Cache Update Algorithms for Multicasting Without File Splitting

Vehicular edge caching (VEC) is expected to support real-time intelligent transportation systems by providing low-latency data services. However, dynamic vehicular environment, such as time-varying data freshness and dynamic data preference, may result in low cache efficiency. Based on the above motivation, this paper designs a system model of freshness-aware VEC system. Accordingly, we formulate the problem of Vehicular Edge Cache Update (VECU) by exploiting the concept of AoI and data heterogeneity for evaluating data freshness, which aims at maximizing the edge cache benefit. On this basis, the Contextual Multi-Armed Bandit for Caching Update (CMAB-CU) algorithm is designed to determine cache update decision by online estimating reward of each arm based on a linear function of dynamic vehicular features and historical observations. Finally, we modeling a simulation model and conduct simulation results, which demonstrates the effectiveness of the proposed algorithm in various service scenarios.

A Closed/Open-Loop cache update strategy by peeking into the future

In this letter, we study the dynamic cache update problem for wireless content caching networks. The cost minimization perspective is considered, wherein the system cost contains three components, including the cache cost, the retrieval cost, and the update cost. Particularly, the update cost lies at the root of the coded caching strategy. To address the non-convex optimization problem, we propose both suboptimal and optimal algorithms. More specifically, we prove that with previous caching state, the cost minimization problem for the current time interval is a 0–1 Knapsack problem, which can be optimally solved by a dynamic programming with pseudo polynomial time complexity. On this basis, the original optimization problem is solved by an iterative manner, resulting in an suboptimal solution. Thereafter, we design the optimal dynamic caching policy, which is derived from the recursive algorithm. Simulation result shows that the suboptimal solution achieves near-optimal performance. It also demonstrates the superiority of our developed strategies compared to various baselines in terms of cost saving. Moreover, counterintuitive insight is judiciously acquired, such as that the high cache capacity does not always reveal positive effect on system’s performance when update cost is taken into consideration.

본 논문에서는 빠르고 정확하게 elephant flow를 발견할 수 있는 알고리즘을 제시한다. 최근 인터넷 사용자의 증가와 다양한 응용 프로그램의 등장으로 인하여, 네트워크 트래픽의 대규모화가 급속히 진행되고 있는 추세이다. 이러한 변화에 따라 네트워크 대역의 상당 부분을 점유하는 elephant flow 가 자주 발생하게 되었다. Elephant flow는 인터넷 트래픽의 관리 (management) 및 서비스 측면에서 네트워크 대역 (network bandwidth)의 불공평한 사용 문제를 유발한다. 본 논문에서는 Elephant flow를 발견하는 방법들 중 하나인 기존 Landmark-LRU 기법에 간단한 메커니즘을 추가시켜, 발견율을 크게 증가시키는 방법을 제시하였다. 그리고 제안하는 개선안을 실제 네트워크에서 추출한 트레이스 (network traces)에 적용하는 시뮬레이션을 통하여 평가하였다. 그 결과로 우리가 제시하는 개선 알고리즘이 효율적인 메모리 비용을 유지하면서 Landmark-LRU 기법보다 더 정확하게 elephant flow를 발견하는 것을 확인할 수 있었다. We proposed a scheme to accurately detect elephant flows. Along the ever increasing traffic trend, certain flows occupy the network heavily in terms of time and network bandwidth. These flows are called elephant flows. Elephant flows raises complicated issues to manage for Internet traffics and services. One of the methods to identify elephant flows is the Landmark LRU cache scheme, which improved the previous method of Least Recently Used scheme. We proposed a cache update algorithm, to further improve the existing Landmark LRU. The proposed scheme improves the accuracy to detect elephant flow while maintaining efficiency of Landmark LRU. We verified our algorithm by simulating on Sangmyung University's wireless real network traces and evaluated the improvement.