-network caching utilization. A binary linear optimization problem is formulated for calculating the optimal path for the cooperative routing. The simulation results show an improvement in the server load and round-trip time provided by the cooperative routing scheme compared with that of the conventional shortest path routing scheme.

On incentive-based inter-domain caching for content delivery in future internet architectures

Leveraging in-network caching of proposed future network architectures such as content-centric networking (CCN) encourages users and ISPs to enjoy performance improvements of networks. However, we raise an argument against a rule of thumb stating that more content storage is better since business relationships in inter-domain networks are based on profit negotiations rather than an efficiently deployed network architecture. The objective of this paper is to identify the optimal cache allowance for a caching mechanism in CCN in inter-domain networks. We propose an incentive model for ISPs and analyze their maximum profits by studying the effects of cache allowance in their routers. The results confirmed from our analysis indicate that the minimum expected round-trip time can be achieved by deploying the maximum cache allowance in all routers. In contrast, the maximum cache allowance in the router connected to clients and the minimum cache allowance in the gateway router of an ISP that connects to the clients provides the maximum profit to that ISP. On the other hand, the maximum cache allowance in the gateway router of an ISP that is connected to clients and the minimum cache allowance in the router close to the server provides the highest profit to an ISP connected to the server.

A dilated-CPU-consumption-based performance prediction for multi-core software routers

Network function virtualization (NFV) raises new possibilities for embedding data plane processing functions, e.g., firewalls, NAT, packet forwarding, etc., on commodity hardware. However, the advantages of flexibility, scalability and low cost of commodity hardware come at a price, such as resource over-provisioning, because the performance of softwarized network functions on shared resources is hardly predictable. This paper addresses the problem of performance prediction for multi-core software routers. Specifically, the performance prediction model is developed to predict the maximum throughput a multi-core software router can perform given assigned resources. Motivated by observations, we first mathematically analyze how many CPU cycles spent for packet forwarding in multi-core processing systems. Our analytical model based on cache contention can capture its nonlinear dilation scaled by the number of CPU cores-called dilated CPU consumption (DCC). We validate the accuracy of DCC with measured data, achieving error less than 8%. We then propose two performance prediction algorithms based on the DCC. The first algorithm relies on CPU utilization statistics (called DCC-u), while its simplified version (called sDCC) does not require CPU utilization statistics. Evaluation with measured data shows that the estimations of DCC-u has error margins less than 3% for a large packet size and 10% for a small packet size, while sDCC provides larger error than DCC-u. Remarkably, our both performance prediction algorithms yield more precise estimation than that of the benchmarking techniques.

LawNFO: A decision framework for optimal location-aware network function outsourcing

By decoupling the network function from the underlying dedicated hardware, enterprises can reap the benefit of outsourcing network functions to the cloud. Due to a lack of analytical tools, one cannot simply justify the worthiness of outsourcing network functions. This paper proposes a decision support framework for network function outsourcing, called Location-aware Network Function Outsourcing (LawNFO), aiming to identify which network function is worth to be outsourced to the cloud. LawNFO constructs a network-function graph and maps the cost of network function processing units and the data transmission between any network-function pairs onto the graph. The network function outsourcing problem is formulated as an optimization problem targeting on the minimum network cost. To solve the optimization problem based on the graph input, we develop an efficient and fast heuristic algorithm, called Node-weighted Contraction (NowCont), that encompasses the graph partitioning and graph augmentation techniques. This paper performs simulation to evaluate the performance of LawNFO with NowCont algorithm in two aspects: 1) the closeness ratio to the optimal solutions and 2) the network cost comparing to that of all-in-house and most-in-cloud network function placement models. The simulation results illustrate that the NowCont algorithm yields the solution closest to the optimal solution. The evaluation through two use cases show that LawNFO is of benefit to the enterprises for the decision of network function outsourcing.

Pending-interest-driven cache orchestration through network function virtualization

Emerging information-centric networking architectures drives a wider focus of content caching mechanisms. Most recent efforts have been attempting for server and user friendliness, i.e., reduction in content-server load and content-delivery time. Those solutions require the prior knowledge of cache state and content popularity exchanged among routers. Challenging tasks are pushed to routers since their explicit coordination is required. Instead of burdening routers, but still keeping the effectiveness of in-network caching, we move the explicit coordination task from routers to a cache orchestrator where the offline optimal caching policy is computed while leaving the simplest cache decision to the routers. By means of the network function virtualization, this paper proposes a cache orchestration lifecycle including three parts, i.e., name hit caching (NHC) policy, pending time history and network cache orchestration. By exploiting NHC policy, a router simply caches the content whose name matches its assigned content name set. Evaluation through simulations demonstrates that NHC policy achieves the lowest content-server load and content downloading time and the highest cache hit ratios in all routers comparing with leaving copy everywhere, probability caching and content-popularity-based cache orchestration policies.

Optimal virtualization of functionality for customer premise equipment

Network function virtualization has been introduced to extend the lifetime of customer premise equipment (CPE). Some functionalities of CPE for serving new service features have been expected to be shifted to a data center. However a lack of network cost analysis causes an argument of the worthiness of this shifting.We propose a determination framework for the functionality virtualization of CPE. Firstly, all functionalities of CPE are transformed to a graph structure. Secondly, the functionality virtualization is determined based on the functionality graph in order to minimize the network cost. The determination problem is formulated as a binary quadratic programming problem; however, searching for the optimal solution is NP-complete. Thus, we propose two heuristic algorithms (i.e., modified Kargers (mKar) and virtualization-cost-based determination (VCD)) to determine which functionality should be virtualized and placed in the data center. This paper performs both theoretical analysis and simulation to evaluate the performance of our algorithms. Those evaluations show that mKar is faster than VCD while VCD provides the solutions closer to the optimum than that of mKar. Conclusive remarks of our findings provide the significant insights into the way of functionality virtualization.

Erlang-k-based packet latency prediction model for optimal configuration of software routers

Providing the optimal configuration for a software router poses a lot of technical challenges that do not present in the dedicated hardware router. One of them is how to characterize performance varying due to different configurations on commodity hardware. This paper addresses the problem of configuring a software router that provides the minimum of average packet latency. Since changing all combinations of hardware configurations of a software router for searching the optimum is cumbersome, we propose a prediction model to accurately estimate the packet latency of a software router. We first analyze the relationship of the packet latency distribution with the configured and observed parameters. Empirical measurements suggest that the Erlang-k distribution is a reasonable model for estimating the packet latency distribution. Motivated by the parameter relationship analysis, we propose a prediction model for packet latency of a software router based on the Erlang- k distribution. Our prediction model requires measurement of only two different configurations, i.e., one and two Rx queues of a network interface card, to predict the average packet latency of all combinations of configurations. We use the measured data from the testbed experiments and the data of curve fitting method to cross-verify the accuracy of our prediction model. Underlying the prediction model, we propose the optimal configuration selection (OCS) algorithm to justify which configuration yields the minimum of average packet latency. Our prediction model based OCS results in the same optimal configuration with the measured data based ones.