Performance Controlled Power Optimization for Virtualized Internet Datacenters

Modern data centers must provide performance assurance for complex system software such as web applications. In addition, the power consumption of data centers needs to be minimized to reduce operating costs and avoid system overheating. In recent years, more and more data centers start to adopt server virtualization strategies for resource sharing to reduce hardware and operating costs by consolidating applications previously running on multiple physical servers onto a single physical server. In this dissertation, several power efficient algorithms are proposed to effectively reduce server power consumption while achieving the required application-level performance for virtualized servers. First, at the server level this dissertation proposes two control solutions based on dynamic voltage and frequency scaling (DVFS) technology and request batching technology. The two solutions share a performance balancing technique that maintains performance balancing among all virtual machines so that they can have approximately the same performance level relative to their allowed peak values. Then, when the workload intensity is light, we adopt the request batching technology by using a controller to determine the time length for periodically batching incoming requests and putting the processor into sleep mode. When the workload intensity changes from light to moderate, request batching is automatically switched to DVFS to increase the processor frequency for performance guarantees. Second, at the datacenter level, this dissertation proposes a performance-controlled power optimization solution for virtualized server clusters with multi-tier applications. The solution utilizes both DVFS and server consolidation strategies for maximized power savings by integrating feedback control with optimization strategies. At the application level, a multi-input-multi-output controller is designed to achieve the desired performance for applications spanning multiple VMs, on a short time scale, by reallocating the CPU resources and DVFS. At the cluster level, a power optimizer is proposed to incrementally consolidate VMs onto the most power-efficient servers on a longer time scale. Finally, this dissertation proposes a VM scheduling algorithm that exploits core performance heterogeneity to optimize the overall system energy efficiency. The four algorithms at the three different levels are demonstrated with empirical results on hardware testbeds and trace-driven simulations and compared against state-of-the-art baselines

Integration and validation of embedded flight software on space-qualified multicore architectures

In the recent decades, the importance of software on space missions has notably increased, reflecting the need to integrate advanced on-board functionalities. With multicore processors being lately introduced to host critical high-performance applications, the complexity to validate software has significantly raised with respect to single core architectures. While there has been a big step forward in avionics after the publication of the CAST-32A paper, the ECSS-E-ST-40C software engineering standard used by the European Space Agency (ESA) is still not providing validation support for multicore processors. Hence, it is expected that standardising guidelines to develop software on such platforms will become a recurring topic in the industry to match the demands of future space exploration missions

Memory region: a system abstraction for managing the complex memory structures of multicore platforms

The performance of modern many-core systems depends on the effective use of their complex cache and memory structures, and this will likely become more pronounced with the impending arrival of on-chip 3D stacked and non-volatile off-chip byte-addressable memory. Yet to date, operating systems have not treated memory as a first class schedulable resource, embracing memory heterogeneity. This dissertation presents a new software abstraction, called ‘memory region’, which denotes the current set of physical memory pages actively used by workloads. Using this abstraction, memory resources can be scheduled for applications to fully exploit a platform's underlying cache and memory system, thereby gaining improved performance and predictability in execution, particularly for the consolidated workloads seen in virtualized and cloud computing infrastructures. The abstraction's implementation in the Xen hypervisor involves the run-time detection of memory regions, the scheduled mapping of these regions to caches to match performance goals, and maintaining region-to-cache mappings using per-cache page tables. This dissertation makes the following specific contributions. First, its region scheduling method proposes that the location of memory blocks rather than CPU utilization is the principal determinant where workloads are run. It proposes a new scheduling method, the region scheduling that the location of memory blocks determines where the workloads are run. Second, treating memory blocks as first-class resources, new methods for efficient cache management are shown to improve application performance as well as the performance of certain operating system functions. Third, explicit memory scheduling makes it possible to disaggregate operating systems, without the need to change OS sources and with only small markups of target guest OS functionality. With this method, OS functions can be mapped to specific desired platform components, such as file system confined to running on specific cores and using only certain memory resources designated for its use. This can improve performance for applications heavily dependent on certain OS functions, by dynamically providing those functions with the resources needed for their current use, and it can prevent performance-critical application functionality from being needlessly perturbed by OS functions used for other purposes or by other jobs. Fourth, extensions of region scheduling can also help applications deal with the heterogeneous memory resources present in future systems, including on-chip stacked DRAM and NUMA or even NVRAM memory modules. More generally, regions scheduling is shown to apply to memory structures with well-defined differences in memory access latencies.Ph.D

Power Modeling and Resource Optimization in Virtualized Environments

The provisioning of on-demand cloud services has revolutionized the IT industry. This emerging paradigm has drastically increased the growth of data centers (DCs) worldwide. Consequently, this rising number of DCs is contributing to a large amount of world total power consumption. This has directed the attention of researchers and service providers to investigate a power-aware solution for the deployment and management of these systems and networks. However, these solutions could be bene\ufb01cial only if derived from a precisely estimated power consumption at run-time. Accuracy in power estimation is a challenge in virtualized environments due to the lack of certainty of actual resources consumed by virtualized entities and of their impact on applications\u2019 performance. The heterogeneous cloud, composed of multi-tenancy architecture, has also raised several management challenges for both service providers and their clients. Task scheduling and resource allocation in such a system are considered as an NP-hard problem. The inappropriate allocation of resources causes the under-utilization of servers, hence reducing throughput and energy e\ufb03ciency. In this context, the cloud framework needs an e\ufb00ective management solution to maximize the use of available resources and capacity, and also to reduce the impact of their carbon footprint on the environment with reduced power consumption. This thesis addresses the issues of power measurement and resource utilization in virtualized environments as two primary objectives. At \ufb01rst, a survey on prior work of server power modeling and methods in virtualization architectures is carried out. This helps investigate the key challenges that elude the precision of power estimation when dealing with virtualized entities. A di\ufb00erent systematic approach is then presented to improve the prediction accuracy in these networks, considering the resource abstraction at di\ufb00erent architectural levels. Resource usage monitoring at the host and guest helps in identifying the di\ufb00erence in performance between the two. Using virtual Performance Monitoring Counters (vPMCs) at a guest level provides detailed information that helps in improving the prediction accuracy and can be further used for resource optimization, consolidation and load balancing. Later, the research also targets the critical issue of optimal resource utilization in cloud computing. This study seeks a generic, robust but simple approach to deal with resource allocation in cloud computing and networking. The inappropriate scheduling in the cloud causes under- and over- utilization of resources which in turn increases the power consumption and also degrades the system performance. This work \ufb01rst addresses some of the major challenges related to task scheduling in heterogeneous systems. After a critical analysis of existing approaches, this thesis presents a rather simple scheduling scheme based on the combination of heuristic solutions. Improved resource utilization with reduced processing time can be achieved using the proposed energy-e\ufb03cient scheduling algorithm

Optical Networks and Interconnects

The rapid evolution of communication technologies such as 5G and beyond, rely on optical networks to support the challenging and ambitious requirements that include both capacity and reliability. This chapter begins by giving an overview of the evolution of optical access networks, focusing on Passive Optical Networks (PONs). The development of the different PON standards and requirements aiming at longer reach, higher client count and delivered bandwidth are presented. PON virtualization is also introduced as the flexibility enabler. Triggered by the increase of bandwidth supported by access and aggregation network segments, core networks have also evolved, as presented in the second part of the chapter. Scaling the physical infrastructure requires high investment and hence, operators are considering alternatives to optimize the use of the existing capacity. This chapter introduces different planning problems such as Routing and Spectrum Assignment problems, placement problems for regenerators and wavelength converters, and how to offer resilience to different failures. An overview of control and management is also provided. Moreover, motivated by the increasing importance of data storage and data processing, this chapter also addresses different aspects of optical data center interconnects. Data centers have become critical infrastructure to operate any service. They are also forced to take advantage of optical technology in order to keep up with the growing capacity demand and power consumption. This chapter gives an overview of different optical data center network architectures as well as some expected directions to improve the resource utilization and increase the network capacity