Allocating several Virtual Machines (VMs) onto a single server helps to increase cloud computing resource utilization and to reduce its operating expense. However, multiplexing VMs with different security levels on a single server gives rise to major VM-to-VM cybersecurity interdependency risks. In this paper, we address the problem of the static VM allocation with cybersecurity loss awareness by modeling it as a two-player zero-sum game between an attacker and a provider. We first obtain optimal solutions by employing the mathematical programming approach. We then seek to find the optimal solutions by quickly identifying the equilibrium allocation strategies in our formulated zero-sum game. We mean by "equilibrium" that none of the provider nor the attacker has any incentive to deviate from one's chosen strategy. Specifically, we study the characteristics of the game model, based on which, to develop effective and efficient allocation algorithms. Simulation results show that our proposed cybersecurity-aware consolidation algorithms can significantly outperform the commonly used multi-dimensional bin packing approaches for large-scale cloud data centers.

Cloud data centers are widely employed to offer reliable cloud services. However, low resource utilization and high power consumption have been great challenges for cloud providers. Moreover, the rapid increase in demand for affordable cloud services magnifies the obstacles for proficient resource management policies. In this paper, we investigate how to improve resource utilization and power consumption in cloud data centers when delivering services with statistically guaranteed Quality of Service (QoS). We assume that the service provider hosts different types of services, each of which has request classes with different QoS requirements. Different from the traditional approaches that distribute workloads with different QoS levels on different Virtual Machines (VMs), we introduce an approach to pack requests of the same service type, even with different QoS requirements, into the same VM, and to remove potential failure requests in time to improve resource usage and energy cost. We formally prove that our algorithm can statistically guarantee QoS conditions in terms of deadline miss ratios. We develop a cloud prototype to empirically validate our proposed methods and algorithm. Our experimental results demonstrate that our approach can significantly outperform other traditional approaches in terms of QoS guarantees, power consumption, resource demand and electricity cost.

The increased resource sharing on multi-core platforms has posed significant challenges on the predictability of real-time systems. Cache memory partitioning has proven to be one of the most effective methods to improve the predictability and also the schedulability of real-time systems. In this paper, we study how to allocate cache memory of a multi-core platform when scheduling fixed-priority hard real-time tasks. As the bounded worst-case execution time (WCET) of a real-time task varies with its cache allocation, the challenges of this problem are twofold: how to judiciously allocate the cache memory among all real-time tasks and how to map real-time tasks to each core to improve the schedulability. To address these challenges, we develop an approach that takes into consideration not only the WCET variations with cache allocations but also the task period relationship and thus can significantly improve the schedulability of real-time tasks. Our simulation results, based on the SPEC CPU2000 benchmarks suite, show that our approach can increase the schedulability of real-time tasks up to four times when compared to other similar scheduling mechanisms.

This work describes workflow modeling using Workflownets and Generalized Stochastic Petri Nets (GSPN) for the Digital Publishing business process and how the dependability attributes are measured in a quantitative form. In our novel approach, these are measured from the workflow model itself, improving its analysis. Applying these measure concepts to the Digital Publishing pre-press process provides a workflow management based on its trustworthiness. The methodology for workflow modeling is introduced and the results for a case study on the preflight stage of the Digital Publishing workflow are presented.