Titlebook: Algorithmic Aspects of Cloud Computing; Third International Dan Alistarh,Alex Delis,George Pallis Conference proceedings 2018 Springer Int

Angiogenesis

https://doi.org/10.1007/978-94-011-0487-6em that automatically decides on the appropriate amount of resources to be provisioned for each node of the Storm workflow topology based on user-defined performance and cost constraints. ARiSTO employs two mechanisms: a ., model-based one, used at bootstrap time to predict the resource-related para

混合

落葉劑

Tight Approximability of the Server Allocation Problem for Real-Time Applicationsrithm. When we assume the triangle inequality only among servers, we propose a polynomial-time 2-approximation algorithm. Both of the algorithms are tight in the sense that we cannot obtain better polynomial-time approximation algorithms unless . = .. Furthermore, we evaluate the practical performan

BALK

尊重

Automatic Scaling of Resources in a Storm Topologyem that automatically decides on the appropriate amount of resources to be provisioned for each node of the Storm workflow topology based on user-defined performance and cost constraints. ARiSTO employs two mechanisms: a ., model-based one, used at bootstrap time to predict the resource-related para

惡臭

高興一回

panorama

Service Chain Placement in SDNswork that contains servers with limited processing power and of links with limited bandwidth, a service chain is a sequence of virtual network functions (VNFs) that service a certain flow request in the network. The allocation of a service chain consists of routing and VNF placement, namely each VNF

curriculum

Tight Approximability of the Server Allocation Problem for Real-Time Applications a set of users and a set of servers. Then, we consider the following communication process between users and servers. First a user sends his/her request to the nearest server. After receiving all the requests from users, the servers share the requests. A user will then receive the data processed fr

古代

Risk Aware Stochastic Placement of Cloud Services: The Case of Two Data Centers much harder due to the dynamic nature of the workload and the fact that while long term statistics about the demand may be known, it is impossible to predict the exact demand in each point in time. As a result, service providers either over allocate resources and hurt the service cost efficiency, o