V-Cache: Towards Flexible Resource Provisioning for Multi-tier Applications in IaaS Clouds

Abstract—Although the resource elasticity offered by Infrastructure-as-a-Service (IaaS) clouds opens up opportunities for elastic application performance, it also poses challenges to application management. Cluster applications, such as multi-tier websites, further complicates the management requiring not only accurate capacity planning but also proper partitioning of the resources into a number of virtual machines. Instead of burdening cloud users with complex management, we move the task of determining the optimal resource configuration for cluster applications to cloud providers. We find that a structural reorganization of multi-tier websites, by adding a caching tier which runs on resources debited from the original resource budget, significantly boosts application performance and reduces resource usage. We propose V-Cache, a machine learning based approach to flexible provisioning of resources for multi-tier applications in clouds. V-Cache transparently places a caching proxy in front of the application. It uses a genetic algorithm to identify the incoming requests that benefit most from caching and dynamically resizes the cache space to accommodate these requests. We develop a reinforcement learning algorithm to optimally allocate the remaining capacity to other tiers. We have implemented V-Cache on a VMware-based cloud testbed. Exper-iment results with the RUBiS and WikiBench benchmarks show that V-Cache outperforms a representative capacity management scheme and a cloud-cache based resource provisioning approach by at least 15 % in performance, and achieves at least 11 % and 21 % savings on CPU and memory resources, respectively. I

On-Line Load Balancing with Task Buffer

On-line load balancing is one of the most important problems for applications with resource allocation. It aims to assign tasks to suitable machines and balance the load among all of the machines, where the tasks need to be assigned to a machine upon arrival. In practice, tasks are not always required to be assigned to machines immediately. In this paper, we propose a novel on-line load balancing model with task buffer, where the buffer can temporarily store tasks as many as possible. Three algorithms, namely LPTCP1_α, LPTCP2_α, and LPTCP3_β, are proposed based on the Longest Processing Time (LPT) algorithm and a variety of planarization algorithms. The planarization algorithms are proposed for reducing the difference among each element in a set. Experimental results show that our proposed algorithms can effectively solve the on-line load balancing problem and have good performance in large scale experiments

Ordonnancement sous contrainte mémoire en domptant la localité des données dans un modèle de programmation à base de tâches

International audienceA now-classical way of meeting the increasing demand for computing speed by HPC applications is the use of GPUs and/or otheraccelerators. Such accelerators have their own memory, which is usually quite limited, and are connected to the main memorythrough a bus with bounded bandwidth. Thus, particular care should be devoted to data locality in order to avoid unnecessary datamovements. Task-based runtime schedulers have emerged as a convenient and efficient way to use such heterogeneous platforms.When processing an application, the scheduler has the knowledge of all tasks available for processing on a GPU, as well astheir input data dependencies. Hence, it is possible to produce a tasks processing order aiming at reducing the total processingtime through three objectives: minimizing data transfers, overlapping transfers and computation and optimizing the eviction ofpreviously-loaded data. In this paper, we focus on how to schedule tasks that share some of their input data (but are otherwiseindependent) on a single GPU. We provide a formal model of the problem, exhibit an optimal eviction strategy, and show thatordering tasks to minimize data movement is NP-complete. We review and adapt existing ordering strategies to this problem,and propose a new one based on task aggregation. We prove that the underlying problem of this new strategy is NP-complete,and prove the reasonable complexity of our proposed heuristic. These strategies have been implemented in the StarPU runtimesystem. We present their performance on tasks from tiled 2D, 3D matrix products, Cholesky factorization, randomized task order,randomized data pairs from the 2D matrix product as well as a sparse matrix product. We introduce a visual way to understandthese performance and lower bounds on the number of data loads for the 2D and 3D matrix products. Our experiments demonstratethat using our new strategy together with the optimal eviction policy reduces the amount of data movement as well as the totalprocessing time

Geographic information extraction from texts

A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction