In this paper, we introduce our Open Source simulation

In this paper, we introduce our Open Source tool-chain providing the parametrization, distributed execution, results post-processing and debugging for OMNeT++-based simulations. While the initial motivation of these tools has been the support of our simulation model of the Reliable Server Pooling (RSerPool) framework, it has been particularly designed with model-independence in mind. That is, it can be easily adapted to other simulation models and therefore may be useful for other users of OMNeT++-based simulation models as well.

In this paper we present a new technique for worst-case analysis of compression algorithms which are based on the Burrows-Wheeler Transform. We deal mainly with the algorithm proposed by Burrows and Wheeler in their first paper on the subject [6], called bw0. This algorithm consists of the following three essential steps: 1) Obtain the Burrows-Wheeler Transform of the text, 2) Convert the transform into a sequence of integers using the move-to-front algorithm, 3) Encode the integers using Arithmetic code or any order-0 encoding (possibly with run-length encoding). We achieve a strong upper bound on the worst-case compression ratio of this algorithm. This bound is significantly better than bounds known to date and is obtained via simple analytical techniques. Specifically, we show that for any input string s, and µ> 1, the length of the compressed string is bounded by µ · |s|Hk(s)+ log(ζ(µ)) · |s | + µgk + O(log n) where Hk is the k-th order empirical entropy, gk is a constant depending only on k and on the size of the alphabet, and ζ(µ) = 1 1 1 µ+ 2 µ+... is the standard zeta function. As part of the analysis we prove a result on the compressibility of integer sequences, which is of independent interest. Finally, we apply our techniques to prove a worst-case bound on the compression ratio of a compression algorithm based on the Burrows-Wheeler Transform followed by distance coding, for which worst-case guarantees have never been given. We prove that the length of the compressed string is bounded by 1.7286 · |s|Hk(s) + gk + O(log n). This bound is better than the bound we give for bw0.

Abstract—Multi-homed Internet sites become more and more widespread, due to the rising dispersal of inexpensive Internet access technologies combined with the growing deployment of resilience-critical applications. Concurrent Multipath Transfer (CMT) denotes the Transport Layer approach to utilise multiple network paths simultaneously, in order to improve application payload throughput. Currently, CMT is a quite hot topic in the IETF – in form of the Multipath TCP (MPTCP) and CMT-SCTP protocol extensions for TCP and SCTP. However, an important issue is still not fully solved: multipath congestion control. In order to support the IETF activities, we have set up a distributed Internet testbed for CMT evaluation. An important tool – which we have developed for multiprotocol Transport Layer performance analysis – is the Open Source NETPERFMETER tool-chain. It supports the parametrisation and processing of measurement runs as well as results collection, post-processing and plotting. However, its key feature is to support multiple Transport Layer protocols, which makes a quantitative comparison of different protocols – including state-of-the-art features like CMT – possible. In this paper, we first introduce NETPERFMETER and then show a proof-of-concept performance evaluation of CMT congestion controls which are currently discussed in the IETF standardisation process of CMT-SCTP. 1234

The best compression algorithm today for English text is based on the Burrows-Wheeler transform. This algorithm (whose common implementation is bzip2) consists of the following three essential steps: 1) Obtain the Burrows-Wheeler transform of the text, 2) Convert the transform into a sequence of integers using the move-to-front algorithm, 3) Encode the integers using arithmetic code or any order-0 encoding (possibly with run length encoding). In this paper we achieve a strong bound on the worst-case compression ratio of this algorithm, that is significantly better than bounds known to date and is obtained via simple analytical techniques. Specifically, for any input string s, and µ> 1, the length of the compressed string is bounded by µ · |s|Hk(s) + log(ζ(µ)) · |s | + gk where Hk is the k-th order empirical entropy, gk is a constant depending only on k and on the size of the alphabet, and ζ(µ) = 1 1 µ + 1 2 µ +... is the standard zeta function. In fact we prove a stronger result: That this bound without the additive term gk holds when we replace Hk(s) by the sum of the logarithms of the integers obtain by the move-to-front encoding of the transform. This refined bound is tight and close to the actual compression achieved in practice. To obtain this result we prove a tight result on the compressibility of integer sequences, which is of independent interest. 1