Proceedings of the Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015) Krakow, Poland

Proceedings of: Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015). Krakow (Poland), September 10-11, 2015

Saving Energy in QoS Networked Data Centers

One of the major challenges that cloud providers face is minimizing power consumption of their data centers. To this point, majority of current research focuses on energy efficient management of resources in the Infrastructure as a Service model using virtualization and through virtual machine consolidation. However, current virtualized data centers are not designed for supporting communication–computing intensive real-time applications, such as, info-mobility applications, real-time video co-decoding. In fact, imposing hard-limits on the overall per-job delay forces the overall networked computing infrastructure to adapt quickly its resource utilization to the (possibly, unpredictable and abrupt) time fluctuations of the offered workload. Jointly, a promising approach for making networked data centers more energy-efficient is the use of traffic engineering-based method to dynamically adapt the number of active servers to match the current workload. Therefore, it is desirable to develop a flexible and robust resource allocation algorithm that automatically adapts to time-varying workload and pays close attention to the consumed energy in computing and communication in virtualized networked data centers (VNetDCs). In this thesis, we propose three new dynamic and adaptive energy-aware algorithms scheduling policies that model and manage VNetDCs. Our focuses are to propose i) admission control of the offered input traffic; ii) balanced control and dispatching of the admitted workload; iii) dynamic reconfiguration and consolidation of the Dynamic Voltage and Frequency Scaling (DVFS)-enabled Virtual Machines (VMs) instantiated onto the parallel computing platform; and, iv) rate control of the traffic injected into the TCP/IP mobile connection. Necessary and sufficient conditions for the feasibility and optimality of the proposed schedulers are also provided in closed-form. Specifically, the first approach, called VNetDC, the optimal minimum-energy scheduler for the joint adaptive load balancing and provisioning of the computing-plus-communication resources. VNetDC platforms have been considered which operate under hard real-time constraints. VNetDC has capability to adapt to the time-varying statistical features of the offered workload without requiring any a priori assumption and/or knowledge about the statistics of the processed data. Green- NetDC is the second scheduling policy that is a flexible and robust resource allocation algorithm that automatically adapts to time-varying workload and pays close attention to the consumed energy in computing and communication in VNetDCs. GreenNetDC not only ensures users the Quality of Service (through Service Level Agreements) but also achieves maximum energy saving and attains green cloud computing goals in a fully distributed fashion by utilizing the DVFS-based CPU frequencies. Finally, the last algorithm tested an efficient dynamic resource provisioning scheduler which applied in Networked Data Centers (NetDCs). This method is connected to (possibly, mobile) clients through TCP/IP-based vehicular backbones The salient features of this algorithm is that: i) It is adaptive and admits distributed scalable implementation; ii) It is capable to provide hard QoS guarantees, in terms of minimum/maximum instantaneous rate of the traffic delivered to the client, instantaneous goodput and total processing delay; and, iii) It explicitly accounts for the dynamic interaction between computing and networking resources, in order to maximize the resulting energy efficiency. Actual performance of the proposed scheduler in the presence of :i) client mobility; ii)wireless fading; iii)reconfiguration and two-thresholds consolidation costs of the underlying networked computing platform; and, iv)abrupt changes of the transport quality of the available TCP/IP mobile connection, is numerically tested and compared against the corresponding ones of some state-of-the-art static schedulers, under both synthetically generated and measured real-world workload traces

Power Analysis and Optimization Techniques for Energy Efficient Computer Systems

Reducing power consumption has become a major challenge in the design and operation of to-day’s computer systems. This chapter describes different techniques addressing this challenge at different levels of system hardware, such as CPU, memory, and internal interconnection network, as well as at different levels of software components, such as compiler, operating system and user applications. These techniques can be broadly categorized into two types: Design time power analysis versus run-time dynamic power management. Mechanisms in the first category use ana-lytical energy models that are integrated into existing simulators to measure the system’s power consumption and thus help engineers to test power-conscious hardware and software during de-sign time. On the other hand, dynamic power management techniques are applied during run-time, and are used to monitor system workload and adapt the system’s behavior dynamically to save energy

Adaptive Resource Management for Uncertain Execution Platforms

Embedded systems are becoming increasingly complex. At the same time, the components that make up the system grow more uncertain in their properties. For example, current developments in CPU design focuses on optimizing for average performance rather than better worst case performance. This, combined with presence of 3rd party software components with unknown properties, makes resource management using prior knowledge less and less feasible. This thesis presents results on how to model software components so that resource allocation decisions can be made on-line. Both the single and multiple resource case is considered as well as extending the models to include resource constraints based on hardware dynam- ics. Techniques for estimating component parameters on-line are presented. Also presented is an algorithm for computing an optimal allocation based on a set of convex utility functions. The algorithm is designed to be computationally efficient and to use simple mathematical expres- sions that are suitable for fixed point arithmetics. An implementation of the algorithm and results from experiments is presented, showing that an adaptive strategy using both estimation and optimization can outperform a static approach in cases where uncertainty is high

MULTI-OBJECTIVE DESIGN AUTOMATION FOR RECONFIGURABLE MULTI-PROCESSOR SYSTEMS

Ph.DDOCTOR OF PHILOSOPH

Energy Saving in QoS Fog-supported Data Centers

One of the most important challenges that cloud providers face in the explosive growth of data is to reduce the energy consumption of their designed, modern data centers. The majority of current research focuses on energy-efficient resources management in the infrastructure as a service (IaaS) model through "resources virtualization" - virtual machines and physical machines consolidation. However, actual virtualized data centers are not supporting communication–computing intensive real-time applications, big data stream computing (info-mobility applications, real-time video co-decoding). Indeed, imposing hard-limits on the overall per-job computing-plus-communication delays forces the overall networked computing infrastructure to quickly adopt its resource utilization to the (possibly, unpredictable and abrupt) time fluctuations of the offered workload. Recently, Fog Computing centers are as promising commodities in Internet virtual computing platform that raising the energy consumption and making the critical issues on such platform. Therefore, it is expected to present some green solutions (i.e., support energy provisioning) that cover fog-supported delay-sensitive web applications. Moreover, the usage of traffic engineering-based methods dynamically keep up the number of active servers to match the current workload. Therefore, it is desirable to develop a flexible, reliable technological paradigm and resource allocation algorithm to pay attention the consumed energy. Furthermore, these algorithms could automatically adapt themselves to time-varying workloads, joint reconfiguration, and orchestration of the virtualized computing-plus-communication resources available at the computing nodes. Besides, these methods facilitate things devices to operate under real-time constraints on the allowed computing-plus-communication delay and service latency. The purpose of this thesis is: i) to propose a novel technological paradigm, the Fog of Everything (FoE) paradigm, where we detail the main building blocks and services of the corresponding technological platform and protocol stack; ii) propose a dynamic and adaptive energy-aware algorithm that models and manages virtualized networked data centers Fog Nodes (FNs), to minimize the resulting networking-plus-computing average energy consumption; and, iii) propose a novel Software-as-a-Service (SaaS) Fog Computing platform to integrate the user applications over the FoE. The emerging utilization of SaaS Fog Computing centers as an Internet virtual computing commodity is to support delay-sensitive applications. The main blocks of the virtualized Fog node, operating at the Middleware layer of the underlying protocol stack and comprises of: i) admission control of the offered input traffic; ii) balanced control and dispatching of the admitted workload; iii) dynamic reconfiguration and consolidation of the Dynamic Voltage and Frequency Scaling (DVFS)-enabled Virtual Machines (VMs) instantiated onto the parallel computing platform; and, iv) rate control of the traffic injected into the TCP/IP connection. The salient features of this algorithm are that: i) it is adaptive and admits distributed scalable implementation; ii) it has the capacity to provide hard QoS guarantees, in terms of minimum/maximum instantaneous rate of the traffic delivered to the client, instantaneous goodput and total processing delay; and, iii) it explicitly accounts for the dynamic interaction between computing and networking resources in order to maximize the resulting energy efficiency. Actual performance of the proposed scheduler in the presence of: i) client mobility; ii) wireless fading; iii) reconfiguration and two-thresholds consolidation costs of the underlying networked computing platform; and, iv) abrupt changes of the transport quality of the available TCP/IP mobile connection, is numerically tested and compared to the corresponding ones of some state-of-the-art static schedulers, under both synthetically generated and measured real-world workload traces

Energy efficiency of dynamic management of virtual cluster with heterogeneous hardware

Cloud computing is an essential part of today's computing world. Continuously increasing amount of computation with varying resource requirements is placed in large data centers. The variation among computing tasks, both in their resource requirements and time of processing, makes it possible to optimize the usage of physical hardware by applying cloud technologies. In this work, we develop a prototype system for load-based management of virtual machines in an OpenStack computing cluster. Our prototype is based on an idea of 'packing' idle virtual machines into special park servers optimized for this purpose. We evaluate the method by running real high-energy physics analysis software in an OpenStack test cluster and by simulating the same principle using the Cloudsim simulator software. The results show a clear improvement, 9-48 %, in the total energy efficiency when using our method together with resource overbooking and heterogeneous hardware.Peer reviewe

DESIGN SPACE EXPLORATION FOR SIGNAL PROCESSING SYSTEMS USING LIGHTWEIGHT DATAFLOW GRAPHS

Digital signal processing (DSP) is widely used in many types of devices, including mobile phones, tablets, personal computers, and numerous forms of embedded systems. Implementation of modern DSP applications is very challenging in part due to the complex design spaces that are involved. These design spaces involve many kinds of configurable parameters associated with the signal processing algorithms that are used, as well as different ways of mapping the algorithms onto the targeted platforms. In this thesis, we develop new algorithms, software tools and design methodologies to systematically explore the complex design spaces that are involved in design and implementation of signal processing systems. To improve the efficiency of design space exploration, we develop and apply compact system level models, which are carefully formulated to concisely capture key properties of signal processing algorithms, target platforms, and algorithm-platform interactions. Throughout the thesis, we develop design methodologies and tools for integrating new compact system level models and design space exploration methods with lightweight dataflow (LWDF) techniques for design and implementation of signal processing systems. LWDF is a previously-introduced approach for integrating new forms of design space exploration and system-level optimization into design processes for DSP systems. LWDF provides a compact set of retargetable application programming interfaces (APIs) that facilitates the integration of dataflow-based models and methods. Dataflow provides an important formal foundation for advanced DSP system design, and the flexible support for dataflow in LWDF facilitates experimentation with and application of novel design methods that are founded in dataflow concepts. Our developed methodologies apply LWDF programming to facilitate their application to different types of platforms and their efficient integration with platform-based tools for hardware/software implementation. Additionally, we introduce novel extensions to LWDF to improve its utility for digital hardware design and adaptive signal processing implementation. To address the aforementioned challenges of design space exploration and system optimization, we present a systematic multiobjective optimization framework for dataflow-based architectures. This framework builds on the methodology of multiobjective evolutionary algorithms and derives key system parameters subject to time-varying and multidimensional constraints on system performance. We demonstrate the framework by applying LWDF techniques to develop a dataflow-based architecture that can be dynamically reconfigured to realize strategic configurations in the underlying parameter space based on changing operational requirements. Secondly, we apply Markov decision processes (MDPs) for design space exploration in adaptive embedded signal processing systems. We propose a framework, known as the Hierarchical MDP framework for Compact System-level Modeling (HMCSM), which embraces MDPs to enable autonomous adaptation of embedded signal processing under multidimensional constraints and optimization objectives. The framework integrates automated, MDP-based generation of optimal reconfiguration policies, dataflow-based application modeling, and implementation of embedded control software that carries out the generated reconfiguration policies. Third, we present a new methodology for design and implementation of signal processing systems that are targeted to system-on-chip (SoC) platforms. The methodology is centered on the use of LWDF concepts and methods for applying principles of dataflow design at different layers of abstraction. The development processes integrated in our approach are software implementation, hardware implementation, hardware-software co-design, and optimized application mapping. The proposed methodology facilitates development and integration of signal processing hardware and software modules that involve heterogeneous programming languages and platforms. Through three case studies involving complex applications, we demonstrate the effectiveness of the proposed contributions for compact system level design and design space exploration: a digital predistortion (DPD) system, a reconfigurable channelizer for wireless communication, and a deep neural network (DNN) for vehicle classification