Towards payment-bound analysis in cloud systems with task-prediction errors

Conference Theme: Change we are leadingIn modern cloud systems, how to optimize user service level based on virtual resources customized on demand is a critical issue. In this paper, we comprehensively analyze the payment bound under a cloud model with virtual machines (VMs), by taking into account that task’s workload may be predicted with errors. The analysis is based on an optimized resource allocation algorithm with polynomial time complexity. We theoretically derive the upper bound of task payment based on a particular margin of workload prediction-error. We also extend the payment-minimization algorithm to adapt to the dynamic changes of host availability over time, and perform the evaluation by a real-cluster environment with 56 VMs deployed. Experiments confirm the correctness of our theoretical inference, and show that our payment-minimization solution can keep 95% of user payments below 1.15 times as large as the theoretical values of the ideal payment with hypothetically accurate information. The ratio for the rest user payments can be limited to about 1.5 at the worst case.postprin

Minimization of cloud task execution length with workload prediction errors

In cloud systems, it is non-trivial to optimize task’s execution performance under user’s affordable budget, especially with possible workload prediction errors. Based on an optimal algorithm that can minimize cloud task’s execution length with predicted workload and budget, we theoretically derive the upper bound of the task execution length by taking into account the possible workload prediction errors. With such a state-of-the-art bound, the worst-case performance of a task execution with a certain workload prediction errors is predictable. On the other hand, we build a close-to-practice cloud prototype over a real cluster environment deployed with 56 virtual machines, and evaluate our solution with different resource contention degrees. Experiments show that task execution lengths under our solution with estimates of worst-case performance are close to their theoretical ideal values, in both non-competitive situation with adequate resources and the competitive situation with a certain limited available resources. We also observe a fair treatment on the resource allocation among all tasks.published_or_final_versio

Proactive Scheduling in Cloud Computing

Autonomic fault aware scheduling is a feature quite important for cloud computing and it is related to adoption of workload variation. In this context, this paper proposes an fault aware pattern matching autonomic scheduling for cloud computing based on autonomic computing concepts. In order to validate the proposed solution, we performed two experiments one with traditional approach and other other with pattern recognition fault aware approach. The results show the effectiveness of the scheme

Efficient Scheme For Payment Minimization And Qos Services To Clients In Cloud

A new technique namely payment minimization error-tolerant algorithm which reduces the payment to clients is proposed in this paper.From cloud organization suppliers' perspective, advantage is a champion amongst the most basic examinations, and it is essentially controlled by the course of action of a cloud organization stage under given business division demand. In any case, a solitary long haul leasing plan is typically grasped to outline a cloud stage, which can't guarantee the organization quality yet prompts bona fide resource waste. In this anticipate, a twofold asset leasing plan is formed firstly in which temporary renting and whole deal renting. Double renting scheme provides profit to service providers but it can’t minimize the payment to clients based on services taken by clients in order to overcome this problem, the proposed scheme isdemonistrated to give better quality service to clients and also minimizes the payments for clients

Proactive Scheduling in Cloud Computing

Autonomic fault aware scheduling is a feature quite important for cloud computing and it is related to adoption of workload variation. In this context, this paper proposes an fault aware pattern matching autonomic scheduling for cloud computing based on autonomic computing concepts.  In order to validate  the proposed solution, we performed two experiments one with traditional approach and other other with pattern recognition fault aware approach. The results show the effectiveness of the scheme

Resource Allocation for Green Cloud Networks under Uncertainty: Stochastic, Robust and Big Data-driven Approaches

University of Minnesota M.S. thesis. September 2016. Major: Electrical/Computer Engineering. Advisor: Georgios Giannakis. 1 computer file (PDF); viii, 139 pages.Major improvements have propelled the development of worldwide Internet systems during the past decade. To meet the growing demand in massive data processing, a large number of geographically-distributed data centers begin to surge in the era of data deluge and information explosion. Along with their remarkable expansion, contemporary cloud networks are being challenged by the growing concerns about global warming, due to their substantial energy consumption. Hence, the infrastructure of future data centers must be energy-efficient and sustainable. Fortunately, supporting technologies of smart grids, big data analytics and machine learning, are also developing rapidly. These considerations motivate well the present thesis, which mainly focuses on developing interdisciplinary approaches to offer sustainable resource allocation for future cloud networks, by leveraging three intertwining research subjects. The modern smart grid has many new features and advanced capabilities including e.g., high penetration of renewable energy sources, and dynamic pricing based demand-side management. Clearly, by integrating these features into the cloud network infrastructure, it becomes feasible to realize its desiderata of reliability, energy-efficiency and sustainability. Yet, full benefits of the renewable energy (e.g., wind and solar) can only be harnessed by properly mitigating its intrinsically stochastic nature, which is still a challenging task. This prompts leveraging the huge volume of historical data to reduce the stochasticity of online decision making. Specifically, valuable insights from big data analytics can enable a markedly improved resource allocation policy by learning historical user and environmental patterns. Relevant machine learning approaches can further uncover “hidden insights” from historical relationships and trends in massive datasets. Targeting this goal, the present thesis systematically studies resource allocation tasks for future sustainable cloud networks under uncertainty. With an eye towards realistic scenarios, the thesis progressively adapts elegant mathematical models, optimization frameworks, and develops low complexity algorithms from three different aspects: stochastic (Chapters 2 and 3), robust (Chapter 4), and big data-driven approaches (Chapter 5). The resultant algorithms are all numerically efficient with optimality guarantees, and most of them are also amenable to a distributed implementation

Mobile data and computation offloading in mobile cloud computing

Le trafic mobile augmente considérablement en raison de la popularité des appareils mobiles et des applications mobiles. Le déchargement de données mobiles est une solution permettant de réduire la congestion du réseau cellulaire. Le déchargement de calcul mobile peut déplacer les tâches de calcul d'appareils mobiles vers le cloud. Dans cette thèse, nous étudions d'abord le problème du déchargement de données mobiles dans l'architecture du cloud computing mobile. Afin de minimiser les coûts de transmission des données, nous formulons le processus de déchargement des données sous la forme d'un processus de décision de Markov à horizon fini. Nous proposons deux algorithmes de déchargement des données pour un coût minimal. Ensuite, nous considérons un marché sur lequel un opérateur de réseau mobile peut vendre de la bande passante à des utilisateurs mobiles. Nous formulons ce problème sous la forme d'une enchère comportant plusieurs éléments afin de maximiser les bénéfices de l'opérateur de réseau mobile. Nous proposons un algorithme d'optimisation robuste et deux algorithmes itératifs pour résoudre ce problème. Enfin, nous nous concentrons sur les problèmes d'équilibrage de charge afin de minimiser la latence du déchargement des calculs. Nous formulons ce problème comme un jeu de population. Nous proposons deux algorithmes d'équilibrage de la charge de travail basés sur la dynamique évolutive et des protocoles de révision. Les résultats de la simulation montrent l'efficacité et la robustesse des méthodes proposées.Global mobile traffic is increasing dramatically due to the popularity of smart mobile devices and data hungry mobile applications. Mobile data offloading is considered as a promising solution to alleviate congestion in cellular network. Mobile computation offloading can move computation intensive tasks and large data storage from mobile devices to cloud. In this thesis, we first study mobile data offloading problem under the architecture of mobile cloud computing. In order to minimize the overall cost for data delivery, we formulate the data offloading process, as a finite horizon Markov decision process, and we propose two data offloading algorithms to achieve minimal communication cost. Then, we consider a mobile data offloading market where mobile network operator can sell bandwidth to mobile users. We formulate this problem as a multi-item auction in order to maximize the profit of mobile network operator. We propose one robust optimization algorithm and two iterative algorithms to solve this problem. Finally, we investigate computation offloading problem in mobile edge computing. We focus on workload balancing problems to minimize the transmission latency and computation latency of computation offloading. We formulate this problem as a population game, in order to analyze the aggregate offloading decisions, and we propose two workload balancing algorithms based on evolutionary dynamics and revision protocols. Simulation results show the efficiency and robustness of our proposed methods