QoS Driven Coordinated Management of Resources to Save Energy in Multi-Core Systems

Reducing the energy consumption of computing systems is a necessary endeavor. However, saving energy should not come at the expense of degrading user experience. To this end, in this thesis, we assume that applications running on multi-core processors are associated with a quality-of-service (QoS) target in terms of performance constraints. This way, hardware resources can be throttled to minimize energy expenditure without violating the QoS requirements. Typical resource management schemes control different resources such as processor cores and on-chip cache memory independently. These approaches are not effective under performance constraints for all applications. Therefore, this thesis presents multi-core resource management schemes that coordinately control several resources in a unified algorithm. This way, the resource manger can find trade-offs between resource allocations to different applications to reduce system-level energy consumption, while still meeting the QoS targets expressed as performance constraints for every application. Implementing a coordinated resource management scheme that dynamically adapts to varying run time behavior of a multi-programmed workload without any prior knowledge about the applications is a challenging task. Two different schemes are presented in this thesis to address this challenge. Both schemes are invoked at regular intervals during program execution. They employ simple and, yet, sufficiently accurate analytical models and a novel hardware technique to predict the effect of different resource allocations on performance and energy for each application. Using a heuristic method, the multi-dimensional system configuration space is pruned in several levels to find the optimum resource settings, with respect to energy efficiency, in a negligible time. In the first scheme a resource management algorithm is presented that coordinates the control of voltage-frequency (VF) of each processor core with partitioning of the on-chip cache space. In the second scheme, a re-configurable processor is considered in which sections of the core micro-architectural resources can be dynamically deactivated to save energy. The resource manager can reactivate these sections, at the proper time, to increase instruction and memory level parallelism (ILP/MLP). This introduces new trade-offs between processor core size, VF settings, and the allocation of cache space for each application. By exploiting these trade-offs, the second scheme improves the energy savings compared to the first scheme considerably. The proposed schemes are evaluated using a novel simulation framework. This framework estimates the effect of different resource management algorithms on full execution of benchmark applications in a multi-programmed workload. According to the experimental results, the proposed schemes can save up to 18% of system energy while respecting the performance constraints of all applications. The average energy savings are 6% and 10% with the first and second schemes, respectively. Further experiments on the first scheme shows that energy savings can potentially improve up to 29% if the users can tolerate a bounded reduction in performance that leads to 40% longer execution time

Coordinated management of DVFS and cache partitioning under QoS constraints to save energy in multi-core systems

Reducing the energy expended to carry out a computational task is important. In this work, we explore the prospects of meeting Quality-of-Service requirements of tasks on a multi-core system while adjusting resources to expend a minimum of energy. This paper considers, for the first time, a QoS-driven coordinated resource management algorithm (RMA) that dynamically adjusts the size of the per-core last-level cache partitions and the per-core voltage–frequency settings to save energy while respecting QoS requirements of every application in multi-programmed workloads run on multi-core systems. It does so by doing configuration-space exploration across the spectrum of LLC partition sizes and Dynamic Voltage–Frequency Scaling (DVFS) settings at runtime at negligible overhead. We show that the energy of 4-core and 8-core systems can be reduced by up to 18% and 14%, respectively, compared to a baseline with even distribution of cache resources and a fixed mid-range core voltage–frequency setting. The energy savings can potentially reach 29% if the QoS targets are relaxed to 40% longer execution time

Dynamic Management of Multi-Core Processor Resources to Improve Energy Efficiency under Quality-of-Service Constraints

With the current technology trends, the number of computers and computation demand is increasing dramatically. In addition to different economic and environmental costs at a large scale, the operational time of battery-powered devices is dependent on how efficiently the computer processors consume energy. Computer processors generally consist of several processing cores and a hierarchy of cache memory that includes both private and shared cache capacity among the cores. A resource management algorithm can adjust the configuration of different core and cache resources at regular intervals during run-time, according to the dynamic characteristics of the workload. A typical resource management policy is to maximize performance, in terms of processing speed or throughput, without exceeding the power and thermal limits. However, this can lead to excessive energy expenditure since a higher performance does not necessarily increase the value of the outcome. For example, increasing the frame-rate of multi-media applications beyond a certain target will not improve user experience considerably. Therefore, applications should be associated with Quality-of-Service (QoS) targets. This way, the resource manager can search for configurations with minimum energy that does not violate the performance constraints of any application. To achieve this goal, we propose several resource management schemes as well as hardware and software techniques for performance and energy modeling, in three papers that constitute this thesis. In the first paper, we demonstrate that, in many cases, independent management of resources such as per-core dynamic voltage-frequency scaling (DVFS) and cache partitioning fails to save a considerable energy without causing any performance degradation. Therefore, we present a coordinated resource management algorithm that saves considerable energy by exploring different combinations of resource allocations to all applications, at regular intervals during run-time. This scheme is based on simplified analytical performance and energy models and a multi-level reduction technique for reducing the dimensions of the multi-core configuration space. In the second paper, we extend the coordinated resource management with dynamic adaptation of the core micro-architectural resources. This way, we include instruction- and memory-level parallelism, ILP and MLP, resp., in the resource trade-offs together with per-core DVFS and cache partitioning. This provides a powerful means to further improve energy savings. Additionally, to enable this scheme, we propose a hardware technique that improves the accuracy of performance and energy prediction for different core sizes and cache partitionings. Finally, in the third paper, we demonstrate that substantial improvements in energy savings are possible by allowing short-term deviations from the baseline performance target. We measure these deviations by introducing a parameter called slack. Based on this, we present Cooperative Slack Management (CSM) that finds opportunities to generate slack at low energy cost and utilize it later to save more energy in the same or even other processor cores. This way, we also ensure that the performance consistently remains ahead of the baseline target in every core

EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design

The radio transceiver of an IoT device is often where most of the energy is consumed. For this reason, most research so far has focused on low power circuit and energy efficient physical layer designs, with the goal of reducing the average energy per information bit required for communication. While these efforts are valuable per se, their actual effectiveness can be partially neutralized by ill-designed network, processing and resource management solutions, which can become a primary factor of performance degradation, in terms of throughput, responsiveness and energy efficiency. The objective of this paper is to describe an energy-centric and context-aware optimization framework that accounts for the energy impact of the fundamental functionalities of an IoT system and that proceeds along three main technical thrusts: 1) balancing signal-dependent processing techniques (compression and feature extraction) and communication tasks; 2) jointly designing channel access and routing protocols to maximize the network lifetime; 3) providing self-adaptability to different operating conditions through the adoption of suitable learning architectures and of flexible/reconfigurable algorithms and protocols. After discussing this framework, we present some preliminary results that validate the effectiveness of our proposed line of action, and show how the use of adaptive signal processing and channel access techniques allows an IoT network to dynamically tune lifetime for signal distortion, according to the requirements dictated by the application

Energy-Efficient Management of Data Center Resources for Cloud Computing: A Vision, Architectural Elements, and Open Challenges

Cloud computing is offering utility-oriented IT services to users worldwide. Based on a pay-as-you-go model, it enables hosting of pervasive applications from consumer, scientific, and business domains. However, data centers hosting Cloud applications consume huge amounts of energy, contributing to high operational costs and carbon footprints to the environment. Therefore, we need Green Cloud computing solutions that can not only save energy for the environment but also reduce operational costs. This paper presents vision, challenges, and architectural elements for energy-efficient management of Cloud computing environments. We focus on the development of dynamic resource provisioning and allocation algorithms that consider the synergy between various data center infrastructures (i.e., the hardware, power units, cooling and software), and holistically work to boost data center energy efficiency and performance. In particular, this paper proposes (a) architectural principles for energy-efficient management of Clouds; (b) energy-efficient resource allocation policies and scheduling algorithms considering quality-of-service expectations, and devices power usage characteristics; and (c) a novel software technology for energy-efficient management of Clouds. We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit. The results demonstrate that Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios.Comment: 12 pages, 5 figures,Proceedings of the 2010 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA 2010), Las Vegas, USA, July 12-15, 201

A Survey of Green Networking Research

Reduction of unnecessary energy consumption is becoming a major concern in wired networking, because of the potential economical benefits and of its expected environmental impact. These issues, usually referred to as "green networking", relate to embedding energy-awareness in the design, in the devices and in the protocols of networks. In this work, we first formulate a more precise definition of the "green" attribute. We furthermore identify a few paradigms that are the key enablers of energy-aware networking research. We then overview the current state of the art and provide a taxonomy of the relevant work, with a special focus on wired networking. At a high level, we identify four branches of green networking research that stem from different observations on the root causes of energy waste, namely (i) Adaptive Link Rate, (ii) Interface proxying, (iii) Energy-aware infrastructures and (iv) Energy-aware applications. In this work, we do not only explore specific proposals pertaining to each of the above branches, but also offer a perspective for research.Comment: Index Terms: Green Networking; Wired Networks; Adaptive Link Rate; Interface Proxying; Energy-aware Infrastructures; Energy-aware Applications. 18 pages, 6 figures, 2 table

Unified clustering and communication protocol for wireless sensor networks

In this paper we present an energy-efficient cross layer protocol for providing application specific reservations in wireless senor networks called the “Unified Clustering and Communication Protocol ” (UCCP). Our modular cross layered framework satisfies three wireless sensor network requirements, namely, the QoS requirement of heterogeneous applications, energy aware clustering and data forwarding by relay sensor nodes. Our unified design approach is motivated by providing an integrated and viable solution for self organization and end-to-end communication is wireless sensor networks. Dynamic QoS based reservation guarantees are provided using a reservation-based TDMA approach. Our novel energy-efficient clustering approach employs a multi-objective optimization technique based on OR (operations research) practices. We adopt a simple hierarchy in which relay nodes forward data messages from cluster head to the sink, thus eliminating the overheads needed to maintain a routing protocol. Simulation results demonstrate that UCCP provides an energy-efficient and scalable solution to meet the application specific QoS demands in resource constrained sensor nodes. Index Terms — wireless sensor networks, unified communication, optimization, clustering and quality of service

Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks

Soaring capacity and coverage demands dictate that future cellular networks need to soon migrate towards ultra-dense networks. However, network densification comes with a host of challenges that include compromised energy efficiency, complex interference management, cumbersome mobility management, burdensome signaling overheads and higher backhaul costs. Interestingly, most of the problems, that beleaguer network densification, stem from legacy networks' one common feature i.e., tight coupling between the control and data planes regardless of their degree of heterogeneity and cell density. Consequently, in wake of 5G, control and data planes separation architecture (SARC) has recently been conceived as a promising paradigm that has potential to address most of aforementioned challenges. In this article, we review various proposals that have been presented in literature so far to enable SARC. More specifically, we analyze how and to what degree various SARC proposals address the four main challenges in network densification namely: energy efficiency, system level capacity maximization, interference management and mobility management. We then focus on two salient features of future cellular networks that have not yet been adapted in legacy networks at wide scale and thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and device-to-device (D2D) communications. After providing necessary background on CoMP and D2D, we analyze how SARC can particularly act as a major enabler for CoMP and D2D in context of 5G. This article thus serves as both a tutorial as well as an up to date survey on SARC, CoMP and D2D. Most importantly, the article provides an extensive outlook of challenges and opportunities that lie at the crossroads of these three mutually entangled emerging technologies.Comment: 28 pages, 11 figures, IEEE Communications Surveys & Tutorials 201