Sharing-Aware Resource Management Algorithms For Virtual Computing Environments

Virtualization technologies in cloud computing are ubiquitous throughout data centers around the world where providers consider operational costs and fast delivery guarantees for a variety of profitable services. These providers should consistently invoke measures for increasing the efficiencies of their virtualized services in a competitive environment where fast entry to market, technology advancement, and service price differentials separate sustaining providers from antiquated ones. Therefore, providers seeking further efficiencies and profit opportunities should consider how their resources are managed in virtual computing environments which leverage memory reclamation techniques, specifically page-sharing; motivating the design of new memory sharing-aware resource management algorithms. In this dissertation, we design families of offline and online sharing-aware algorithms for resource management in virtual computing environments and investigate their properties and relationships to various sharing models. Our contribution consists of the design of new online and approximation algorithms offering relevant performance guarantees and their applications to next-generation virtualization technologies

Network-constrained packing of brokered workloads in virtualized environments

Providing resource allocation with performance predictability guarantees is increasingly important in cloud platforms, especially for data-intensive applications, in which performance depends greatly on the available rates of data transfer between the various computing/storage hosts underlying the virtualized resources assigned to the application. Existing resource allocation solutions either assume that applications manage their data transfer between their virtualized resources, or that cloud providers manage their internal networking resources.With the increased prevalence of brokerage services in cloud platforms, there is a need for resource allocation solutions that provides predictability guarantees in settings, in which neither application scheduling nor cloud provider resources can be managed/controlled by the broker. This paper addresses this problem, as we define the Network-Constrained Packing (NCP)problem of finding the optimal mapping of brokered resources to applications with guaranteed performance predictability. We prove that NCP is NP-hard, and we define two special instances of the problem, for which exact solutions can be found efficiently. We develop a greedy heuristic to solve the general instance of the NCP problem, and we evaluate its efficiency using simulations on various application workloads, and network models.This work is supported by NSF CISE CNS Award #1347522, # 1239021, # 1012798