A statistical approach to virtual server resource management

Abstract

Resource management is of key importance in a wide array of computer and network environments. Failure to effectively allocate resources necessary for smooth infrastructure operation may result in impediments in successful service provisioning. This is especially prominent in environments whose workloads present fluctuating resource demand, such as in Cloud-based infrastructures. The mathematical science of statistics offers an extensive theoretical and practical toolbox that can be of potential use in tackling such problems in the information technology domain. We propose a novel approach to resource management of virtualized data center components. We have worked toward creating a mechanism that allows for dynamic resource allocation, adapting to the changing demand patterns. Our system incorporates a resource controller, based on Statistical Process Control, which permits the online management of a virtual machine's processor and memory capacity through real-time analysis of its observed performance. We successfully demonstrate our approach, with real data, on an architecture running the core banking environment of a financial institution. The controller successfully manages the resource allocation of the virtual machine and stabilizes its performance while business workloads are being processed. Our approach can be extended to realize different workload models and to manage other types of hypervisor provisioned resources.

Cloud Computing Virtual Resource Dynamic System Allocation and Application Based on System Architecture

“…Split the task request to be spread across as many physical servers as possible. This can reduce the impact of host failures and improve the application performance of tasks [16]. (3) Load distribution.…”

“…(1) Optimization objective: To evaluate the quality of the scheduling strategy, the scheduling center needs to define various objective functions, such as minimum cost, maximum resource utilization rate, maximum benefit, and maximum satisfaction of user requirements, etc. [20].…”

Adaptive video streaming testbed design for performance study and assessment of QoE

“…CPU usage and battery power consumption for the VMs and the entire virtualized system are measured by using the Linux monitoring tools. In the VMs, 39,40 the number of cycles executed per second is analyzed when a user watches videos of different segment lengths. The average CPU usage is influenced by some factors such as high encoded video quality, quality video switching, CPU environment, and the behavior of the video playback application and availability of clients' throughput.…”

Comprehensive formalization and modeling of the network and system virtualization process

“…Our proposed model's positioning in the information modeling realm of Computing and Networking is illustrated in [186]. The verification methodology is described in [187], and [188]. Lastly, our approach's implementation in the datacenter context is further explored in [189].…”