Datacenter networks commonly facilitate the transmission of data in distributed computing frameworks through coflows, which are collections of parallel flows associated with a common task. Most of the existing research has concentrated on scheduling coflows to minimize the time required for their completion, i.e., to optimize the average dispatch rate of coflows in the network fabric. Nevertheless, modern applications often produce coflows that are specifically intended for online services and mission-crucial computational tasks, necessitating adherence to specific deadlines for their completion. In this paper, we introduce$\mathtt {WDCoflow}$, a new algorithm to maximize the weighted number of coflows that complete before their deadline. By combining a dynamic programming algorithm along with parallel inequalities, our heuristic solution performs at once coflow admission control and coflow prioritization, imposing a$\sigma$-order on the set of coflows. With extensive simulation, we demonstrate the effectiveness of our algorithm in improving up to$3\times$more coflows that meet their deadline in comparison the best SoA solution, namely$\mathtt {CS\rm{-}MHA}$. Furthermore, when weights are used to differentiate coflow classes,$\mathtt {WDCoflow}$is able to improve the admission per class up to$4\times$, while increasing the average weighted coflow admission rate.

With the advent of big data applications, coflow scheduling has become a cornerstone for the engineering of traffic in datacenters. Minimizing the average weighted Coflow Completion Times (CCT) is a crucial step to minimize the execution time of jobs running in distributed computing frameworks. In this paper, we present a new$\sigma $-order coflow scheduling solution, ONE-PARIS, an online semi-clairvoyant and semi-distributed implementation suitable to minimize the weighted CCT in production environments. We achieves this through ONE-PARIS scheduler for ordering coflows and a decentralized resource allocation mechanism, called Sync-Rate, enabling to respect the order of priority of coflows provided by ONE-PARIS and ensuring efficient synchronization between flows of the same coflow in order to free up bandwidth for low-priority flows. Extensive simulations on both synthetic and real traffics show that our proposed coflow scheduler outperforms other state-of-art schemes.

The average coflow completion time (CCT) is the standard performance metric in coflow scheduling. However, standard CCT minimization may introduce unfairness between the data transfer phase of different computing jobs. Thus, while progress guarantees have been introduced in the literature to mitigate this fairness issue, the trade-off between fairness and efficiency of data transfer is hard to control. This paper introduces a fairness framework for coflow scheduling based on the concept of slowdown, i.e., the performance loss of a coflow compared to isolation. By controlling the slowdown it is possible to enforce a target coflow progress while minimizing the average CCT. In the proposed framework, the minimum slowdown for a batch of coflows can be determined in polynomial time. By showing the equivalence with Gaussian elimination, slowdown constraints are introduced into primal-dual iterations of the CoFair algorithm. The algorithm extends the class of the$\sigma$-order schedulers to solve the fair coflow scheduling problem in polynomial time. It provides a 4-approximation of the average CCT w.r.t. an optimal scheduler. Extensive numerical results demonstrate that this approach can trade off average CCT for slowdown more efficiently than existing state of the art schedulers.

Reducing Coflow Completion Time (CCT) has a significant impact on data-intensive application performance in datacenter networks. An efficient allocation of network resources allows for accelerating the computations to be performed. In this paper, we propose a new scheduler, named ELITE, to minimize the Weighted Coflow Completion Time (WCCT). Our scheduling algorithm is a 2-approximation of the optimal and the rate allocation can achieve a 4-approximation as long as the scheduling priority is respected. We also present a new rate allocation procedure, named RACO, that shows near-optimal performance when combined with our scheduling algorithms. We also propose a low complexity online scheduler, named LSPRT to attain near-optimal performance in online setting. With extensive simulations, we demonstrate the effectiveness of our algorithms by measuring the performance gain of ELITE and LSPRT over previous solutions in the literature. In particular, ELITE and LSPRT perform about 44 % better than Varys, while Sincronia achieves only 32 % against Varys.

The goal of this paper is to study the behaviour of viewcount in YouTube. We first propose several bio-inspired models for the evolution of the viewcount of YouTube videos. We show, using a large set of empirical data, that the viewcount for 90% of videos in YouTube can indeed be associated to at least one of these models, with a Mean Error which does not exceed 5%. We derive automatic ways of classifying the viewcount curve into one of these models and of extracting the most suitable parameters of the model. We study empirically the impact of videos' popularity and category on the evolution of its viewcount. We finally use the above classification along with the automatic parameters extraction in order to predict the evolution of videos' viewcount.