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Modern cluster managers like Borg, Omega and Kubernetes rely on the state-reconciliation principle to be highly resilient and extensible. In these systems, all cluster-management logic is embedded in a loosely coupled collection of microservices called controllers. Each controller independently observes the current cluster state and issues corrective actions to converge the cluster to a desired state. However, the complex distributed nature of the overall system makes it hard to build reliable and correct controllers – we find that controllers face myriad reliability issues that lead to severe consequences like data loss, security vulnerabilities, and resource leaks. We present Sieve, the first automatic reliability-testing tool for cluster-management controllers. Sieve drives controllers to their potentially buggy corners by systematically and extensively perturbing the controller’s view of the current cluster state in ways it is expected to tolerate. It then compares the cluster state’s evolution with and without perturbations to detect safety and liveness issues. Sieve’s design is powered by a fundamental opportunity in state-reconciliation systems – these systems are based on state-centric interfaces between the controllers and the cluster state; such interfaces are highly transparent and thereby enable fully-automated reliability testing. To date, Sieve has efficiently found 46 serious safety and livenessmore »bugs (35 confirmed and 22 fixed) in ten popular controllers with a low false-positive rate of 3.5%.« less

We measure the sunspot areas of activity cycle 24 using ten years of continuum images from the Helioseismic and Magnetic Imager, and compare them with the peak flare soft X-ray flux from the Geostationary Operational Environmental Satellite. We find that the sunspot area in our sample is positively correlated with the magnitude of the largest flare they produce. Complex spot groups withβγδ magnetic classification tend to be larger and more likely to produce intense flares. Our findings are qualitatively consistent with previous studies.

Abstract With the aim of investigating how the magnetic field in solar active regions (ARs) controls flare activity, i.e., whether a confined or eruptive flare occurs, we analyze 106 flares of Geostationary Operational Environmental Satellite class ≥M1.0 during 2010–2019. We calculate mean characteristic twist parameters α FPIL within the “flaring polarity inversion line” region and α HFED within the area of high photospheric magnetic free energy density, which both provide measures of the nonpotentiality of the AR core region. Magnetic twist is thought to be related to the driving force of electric current-driven instabilities, such as the helical kink instability. We also calculate total unsigned magnetic flux (Φ AR ) of ARs producing the flare, which describes the strength of the background field confinement. By considering both the constraining effect of background magnetic fields and the magnetic nonpotentiality of ARs, we propose a new parameter α /Φ AR to measure the probability for a large flare to be associated with a coronal mass ejection (CME). We find that in about 90% of eruptive flares, α FPIL /Φ AR and α HFED /Φ AR are beyond critical values (2.2 × 10 −24 and 3.2 × 10 −24 Mm −1 Mx −1more »), whereas they are less than critical values in ∼80% of confined flares. This indicates that the new parameter α /Φ AR is well able to distinguish eruptive flares from confined flares. Our investigation suggests that the relative measure of magnetic nonpotentiality within the AR core over the restriction of the background field largely controls the capability of ARs to produce eruptive flares.« less

Abstract Violent solar flares and coronal mass ejections (CMEs) are magnetic phenomena. However, how magnetic fields reconnecting in the flare differ from nonflaring magnetic fields remains unclear owing to the lack of studies of the flare magnetic properties. Here we present a first statistical study of flaring (highlighted by flare ribbons) vector magnetic fields in the photosphere. Our systematic approach allows us to describe the key physical properties of solar flare magnetism, including distributions of magnetic flux, magnetic shear, vertical current, and net current over flaring versus nonflaring parts of the active region (AR), and compare these with flare/CME properties. Our analysis suggests that while flares are guided by the physical properties that scale with AR size, like the total amount of magnetic flux that participates in the reconnection process and the total current (extensive properties), CMEs are guided by mean properties, like the fraction of the AR magnetic flux that participates (intensive property), with little dependence on the amount of shear at the polarity inversion line (PIL) or the net current. We find that the nonneutralized current is proportional to the amount of shear at the PIL, providing direct evidence that net vertical currents are formed as a resultmore »of any mechanism that could generate magnetic shear along the PIL. We also find that eruptive events tend to have smaller PIL fluxes and larger magnetic shears than confined events. Our analysis provides a reference for more realistic solar and stellar flare models. The database is available online and can be used for future quantitative studies of flare magnetism.« less

Abstract We present a new data product, called Space-Weather MDI Active Region Patches (SMARPs), derived from maps of the solar surface magnetic field taken by the Michelson Doppler Imager on board the Solar and Heliospheric Observatory. Together with the Space-Weather HMI Active Region Patches (SHARPs), derived from similar maps taken by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory, these data provide a continuous and seamless set of maps and keywords that describe every active region observed over the last two solar cycles, from 1996 to the present day. In this paper, we describe the SMARP data and compare it to the SHARP data.

Abstract Emerging dimming occurs in isolated solar active regions (ARs) during the early stages of magnetic flux emergence. Observed by the Atmospheric Imaging Assembly, it features a rapid decrease in extreme-ultraviolet (EUV) emission in the 171 Å channel images, and a simultaneous increase in the 211 Å images. Here, we analyze the coronal thermodynamic and magnetic properties to probe its physical origin. We calculate the time-dependent differential emission measures for a sample of 18 events between 2010 and 2012. The emission measure (EM) decrease in the temperature range is well correlated with the EM increase in over eight orders of magnitude. This suggests that the coronal plasma is being heated from the quiet-Sun, sub-MK temperature to 1–2 MK, more typical for ARs. Potential field extrapolation indicates significant change in the local magnetic connectivity: the dimming region is now linked to the newly emerged flux via longer loops. We conclude that emerging dimming is likely caused by coronal heating episodes, powered by reconnection between the emerging and the ambient magnetic fields.

Modern datacenter infrastructures are increasingly architected as a cluster of loosely coupled services. The cluster states are typically maintained in a logically centralized, strongly consistent data store (e.g., ZooKeeper, Chubby and etcd), while the services learn about the evolving state by reading from the data store, or via a stream of notifications. However, it is challenging to ensure services are correct, even in the presence of failures, networking issues, and the inherent asynchrony of the distributed system. In this paper, we identify that partial histories can be used to effectively reason about correctness for individual services in such distributed infrastructure systems. That is, individual services make decisions based on observing only a subset of changes to the world around them. We show that partial histories, when applied to distributed infrastructures, have immense explanatory power and utility over the state of the art. We discuss the implications of partial histories and sketch tooling for reasoning about distributed infrastructure systems.