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With today's rapidly-evolving cloud landscape embracing continuous integration and delivery, users of cloud systems must monitor software running on their containers and virtual machines (VMs) to ensure compliance, security, and efficiency. Traditional solutions to this problem rely on manually-created rules that identify software installations and modifications, but these require expert authors and are often unmaintainable. Recently, automated techniques for software discovery have emerged. Some techniques use examples of software to train machine learning models to predict which software has been installed on a system. Others leverage knowledge of packaging practices to aid in discovery without requiring any pre-training, but these practice-based methods cannot provide precise-enough information to perform discovery by themselves. This paper introduces Praxi, a new software discovery method that builds upon the strengths of prior approaches by combining the accuracy of learning-based methods with the efficiency of practice-based methods. In tests using samples collected on real-world cloud systems, Praxi correctly classifies installations at least 97.6% of the time, while running 14.8 times faster and using 87% less disk space than a similar learning-based method. Using a diverse software dataset, this paper quantitatively compares Praxi to systematic rule-, learning-, and practice-based methods, and discusses the best uses for each.

Wilson disease (WD) is a chronic disorder of copper metabolism which may affect patient's quality of life (QOL).Our aim was to assess the relationship between mental QOL (M-QOL) and physical QOL (P-QOL) and severity of the liver, neurological disease and mental health in patients with WD.At enrollment into our multisite international WD registry, adults (n = 62) were administered examinations assessing QOL (Short-Form 12-Item Health Survey), cognition, and mood. Patients also underwent hepatology and neurological assessments.Patients had lower M-QOL than P-QOL scores, P = 0.0006. Patients with major depressive disorder (n = 22) had worse M-QOL scores, P = 0.0017 but not P-QOL. We found no association with impaired cognition (n = 37) and QOL. The P-QOL scores have a moderate negative association with neurological disease severity based on the Unified Wilson Disease Rating Scale score (total [r = -0.38, P0.003], part 2 [r = -0.50, P0.0001], and part 3 [r = -0.37, P = 0.004]). M-QOL was not associated with Unified Wilson Disease Rating Scale scores. Worse P-QOL, but not M-QOL, was found in higher cirrhosis severity indicated by Child-Pugh (r = -0.80, P = 0.002) and Model for End Stage Liver Disease scores (r = -0.64, P = 0.03).M-QOL was associated with depression but not cognitive impairment, neurological disease, or liver disease severity, suggesting that mental health issues may affect overall QOL independent of the degree of liver or neurological disease. P-QOL was affected by the severity of neurological and liver disease but not mental health but also contributes to overall QOL in WD. An appreciation of the range of problems that affect QOL in adults with WD will help health care providers address issues that could improve overall well-being. The Short-Form 12-Item Health Survey may provide a useful instrument for QOL surveillance in WD.

Modern datacenters need to tackle efficiently the increasing demand for computing resources while minimizing energy usage and monetary costs. Power market operators have recently introduced emerging demand-response programs, in which electricity consumers regulate their power usage following provider requests to reduce monetary costs. Among different programs, regulation service (RS) reserves are particularly promising for datacenters due to the high credit gain possibilities and datacenters’ flexibility in regulating their power consumption. Therefore, it is essential to develop bidding strategies for datacenters to participate in emerging power markets together with power management policies that are aware of power market requirements at runtime. In this paper we propose ECOGreen, a holistic strategy to jointly optimize the datacenter RS problem and virtual machine (VM) allocation that satisfies the hour-ahead power market constraints in the presence of electrical energy storage (EES) and renewable energy. We first find the best power and reserve bidding values as well as the number of active servers in a fast analytical way that works well in practice. Then, we present an online adaptive policy that modulates datacenter power consumption by controlling VMs CPU resource limits and efficiently utilizing demand-side EES and renewable power, while guaranteeing quality-of-service (QoS) constraints. Our results demonstrate that ECOGreen can provide 76 percent of the datacenter power consumption on average as reserves to the market, due to largely operating on renewable sources and EES. This translates into ECOGreen saving up to 71 percent electricity costs when compared to other state-of-the-art datacenter electricity cost minimization techniques that participate in the power market.

Microservice architectures are widely used in cloud-native applications as their modularity allows for independent development and deployment of components. With the many complex interactions occurring in between components, it is difficult to determine the effects of a particular microservice rollout. Site Reliability Engineers must be able to determine with confidence whether a new rollout is at fault for a concurrent or subsequent performance problem in the system so they can quickly mitigate the issue. We present Tritium, a cross-layer analytics system that synthesizes several types of data to suggest possible causes for Service Level Objective (SLO) violations in microservice applications. It uses event data to identify new version rollouts, tracing data to build a topology graph for the cluster and determine services potentially affected by the rollout, and causal impact analysis applied to metric time-series to determine if the rollout is at fault. Tritium works based on the principle that if a rollout is not responsible for a change in an upstream or neighboring SLO metric, then the rollout's telemetry data will do a poor job predicting the behavior of that SLO metric. In this paper, we experimentally demonstrate that Tritium can accurately attribute SLO violations to downstream rollouts and outline the steps necessary to fully realize Tritium.

Online experimentation is an agile software development practice that plays an essential role in enabling rapid innovation. Existing solutions for online experimentation in Web and mobile applications are unsuitable for cloud applications. There is a need for rethinking online experimentation in the cloud to advance the state-of-the-art by considering the unique challenges posed by cloud environments. In this paper, we introduce Iter8, an open-source system that enables practitioners to deliver code changes to cloud applications in an agile manner while minimizing risk. Iter8 embodies our novel mathematical formulation built on online Bayesian learning and multi-armed bandit algorithms to enable online experimentation tailored for the cloud, considering both SLOs and business concerns, unlike existing solutions. Using Iter8, practitioners can safely and rapidly orchestrate various types of online experiments, gain key insights into the behavior of cloud applications, and roll out the optimal versions in an automated and statistically rigorous manner.

Multivariate time series are used in many science and engineering domains, including health-care, astronomy, and high-performance computing. A recent trend is to use machine learning (ML) to process this complex data and these ML-based frameworks are starting to play a critical role for a variety of applications. However, barriers such as user distrust or difficulty of debugging need to be overcome to enable widespread adoption of such frameworks in production systems. To address this challenge, we propose a novel explainability technique, CoMTE, that provides counterfactual explanations for supervised machine learning frameworks on multivariate time series data. Using various machine learning frameworks and data sets, we compare CoMTE with several state-of-the-art explainability methods and show that we outperform existing methods in comprehensibility and robustness. We also show how CoMTE can be used to debug machine learning frameworks and gain a better understanding of the underlying multivariate time series data.

Demand response programs help stabilize the electricity grid by providing monetary stimulus to consumers if they regulate their power consumption following market requirements. Regulation service, a market that requires participants to regulate power by following a signal updated every few seconds, is particularly beneficial to HPC data centers since data centers are capable of increasing/decreasing power consumption owing to the flexibility in running workloads and the availability of power control mechanisms. While prior works have explored how data centers can provide regulation service reserves, Quality-of-Service (QoS) provisioning for the jobs running at the data centers has not been considered. In this work, we propose an Adaptive policy with QoS Assurance that enables data centers to participate in regulation service programs with assurance on job QoS. Our policy regulates data center power through job scheduling and server power capping. QoS assurance is achieved by applying a queueing-theoretic result to our job scheduling strategy. We evaluate our policy by experiments on a real cluster. Our results demonstrate that the proposed policy reduces electricity costs by 25-56% while providing QoS assurance. On the other hand, the baseline policies cannot meet QoS constraints in 9 of the 14 workload traces tested.

Effective power management in a data center is critical to ensure that power delivery constraints are met while maximizing the performance of users’ workloads. Power limiting is needed in order to respond to greater-than-expected power demand. HPC sites have generally tackled this by adopting one of two approaches: (1) a system-level power management approach that is aware of the facility or site-level power requirements, but is agnostic to the application demands; OR (2) a job-level power management solution that is aware of the application design patterns and requirements, but is agnostic to the site-level power constraints. Simultaneously incorporating solutions from both domains often leads to conflicts in power management mechanisms. This, in turn, affects system stability and leads to irreproducibility of performance. To avoid this irreproducibility, HPC sites have to choose between one of the two approaches, thereby leading to missed opportunities for efficiency gains.This paper demonstrates the need for the HPC community to collaborate towards seamless integration of system-aware and application-aware power management approaches. This is achieved by proposing a new dynamic policy that inherits the benefits of both approaches from tight integration of a resource manager and a performance-aware job runtime environment. An empirical comparison of this integrated management approach against state-of-the-art solutions exposes the benefits of investing in end-to-end solutions to optimize for system-wide performance or efficiency objectives. With our proposed system–application integrated policy, we observed up to 7% reduction in system time dedicated to jobs and up to 11% savings in compute energy, compared to a baseline that is agnostic to system power and application design constraints.

Power densities of modern mobile system-on-a-chip designs can quickly exceed the thermal design limits during typical application use such as gaming or Web browsing. Resulting high temperatures lead to frequent thermal throttling and significant loss in quality-of-service (QoS) delivered to users. Thus, a joint consideration of thermal constraints and QoS requirements is essential to maximize the overall user experience. Prior techniques either rely on users to determine the best tradeoff point between QoS and temperature, or greedily utilize the thermal headroom to maximize performance, causing QoS to drop below user tolerable levels over extended durations of use. This paper introduces the MAESTRO framework to automatically manage QoS at runtime depending on application characteristics and thermal constraints. MAESTRO builds on the observation that increased temperatures can be tolerated for applications with bursty compute patterns due to idle periods between activities, while causing large QoS degradations for long-running applications with continuous computations. MAESTRO: 1) detects such continuous computations that are susceptible to throttling; 2) proactively finds a QoS level to balance user experience and temperature; and 3) performs closed-loop DVFS and thermally efficient thread mapping to meet the target QoS on a heterogeneous multicore CPU. Such application-adaptive control of QoS-temperature tradeoffs allows MAESTRO to sustain a target QoS level within a user tolerable range for longer durations without sacrificing the performance of latency-sensitive bursty computations. Evaluations on a real system prototype validates MAESTRO’s ability to accurately detect potential throttling-induced QoS degradations and demonstrates 41% to $6.7\boldsymbol {\times }$ longer durations of sustained QoS compared to state-of-the-art for a set of mobile applications.

As the size and complexity of high performance computing (HPC) systems grow in line with advancements in hardware and software technology, HPC systems increasingly suffer from performance variations due to shared resource contention as well as software- and hardware-related problems. Such performance variations can lead to failures and inefficiencies, which impact the cost and resilience of HPC systems. To minimize the impact of performance variations, one must quickly and accurately detect and diagnose the anomalies that cause the variations and take mitigating actions. However, it is difficult to identify anomalies based on the voluminous, high-dimensional, and noisy data collected by system monitoring infrastructures. This paper presents a novel machine learning based framework to automatically diagnose performance anomalies at runtime. Our framework leverages historical resource usage data to extract signatures of previously-observed anomalies. We first convert collected time series data into easy-to-compute statistical features. We then identify the features that are required to detect anomalies, and extract the signatures of these anomalies. At runtime, we use these signatures to diagnose anomalies with negligible overhead. We evaluate our framework using experiments on a real-world HPC supercomputer and demonstrate that our approach successfully identifies 98 percent of injected anomalies and consistently outperforms existing anomaly diagnosis techniques.

Power market operators have recently introduced smart grid demand response (DR), in which electricity consumers regulate their power usage following market requirements. DR helps stabilize the grid and enables integrating a larger amount of intermittent renewable power generation. Data centers provide unique opportunities for DR participation due to their flexibility in both workload servicing and power consumption. While prior studies have focused on data center participation in legacy DR programs such as dynamic energy pricing and peak shaving, this article studies data centers in emerging DR programs, i.e., demand side capacity reserves. Among different types of capacity reserves, regulation service reserves (RSRs) are especially attractive due to their relatively higher value. This article proposes EnergyQARE , the Energy and Q uality-of-Service (QoS) A ware R SR E nabler, an approach that enables data center RSR provision in real-life scenarios. EnergyQARE not only provides a bidding strategy in RSR provision, but also contains a runtime policy that adaptively modulates data center power through server power management and server provisioning based on workload QoS feedback. To reflect real-life scenarios, this runtime policy handles a heterogeneous set of jobs and considers transition time delay of servers. Simulated numerical results demonstrate that in a general data center scenario, EnergyQARE provides close to 50% of data center average power consumption as reserves to the market and saves up to 44% in data center electricity cost, while still meeting workload QoS constraints. Case studies in this article show that the percentages of savings are not sensitive to a specific type of non-interactive workload, or the size of the data center, although they depend strongly on data center utilization and parameters of server power states.

Demand response programs offer an opportunity for large power consumers to save on electricity costs by modulating their power consumption in response to demand changes in the electricity grid. Multiple types of such programs exist; for example, regulation service programs enable a consumer to bid for a sustainable amount of power draw over a time period, along with a reserve amount they are able to provide at request of the electricity service provider. Data centers offer unique capabilities to participate in these programs since they have significant capacity to modify their power consumption through workload scheduling and CPU power limiting. This paper proposes a novel power management policy and a bidding policy that enable data centers to participate in regulation service programs under real-world constraints. The power management policy schedules computing jobs and applies server power-capping under both the constraints of power programs and the constraints of job Quality-of-Service (QoS). Simulations with workload traces from a real data center show that the proposed policies enable data centers to meet both the requirement of regulation service programs and the QoS requirement of jobs. We demonstrate that, by applying our policies, data centers can save their electricity costs by 10% while abiding by all the QoS constraints in a real-world scenario.

Performance variation diagnosis in High-Performance Computing (HPC) systems is a challenging problem due to the size and complexity of the systems. Application performance variation leads to premature termination of jobs, decreased energy efficiency, or wasted computing resources. Manual root-cause analysis of performance variation based on system telemetry has become an increasingly time-intensive process as it relies on human experts and the size of telemetry data has grown. Recent methods use supervised machine learning models to automatically diagnose previously encountered performance anomalies in compute nodes. However, supervised machine learning models require large labeled data sets for training. This labeled data requirement is restrictive for many real-world application domains, including HPC systems, because collecting labeled data is challenging and time-consuming, especially considering anomalies that sparsely occur.

International audience; 2.5D manycore systems running parallel applications are severely bottlenecked by network-on-chip (NoC) latencies and bandwidth. Traditionally, network-on-chips are composed of electrical links that exhibit constrained bandwidth, increased energy consumption at high-speed communication, and long latencies. Photonic Network-on-Chips (PNoCs) have been shown to provide high bandwidth at low latencies and negligible data-dependent power. However, the power overheads of lasers, thermal tuning, and electrical-optical conversion present major challenges against wide-scale adoption of PNoCs. A primary factor that impacts PNoC power is the number of activated laser wavelengths in the system. Applications dynamic bandwidth needs provide the opportunity to selectively deactivate laser wavelengths when there is a lower bandwidth demand to alleviate high PNoC power concerns. This paper analyzes dynamic PNoC activity of applications at runtime so as to select laser wavelengths depending on an applications bandwidth requirements. The paper then proposes PROWAVES, a proactive runtime wavelength selection policy that forecasts the bandwidth needs and activates the minimum laser wavelengths for each application phase. We develop a cross-layer simulation framework to model the system performance, PNoC power and transient thermal distribution in a manycore system with PNoCs. We compare PROWAVES with prior system-level policies and our simulation results on a 2.5D system demonstrate that PROWAVES provides 18% and 33% power savings with only 1% and 5% loss in performance respectively, compared to activating all laser wavelengths in the system.

With the growth in server traffic and component diversity in cloud systems, administrators face the increasingly onerous task of monitoring system activity. Failure to keep track of the contents of virtual servers can limit overall efficiency and create security risks for users. Prior work in software discovery attempted to address this problem by identifying applications based on file system activity. While some of these methods have claimed to be extensible to detection of specific versions of an application, version detection has yet to be demonstrated. In this paper, we propose version detection algorithms that operate on top of Praxi, an existing open-source software discovery tool. These algorithms introduce a rule-based component to differentiate between versions, whose file system footprints can appear very similar. We find that our best method achieves up to 99.9% accuracy in version detection experiments compared to Praxi's original 94% accuracy, albeit at the cost of increased runtime. This work confirms the feasibility of version detection in software discovery and provides a starting point for implementing this feature in software discovery tools.

While much of the software running on today's serverless platforms is written in easily-analyzed high-level interpreted languages, many performance-conscious users choose to deploy their applications as container-encapsulated compiled binaries on serverless container platforms such as AWS Fargate or Google Cloud Run. Modern CI/CD workflows make this deployment process nearly-instantaneous, leaving little time for in-depth manual application security reviews. This combination of opaque binaries and rapid deployment prevents cloud developers and platform operators from knowing if their applications contain outdated, vulnerable, or legally-compromised code. This paper proposes Approximate Concrete Execution (ACE), a just-in-time binary analysis technique that enables automatic software component discovery for serverless binaries. Through classification and search engine experiments with common cloud software packages, we find that ACE scans binaries 5.2x faster than a state-of-the-art binary analysis tool, minimizing the impact on deployment and cold-start latency while maintaining comparable recall.

Demand Response (DR) policies define the interactions between an energy supplier and its consumers and allow for customer energy regulation given a supplier request. Given the high flexibility and controllability of Data Centers (DC), they are promising candidates to participate in DR for power grid stabilization. In this work, we consider the setting where an energy supply deficit event occurs and must be addressed to avoid grid strain. We present two novel frameworks for DR, where a load aggregator offers price incentives to a set of consumer DCs so they can dynamically adjust their electricity consumption and provide DR to the grid via server usage reductions. Modeling DCs using realistic cost functions based on Quality of Service (QoS) requirements of the DC workloads, we present a data-driven inverse optimization method to estimate DC cost function parameters for precise and efficient pricing and provide an algorithm for solving the inverse problem. Experimental results on two test cases demonstrate the benefits of our proposed DR mechanisms for energy control.

Photonic network-on-chips, despite their low-latency and high-bandwidth-density advantages in large manycore systems, suffer from high power overhead. This overhead is further exacerbated by the high bandwidth demands of data-centric applications. Prior works utilize bandwidth allocation policies at system-level to minimize photonic power and provide required bandwidth for applications. We present an approach to minimize the bandwidth requirements by instrumenting an application at the software level. This instrumented information is used to assist bandwidth allocation at system-level, thereby reducing the photonic power. We instrument PageRank application and demonstrate 35% lower power using instrumentation-assisted bandwidth allocation on PageRank running real-world graphs compared to bandwidth allocation on uninstrumented PageRank.

We propose a design automation methodology to help design of energy-efficient Mono3D DNN accelerators with safe on-chip temperature for mobile systems. We introduce an optimizer capable of investigating the impact of different aspect ratios of the chip and chip footprint specifications, and selecting energy-efficient accelerators under user-specified thermal and performance constraints. We also demonstrate that using our optimizer we can reduce energy consumption by 1.6× and area by 2× with a maximum of 9.5% increase in latency compared to a Mono3D DNN accelerator optimized only for performance.

In modern high-performance computing (HPC) systems, network congestion is an important factor that contributes to performance degradation. However, how network congestion impacts application performance is not fully understood. As Aries network, a recent HPC network architecture featuring a dragonfly topology, is equipped with network counters measuring packet transmission statistics on each router, these network metrics can potentially be utilized to understand network performance. In this work, by experiments on a large HPC system, we quantify the impact of network congestion on various applications' performance in terms of execution time, and we correlate application performance with network metrics. Our results demonstrate diverse impacts of network congestion: while applications with intensive MPI operations (such as HACC and MILC) suffer from more than 40% extension in their execution times under network congestion, applications with less intensive MPI operations (such as Graph500 and HPCG) are mostly not affected. We also demonstrate that a stall-to-flit ratio metric derived from Aries network counters is positively correlated with performance degradation and, thus, this metric can serve as an indicator of network congestion in HPC systems.

Background Wilson Disease (WD) can result in a number of physical and mental health issues due to copper toxicity affecting the liver and brain. It is no longer acceptable to look at survival alone as an outcome measure in chronic disease. Our aim was to a) assess quality of life (QOL) in WD b) assess the relationship between mental health QOL (M-QOL) and physical health QOL (P-QOL) and severity of liver and neurological disease and mental health issues (major depressive disorder (MDD) and cognitive impairment). Methods Adult WD patients in the WD registry study (n=52), were evaluated at enrollment over 1.5 y using questionnaires and administered exams assessing QOL (SF-12), cognition (MOCA) and mood (MINI-7). Patients also underwent hepatology and neurological assessments (UWDRS). Results Adult patients with WD had lower M-QOL scores compared to P-QOL scores median 50.2 (range 19.3–62.2) vs. 56.0 (range 23.9–64.7), p=0.0018 indicating that the burden of mental health issues in WD is greater than that of physical health issues on quality of life. Eleven patients had cirrhosis based on review of imaging, APRI and Fib4 scores. There was no significant difference in M-QOL scores in patients with cirrhosis vs. those without (median 54.5 (range 26.2–59.7) vs 46.5 (range 19.3–62.2), p = 0.11. Similarly, there was no significant difference in P-QOL in patients with cirrhosis vs. those without (median 55.7 (range 44.4–59.9) vs. 56.3 (range 23.9–64.7), p = 1.00. In those with cirrhosis, higher Child-Pugh scores were associated with a worse P-QOL (r=-0.84, p=-.0011) and M-QOL (r=-0.60, p=0.0488). Patients with lifetime MDD (n=22) had worse M-QOL scores compared to those without MDD (median 42.3 vs 54.6, p The P-QOL scores have a moderate negative association with the neurological UWDRS II Score (r=-0.44, p=0.001), UWDRS III Score (r=-0.42, p= 0.002) and total UWDRS score (r=-0.44, p=0.001). There are no associations with M-QOL and neurological UWDRS scores. Conclusions While overall QOL in WD is affected by both mental and physical health, patients with WD have worse M-QOL than P-QOL scores and mental health issues may affect WD patient’s QOL independent of their degree of liver or neurologic disease. Multivariate regression will be performed to evaluate if mental health issues are independently associated with QOL.

Introduction Mental health problems are underappreciated in Wilson disease (WD) (Schaefer et al. 2016). We aimed to determine: i) the prevalence and clinical features of major depressive disorder (MDD) in adults with WD ii) whether MDD correlates with liver and neurological disease and quality of life (QOL). Method A multi-site international WD registry was developed and initiated in December 2017. At enrolment adults (n=55) were evaluated using questionnaires and administered exams assessing cognition, mood, psychosis, substance use, anxiety, perceived stress, personality change and QOL. MDD was determined using the investigator led MINI-7 questionnaire. Patients also underwent hepatology and neurological assessments (UWDRS). We reviewed patient reported depression at first presentation and diagnosis. Statistical analysis using SAS9.4 (SAS Institute; Cary, NC) included summary statistics, Wilcoxon Rank Sum test and categorical comparisons using Chi-squared or Fisher’s exact test. Results Depression was reported in 26% at first presentation of WD (33% at diagnosis). At enrolment 35% had a lifetime history of MDD. At evaluation 5 patients with lifetime MDD took antidepressants; 1 without. At enrolment 36.33% were suffering from symptoms of current depression based on the PHQ-9 (self-administered). Cirrhosis was present in 24% based on imaging, APRI and Fib4 scores. There was no association between cirrhosis and depression symptomology (PHQ-9 score >9). In those with depression symptomology, 1 had cirrhosis and 6 did not. Liver disease severity did not differ in cirrhotics with MDD and those without (median Child-Pugh 5 (5–9) vs. 5 (5–9), p=0.13). Two patients (1 with MDD) took medication for hepatic encephalopathy. No significant difference in neurological total UWDRS scores was found in those with MDD vs. without (median 8 (0–73) vs. 4 (0–40), p=0.22). A neurological UWDRS total score >0 was not associated with lifetime MDD status (79% vs. 81%, p=1.00). Patients with MDD had worse mental health QOL (median 43 vs. 52.7 p=0.042), more suicidal ideation (32% vs. 8%, p=0.05), higher anxiety (p=0.008), higher perceived stress (median 19 vs. 9, p=0.007), and more neuroticism (median 7 vs. 4.5, p=0.006). We found no significant difference in physical health QOL in MDD (median 55.6 vs. 56.3, p=0.69). Conclusion MDD is highly prevalent in WD and associated with worse mental health QOL. The risk appears higher than other liver diseases (Lee et al. 2013. Psychosomatics, 4:52–9). We did not find an association between the severity of liver, neurological disease and MDD. Screening for depression should be considered in patients with WD.

International audience; Silicon photonics technology is now gaining maturity with increasing levels of design complexity from devicesto large photonic integrated circuits. Close integration of control electronics with 3D assembly of photonics and CMOS opens the way to high-performance computing architectures partitioned in chiplets connected by optical NoC on silicon photonic interposers.In this paper, we give an overview of our works on optical links and NoC for manycore systems, from low-level control of photonic devices to high-level system optimization of the optical communications. We detail the POPSTAR optical NoC topology and architecture (Processors On Photonic Silicon interposer Terascale ARchitecture) with electro-optical interface chiplets, the corresponding nested spiral topology for single-writer multiplereader links and the associated control electronics, in charge of high-speed drivers, thermal stabilization and handling of the protocol stack, from data integrity to flow-control, routing and arbitration of the optical communications. The strengths and opportunities for this architecture will be discussed, with a shift insystem & implementation constraints with respect to previous optical NoC proposals, and new challenges to be addressed

Emerging data-driven applications such as graph processing applications are characterized by their excessive memory footprint and abundant parallelism, resulting in high memory bandwidth demand. As the scale of datasets for applications is reaching orders of TBs, performance limitation due to bandwidth demands is a major concern. Traditional on-chip electrical networks fail to meet such high bandwidth demands due to increased energy-per-bit or physical limitations with pin counts. Silicon photonic networks have emerged as a promising alternative to electrical interconnects, owing to their high bandwidth density and low energy-per-bit communication with negligible data-dependent power. Wide-scale adoption of silicon photonics at chip level, however, is hampered by their high sensitivity to process and thermal variations, high laser power due to losses along the network, and power consumption of the electrical-optical conversion. Device-level technological innovations to mitigate these issues are promising, yet they do not consider the system-level implications of the applications running on manycore systems with photonic networks. This work aims to bridge the gap between the system-level attributes of applications with the underlying architectural and device-level characteristics of silicon photonic networks to achieve energy-efficient computing. We particularly focus on graph applications, which involve unstructured yet abundant parallel memory accesses that stress the on-chip communication networks, and develop a cross-layer framework to evaluate 2.5D systems with silicon photonic networks. We demonstrate 38% power savings through system-level management using wavelength selection policies with only 1% loss in system performance and further evaluate architectural design choices on 2.5D systems with photonic networks.

© 2020 EDAA. Future high-performance chips will require new cooling technologies that can extract heat efficiently. Two-phase cooling is a promising processor cooling solution owing to its high heat transfer rate and potential benefits in cooling power. Two-phase cooling mechanisms, including microchannel-based two-phase cooling or two-phase vapor chambers (VCs), are typically modeled by computing the temperature-dependent heat transfer coefficient (HTC) of the evaporator or coolant using an iterative simulation framework. Precomputed HTC correlations are specific to a given cooling system design and cannot be applied to even the same cooling technology with different cooling parameters (such as different geometries). Another challenge is that HTC correlations are typically calculated with computational fluid dynamics (CFD) tools, which induce long design and simulation times. This paper introduces a learning-based temperature-dependent HTC simulation framework that is used to model a two-phase cooling solution with a wide range of cooling design parameters. In particular, the proposed framework includes a compact thermal model (CTM) of two-phase VCs with hybrid wick evaporators (of nanoporous membrane and microchannels). We build a new simulation tool to integrate the proposed simulation framework and CTM. We validate the proposed simulation framework as well as the new CTM through comparisons against a CFD model. Our simulation framework and CTM achieve a speedup of 21 × with an average error of 0.98° C (and a maximum error of 2.59° C). We design an optimization flow for hybrid wicks to select the most beneficial hybrid wick geometries. Our flow is capable of finding a geometry- coolant combination that results in a lower (or similar) maximum chip temperature compared to that of the best coolant-geometry pair selected by grid search, while providing a speedup of 9.4 x.

With today's rapidly-evolving cloud software landscape, users of cloud systems must constantly monitor software running on their containers and virtual machines (VMs) to ensure compliance, security, and efficiency. Traditional solutions to this problem rely on manually-created rules that identify software installations and modifications, but these require expert authors and are often unmaintainable. More recent automated techniques leverage knowledge of packaging practices to aid in discovery without requiring any pre-training, but these practice-based methods cannot provide precise-enough information to perform discovery by themselves. Other approaches use machine learning models to facilitate discovery of software present in a training corpus, but prior approaches have high runtime and storage requirements. This demonstration features Praxi, a new software discovery method that builds upon the strengths of prior approaches by combining the accuracy of learning-based methods with the efficiency of practice-based methods. We demonstrate Praxi's training and detection process in real time while allowing laptop-equipped participants to follow along using a provided remote virtual machine.

The ability to repeat the execution of a program is a fundamental requirement in many areas of computing from computer system evaluation to software engineering. Reproducing executions of mobile apps, in particular, has proven difficult under real-life scenarios due to multiple sources of external inputs and interactive nature of the apps. Previous works that provide record/replay functionality for mobile apps are restricted to particular input sources (e.g., touchscreen events) and present deployment challenges due to intrusive modifications to the underlying software stack. Moreover, due to their reliance on record and replay of device specific events, the recorded executions cannot be reliably reproduced across different platforms. In this paper, we present a new practical approach, RandR, for record and replay of Android applications. RandR captures and replays multiple sources of input (i.e., UI and network) without requiring source code (OS or app), administrative device privileges, or any special platform support. RandR achieves these qualities by instrumenting a select set of methods at runtime within an application's own sandbox. In addition, to enable portability of recorded executions across different platforms for replay, RandR contextualizes UI events as interactions with particular UI components (e.g., a button) as opposed to relying on platform specific features (e.g., screen coordinates). We demonstrate RandR's accurate cross-platform record and replay capabilities using over 30 real-world Android apps across a variety of platforms including emulators as well as commercial off-the-shelf mobile devices deployed in real life.

Background & Aims Both existing clinical criteria and genetic testing have significant limitations for the diagnosis of Wilson disease (WD), often creating ambiguities in patient identification and leading to delayed diagnosis and ineffective management. ATP7B protein concentration, indicated by direct measurement of surrogate peptides from patient dried blood spot samples, could provide primary evidence of WD. ATP7B concentrations were measured in patient samples from diverse backgrounds, diagnostic potential is determined, and results are compared with biochemical and genetic results from individual patients. Methods Two hundred and sixty-four samples from biorepositories at 3 international and 2 domestic academic centers and 150 normal controls were obtained after Institutional Review Board approval. Genetically or clinically confirmed WD patients with a Leipzig score >3 and obligate heterozygote (carriers) from affected family members were included. ATP7B peptide measurements were made by immunoaffinity enrichment mass spectrometry. Results Two ATP7B peptides were used to measure ATP7B protein concentration. Receiver operating characteristics curve analysis generates an area under the curve of 0.98. ATP7B peptide analysis of the sequence ATP7B 887 was found to have a sensitivity of 91.2%, specificity of 98.1%, positive predictive value of 98.0%, and a negative predictive value of 91.5%. In patients with normal ceruloplasmin concentrations (>20 mg/dL), 14 of 16 (87.5%) were ATP7B-deficient. In patients without clear genetic results, 94% were ATP7B-deficient. Conclusions Quantification of ATP7B peptide effectively identified WD patients in 92.1% of presented cases and reduced ambiguities resulting from ceruloplasmin and genetic analysis. Clarity is brought to patients with ambiguous genetic results, significantly aiding in noninvasive diagnosis. A proposed diagnostic score and algorithm incorporating ATP7B peptide concentrations can be rapidly diagnostic and supplemental to current Leipzig scoring systems.

Photonic network-on-chip (PNoC) is a promising candidate to replace traditional electrical NoC in manycore systems that require substantial bandwidths. The photonic links in the PNoC comprise laser sources, optical ring resonators, passive waveguides, and photodetectors. Reliable link operation requires laser sources and ring resonators to have matching optical frequencies. However, inherent thermal sensitivity of photonic devices and manufacturing process variations can lead to a frequency mismatch. To avoid this mismatch, micro-heaters are used for thermal trimming and tuning, which can dissipate a significant amount of power. This paper proposes a novel FreqAlign workload allocation policy, accompanying an adaptive frequency tuning ( AFT ) policy, that is capable of reducing thermal tuning power of PNoC. FreqAlign uses thread allocation and thread migration to control temperature for matching the optical frequencies of ring resonators in each photonic link. The AFT policy reduces the remaining optical frequency difference among ring resonators and corresponding on-chip laser sources by hardware tuning methods. We use a full modeling stack of a PNoC that includes a performance simulator, a power simulator, and a thermal simulator with a temperature-dependent laser source power model to design and evaluate our proposed policies. Our experimental results demonstrate that FreqAlign reduces the resonant frequency gradient between ring resonators by 50%–60% when compared to existing workload allocation policies. Coupled with AFT , FreqAlign reduces localized thermal tuning power by 19.28 W on average, and is capable of saving up to 34.57 W when running realistic loads in a 256-core system without any performance degradation.

As the number of cores per server node increases, designing multi-threaded applications has become essential to efficiently utilize the available hardware parallelism. Many application domains have started to adopt multi-threaded programming; thus, efficient management of multi-threaded applications has become a significant research problem. Efficient execution of multi-threaded workloads on cloud environments, where applications are often consolidated by means of virtualization, relies on understanding the multi-threaded specific characteristics of the applications. Furthermore, energy cost and power delivery limitations require data center server nodes to work under power caps, which bring additional challenges to runtime management of consolidated multi-threaded applications. This article proposes a dynamic resource allocation technique for consolidated multi-threaded applications for power-constrained environments. Our technique takes into account application characteristics specific to multi-threaded applications, such as power and performance scaling, to make resource distribution decisions at runtime to improve the overall performance, while accurately tracking dynamic power caps. We implement and evaluate our technique on state-of-the-art servers and show that the proposed technique improves the application performance by up to 21% under power caps compared to a default resource manager.

Modern high performance computing (HPC) systems, including supercomputers, routinely suffer from substantial performance variations. The same application with the same input can have more than 100% performance variation, and such variations cause reduced efficiency and wasted resources. There have been recent studies on performance variability and on designing automated methods for diagnosing "anomalies" that cause performance variability. These studies either observe data collected from HPC systems, or they rely on synthetic reproduction of performance variability scenarios. However, there is no standardized way of creating performance variability inducing synthetic anomalies; so, researchers rely on designing ad-hoc methods for reproducing performance variability. This paper addresses this lack of a common method for creating relevant performance anomalies by introducing HPAS, an HPC Performance Anomaly Suite, consisting of anomaly generators for the major subsystems in HPC systems. These easy-to-use synthetic anomaly generators facilitate low-effort evaluation and comparison of various analytics methods as well as performance or resilience of applications, middleware, or systems under realistic performance variability scenarios. The paper also provides an analysis of the behavior of the anomaly generators and demonstrates several use cases: (1) performance anomaly diagnosis using HPAS, (2) evaluation of resource management policies under performance variations, and (3) design of applications that are resilient to performance variability.

Ultra-high power densities that are expected in future processors cannot be efficiently mitigated by conventional cooling solutions. Using two-phase vapor chambers (VCs) with micropillar wick evaporators is an emerging cooling technique that can effectively remove high heat fluxes through the evaporation process of a coolant. Two-phase VCs with micropillar wicks offer high cooling efficiency by leveraging a capillary-driven flow, where the coolant is passively driven by the wicking structure that eliminates the need for an external pump. Thermal models for such emerging cooling technologies are essential to evaluate their impact on future processors. Existing thermal models for two-phase VCs use computational fluid dynamics (CFD) modules, which incur long design and simulation times. This paper presents a fast and accurate compact thermal model for two-phase VCs with micropillar wicks. Our model achieves a maximum error of 1.25°C with a speedup of 214x in comparison to a CFD model. Using our proposed thermal model, we build an optimization flow that selects the best cooling solution and its cooling parameters to minimize the cooling power under a temperature constraint for a given processor and power profile. We then demonstrate our optimization flow on different chip sizes and hot spot distributions to choose the optimal cooling technique among VCs, microchannel-based two-phase cooling, liquid cooling via microchannels, and a hybrid cooling technique with thermoelectric coolers and liquid cooling with microchannels.

In Demand Response (DR), consumers regulate their power based on requests from an energy supplier. Data Centers (DC) are among the promising candidates to perform DR to help stabilize the power grid due to their flexibility and controllability. In this work, we present a novel framework for offering incentives to DCs so they can dynamically adjust their electricity consumption and provide DR to the grid. Coordination between an Independent System Operator (ISO) and DCs is done through pricing where the ISO computes optimal prices which elicit desired responses from the DCs. We model DCs using realistic cost functions based on Quality of Service (QoS) requirements of the DC workloads and present an inverse optimization approach to cost function parameter estimation for precise and efficient pricing along with simulation results that highlight the strength of our approach.

Demand response helps stabilize the power grid and offers opportunities for consumers to reduce their cost by regulating their power consumption to follow market requirements. Regulation service reserves (RSRs) is a specific form of a demand response program, requiring participants to regulate their power to follow a dynamically-changing target that is updated every few seconds. In return, participants' electricity bill is reduced in proportion to the reserves they provide. Data centers are significant power consumers, and they are good candidates to participate in RSRs because they have the flexibility to regulate their power consumption through various strategies. Previous work in this area has proposed power regulation policies that enable data centers to participate in RSRs, but without providing guarantees on the Quality-of-Service (QoS) of the jobs running on the data center. This paper develops a strategy for a data center to provide RSR while offering QoS guarantees, expressed in terms of the sojourn time of jobs. The proposed policy regulates data center power through power capping and job scheduling, following the spirit of a Generalized Processor Sharing (GPS) policy. Parameters in our policy are calculated so as to minimize the electricity cost under QoS constraints. The key in guaranteeing QoS is to determine an acceptable range for policy parameters using a queueing theoretic result for delay. We evaluate our policy in both large-scale simulations as well as real-system experiments on a small cluster. We demonstrate that our policy enables data centers to participate in RSRs and reduces their electricity bill by 14-51% while providing guarantees on the QoS of the jobs.

Photonic Network-on-Chips (PNoCs) offer promising benefits over Electrical Network-on-Chips (ENoCs) in many-core systems owing to their lower latencies, higher bandwidth, and lower energy-per-bit communication with negligible data-dependent power. These benefits, however, are limited by a number of challenges. Microring resonators (MRRs) that are used for photonic communication have high sensitivity to process variations and on-chip thermal variations, giving rise to possible resonant wavelength mismatches. State-of-the-art microheaters, which are used to tune the resonant wavelength of MRRs, have poor efficiency resulting in high thermal tuning power. In addition, laser power and high static power consumption of drivers, serializers, comparators, and arbitration logic partially negate the benefits of the sub-pJ operating regime that can be obtained with PNoCs. To reduce PNoC power consumption, this paper introduces WAVES, a wavelength selection technique to identify and activate the minimum number of laser wavelengths needed, depending on an application’s bandwidth requirement. Our results on a simulated 2.5D manycore system with PNoC demonstrate an average of 23% (resp. 38%) reduction in PNoC power with only

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.

Integrated flow cell array (FCA) is an emerging technology, targeting the cooling and power delivery challenges of modern 2D/3D Multi-Processor Systems-on-Chip (MPSoCs). In FCA, electrolytic solutions are pumped through microchannels etched in the silicon of the chips, removing heat from the system, while, at the same time, generating power on-chip. In this work, we explore the impact of FCA system design on various 3D architectures and propose a methodology to optimize a 3D MPSoC with integrated FCA to run a given workload in the most energy-efficient way. Our results show that an optimized configuration can save up to 50% energy with respect to sub-optimal 3D MPSoC configurations.

Modern cloud applications are designed in a highly configurable way to ensure increased reusability and portability. With the growing complexity of these applications, configuration errors (i.e., misconfigurations) have become major sources of service outages and disruptions. While some research has so far focused on detecting errors in configurations that are represented as well-structured key-value pairs, the configurations of cloud applications are typically stored in text files with application-specific syntax and in unlabeled file system locations, limiting the use of existing error detection tools. This paper introduces ConfEx, a framework that enables discovery and extraction of text-based configurations in multi-tenant cloud platforms and cloud image repositories for configuration analysis and validation. ConfEx uses a novel vocabulary-based technique to identify text-based configuration files in cloud system instances with unlabeled content, and leverages existing configuration parsers to extract the information in these files. We show that ConfEx achieves over 98% precision and recall in identifying configuration files on 3893 popular Docker Hub images and we also demonstrate a use case of ConfEx for detecting injected misconfigurations via outlier analysis.

The dragonfly network topology has attracted attention in recent years owing to its high radix and constant diameter. However, the influence of job allocation on communication time in dragonfly networks is not fully understood. Recent studies have shown that random allocation is better at balancing the network traffic, while compact allocation is better at harnessing the locality in dragonfly groups. Based on these observations, this paper introduces a novel allocation policy called Level-Spread for dragonfly networks. This policy spreads jobs within the smallest network level that a given job can fit in at the time of its allocation. In this way, it simultaneously harnesses node adjacency and balances link congestion. To evaluate the performance of Level-Spread, we run packet-level network simulations using a diverse set of application communication patterns, job sizes, and communication intensities. We also explore the impact of network properties such as the number of groups, number of routers per group, machine utilization level, and global link bandwidth. Level-Spread reduces the communication overhead by 16% on average (and up to 71%) compared to the state-of-the-art allocation policies.

The popularity of Android and the personal information stored on these devices attract the attention of regular cyber-criminals as well as nation state adversaries who develop malware that targets this platform. To identify malicious Android apps at a scale (e.g., Google Play contains 3.7M Apps), state-of-the-art mobile malware analysis systems inspect the execution of apps in emulation-based sandboxes. An emerging class of evasive Android malware, however, can evade detection by such analysis systems through ceasing malicious activities if an emulation sandbox is detected. Thus, systematically uncovering potential methods to detect emulated environments is crucial to stay ahead of adversaries. This work uncovers the detection methods based on discrepancies in instruction-level behavior between software-based emulators and real ARM CPUs that power the vast majority of Android devices. To systematically discover such discrepancies at scale, we propose the Proteus system. Proteus performs large-scale collection of application execution traces (i.e., registers and memory) as they run on an emulator and on accurate software models of ARM CPUs. Proteus automatically identifies the instructions that cause divergent behavior between emulated and real CPUs and, on a set of 500K test programs, identified 28K divergent instances. By inspecting these instances, we reveal 3 major classes of root causes that are responsible for these discrepancies. We show that some of these root causes can be easily fixed without introducing observable performance degradation in the emulator. Thus, we have submitted patches to improve resilience of Android emulators against evasive malware.

Modern supercomputers are shared among thousands of users running a variety of applications. Knowing which applications are running in the system can bring substantial benefits: knowledge of applications that intensively use shared resources can aid scheduling; unwanted applications such as cryptocurrency mining or password cracking can be blocked; system architects can make design decisions based on system usage. However, identifying applications on supercomputers is challenging because applications are executed using esoteric scripts along with binaries that are compiled and named by users.

While the growth of cloud-based technologies has benefited the society tremendously, it has also increased the surface area for cyber attacks. Given that cloud services are prevalent today, it is critical to devise systems that detect intrusions. One form of security breach in the cloud is when cyber-criminals compromise Virtual Machines (VMs) of unwitting users and, then, utilize user resources to run time-consuming, malicious, or illegal applications for their own benefit. This work proposes a method to detect unusual resource usage trends and alert the user and the administrator in real time. We experiment with three categories of methods: simple statistical techniques, unsupervised classification, and regression. So far, our approach successfully detects anomalous resource usage when experimenting with typical trends synthesized from published real-world web server logs and cluster traces. We observe the best results with unsupervised classification, which gives an average F1-score of 0.83 for web server logs and 0.95 for the cluster traces.

As mobile system-on-chips incorporate multicore processors with high power densities, high chip temperatures are becoming a rising concern in mobile processors. Modern smartphones are limited in their cooling capabilities and employ CPU throttling mechanisms to avoid thermal emergencies by sacrificing performance. Traditional throttling techniques aim at achieving maximum utilization of the available thermal headroom so as to minimize the performance penalty at a given time. This letter demonstrates that such greedy techniques lead to fast elevation of temperature on other system components and cause substantially suboptimal performance over increased durations of phone activity. Through experiments on a commercial smartphone, we characterize the impact of application duration on throttling-induced performance loss and propose quality-of-service (QoS) tuning as an effective way of providing the mobile system user with consistent performance levels over extended application durations. The proposed QoS-aware frequency capping technique achieves up to 56% improvement in performance sustainability.

Performance and energy are critical aspects in high performance computing (HPC) data centers. Highly parallel HPC applications that require multiple nodes usually run for long durations in the range of minutes, hours or days. As the threads of parallel applications communicate with each other intensively, the communication cost of these applications has a significant impact on data center performance. Energy consumption has also become a first-order constraint of HPC data centers. Nearly half of the energy in the computing clusters today is consumed by the cooling infrastructure. Existing job allocation policies either target improving the system performance or reducing the cooling energy cost of the server nodes. How to optimize the system performance while minimizing the cooling energy consumption is still an open question. This paper proposes a job allocation methodology aimed at jointly reducing the communication cost and the cooling energy of HPC data centers. In order to evaluate and validate our optimization algorithm, we implement our joint job allocation methodology in the structural simulation toolkit (SST) – a simulation framework for large-scale data centers. We evaluate our joint optimization algorithm using traces extracted from real-world workloads. Experimental results show that, in comparison to performance-aware job allocation algorithms, our algorithm achieves comparable running times and reduces the cooling power by up to 42.21% across all the jobs.

Dynamic frequency and voltage scaling (DVFS) techniques have been widely used for meeting energy constraints. Single-chip many-core systems bring new challenges owing to the large number of operating points and the shift to message passing from shared memory communication. DVFS, however, has been mostly studied on single-chip systems with one or few cores, without considering the impact of the communication among cores. This paper evaluates the impact of voltage and frequency scaling on the performance and power of many-core systems with message passing (MP) based communication, and proposes a power management policy that leverages the communication pattern information to efficiently traverse the search space for finding the optimal voltage and frequency operating point. We conduct experiments on a 48-core Intel Single-Chip Cloud Computer (SCC), as our target many-core platform. The paper first introduces the runtime monitoring infrastructure and the application suite we have designed for an in-depth evaluation of the SCC. We then quantify the effects of frequency perturbations on performance and energy efficiency. Experimental results show that runtime communication patterns lead to significant differences in power/performance tradeoffs in many-core systems with MP-based communication. We show that the proposed power management policy achieves up to the 70% energy-delayproduct (EDP) improvements compared to existing DVFS policies, while meeting the performance constraints.

Network messaging delay historically constitutes a large portion of the wall-clock time for High Performance Computing (HPC) applications, as these applications run on many nodes and involve intensive communication among their tasks. Dragonfly network topology has emerged as a promising solution for building exascale HPC systems owing to its low network diameter and large bisection bandwidth. Dragonfly includes local links that form groups and global links that connect these groups via high bandwidth optical links. Many aspects of the dragonfly network design are yet to be explored, such as the performance impact of the connectivity of the global links, i.e., global link arrangements, the bandwidth of the local and global links, or the job allocation algorithm. This paper first introduces a packet-level simulation framework to model the performance of HPC applications in detail. The proposed framework is able to simulate known MPI (message passing interface) routines as well as applications with custom-defined communication patterns for a given job placement algorithm and network topology. Using this simulation framework, we investigate the coupling between global link bandwidth and arrangements, communication pattern and intensity, job allocation and task mapping algorithms, and routing mechanisms in dragonfly topologies. We demonstrate that by choosing the right combination of system settings and workload allocation algorithms, communication overhead can be decreased by up to 44%. We also show that circulant arrangement provides up to 15% higher bisection bandwidth compared to the other arrangements, but for realistic workloads, the performance impact of link arrangements is less than 3%.

With the growing complexity and scale of high performance computing (HPC) systems, application performance variation has become a significant challenge in efficient and resilient system management. Application performance variation can be caused by resource contention as well as software- and firmware-related problems, and can lead to premature job termination, reduced performance, and wasted compute platform resources. To effectively alleviate this problem, system administrators must detect and identify the anomalies that are responsible for performance variation and take preventive actions. However, diagnosing anomalies is often a difficult task given the vast amount of noisy and high-dimensional data being collected via a variety of system monitoring infrastructures.

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.

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.