Distribution Linux | EPFL Graph Search

vignette|Logos de GNU et Linux Une distribution Linux, appelée aussi distribution GNU/Linux lorsqu'elle contient les logiciels du projet GNU, est un ensemble cohérent de logiciels, la plupart étant des logiciels libres, assemblés autour du noyau Linux, et formant un système d'exploitation pleinement opérationnel. Le terme « distribution » (potentiellement calqué sur le mot anglais distribution) est employé car il s'agit de distribuer une collection de logiciels compatibles avec Linux et sélectionnés par les mainteneurs de la distribution. Il existe une très grande variété de distributions Linux, chacune ayant des objectifs et une philosophie particulière. Les éléments les différenciant principalement sont : la convivialité (facilité de mise en œuvre), l'intégration (taille du parc de logiciels validés distribués), la notoriété (communauté informative pour résoudre les problèmes), leur fréquence de mise à jour, leur gestion des paquets et le mainteneur de la distribution (généralement

Energy Savings for Cellular Network with Evaluation of Impact on Data Traffic Performance

Jean-Yves Le Boudec, Byongkwon Moon

We present a concrete methodology for saving energy in future and contemporary cellular networks. It is based on re-arranging the user-cell association so as to allow shutting down under-utilized parts of the network. We consider a hypothetical static case where we have complete knowledge of stationary user locations and thus the results represent an upper bound of potential energy savings. We formulate the problem as a binary integer programming problem, thus it is NP-hard, and we present a heuristic approximation method. We simulate the methodology on an example real cellular network topology with traffic- and user distribution generated according to recently measured patterns. Further, we evaluate the energy savings, using realistic energy profiles, and the impact on the user-perceived network performance, represented by delay and throughput, at various times of day. The general findings conclude that up to 50% energy may be saved in less busy periods, while the performance effects remain limited. We conclude that practical, real-time user-cell re-allocation methodology, taking into account user mobility predictions, may thus be feasible and bring significant energy savings at acceptable performance impact.

2010

Rack-Scale Memory Pooling for Datacenters

Stanko Novakovic

The rise of web-scale services has led to a staggering growth in user data on the Internet. To transform such a vast raw data into valuable information for the user and provide quality assurances, it is important to minimize access latency and enable in-memory processing. For more than a decade, the only practical way to accommodate for ever-growing data in memory has been to scale out server resources, which has led to the emergence of large-scale datacenters and distributed non-relational databases (NoSQL). Such horizontal scaling of resources translates to an increasing number of servers that participate in processing individual user requests. Typically, each user request results in hundreds of independent queries targeting different NoSQL nodes - servers, and the larger the number of servers involved, the higher the fan-out. To complete a single user request, all of the queries associated with that request have to complete first, and thus, the slowest query determines the completion time. Because of skewed popularity distributions and resource contention, the more servers we have, the harder it is to achieve high throughput and facilitate server utilization, without violating service level objectives. This thesis proposes rack-scale memory pooling (RSMP), a new scaling technique for future datacenters that reduces networking overheads and improves the performance of core datacenter software. RSMP is an approach to building larger, rack-scale capacity units for datacenters through specialized fabric interconnects with support for one-sided operations, and using them, in lieu of conventional servers (e.g. 1U), to scale out. We define an RSMP unit to be a server rack connecting 10s to 100s of servers to a secondary network enabling direct, low-latency access to the global memory of the rack. We, then, propose a new RSMP design - Scale-Out NUMA that leverages integration and a NUMA fabric to bridge the gap between local and remote memory to only 5× difference in access latency. Finally, we show how RSMP impacts NoSQL data serving, a key datacenter service used by most web-scale applications today. We show that using fewer larger data shards leads to less load imbalance and higher effective throughput, without violating applications¿ service level objectives. For example, by using Scale-Out NUMA, RSMP improves the throughput of a key-value store up to 8.2× over a traditional scale-out deployment.

EPFL2017

Rack-Scale Memory Pooling for Datacenters

Stanko Novakovic

EPFL2017