By 2027, over 50 % of enterprises are expected to adopt industry cloud platforms [1], driving potential EBITDA value of $3 trillion by 2030 [2]. In this landscape, software providers rely on Infrastructure-as-a-Service (IaaS) providers to access tailored virtualized resources based on usage. Optimizing resource utilization is crucial to reducing operating costs and maintaining quality standards for SaaS and IaaS providers. This creates an essential need for dynamic scaling mechanisms to adjust resources according to workload variations. The Kubernetes resource Horizontal Pod Autoscaler (HPA) has limitations in scaling applications. However, AI-based algorithms, particularly Reinforcement Learning (RL), offer promising solutions. AI-based methods excel in overcoming fixed parameter constraints, handling sudden load spikes, and supporting custom parameters. We present an RL-based framework for auto scaling applications, demonstrating results from experimental evaluation.

Recent advances in Large Language Model (LLM) based Generative AI techniques have made it feasible to translate enterpriselevel code from legacy languages such as COBOL to modern languages such as Java or Python. While the results of LLM-based automatic transformation are encouraging, the resulting code cannot be trusted to correctly translate the original code. We propose a framework and a tool to help validate the equivalence of COBOL and translated Java. The results can also help repair the code if there are some issues and provide feedback to the AI model to improve. We have developed a symbolic-execution-based test generation to automatically generate unit tests for the source COBOL programs which also mocks the external resource calls. We generate equivalent JUnit test cases with equivalent mocking as COBOL and run them to check semantic equivalence between original and translated programs. Demo Video: https://youtu.be/aqF_agNP-lU

In this paper we provide a high performance solution to the problem of committing transactions while enforcing a pre-defined order. We provide the design and implementation of three algorithms, which deploy a specialized cooperative transaction execution model. This model permits the propagation of written values along the chain of ordered transactions. We show that, even in the presence of data conflicts, the proposed algorithms outperform single threaded execution, and other baseline and specialized state-of-the-art competitors (e.g., STMLite). The maximum speedup achieved in micro benchmarks, STAMP, PARSEC and SPEC200 applications is in the range of 4.3x -- 16.5x.

We discuss the problem of constructing efficient temporal indexes on Hyperledger Fabric, a popular Blockchain platform. The temporal nature of the data inserted by Fabric transactions can be leveraged to support various use-cases. This requires that temporal queries be processed efficiently on this data. Currently this presents significant challenges as this data is organized on file-system, is exposed via limited API and does not support temporal indexes. In a prior work [1], we presented two models for creating temporal indexes on Fabric which overcome these limitations and improve the performance of temporal queries on Fabric. The first model creates a copy of each event inserted and stores temporally close events together on Fabric. The second model keeps the event count intact but tags metadata to each event s.t. temporally close events share the same metadata. In this paper, we present variants on these two models which are better able to handle the skew present in Fabric data. We discuss the details and show that these variants significantly outperform the approaches presented in [1] when Fabric data contains skew. We also discuss the performance tradeoffs among these variants across various dimensions - data storage, query performance, event insertion time etc.

In this paper, we discuss the problem of efficiently handling temporal queries on Hyperledger Fabric, a popular implementation of Blockchain technology. The temporal nature of the data inserted by the Hyperledger Fabric transactions can be leveraged to support various use-cases. This requires that the temporal queries be processed efficiently on this data. Currently this presents significant challenges as this data is organized on file-system, is exposed to users via a limited API and does not support any temporal indexes. We present two models for overcoming these limitations and improving the performance of temporal queries on Fabric. The first model creates a copy of each event inserted by a Fabric transaction and stores temporally close events together on Fabric. The second model keeps the event count intact but tags some metadata to each event being inserted on Fabric s.t. temporally close events share the same metadata. We discuss these two models in detail and show that these two models significantly outperform the naive ways of handling temporal queries on Fabric. We also discuss the performance trade-offs for these two models across various dimensions - data storage, query performance, data ingestion time etc.

Transactional memory (TM) facilitates the development of concurrent applications by letting a programmer designate certain code blocks as atomic. The common approach to stating TM correctness is through a consistency condition that restricts the possible TM executions. Unfortunately, existing consistency conditions fall short of formalizing the intuitive semantics of atomic blocks through which programmers use a TM. To close this gap, we formalize programmer expectations as observational refinement between TM implementations. This states that properties of a program using a concrete TM implementation can be established by analyzing its behavior with an abstract TM, serving as a specification of the concrete one. We show that a variant of Transactional Memory Specification (TMS), a TM consistency condition, is equivalent to observational refinement for a programming language where local variables are rolled back upon a transaction abort. We thereby establish that TMS is the weakest acceptable condition for this case. We then propose a new consistency condition, called Strong Transactional Memory Specification (STMS) , and show that it is equivalent to observational refinement for a language where local variables are not rolled back upon aborts. Finally, we show that under certain natural assumptions on TM implementations, STMS is equivalent to a variant of a well-known condition of opacity. Our results suggest a new approach to evaluating TM consistency conditions and enable TM implementors and language designers to make better-informed decisions.

Hadoop as a service (HaaS), also known as Hadoop in the cloud, is a big data analytics framework that stores and analyzes data in the cloud using Hadoop/Spark. In this paper, we discuss the importance of providing provenance capabilities in context of Hadoop as a service (HaaS) framework. We first review the state of the art in provenance tracking in context of databases and work-flow processing, in context of cloud and in context of big data analytics frameworks like Hadoop and Spark. We next identify a number of provenance capabilities which have been developed in context of databases and workflow processing but the corresponding solutions have not been developed in context of Hadoop or Spark. We argue that developing these solutions is important so that a comprehensive provenance aware Hadoop as a Service (HaaS) can be provided on cloud. The paper ends by identifying some research challenges in developing these provenance capabilities.

Transactional memory allows the user to declare sequences of instructions as speculative transactions that can either commit or abort. If a transaction commits, it appears to be executed sequentially, so that the committed transactions constitute a correct sequential execution. If a transaction aborts, none of its instructions can affect other transactions. The popular criterion of opacity requires that the views of aborted transactions must also be consistent with the global sequential order constituted by committed ones. This is believed to be important, since inconsistencies observed by an aborted transaction may cause a fatal irrecoverable error or waste of the system in an infinite loop. Intuitively, an opaque implementation must ensure that no intermediate view a transaction obtains before it commits or aborts can be affected by a transaction that has not started committing yet, so called deferred-update semantics. In this paper, we intend to grasp this intuition formally. We propose a variant of opacity that explicitly requires the sequential order to respect the deferred-update semantics. Unlike opacity, our property also ensures that a serialization of a history implies serializations of its prefixes. Finally, we show that our property is equivalent to opacity if we assume that no two transactions commit identical values on the same variable, and present a counter-example for scenarios when the “unique-write” assumption does not hold.

Transactional memory (TM) has been hailed as a paradigm for simplifying concurrent programming. While several consistency conditions have been suggested for TM, they fall short of formalizing the intuitive semantics of atomic blocks, the interface through which a TM is used in a programming language.

Computing convex hull for a given set of points is one of the most explored problems in the area of computational geometry (CG). If the set of points is distributed among a set of parties who jointly wish to compute the convex hull, each party can send his points to every other party, and can then locally compute the hull using any of the existing algorithms in CG. However such an approach does not work if the parties wish to compute the convex hull securely, i.e., no party wishes to reveal any of his input points to any other party apart from those that are part of the answer. The problem of secure computation of convex hull for two parties was first introduced by Du and Atallah (NSPW '01). The first solution to the problem was given by Wang et. al(ARES '08). However, the proposed solution was based on well known algorithms for computing convex hull in CG which are proven to be sub-optimal. We propose a new solution for secure computation of convex hull with a considerable improvement in computational complexity. We further show how to extend our two-party protocol for the case of any number of parties.