Algorithmes d’ordonnancement des tâches dans un environnement Cloud

Les systèmes distribués à grande échelle comme les Grilles ou les Nuages (Clouds) [8] sont fondamentalement dynamiques et instables, et il est également réaliste de considérer que certaines ressources vont subir des défaillances pendant leur utilisation. La panne d’une ressource peut affecter l’entière exécution des applications qui nécessitent la disponibilité de plusieurs ressources en même temps. Afin de pouvoir gérer des plates-formes dynamiques à grande échelle, il faut se tourner vers des algorithmes d'ordonnancement et d'équilibrage de charge décentralisés, de telle sorte que le système puisse passer à l'échelle, sans que les performances de la plate-forme soient limitées par celle du noeud en charge de l'ordonnancement. Dans ce papier, nous présentons un état de l’art sur les algorithmes d'ordonnancement et d'équilibrage de charge destinés pour les Clouds. Nous proposons comme synthèse une classification de ces algorithmes sur la base de critères et de dimensions que nous avons définis à cet effet

Scheduling Parallel Tasks onto Opportunistically Available Cloud Resources

We consider the problem of opportunistically scheduling low-priority tasks onto underutilized computation resources in the cloud left by high-priority tasks. To avoid conflicts with high-priority tasks, the scheduler must suspend the low-priority tasks (causing waiting), or move them to other underutilized servers (causing migration), if the high-priority tasks resume. The goal of opportunistic scheduling is to schedule the low-priority tasks onto intermittently available server resources while minimizing the combined cost of waiting and migration. Moreover, we aim to support multiple parallel low-priority tasks with synchronization constraints. Under the assumption that servers' availability to low-priority tasks can be modeled as ON/OFF Markov chains, we have shown that the optimal solution requires solving a Markov Decision Process (MDP) that has exponential complexity, and efficient solutions are known only in the case of homogeneously behaving servers. In this paper, we propose an efficient heuristic scheduling policy by formulating the problem as restless Multi-Armed Bandits (MAB) under relaxed synchronization. We prove the index ability of the problem and provide closed-form formulas to compute the indices. Our evaluation using real data center traces shows that the performance result closely matches the prediction by the Markov chain model, and the proposed index policy achieves consistently good performance under various server dynamics compared with the existing policies

United States Air Force fighter jet maintenance Models : effectiveness of index policies

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2013.Cataloged from PDF version of thesis. "June 2013."Includes bibliographical references (pages 107-109).As some of the most technically complex systems in the world, United States fighter aircraft require a complex logistics system to sustain their reliable operation and ensure that the day-to-day Air Force missions can be satisfied. While there has been a lot of attention among academics and practitioners regarding the study of this complex logistics system, most of the focus has been on availability of spare parts that are indeed essential for the smooth operations of the fighter aircraft. However, in recent years there has been an increasing awareness that maintenance resources are an equally important enabler and should be considered together with inventory issues. The maintenance resources required to repair the fighter aircraft are expensive and therefore limited. Moreover, there are various types of maintenance that compete for the same resources. It .is therefore imperative that the allocation of maintenance resources is done as efficiently as possible. In this thesis, we study two areas of fighter aircraft maintenance that could significantly benefit from improved resource allocation and scheduling strategies. We use quantitative and qualitative data from Air Force data-bases and logistics personnel to develop an innovative modeling framework to capture these challenging maintenance problems. This modeling framework is based on a generalization of the of the well-known multi-armed bandit superprocess problem. Using these models, we develop index policies which provide intuitive, easily implemented, and effective rules for scheduling maintenance activities and allocating maintenance resources. These policies seem to improve on existing best practices within the Air Force, and perform well in extensive data-driven simulated computational experiments. The first area is focused on the challenges of scheduling maintenance for the low observable (stealth) capabilities of the F-22 Raptor, specifically, maintenance of the outer coating of the aircraft that is essential to maintain its radar invisibility. In particular, we generate index policies that efficiently schedule which aircraft should enter low observable maintenance, how long they should be worked on, and which aircraft should fly in order to maximize the stealth capability of the fleet. Secondly, we model the maintenance process of the F100-229 engine, which is the primary propulsion method used in the F-16C/D and F-15E aircraft. In particular, we generate index policies to decide which engines should take priority over others, and whether or not certain components of the engines should be repaired or replaced. The policies address both elective (planned) and unplanned maintenance tasks.by John M. Kessler.S.M

Automating SLA enforcement in the cloud computing

Cloud computing is playing an increasingly important role, not only by facilitating digital trading platforms but also by transforming conventional services from client-server models to cloud computing. This domain has given the global economic and technological benefits, it offers to both the service providers and service subscribers. Digital marketplaces are no longer limited only to trade tangible commodities but also facilitates enormous service virtualization across various industries. Software as a Service (SaaS) being the largest service segment, dominates the global cloud migration. Infrastructure as a Service (IaaS) and cloud-based application development also known as Platform as a Service (PaaS) are also next-generation computing platforms for their ultimate futuristic demand by both, public and private sector. These service segments are now hosted on cloud platforms to compute, store, and network, an enormous amount of service requests, which process data incredibly fast and economically. Organizations also perform data analytics and other similar computing amenities to manage their business without maintaining on-premise computing infrastructures which are hard to maintain. This computing capability has extensively improved the popularity and increased the demand for cloud services to an extent, that businesses worldwide are heavily migrating their computing resources to these platforms. Diverse cloud service providers take the responsibility of provisioning such cloud-based services for subscribers. In return, a certain subscription fee is charged to them periodically and depending upon the service package, availability and security. On the flip side, such intensive technology shift and outsourcing reliance have also introduced scenarios that any failure on their part leads to serious consequences to the business community at large. In recent years technology industry has observed critical and increased service outages at various cloud service providers(CSP) such as Amazon AWS, Microsoft, Google, which ultimately interrupts the entire supply chain and causes several well-known web services to be taken offline either due to a human error, failed change control implementation or in more recently due to targeted cyber-attacks like DDoS. These web-based solutions such as compute, storage, network or other similar services are provisioned to cloud service subscribers (CSS) platforms. Regardless of a cloud service deployment, a legal binding such as a Service Level Agreement (SLA) is signed between the CSP and CSS. The SLA holds a service scope and guarantees in case of failure. There are probabilities where these SLA may be violated, revoked, or dishonoured by either party, mostly the CSP. An SLA violation along with an unsettled dispute leads to some financial losses for the service subscribers or perhaps cost them their business reputation. Eventually, the subscriber may request some form of compensation from the provider such as a service credit or a refund. In either case, the burden of proof lies with the subscribers, who have to capture and preserve those data or forensically sound system or service logs, supporting their claims. Most of the time, this is manually processed, which is both expensive and time-consuming. To address this problem, this research first analyses the gaps in existing arrangements. It then suggests automation of SLA enforcement within cloud environments and identifies the main properties of a solution to the problem covering various other avenues associated with the other operating environments. This research then subsequently proposes architectures, based on the concept of fair exchange, and shows that how intelligently the approach enforces cloud SLA using various techniques. Furthermore, by extending the research scope covering two key scenarios (a) when participants are loss averse and (b) when interacting participants can act maliciously. Our proposed architectures present robust schemes by enforcing the suggested solutions which are effective, efficient, and most importantly resilient to modern-day security and privacy challenges. The uniqueness of our research is that it does not only ensure the fairness aspect of digital trading but it also extends and logically implements a dual security layer throughout the service exchange. Using this approach protects business participants by securely automating the dispute resolutions in a more resilient fashion. It also shields their data privacy and security from diverse cyber challenges and other operational failures. These architectures are capable of imposing state-of-the-art defences through integrated secure modules along with full encryption schemes, mitigating security gaps previously not dealt with, based upon fair exchange protocols. The Protocol also accomplishes achieving service exchange scenarios either with or without dispute resolution. Finally, our proposed architectures are automated and interact with hardcoded procedures and verifications mechanism using a variant of trusted third parties and trusted authorities, which makes it difficult to cause potential disagreements and misbehaviours during a cloud-based service exchange by enforcing SLA