Showing papers by "Ayse K. Coskun published in 2016"

Providing Sustainable Performance in Thermally Constrained Mobile Devices

Abstract: State-of-the-art smartphones can generate excessive amounts of heat during high computational activity or long durations of use. While throttling mechanisms ensure safe component and outer skin level temperatures, frequent throttling can largely degrade the user-perceived performance. This work explores the impact of multiple different thermal constraints in a real-life smartphone on user experience. In addition to high processor temperatures, which have traditionally been a major point of interest, we show that applications can also quickly elevate battery and device skin temperatures to critical levels. We introduce and evaluate various thermally-efficient runtime management techniques that slow down heating under performance guarantees so as to sustain a desirable performance for maximum durations. Our techniques achieve up to 8x longer sustainable QoS.

QScale: thermally-efficient QoS management on heterogeneous mobile platforms

Abstract: Single-ISA heterogeneous mobile processors integrate low-power and power-hungry CPU cores together to combine energy efficiency with high performance. While running computationally demanding applications, current power management and scheduling techniques greedily maximize quality-of-service (QoS) within thermal constraints using power-hungry cores. We show that such an approach delivers short bursts of high QoS, but also causes severe QoS loss over time due to thermal throttling. To provide mobile users with sustainable QoS over extended durations, this paper proposes QScale. QScale is a novel thermally-efficient QoS management framework for mobile devices with heterogeneous multi-core CPUs. QScale leverages two novel observations to provide thermally-efficient QoS: (1) threads of a mobile application exhibit significant heterogeneity, which can be exploited during scheduling; (2) thermal efficiency of core allocation decisions is significantly altered by thermal interactions across system-on-a-chip (SoC) components and application characteristics. QScale coordinates closed-loop frequency control with thermally-efficient scheduling to deliver the desired QoS with minimal exhaustion of processor thermal headroom. Our experiments on a state-of-the-art heterogeneous mobile platform show that QScale meets target QoS levels while minimizing heating, achieving up to 8× longer durations of sustainable QoS.

Adapt&Cap: Coordinating System- and Application-Level Adaptation for Power-Constrained Systems

Abstract: The paper proposes an adaptive framework for modern data centers that jointly manages application- and system-level (dynamic voltage and frequency scaling) adaptation to improve energy efficiency of multicore servers. The results of applying this framework show that significant power savings and performance improvements are possible with respect to current data center management techniques.

Cross-layer floorplan optimization for silicon photonic NoCs in many-core systems

Abstract: Many-core chip architectures are now feasible, but the power consumption of electrical networks-on-chip does not scale well. Silicon photonic NoCs (PNoCs) are more scalable and power efficient, but floorplan optimization is challenging. Prior work optimizes PNoC floorplans through simultaneous place and route, but does not address cross-layer effects that span optical and electrical boundaries, chip thermal profiles, or effects of job scheduling policies. This paper proposes a more comprehensive, cross-layer optimization of the silicon PNoC and core cluster floorplan. Our simultaneous placement (locations of router groups and core clusters) and routing (waveguide layout) considers scheduling policy, thermal tuning, and heterogeneity in chip power profiles. The core of our optimizer is a mixed-integer linear programming formulation that minimizes NoC power, including (1) laser source power due to propagation, bend and crossing losses; (2) electrical and electrical-optical-electrical conversion power; and (3) thermal tuning power. Our experiments vary numbers of cores, optical data rate per wavelength, number of waveguides and other parameters to investigate scalability and tradeoffs through a large design space. We demonstrate how the optimal floorplan changes with cross-layer awareness: metrics of interest such as optimal waveguide length or thermal tuning power change significantly (up to 4X) based on power and utilization levels of cores, chip and cluster aspect ratio, and laser source sharing mechanism. Exploration of a large solution space is achieved with reasonable runtimes, and is perfectly parallelizable. Our optimizer thus affords designers with more accurate, cross-layer chip planning decision support to accelerate adoption of PNoC-based solutions.

Seeing into a Public Cloud: Monitoring the Massachusetts Open Cloud

Abstract: Cloud users today have little visibility into the performance characteristics, power consumption, and utilization of cloud resources; and the cloud has little visibility into user application performance requirements and critical metrics such as response time and throughput. This paper outlines new efforts to reduce the information gap between the cloud users and the cloud. We first present a scalable monitoring platform to collect and retain rich information on a regional public cloud. Second, we present two motivating use cases that leverage the collected information: (1) Participation in emerging smart grid demand response programs in order to reduce datacenter energy costs and stabilize power grid demands, (2) budgeting available power to applications via peak shaving. This work is done in the context of the Massachusetts Open Cloud (MOC), a new public cloud project that has a central goal of enabling cloud research.

Automated system change discovery and management in the cloud

Abstract: Emerging cloud service platforms are hosting hundreds of thousands of virtual machine instances, each of which evolves differently from the time they are provisioned. As a result, cloud service operators are facing great challenges in continuously managing, monitoring, and maintaining a large number of diversely evolving systems, and discovering potential resilience and vulnerability issues in a timely manner. In this paper, we introduce an automated cloud analytics solution that is based on using machine learning for system change discovery and management. The learning-based approaches we introduce are widely used in multimedia and web content analysis, but application of these to the cloud management context is a novel aspect of our work. We first propose multiple feature extraction methods to generate condensed “fingerprints” from the comprehensive system metadata recorded during the system changes. We then build an adaptive knowledge base using all known fingerprint samples. We evaluate different machine learning algorithms as part of the proposed discovery and identification framework. Experimental results that are gathered from several real-life systems demonstrate that our approach is fast and accurate for system change discovery and management in emerging cloud services.

DeltaSherlock: Identifying changes in the cloud

Abstract: To track security and compliance requirements and perform problem diagnosis, administrators of cloud computing systems need to monitor significant system changes occurring on the set of cloud instances under their supervision. Considering the large number of instances (virtual machines, containers) possibly operating under multiple configurations, this is a difficult-to-track process. Standard solutions to this problem rely on manually-created rules to identify changes. These techniques suffer from a limited scope, rely on domain expertise, and are time-consuming and error-prone. Recently, more streamlined approaches that automatically determine the type of individual system changes have been proposed, but these techniques assume that system states right before and after each individual change can be captured, a rather difficult requirement to enforce in real world usage. This paper proposes DeltaSherlock, a practical system change discovery framework that can capture system states on-demand and detect multiple system changes between them. We evaluate DeltaSherlock over 25,000 system changes caused by software installations collected from virtual machines (VMs) deployed over a commercial cloud. DeltaSherlock can accurately identify multiple software installations with 96.8% accuracy when supplied with a non-overlapping record of system changes and with 77.8% accuracy when supplied with random irregular observations possibly containing overlapping or incomplete changes.