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. Résumé,

, Les utilisateurs mobiles s'attendent à pouvoir utiliser des applications intensives en calcul, ce qui requière la disponibilité de ressources importantes, telles que jeux vidéo, reconnaissance faciale et vocale. Cependant, en raison des contraintes de légèreté, de maniabilité, de coût et de compacité, en plus de la sûreté et la mobilité, les terminaux sont nécessairement conçus avec des ressources contraintes, bien qu'en forte progression, qui incluent une capacité limitée de calcul CPU/GPU, une faible autonomie de batterie, et un espace mémoire restreint. Étant donné ces limitations, les applications exigeantes en ressources ne s'exécutent pas de la même manière sur ces terminaux en comparaison avec des architectures X86 (ex. PC de bureau ou serveur) et certaines applications ne peuvent tout simplement pas être utilisées sur ces terminaux. Pour pallier à ces problèmes, deux options s'offrent aux industriels et aux chercheurs : ? Augmentation matérielle : consiste à améliorer les capacités matérielles des terminaux, De nos jours, les terminaux tels que smartphones sont des dispositifs omniprésents, offrant des applications et des informations continuellement ''à portée de notre main'' allant de simples services de communications à des services multimédias

, ? Augmentation logicielle : consiste à utiliser les ressources d'infrastructure distante pour délester du calcul afin de conserver les ressources locales

, conçu pour exécuter des systèmes d'exploitation complexes, y compris Linux, Android et Microsoft Windows. Samsung et Qualcomm conçoivent de nouvelles générations de smartphones, qui intègrent des GPU, tels que Mali ou Adreno. Concernant l'autonomie de la batterie, de nombreux efforts ont été déployés pour récolter de l'énergie à travers le mouvement, l'énergie solaire et les radiations sans fil. Ces solutions sont encore en phase d'étude et ne peuvent malheureusement pas renouveler notablement l'énergie emmagasinée dans les batteries, Plusieurs études ont été menées pour augmenter les performances des terminaux. Les fabricants proposent des processeurs multi-coeurs à fréquence d'horloge assez élevée. L'industrie ARM propose différents types de microprocesseurs qui répondent à des exigences de performance, de puissance et de coûts, tels que le Cortex-A qui intègre une unité de gestion de la mémoire (MMU)

, Mobile Cloud Computing'' (MCC) est envisagée comme une solution prometteuse pour relever les défis précédemment cités. Plusieurs approches ont été proposées dans le contexte du MCC; notamment le ''load sharing'' et l'exécution à distance qui ont évolué vers le concept de délestage de calcul ou, L'augmentation logicielle (ou ''computation offloading

, une partie ou la totalité d'une application intensive en calcul. Cette technique offre ainsi un moyen attrayant pour réduire le temps d'exécution exigé par les utilisateurs mobiles et/ou économiser la consommation énergétique sur leurs terminaux. Durant la période d'exécution à distance, soit le terminal continue à exécuter d'autres tâches, soit il attend les résultats d'exécution du serveur en charge de l'exécution à distance. À la fin de cette dernière, les résultats d'exécution de la partie d'application « migrée » sur le serveur sont utilisés par l'application exécutée sur le terminal

, Phase de profilage de l'application : consiste à disséquer l'application afin d'estimer la consommation en ressource CPU/GPU, batterie et mémoire. L'instrumentation du code est une des techniques utilisées pour mesurer les performances

, Elle consiste à insérer des instructions de mesure de performance à des endroits bien précis dans le programme dépendants de la granularité choisie qui varie d'un module (i.e. un ensemble de classes) à une fonction ou un bloc d'instructions dans une classe

, l'application est représentée sous forme d'un modèle mathématique utilisant la théorie des graphes, la programmation linéaire, la théorie des jeux ou encore les processus de décision Markovien. La représentation tient compte de la granularité définie précédemment. Un exemple de représentation pourrait être un graphe pondéré où les sommets du graphe représentent les composantes de l'application (ex., fonctions, classes ou modules selon la granularité choisie) et les arcs décrivent les interactions entre ces composantes. Les sommets (arcs, respectivement) sont valués avec les performances obtenues dans la phase de profilage comme la consommation en CPU/GPU, mémoire, énergie

, une décision est prise pour chaque composante logicielle de l'application, à savoir si elle doit être déportée sur un serveur ou exécutée en local sur le terminal. La décision est basée sur la résolution d'une fonction objectif dont les critères consistent par exemple à améliorer les performances (ou le temps de réponse), réduire la consommation énergétique sur le terminal ou encore combiner deux ou plusieurs paramètres. Plusieurs algorithmes sont proposés pour résoudre la fonction objectif. Nous citons les algorithmes de découpage de graphes min

, Toutes les composantes qui seront exécutées sur le terminal seront regroupées dans la même partition locale, tandis que les composantes qui maximisent le gain, en étant exécutées à distance, seront rassemblées dans la partition distante. Le partitionnement de graphes est connu pour être NPcomplexe, donc des heuristiques (ex. l'algorithme branch and bound, les algorithmes gloutons ou les algorithmes génétiques) sont proposées pour remplacer ces algorithmes min-cut/max-flow

, cette étape consiste à établir un lien de communication entre le terminal qui représente en général le client, et le serveur distant. Ce lien sera utilisé pour des communications entre le client et le serveur pour l'échange des données, de code, et de résultats

, Selon que ces étapes soient réalisées au moment de l'exécution de l'application ou avant, nous distinguons trois approches de délestage de calcul : a. Délestage statique : il se produit avant le démarrage de l'application. Dans ce type de délestage, l'application est partitionnée seulement une fois en deux partitions : la partition locale qui contient les composantes logicielles qui doivent s'exécuter localement sur le terminal et la partition distante qui

, L'application est ainsi partitionnée avant son exécution, durant les phases de développement ou d'installation

, Cette approche s'adapte mieux aux variations de l'environnement (ex. latence, qualité du réseau, bande passante, énergie restante sur le terminal, sa charge de travail et celle du serveur) durant l'exécution. Les différentes étapes de délestage sont effectuées durant l

, Délestage hybride : pour tirer profit des deux approches (statique et dynamique), l'approche hybride ou semi-dynamique est proposée. Dans cette approche, une partie de la décision est prise durant la phase de design de l'application par le programmeur ou durant la phase d'installation par des outils d'analyse statiques. L'autre partie est partitionnée durant la phase d

. Le-serveur-distant and . De-délestage, Différents travaux de recherche sont menés pour soulever ces verrous. Certains sont résolus, tels que la latence du réseau, en utilisant le ''Multi-access Edge Computing (MEC)'' ou le ''Fog Computing'' dans des solutions de délestage de calcul. En termes de communications réseau, des technologies d'accès multi-radio (multi-RAT) et une utilisation de la bande passante à la demande pour améliorer le débit sont proposées. Certains autres défis restent à résoudre, tels que la latence du Framework, son architecture