Adaptive runtime techniques for power and resource management on multi-core systems

Energy-related costs are among the major contributors to the total cost of ownership of data centers and high-performance computing (HPC) clusters. As a result, future data centers must be energy-efficient to meet the continuously increasing computational demand. Constraining the power consumption of the servers is a widely used approach for managing energy costs and complying with power delivery limitations. In tandem, virtualization has become a common practice, as virtualization reduces hardware and power requirements by enabling consolidation of multiple applications on to a smaller set of physical resources. However, administration and management of data center resources have become more complex due to the growing number of virtualized servers installed in data centers. Therefore, designing autonomous and adaptive energy efficiency approaches is crucial to achieve sustainable and cost-efficient operation in data centers. Many modern data centers running enterprise workloads successfully implement energy efficiency approaches today. However, the nature of multi-threaded applications, which are becoming more common in all computing domains, brings additional design and management challenges. Tackling these challenges requires a deeper understanding of the interactions between the applications and the underlying hardware nodes. Although cluster-level management techniques bring significant benefits, node-level techniques provide more visibility into application characteristics, which can then be used to further improve the overall energy efficiency of the data centers. This thesis proposes adaptive runtime power and resource management techniques on multi-core systems. It demonstrates that taking the multi-threaded workload characteristics into account during management significantly improves the energy efficiency of the server nodes, which are the basic building blocks of data centers. The key distinguishing features of this work are as follows: We implement the proposed runtime techniques on state-of-the-art commodity multi-core servers and show that their energy efficiency can be significantly improved by (1) taking multi-threaded application specific characteristics into account while making resource allocation decisions, (2) accurately tracking dynamically changing power constraints by using low-overhead application-aware runtime techniques, and (3) coordinating dynamic adaptive decisions at various layers of the computing stack, specifically at system and application levels. Our results show that efficient resource distribution under power constraints yields energy savings of up to 24% compared to existing approaches, along with the ability to meet power constraints 98% of the time for a diverse set of multi-threaded applications

Adaptive Power and Resource Management Techniques for Multithreaded Workloads

Abstract-As today's computing trends are moving towards the cloud, meeting the increasing computational demand while minimizing the energy costs in data centers has become essential. This work introduces two adaptive techniques to reduce the energy consumption of the computing clusters through power and resource management on multi-core processors. We first present a novel power capping technique to constrain the power consumption of computing nodes. Our technique combines Dynamic Voltage-Frequency Scaling (DVFS) and thread allocation on multi-core systems. By utilizing machine learning techniques, our power capping method is able to meet the power budgets 82% of the time without requiring any power measurement device and reduces the energy consumption by 51.6% on average in comparison to the state-of-the-art techniques. We then introduce an autonomous resource management technique for consolidated multi-threaded workloads running on multi-core servers. Our technique first classifies applications according to their energy efficiency measure, then proportionally allocates resources for co-scheduled applications to improve the energy efficiency. The proposed technique improves the energy efficiency by 17% in comparison to state-of-the-art co-scheduling policies. I. INTRODUCTION Energy-related costs are among the major contributors to the total cost of ownership of today's data centers and high performance computing (HPC) clusters. Therefore, future computing clusters are required to be energy-efficient in order to be able to meet the continuously increasing computational demand. Moreover, administration and management of the data center resources has become significantly complex, due to increasing number of servers installed on data centers. Therefore, designing autonomous techniques to optimally manage the limited data center resources is essential to achieve sustainability in the cloud era. The achievable maximum performance of a computing cluster is determined by (1) infrastructural/cost limitations (e.g, power delivery, cooling capacity, electricity cost) and/or (2) available hardware resources (e.g., CPU, disk size). Optimizing the performance under such constraints (i,e., power, resource) is critically important to improve the energy efficiency, therefore to reduce to cost of computing. Moreover, the emergence of multi-threaded applications on cloud resources bring additional challenges for optimizing the performanceenergy tradeoffs under resource constraints, due to their complex characteristics such as performance scalability and intercore communication. In this work, we present two adaptive management techniques for multi-threaded workloads to improve the energ

Message passing-aware power management on many-core systems

Dynamic frequency and voltage scaling (DVFS) techniques have been widely used for meeting energy constraints. Single-chip many-core systems bring new challenges owing to the large number of operating points and the shift to message passing from shared memory communication. DVFS, however, has been mostly studied on single-chip systems with one or few cores, without considering the impact of the communication among cores. This paper evaluates the impact of voltage and frequency scaling on the performance and power of many-core systems with message passing (MP) based communication, and proposes a power management policy that leverages the communication pattern information to efficiently traverse the search space for finding the optimal voltage and frequency operating point. We conduct experiments on a 48-core Intel Single-Chip Cloud Computer (SCC), as our target many-core platform. The paper first introduces the runtime monitoring infrastructure and the application suite we have designed for an in-depth evaluation of the SCC. We then quantify the effects of frequency perturbations on performance and energy efficiency. Experimental results show that runtime communication patterns lead to significant differences in power/performance tradeoffs in many-core systems with MP-based communication. We show that the proposed power management policy achieves up to the 70% energy-delayproduct (EDP) improvements compared to existing DVFS policies, while meeting the performance constraints