Abstract not found

Autonomic Computing is a concept that brings together many fields of computing with the purpose of creating computing systems that self-manage. In its early days it was criticised as being a “hype topic” or a rebadging of some Multi Agent Systems work. In this survey, we hope to show that this was not indeed ’hype ’ and that, though it draws on much work already carried out by the Computer Science and Control communities, its innovation is strong and lies in its robust application to the specific self-management of computing systems. To this end, we first provide an introduction to the motivation and concepts of autonomic computing and describe some research that has been seen as seminal in influencing a large proportion of early work. Taking the components of an established reference model in turn, we discuss the works that have provided significant contributions to that area. We then look at larger scaled systems that compose autonomic systems illustrating the hierarchical nature of their architectures. Autonomicity is not a well defined subject and as such different systems adhere to different degrees of Autonomicity, therefore we cross-slice the body of work in terms of these degrees. From this we list the key applications of autonomic computing and discuss the research work that is missing and what we believe the community should be considering.

Abstract-- We present a technique that controls the peak power consumption of a high-density server by implementing a feedback controller that uses precise, system-level power measurement to periodically select the highest performance state while keeping the system within a fixed power constraint. A control theoretic methodology is applied to systematically design this control loop with analytic assurances of system stability and controller performance, despite unpredictable workloads and running environments. In a real server we are able to control power over a 1 second period to within 1 W and over an 8 second period to within 0.1 W. Conventional servers respond to power supply constraint situations by using simple open-loop policies to set a safe performance level in order to limit peak power consumption. We show that closed-loop control can provide higher performance under these conditions and implement this technique on an IBM BladeCenter HS20 server. Experimental results demonstrate that closed-loop control provides up to 82 % higher application performance compared to open-loop control and up to 17 % higher performance compared to a widely used ad-hoc technique. 1.

Data center power infrastructure incurs massive capital costs, which typically exceed energy costs over the life of the facility. To squeeze maximum value from the infrastructure, researchers have proposed over-subscribing power circuits, relying on the observation that peak loads are rare. To ensure availability, these proposals employ power capping, which throttles server performance during utilization spikes to enforce safe power budgets. However, because budgets must be enforced locally—at each power distribution unit (PDU)—local utilization spikes may force throttling even when power delivery capacity is available elsewhere. Moreover, the need to maintain reserve capacity for fault tolerance on power delivery paths magnifies the impact of utilization spikes. In this paper, we develop mechanisms to better utilize installed power infrastructure, reducing reserve capacity margins and avoiding performance throttling. Unlike conventional high-availability data centers, where collocated servers share identical primary and secondary power feeds, we reorganize power feeds to create shuffled power distribution topologies. Shuffled topologies spread secondary power feeds over numerous PDUs, reducing reserve capacity requirements to tolerate a single PDU failure. Second, we propose Power Routing, which schedules IT load dynamically across redundant power feeds to: (1) shift slack to servers with growing power demands, and (2) balance power draw across AC phases to reduce heating and improve electrical stability. We describe efficient heuristics for scheduling servers to PDUs (an NP-complete problem). Using data collected from nearly 1000 servers in three production facilities, we demonstrate that these mechanisms can reduce the required power infrastructure capacity relative to conventional high-availability data centers by 32 % without performance degradation.

As chip multiprocessors (CMPs) become the main trend in processor development, various power and thermal management strategies have recently been proposed to optimize system performance while controlling the power or temperature of a CMP chip to stay below a constraint. The availability of per-core DVFS (dynamic voltage and frequency scaling) also makes it possible to develop advanced management strategies. However, most existing solutions rely on open-loop search or optimization with the assumption that power can be estimated accurately, while others adopt oversimplified feedback control strategies to control power and temperature separately, without any theoretical guarantees. In this paper, we propose a chip-level power control algorithm that is systematically designed based on optimal control theory. Our algorithm can precisely control the power of a CMP chip to the desired set point while maintaining the temperature of each core below a specified threshold. Furthermore, an online model estimator is designed to achieve analytical assurance of control accuracy and system stability, even in the face of significant workload variations or unpredictable chip or core variations. Empirical results on a physical testbed show that our controller outperforms two state-of-the-art control algorithms by having better SPEC benchmark performance and more precise power control. In addition, extensive simulation results demonstrate the efficacy of our algorithm for various CMP configurations.

It has recently become clear that power management is of critical importance in modern enterprise computing environments. The traditional drive for higher performance has influenced trends towards consolidation and higher densities, artifacts enabled by virtualization and new small form factor server blades. The resulting effect has been increased power and cooling requirements in data centers which elevate ownership costs and put more pressure on rack and enclosure densities. To address these issues, in this paper, we enable power-efficient management of enterprise workloads by exploiting a fundamental characteristic of data centers: “platform heterogeneity”. This heterogeneity stems from the architectural and management-capability variations of the underlying platforms. We define an intelligent workload allocation method that leverages heterogeneity characteristics and efficiently maps workloads to the best fitting platforms, significantly improving the power efficiency of the whole data center. We perform this allocation by employing a novel analytical prediction layer that accurately predicts workload power/performance across different platform architectures and power management capabilities. This prediction infrastructure relies upon platform and workload descriptors that we define as part of our work. Our allocation scheme achieves on average 20 % improvements in power efficiency for representative heterogeneous data center configurations, highlighting the significant potential of heterogeneity-aware management. 1

Today’s data centers face two critical challenges. First, various customers need to be assured by meeting their required service-level agreements such as response time and throughput. Second, server power consumption must be controlled in order to avoid failures caused by power capacity overload or system overheating due to increasing high server density. However, existing work controls power and application-level performance separately, and thus, cannot simultaneously provide explicit guarantees on both. In addition, as power and performance control strategies may come from different hardware/software vendors and coexist at different layers, it is more feasible to coordinate various strategies to achieve the desired control objectives than relying on a single centralized control strategy. This paper proposes Co-Con, a novel cluster-level control architecture that coordinates individual power and performance control loops for virtualized server clusters. To emulate the current practice in data centers, the power control loop changes hardware power states with no regard to the application-level performance. The performance control loop is then designed for each virtual machine to achieve the desired performance even when the system model varies significantly due to the impact of power control. Co-Con configures the two control loops rigorously, based on feedback control theory, for theoretically guaranteed control accuracy and system stability. Empirical results on a physical testbed demonstrate that Co-Con can simultaneously provide effective control on both application-level performance and underlying power consumption.

Electrical power management in large-scale IT systems such as commercial datacenters is an application area of rapidly growing interest from both an economic and ecological perspective, with billions of dollars and millions of metric tons of CO2 emissions at stake annually. Businesses want to save power without sacrificing performance. This paper presents a reinforcement learning approach to simultaneous online management of both performance and power consumption. We apply RL in a realistic laboratory testbed using a Blade cluster and dynamically varying HTTP workload running on a commercial web applications middleware platform. We embed a CPU frequency controller in the Blade servers’ firmware, and we train policies for this controller using a multi-criteria reward signal depending on both application performance and CPU power consumption. Our testbed scenario posed a number of challenges to successful use of RL, including multiple disparate reward functions, limited decision sampling rates, and pathologies arising when using multiple sensor readings as state variables. We describe innovative practical solutions to these challenges, and demonstrate clear performance improvements over both hand-designed policies as well as obvious “cookbook ” RL implementations. 1

The large amount of energy consumed by Internet services represents significant and fast-growing financial and environmental costs. Increasingly, services are exploring dynamic methods to minimize energy costs while respecting their service-level agreements (SLAs). Furthermore, it will soon be important for these services to manage their usage of “brown energy ” (produced via carbon-intensive means) relative to renewable or “green ” energy. This paper introduces a general, optimization-based framework for enabling multi-data-center services to manage their brown energy consumption and leverage green energy, while respecting their SLAs and minimizing energy costs. Based on the framework, we propose policies for request distribution across the data centers. Our policies can be used to abide by caps on brown energy consumption, such as those that might arise from Kyotostyle carbon limits, from corporate pledges on carbon-neutrality, or from limits imposed on services to encourage brown energy conservation. We evaluate our framework and policies extensively through simulations and real experiments. Our results show how our policies allow a service to trade off consumption and cost. For example, using our policies, the service can reduce brown energy consumption by 24 % for only a 10 % increase in cost, while still abiding by SLAs. 1

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