Traffic Redundancy Elimination Research Articles

Traffic Redundancy Elimination over a Programmable Data Plane: Design and Implementation

Traffic redundancy elimination (TRE) is a key solution for saving networking resources by removing redundant traffic transfers in networks. However, current TRE systems mainly rely on end-side software programs at the cost of packet processing performance due to their complex computational requirements. This makes it difficult to deploy the TRE technique for various networking scenarios in practice. This study proposes TRE-PRO, a TRE system over a programmable data plane (PDP) paradigm. PDP provides flexible and high-performance packet processing that enables the implementation of on-path deployable and high-performance TRE systems. This article explores the design and implementation of TRE-PRO in the state-of-the-art programmable switch architecture. It is shown that TRE-PRO overcomes the shortcomings of existing TRE systems, such as packet processing delay. The performance evaluation results show that TRE-PRO can eliminate redundant traffic at the modern programmable switch without compromising any processing delay.

Pache: A Packet Management Scheme of Cache in Data Center Networks

The communication traffic among servers is a principal bottleneck for data center networks (DCNs), in which redundant traffic has a significant impact on the performance of DCNs. In this paper, we propose Pache , a distributed and efficient cache scheme to achieve traffic redundancy elimination in DCNs. In Pache, nodes cache packets to reduce bandwidth consumption and gain performance revenue. Each cache node manages the local cache and improves cache efficiency with counting bloom filter, hash table and Trie. Each cache node broadcasts the local cache information by a bloom filter to other servers, and servers have cache information tables (cache-table) to record them, which achieves a cache sharing mechanism. Moreover, Pache employs false positive information table (fp-table) to reduce the false positive rate caused by bloom filters. A server determines an original packet or the corresponding fingerprint packet to send by querying cache-table and fp-table. Cache node placement mechanism is also designed to select which node to cache a packet. For evaluating the performance of Pache, we execute extensive simulations and conduct a case study on Amazon EC2 platform from different aspects. The results show that Pache can eliminate about 40 percent redundant traffic on average, and only increase 10 percent runtime, and it is also a scalable and feasible cache scheme in DCNs.

A Predictive Acknowledgement Model for Enhancing Traffic Redundancy Elimination in Cloud

Due to the huge increase in the utilization of cloud storage in recent days, it leads to a massive growth in data traffic from application based servers to applications like smart phones, which not only influences batteries and computational capacities but as well swamp down multi-hopping strategies during data transmission. To resolve this crisis, traffic redundancy elimination (TRE) is an effectual solution, where the chunks to be transmitted will be directly fetched out from the receivers’ cache. Moreover, prevailing solutions cannot be directly applied or it is not appropriate for smart phones owing to its energy overhead ad high computation that is imposed on the applications. In order to overcome this problem, in this investigation, a novel a Predictive Acknowledgement for Eliminating Traffic (PACKET) is proposed which comprises of three significant elements. Initially, every application possess a clone in cloud that are responsible for calculating intensive tasks like detecting redundancy and parsing traffic. Secondly, consider that every cloud user has some specific applications like Facebook to be used in regular day to day life, every clones of cloud has to selectively determine the applications that are most frequently utilized and also reduce the high redundancy ratio. Thirdly, some cloud users always possess certain common applications; the proposed PACKET clusters those clones to co-operatively perform redundancy detection so as to diminish cache resource consumption in cloud. The simulation is carried out in MATLAB environment; the traces of applications are collected from online available data and are utilized for simulation purpose. Experimental outcomes demonstrates that PACKET can attain much higher hit ratio, reduced E2E delay, increased E2E throughput, energy efficiency and effectual bandwidth utilization in contrast to existing approaches. The proposed PACKET shows better and efficient trade-off than prevailing techniques.

Optimized and Secure Multiple Predictions Based Traffic Redundancy Elimination - A Survey

Optimized and Secure Multiple Predictions Based Traffic Redundancy Elimination - A Survey

소프트웨어 정의 무선 메쉬 네트워크에서의 경량화된 중복 제거 기법

낮은 비용으로 무선 네트워킹 인프라를 구축할 수 있는 무선 메쉬 네트워크에서는 제한된 무선 자원을 효율적으로 이용하기 위해 패킷 전송(특히, 불필요하게 중복되는 패킷 전송)을 신중하게 처리해야 한다. 본 논문에서는 컨트롤러를 통한 중앙 집중식의 관리가 가능한 소프트웨어 정의 네트워킹 기반의 무선 메쉬 네트워크에서 불필요하게 중복 전송되는 데이터의 양을 감소시키기 위해 경량화된 중복 제거기법을 제안한다. 제안하는 중복 제거 기법은 감소되는 트래픽 양을 극대화하기 위해 컨트롤러가 1) 기계학습 기반의 정보 요청, 2) ID기반의 소스 라우팅, 3) 인기도 기반의 캐쉬 업데이트를 통해 중복 제거 효과를 극대화시킬 수 있는 최적의 경로를 결정한다. 시뮬레이션 결과는 제안하는 기법을 통해 전체 트래픽 부하를 18.34%-48.89% 만큼 감소시킬 수 있음을 보여준다.

$\mathsf{REboost}$ : Improving Throughput in Wireless Networks Using Redundancy Elimination

Traffic redundancy elimination (RE) is an attractive approach to improve the throughput in bandwidth-limited networks. While previous studies show that the RE is useful for improving the throughput in such networks, we observed that the RE would not be an effective solution in wireless networks. We found the TCP congestion control cannot take advantage of the RE, without knowing how the underlying RE system manipulates each TCP packet. In this letter, we present a novel technique called REboost to enable the TCP layer to be aware of the underlying RE system and improve the throughput. Our evaluation with a prototype shows that REboost significantly improves the throughput compared with the previous RE systems.

CoRE: Cooperative End-to-End Traffic Redundancy Elimination for Reducing Cloud Bandwidth Cost

The pay-as-you-go service model impels cloud customers to reduce the usage cost of bandwidth. Traffic Redundancy Elimination (TRE) has been shown to be an effective solution for reducing bandwidth costs, and thus has recently captured significant attention in the cloud environment. By studying the TRE techniques in a trace driven approach, we found that both short-term (time span of seconds) and long-term (time span of hours or days) data redundancy can concurrently appear in the traffic, and solely using either sender-based TRE or receiver-based TRE cannot simultaneously capture both types of traffic redundancy. Also, the efficiency of existing receiver-based TRE solution is susceptible to the data changes compared to the historical data in the cache. In this paper, we propose a Cooperative end-to-end TRE solution (CoRE) that can detect and remove both short-term and long-term redundancy through a two-layer TRE design with cooperative operations between layers. An adaptive prediction algorithm is further proposed to improve TRE efficiency through dynamically adjusting the prediction window size based on the hit ratio of historical predictions. Besides, we enhance CoRE to adapt to different traffic redundancy characteristics of cloud applications to improve its operation cost. Extensive evaluation with several real traces show that CoRE is capable of effectively identifying both short-term and long-term redundancy with low additional cost while ensuring TRE efficiency from data changes.

Robust energy-aware routing with redundancy elimination

Many studies in literature have shown that energy-aware routing (EAR) can significantly reduce energy consumption for backbone networks. Also, as an arising concern in networking research area, the protocol-independent traffic redundancy elimination (RE) technique helps to reduce (a.k.a compress) traffic load on backbone network. Motivation from a formulation perspective, we first present an extended model of the classical multi-commodity flow problem with compressible flows. Moreover, our model is robust with fluctuation of traffic demand and compression rate. In details, we allow any set of a predefined size of traffic flows to deviate simultaneously from their nominal volumes or compression rates. As an applicable example, we use this model to combine redundancy elimination and energy-aware routing to increase energy efficiency for a backbone network. Using this extra knowledge on the dynamics of the traffic pattern, we are able to significantly increase energy efficiency for the network. We formally define the problem and model it as a Mixed Integer Linear Program (MILP). We then propose an efficient heuristic algorithm that is suitable for large networks. Simulation results with real traffic traces on Abilene, Geant and Germany50 networks show that our approach allows for 16–28% extra energy savings with respect to the classical EAR model.

English

During the last few years there has been a tremendous and efficient improvement in the computational world and one of the area that is leaving its footprints is cloud computing. One of the main trending issue in this computing is traffic redundancy elimination (TRE).Cloud-based TRE needs to apply a judicious use of cloud resources so that the bandwidth cost reduction is optimized together with the additional cost of TRE computation . Here, I present an approach that is suitable for cloud computing customers called Hybrid PACK(predictive acknowledgements) regarding TRE systems. Unlike other techniques PACK’s main advantage is its capability of offloading the cloud-server TRE effort to the end clients ,and hence minimizing the overhead and processing costs induced by the TRE algorithm. In the present scenario the server keeps track of all the end clients. But this is not the case with the PACK as in this mechanism a client can manage his own status and hence the server is offloaded. This makes the PACK suitable for pervasive computing .PACK is based on a novel TRE technique ,which allows the client as well as server to use newly received chunks to identify previously received chunk chains, which in turn can be used as reliable predictors to future transmitted chunks. I here present a hybrid implementation of the PACK in which there is an active involvement of client and server and moreover the chunking signatures are done using SHA 2 (secure hash algorithm) in order to process the any size of chunks.

PACK: Prediction-Based Cloud Bandwidth and Cost Reduction System

In this paper, we present PACK (Predictive ACKs), a novel end-to-end traffic redundancy elimination (TRE) system, designed for cloud computing customers. Cloud-based TRE needs to apply a judicious use of cloud resources so that the bandwidth cost reduction combined with the additional cost of TRE computation and storage would be optimized. PACK's main advantage is its capability of offloading the cloud-server TRE effort to end-clients, thus minimizing the processing costs induced by the TRE algorithm. Unlike previous solutions, PACK does not require the server to continuously maintain clients' status. This makes PACK very suitable for pervasive computation environments that combine client mobility and server migration to maintain cloud elasticity. PACK is based on a novel TRE technique, which allows the client to use newly received chunks to identify previously received chunk chains, which in turn can be used as reliable predictors to future transmitted chunks. We present a fully functional PACK implementation, transparent to all TCP-based applications and network devices. Finally, we analyze PACK benefits for cloud users, using traffic traces from various sources.

Data Centers as Software Defined Networks: Traffic Redundancy Elimination with Wireless Cards at Routers

We propose a novel architecture of data center networks (DCN), which adds wireless network card to both servers and routers. Existing traffic redundancy elimination (TRE) mechanisms reduce link loads and increase network capacity in several environments by removing strings that have appeared in earlier packets through encoding and decoding them several hops downstream. This article is the first to explore TRE mechanisms in large-scale DCNs and the first to exploit cooperative TRE among servers. Moreover, it also achieves the `logically centralized' control over the physically distributed states in emerging software defined networks (SDN) paradigm, by sharing information among servers and routers in data centers with wireless cards. We first formulate the TREDaCeN (TRE in Data Center Networks) problem and reduce the cycle cover problem to prove that finding an optimal caching task assignment for TREDaCeN problem is NP-hard. We further describe an offline TREDaCeN algorithm which is proved to have good approximation ratio. We then discuss efficient online zero-delay and semi-distributed implementations of TREDaCeN supported by physical proximity of servers and routers, enabling status updates in a single wireless transmission, using an efficient prioritized schedule. We also address online cache replacement and consistency of information in servers and routers with and without delay. Our framework is tested on different parameters and shows superior performance in comparison to other mechanisms (imported directly to this setting). Our results show the robustness and the trade-off between the `logically centralized' implementation and the overhead on handling inconsistency of distributed information in DCN.

The power of prediction

In this paper we present PACK (Predictive ACKs), a novel end-to-end Traffic Redundancy Elimination (TRE) system, designed for cloud computing customers. Cloud-based TRE needs to apply a judicious use of cloud resources so that the bandwidth cost reduction combined with the additional cost of TRE computation and storage would be optimized. PACK's main advantage is its capability of offloading the cloud-server TRE effort to end-clients, thus minimizing the processing costs induced by the TRE algorithm. Unlike previous solutions, PACK does not require the server to continuously maintain clients' status. This makes PACK very suitable for pervasive computation environments that combine client mobility and server migration to maintain cloud elasticity. PACK is based on a novel TRE technique, which allows the client to use newly received chunks to identify previously received chunk chains, which in turn can be used as reliable predictors to future transmitted chunks. We present a fully functional PACK implementation, transparent to all TCP-based applications and network devices. Finally, we analyze PACK benefits for cloud users, using traffic traces from various sources.