Large Language Models (LLMs) trained on code are revolutionizing the software development process. Increasingly, code LLMs are being integrated into software development environments to improve the productivity of human programmers, and LLM-based agents are beginning to show promise for handling complex tasks autonomously. Realizing the full potential of code LLMs requires a wide range of capabilities, including code generation, fixing bugs, explaining and documenting code, maintaining repositories, and more. In this work, we introduce the Granite series of decoder-only code models for code generative tasks, trained with code written in 116 programming languages. The Granite Code models family consists of models ranging in size from 3 to 34 billion parameters, suitable for applications ranging from complex application modernization tasks to on-device memory-constrained use cases. Evaluation on a comprehensive set of tasks demonstrates that Granite Code models consistently reaches state-of-the-art performance among available open-source code LLMs. The Granite Code model family was optimized for enterprise software development workflows and performs well across a range of coding tasks (e.g. code generation, fixing and explanation), making it a versatile all around code model. We release all our Granite Code models under an Apache 2.0 license for both research and commercial use.

Industries such as banking, telecom and airlines - often have large software systems that are several decades old. Many of these systems are written in old programming languages such as COBOL, PL/1, Assembler, etc. In many cases, the documentation is not updated, and those who developed/designed these systems are no longer around. Understanding these systems for either modernization or even regular maintenance has been a challenge. An extensive application may have natural boundaries based on its code dependencies and architecture. There are also other logical boundaries in an enterprise setting driven by business functions, data domains, etc. Due to these complications, the system architects generally plan their modernization across these logical boundaries in parts, thereby adopting an incremental approach for the modernization journey of the entire system. In this work, we present a software system analysis tool that allows a subject matter expert (SME) or system architect to analyze a large software system incrementally. We analyze the source code and other artifacts (such as data schema) to create a knowledge graph using a customizable ontology/schema. Entities and relations in our ontology can be defined for any combination of programming languages and platforms. Using this knowledge graph, the analyst can then define logical boundaries around dependent entities (e.g. Programs, Transactions, Database Tables etc.). Our tool then presents different views showcasing the dependencies from the newly defined boundary to/from the other logical groups of the system. This exercise is repeated interactively to 1) Identify the entities and groupings of interest for a modernization task and 2) Understand how a change in one part of the system may affect the other parts. To validate the efficacy of our tool, we provide an initial study of our system on two client applications.

Cluster rightsizing facilitates cost-performance trade-off in resource-constrained clouds. Multidimensional bin-packing algorithms can address this rightsizing problem, but these assume that every task on the cluster is always active. In contrast, real-world tasks may be active only during specific time-periods, which allows reusing resources via time sharing and optimal packing. This motivates our generalized problem of rightsizing for time-limited tasks: given a timeline, time-periods and resource demands for tasks, the objective is to place the tasks on a minimum cost cluster of nodes without violating node capacities at any time instance. We design a baseline two-phase algorithm that performs penalty-based mapping of task to node-type and then, solves each node-type independently. We prove that the algorithm has an approximation ratio of O(D. min(m, T)), where D, m and $T$ are the number of resources, node-types and timeslots, respectively, We then present an improved linear programming based mapping strategy, enhanced further with a cross-node-type filling mechanism. Our experiments on synthetic and real-world cluster traces show significant cost reduction by LP-based mapping compared to the baseline, and the filling mechanism improves further to produce solutions within 20% of (a lower-bound to) the optimal solution.

We present Move2Kube, a replatforming framework that automates the transformation of the deployment specification and development pipeline of an application from a non-Kubernetes platform to a Kubernetes-based one, minimizing changes to the application's functional implementation and architecture. Our contributions include: (1) a standardized intermediate representation to which diverse application deployment artifacts could be translated, (2) an extension framework for adding support for new source platforms, and target artifacts while allowing customization as per organizational standards. We provide initial evidence of its effectiveness in terms of effort reduction, and highlight the current research challenges and future lines of work. Move2Kube is being developed as an open source community project and it is available at https://move2kube.konveyor.io/

In this paper, we propose a novel approach for monolith decomposition, that maps the implementation structure of a monolith application to a functional structure that in turn can be mapped to business functionality. First, we infer the classes in the monolith application that are distinctively representative of the business functionality in the application domain. This is done using formal concept analysis on statically determined code flow structures in a completely automated manner. Then, we apply a clustering technique, guided by the inferred representatives, on the classes belonging to the monolith to group them into different types of partitions, mainly: 1) functional groups representing microservice candidates, 2) a utility class group, and 3) a group of classes that require significant refactoring to enable a clean microservice architecture. This results in microservice candidates that are naturally aligned with the different business functions exposed by the application. A detailed evaluation on four publicly available applications show that our approach is able to determine better quality microservice candidates when compared to other existing state of the art techniques. We also conclusively show that clustering quality metrics like modularity are not reliable indicators of microservice candidate goodness.

Robust resource demand prediction is crucial for efficient allocation of resources to service requests in a distributed service delivery system. There are two problems in resource demand prediction: firstly to estimate the volume of service requests that come in at different time points and at different geo-locations, secondly to estimate the resource demand given the estimated volume of service requests. While a lot of literature exists to address the first problem, in this work, we have proposed a data-driven statistical method for robust resource demand prediction to address the second problem. The method automates the identification of various system operational characteristics and contributing factors that influence the system behavior to generate an adaptive low variance resource demand prediction model. Factors can be either continuous or categorical in nature. The method assumes that each service request resolution involves multiple tasks. Each task is composed of multiple activities. Each task belongs to a task type, based on the type of the resource it requires to resolve that task. Our method supports configurable tasks per service request, and configurable activities per task. The demand prediction model produces an aggregated resource demand required to resolve all the activities under a task by activity sequence modeling; and aggregated resource demand by resource type, required to resolve all the activities under a service request by task sequence modeling.

I ncreasing society's resilience to high-impact natural events requires coordinated research and new investments in observation and prediction.To enable all nations to benefit from these investments, scientific and technical advancements need to be more accessible and usable.The new research strategy of the World Meteorological Organization (WMO), dating from the 2017 Science Summit, highlights the benefit of seamless Earth system science.The impacts of weather extremes and environmental change can only be mitigated-or adapted to-through this seamless approach, which would break down the artificial barriers created by the different observing systems, models, time and space scales, and compartments of the Earth system.

Memory design specifications typically include yield requirements, apart from performance and power requirements. These yield requirements are usually specified for the entire memory array at some supply voltage and temperature conditions. For example, the designer may be comfortable with an array failure probability of one in a thousand at 100 C and 1 V supply; i.e., Ff,array 10 . However, how does this translate to a yield requirement for the memory cell? What is the maximum cell failure probability, Ff,cell, allowed so as to satisfy this array failure probability requirement? We will answer these questions and in the process understand the relevance of extreme statistics in memory design.

Whether on an urban or planetary scale, covering time scales of a few minutes or a few decades, the societal need for more accurate weather, climate, water, and environmental information has led to a more seamless thinking across disciplines and communities. This challenge, at the intersection of scientific research and society's need, is among the most important scientific and technological challenges of our time. The "Science Summit on Seamless Research for Weather, Climate, Water, and Environment" organized by the World Meteorological Organization (WMO) in 2017, has brought together researchers from a variety of institutions for a cross-disciplinary exchange of knowledge and ideas relating to seamless Earth system science. The outcomes of the Science Summit, and the interactions it sparked, highlight the benefit of a seamless Earth system science approach. Such an approach has the potential to break down artificial barriers that may exist due to different observing systems, models, time and space scales, and compartments of the Earth system. In this context, the main future challenges for research infrastructures have been identified. A value cycle approach has been proposed to guide innovation in seamless Earth system prediction. The engagement of researchers, users, and stakeholders will be crucial for the successful development of a seamless Earth system science that meets the needs of society.

We present a machine-learning based approach for early detection of issues emerging in refrigeration and cold-storage systems that has the following desirable features: 1) Minimal sensor dependencies: only requires temperature readings and defrost state from the refrigeration cases 2) high precision, and 3) high generalizability of the learnt model. We achieve this by casting the time-series prediction problem as a classification problem, wherein we craft a set of features that capture key time-series characteristics specific to defrost and operating regimes. Our feature extraction employs seasonality-trend decomposition and pattern learning using dynamic time warping and clustering. The extracted features are used to learn a random forest-based binary classifier that can indicate the presence or absence of an issue in any given refrigeration case at any given time. We validate our approach on real data from 2265 refrigeration cases from several large supermarkets. The approach achieves a precision of 89%, lead time of approximately seven days, and a recall of 46% when evaluated on unseen cases.

Location based services inside smart buildings are dependent on scalable localization methods. However, for enterprises, privacy of individual employees is a major concern. In this paper, we present a privacy aware occupancy sensing mechanism for large scale enterprises with multiple floors in multiple buildings of multiple cities. This is achieved through Wi-Fi fingerprint based localization methods implemented on edge devices. We present some preliminary results on occupancy sensing from our pilot study inside the office spaces of IBM India.

In this work we develop a machine learning based model for computing a probabilistic prediction of the number of distribution grid customers that will lose power during a given severe weather event. The model takes as input a prediction of damage counts at the level of substation regions or service regions, as proposed in earlier work, and generates the customer count impact forecast in aggregate for the entire service territory (or a large region thereof). The relationship between damage count and customer count is highly noisy in general, given the branching structure of distribution grids. Here we exploit the fact that the noise reduces as the damage count increases and develop a Tobit model applicable for severe weather events. We validate the forecasting system using data from a utility.

A number of key technological, social, and business disruptions will drive a new generation of smarter energy applications. The disruptions include the following: 1) large sensor deployments, resulting in a huge increase in data volumes and variety, 2) a move toward clean energy and intermittent renewable energy sources, and 3) a move to highly distributed energy resources. To enable resilient and efficient power delivery, with these disruptions, will require a host of new applications that analyze large amounts and varieties of data in the context of the connected grid and perform analysis, visualization, and control in real-time with very low latency. In this paper, we present a set of capabilities that enable such applications, and a software and hardware platform that combines these capabilities to enable rapid development of a wide array of high-performance and analytics-rich applications. These capabilities include: 1) high-performance time-series ingestion, 2) a flexible data model that spans multiple contexts, 3) high-performance, in-memory analysis of time-varying, hierarchical graphs, 4) data service for co-presenting real-time and static spatiotemporal data for real-time web-based visualization, and 5) a seamless combination of event-based and service-oriented programming models.

Electric utilities spend a large amount of their resources and budget on managing unplanned outages, the majority of which are driven by weather. The weather is the largest contributing factor for power outages faced by the population in the United States and several other countries. A major ongoing effort by utilities is to improve their emergency preparedness process, in order to 1) reduce outage time, 2) reduce repair and restoration costs, and 3) improve customer satisfaction. We present an approach called Outage Prediction and Response Optimization (OPRO) to improve emergency preparedness by combining a) localized and highly accurate weather prediction, b) damage prediction, c) infrastructure health-aware damage hotspot analysis, and d) optimal resource planning. The combination of these capabilities can enable utilities to initiate their storm preparation process 1 to 2 days in advance of the storm and precisely plan their resource schedules and escalation stance. This would be A. profound change to the business process of utilities, which today tends to be reactionary once the storm hits. We describe these capabilities and their effectiveness in terms of metrics relevant to a utility, the related use cases, and the overall business process that brings them together in the context of a real U.S. utility.

Energy cost is one of the significant contributors to the operational expenses of commercial buildings. In developing countries facing problems of frequent power outages and deficient grid connectivity, diesel generators are used as backup power source which significantly increase the costs incurred in management of commercial establishments. Integration of information and communication technologies to building management systems provides a reliable platform to analyze various aspects of the building such as energy consumption trends and occupancy inferences thereby proposing reactive or pro-active strategies directed towards efficient and cost-effective building management. Usually, this potential of data available to building management agencies stays untapped in developing countries. In this paper, we take a data-driven approach to understand various operational aspects of a commercial establishment. To demonstrate the scope for optimization of building operations by exploiting the energy consumption data, a pilot study was conducted in an IT office building in India.

Efficient, resilient, and safe operation of an electric utility is dependent on the local weather conditions at the scale of its infrastructure. This sensitivity to weather includes such factors as damage to distribution or transmission systems due to relative extremes in precipitation or wind, determining electricity demand and load, and power generation from renewable facilities. Hence, the availability of highly focused weather predictions has the potential to enable proactive planning for the effect of weather on utility systems. Often, such information is simply unavailable. The initial step to address this gap is the application of state-of-the-art physical weather models at the spatial scale of the utility's infrastructure, calibrated to avoid this mismatch in predictability. The results of such a model are then coupled to a data-driven stochastic model to represent the weather impacts. The deployment of such methods requires an abstraction of the weather forecasting component to drive the model coupling.

Advanced analytical applications that will enable the smart grid need to analyze the connectivity of the power grid under multiple different operating scenarios, taking into account time-varying topology of the grid. This paper proposes a highly memory-efficient representation of the power grid that enables efficient construction of multiple topological and operational states in memory for high-performance graph analysis. The proposed representation exploits repeating patterns in the grid and uses a hierarchical graph as the core model. Time-varying topology and operational conditions are modeled as mapping functions on this hierarchical graph, so as to avoid construction of multiple graphs to represent multiple topologies. The efficiency and performance of the proposed representation is demonstrated on a large real-world distribution electrical grid.

A major ongoing effort by utilities is to improve their emergency preparedness process for weather events, in order to: 1) reduce outage time 2) reduce repair and restoration costs and 3) improve customer satisfaction. This paper proposes a method for forecasting the number of damages of different types that will result from a weather event, up to 3 days before the event actually occurs. The proposed method overcomes practical issues with sparsity of historical damage and weather records by 1) using a spatial clustering-based scheme to work even in cases where there are very few historical incidents of damage, 2) combining data from multiple weather observation networks, 3) using weather hindcast data and 4) accounting for variability in damage susceptibility across different substation regions. The performance of the method is evaluated on real utility data.