Data preparation is the first and a very important step towards any Large Language Model (LLM) development. This paper introduces an easy-to-use, extensible, and scale-flexible open-source data preparation toolkit called Data Prep Kit (DPK). DPK is architected and designed to enable users to scale their data preparation to their needs. With DPK they can prepare data on a local machine or effortlessly scale to run on a cluster with thousands of CPU Cores. DPK comes with a highly scalable, yet extensible set of modules that transform natural language and code data. If the user needs additional transforms, they can be easily developed using extensive DPK support for transform creation. These modules can be used independently or pipelined to perform a series of operations. In this paper, we describe DPK architecture and show its performance from a small scale to a very large number of CPUs. The modules from DPK have been used for the preparation of Granite Models [1] [2]. We believe DPK is a valuable contribution to the AI community to easily prepare data to enhance the performance of their LLM models or to fine-tune models with Retrieval-Augmented Generation (RAG).

IT compliance is an area of increasing attention and capital spend in enterprise IT environments. Enforcing compliance is a complex process, which involves following regulatory requirements coming from many, often overlapping sources, and mapping those requirements against a controls framework that implements them on the ground. In this paper, we propose a solution for streamlining the process of analyzing, monitoring and enforcing compliance in the cloud. We rely on text classification methodologies to match both regulatory requirements and controls against a common hierarchy. Finally, we explain how to use the text classification techniques to analyze the regulatory requirements, and match them to executable code that enforces these requirements in the cloud infrastructure components, such as in virtual machines and containers.

IT compliance is an area of increasing attention and capital spend in enterprise IT environments. We present "Continuous Compliance", a framework that allows a managed IT services provider to automate the overall process of keeping IT assets conformant with enterprise policies, regulatory frameworks, and other best practices. Our framework applies to all cloud layers and service models: Infrastructure-, Platform-, and Software-as-a-Service. We describe our framework design, its operation, and the post-process analytics and reporting. We also examine remediation reports gathered from over 2,000 servers for a seven month period, graph the incidence of repeated remediations, and explore some reasons for gradually subsiding remediations.

In this paper, we investigate the benefits of adding autonomic capabilities inside the operating system. We have developed and implemented a solution that focuses on three use cases (continuous file permission compliance, dynamic disk cleanup, and accidental removal protection) for the file system, and encapsulates all the respective file system monitoring, troubleshooting and error remedial operations in a Linux kernel module. The main benefits of this approach are the capability to detect issues instantly when they occur, and fix these issues transparently, with the invoking applications being unaware of their occurrence. These capabilities are not present in external agent architectures, including contemporary configuration management systems, like Puppet, Chef, or CFEngine. We have built a prototype and evaluated the performance of the most resource intensive use case, dynamic disk cleanup, using the FileBench file system benchmarking tool.

Endpoint management, including patching, health checking, configuration etc., is a key function for data center and cloud management. Managing multiple nodes through automation tools or scripts significantly increases efficiency. However, the risks of adverse impact due to excessive privilege or human error may propagate to a large pool of endpoints and lead to massive service disruptions and SLA (Service Level Agreement) violations. In this paper, we present a system that proactively and systematically manages the risk throughout the lifecycle phases of automation. We present a prototype implementation consisting of an authorization mechanism that guarantees the right level of eligibility and privilege of accessing the automation content (during the deployment stage), and an execution validator that controls the risk of human error which may cause massive damage to the infrastructure (during execution of the automation content). Our current implementation has been deployed to more than a dozen customer environments and achieved an efficiency gain of 58% with high execution accuracy.

We present a peer-to-peer service management middleware that dynamically allocates system resources to a large set of applications. The system achieves scalability in number of nodes (1000s or more) through three decentralized mechanisms that run on different time scales. First, overlay construction interconnects all nodes in the system for exchanging control and state information. Second, request routing directs requests to nodes that offer the corresponding applications. Third, application placement controls the set of offered applications on each node, in order to achieve efficient operation and service differentiation. The design supports a large number of applications (100s or more) through selective propagation of configuration information needed for request routing. The control load on a node increases linearly with the number of applications in the system. Service differentiation is achieved through assigning a utility to each application, which influences the application placement process. Simulation studies show that the system operates efficiently for different sizes, adapts fast to load changes and failures and effectively differentiates between different applications under overload.

Resource management poses particular challenges in large-scale systems, such as server clusters that simultaneously process requests from a large number of clients. A resource management scheme for such systems must scale both in the in the number of cluster nodes and the number of applications the cluster supports. Current solutions do not exhibit both of these properties at the same time. Many are centralized, which limits their scalability in terms of the number of nodes, or they are decentralized but rely on replicated directories, which also reduces their ability to scale. In this paper, we propose novel solutions to request routing and application placement- two key mechanisms in a scalable resource management scheme. Our solution to request routing is based on selective update propagation, which ensures that the control load on a cluster node is independent of the system size. Application placement is approached in a decentralized manner, by using a distributed algorithm that maximizes resource utilization and allows for service differentiation under overload. The paper demonstrates how the above solutions can be integrated into an overall design for a peer-to-peer management middleware that exhibits properties of self-organization. Through complexity analysis and simulation, we show to which extent the system design is scalable. We have built a prototype using accepted technologies and have evaluated it using a standard benchmark. The testbed measurements show that the implementation, within the parameter range tested, operates efficiently, quickly adapts to a changing environment and allows for effective service differentiation by a system administrator.

We present a decentralized design that dynamically allocates resources to multiple services inside a global server cluster. The design supports QoS objectives (maximum response time and maximum loss rate) for each service. A system administrator can modify policies that assign relative importance to services and, in this way, control the resource allocation process. Distinctive features of our design are the use of an epidemic protocol to disseminate state and control information, as well as the decentralized evaluation of utility functions to control resource partitioning among services. Simulation results show that the system operates both effectively and efficiently; it meets the QoS objectives and dynamically adapts to load changes and to failures. In case of overload, the service quality degrades gracefully, controlled by the cluster policies.

We present a decentralized design for a server cluster that supports a single service with response time guarantees. Three distributed mechanisms represent the key elements of our design. Topology construction maintains a dynamic overlay of cluster nodes. Request routing directs service requests towards available servers. Membership control allocates/releases servers to/from the cluster, in response to changes in the external load. We advocate a decentralized approach, because it is scalable, fault-tolerant, and has a lower configuration complexity than a centralized solution. We demonstrate through simulations that our system operates efficiently by comparing it to an ideal centralized system. In addition, we show that our system rapidly adapts to changing load. We found that the interaction of the various mechanisms in the system leads to desirable global properties. More precisely, for a fixed connectivity c (i.e., the number of neighbors of a node in the overlay), the average experienced delay in the cluster is independent of the external load. In addition, increasing c increases the average delay but decreases the system size for a given load. Consequently, the cluster administrator can use c as a management parameter that permits control of the tradeoff between a small system size and a small experienced delay for the service.

The paper contributes towards engineering self-stabilizing networks and services. We propose the use of navigation patterns, which define how information for state updates is disseminated in the system, as fundamental building blocks for self-stabilizing systems. We present two navigation patterns for self-stabilization: the progressive wave pattern and the stationary wave pattern. The progressive wave pattern defines the update dissemination in Internet routing systems running the DUAL and OSPF (open shortest path first) protocols. Similarly, the stationary wave pattern defines the interactions of peer nodes in structured peer-to-peer systems, including Chord, Pastry, Tapestry, and CAN. It turns out that the two patterns are related. They both disseminate information in the form of waves, i.e, sets of messages that originate from single events. Patterns can be instrumented to obtain wave statistics, which enables monitoring the process of self-stabilization in a system. We focus on Internet routing and peer-to-peer systems, since we believe that studying these (existing) systems can lead to engineering principles for self-stabilizing systems in various application areas.