Constraining the temperature of computing systems has become a dominant aspect in the design of integrated circuits. The supply voltage decrease has lost its pace even though the feature size is shrinking constantly. This results in an increased number of transistors per unit of area and hence a growing power density. Researchers started investigating dynamic thermal management techniques to address the tradeoff between performance and temperature. Hardware dynamic thermal management can guarantee safety but, at the same time, can negatively affect established service-level agreements. On the other hand, software solutions rely on hardware for safety but does not indiscriminately trade-off performance for temperature.We propose ThermOS, an extension for commodity operating systems that harnesses formal feedback control and idle cycle injection to decrease thermal emergencies while showing better efficiency than commodity and cutting edge techniques.
Abstract-Self-Aware Adaptive computing systems are capable of adapting their behavior and resources thousands of times based on changing environmental conditions and demands. This allows them to automatically find the best way to accomplish a given goal with the resources at hand. This capability would benefit the full range of computer systems, from embedded devices to servers to supercomputers. Although such a system may seem rather far fetched, we believe that basic semiconductor technology, computer architecture and software systems have advanced to the point that the time is ripe to realize such a system.In this paper we present an implementation of an FPGAbased Self-Aware Adaptive computing system which blends techniques developed in different research fields, i.e, monitoring, decision making, and self-adaptation. The result is a system built on top of a set of enabling technology that proves the effectiveness of using Self-Aware Adaptive computing systems. We used the Application Heartbeats to assess performance goals and to inspect application progress and the Implementation Switch Service to switch between different implementations of the same algorithm (both in software and in hardware) at runtime. Preliminary results show the effectiveness and the usability of the proposed approach.
Traditionally, hypervisors, operating systems, and runtime systems have been providing an abstraction layer over the bare-metal hardware. Traditional abstractions, however, do not consider for nonfunctional requirements such as system-level constraints or users' objectives. As these requirements are gaining increasing importance, researchers are looking into making user-specified and systemlevel objectives first-class citizens in the computer systems' realm.This paper describes the Autonomic Operating System (AcOS) project; AcOS enhances commodity operating systems with an autonomic layer that enables self-* properties through adaptive resource allocation. With AcOS, we investigate intelligent resource allocation to achieve user-specified service-level objectives on application performance and to respect system-level thresholds on CPU temperature. We give a broad overview of AcOS, elaborate on its achievements, and discuss research perspectives.
Ideally, the pay-as-you-go model of Infrastructure as a Service (IaaS) clouds should enable users to rent just enough resources (e.g., CPU or memory bandwidth) to fulfill their service level objectives (SLOs). Achieving this goal is hard on current IaaS offers, which require users to explicitly specify the amount of resources to reserve; this requirement is nontrivial for users, because estimating the amount of resources needed to attain application-level SLOs is often complex, especially when resources are virtualized and the service provider colocates virtual machines (VMs) on host nodes. For this reason, users who deploy VMs subject to SLOs are usually prone to overprovisioning resources, thus resulting in inflated business costs.This article tackles this issue with AutoPro: a runtime system that enhances IaaS clouds with automated and fine-grained resource provisioning based on performance SLOs. Our main contribution with AutoPro is filling the gap between application-level performance SLOs and allocation of a contended resource, without requiring explicit reservations from users. In this article, we focus on CPU bandwidth allocation to throughput-driven, compute-intensive multithreaded applications colocated on a multicore processor; we show that a theoretically sound, yet simple, control strategy can enable automated fine-grained allocation of this contended resource, without the need for offline profiling. Additionally, AutoPro helps service providers optimize infrastructure utilization by provisioning idle resources to best-effort workloads, so as to maximize node-level utilization.Our extensive experimental evaluation confirms that AutoPro is able to automatically determine and enforce allocations to meet performance SLOs while maximizing node-level utilization by supporting batch workloads on a best-effort basis.
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