2021 SDC India Abstracts

Back to Top

Day One Track One Abstracts 

Storage - A LinchPin for Cyber Resiliency  

Akhtar Ali, Vice President, IBM

Abstract

Since 4,000 B.C. when the Sumerians invented the first system of writing, they used clay tokens to represent the trades and deals they were making. To prevent tampering with these tokens, they then sealed the tokens in clay jars. This ensured that the data would remain untouched by any people and would securely preserve data over time.  Data Protection has evolved quite a bit since then.  From traditional backup to recover to to modern data protection, it morphed to Data Resilience which is where we are today. The session provides guidance on the best practices necessary to Cyber Resiliency.  In addition, it insights the technologies available from IBM to help implement these best practices.

Transforming Monolith to Microservices  

Parmeshwr Prasad, Software Principal Engineer & Rahul Vishwakarma, Software Quality Senior Engineer, Dell Technologies

Abstract

There is a boundless inclination towards transforming the applications from monolith to microservices-based architecture across the enterprise. However, even before joining the race, there are many crucial questions to be answered first. For instance – What will be the scalability of the designed microservices? What migration patterns can be adopted? Is microservices required for the application? 
With some real-world example from enterprise applications, in this talk, we will cover domain-driven design to organizational change models and understand if starting this journey is even suitable for the specific application. Further, we dive deeper into the technical aspects of decomposing a monolith, exploring real-world examples and extracting the migration patterns. We conclude by discussing the pain points and the challenges faced by the enterprise when the microservices architecture grows.

Unify Data and Storage Management with SODA ODF  

Steven Tan, VP & CTO Cloud Solution, Storage at Futurewei/Board Chairman at SODA Foundation

Abstract

The Open Data Framework (ODF) unifies data and storage management from the core, to cloud and to edge. In this talk, we will show how ODF simplifies Kubernetes storage management, provides data protection for applications, and connect data on-prem to clouds. We will also be introducing how ODF can be extended with other SODA projects such as DAOS - a distributed asynchronous object storage for HPC, ZENKO - a multicloud data controller with search functionality, CORTX - an object storage optimized for mass capacity storage and others (YIG, LINSTOR, OpenEBS). SODA Foundation is a Linux Foundation project focused on building an ecosystem of open-source data management and storage software for data autonomy.

Hybrid Data Management and Monitoring for Container Storages  

Sanil Kumar D, TOC, AWG Lead & Amit Kumar Roushan, System Architect, SODA Foundation

Abstract

Container Storage solutions are getting increased traction. Most of the vendors working on Storage as a service kind of solutions with their storage products. In this session, we will explore potential solutions for hybrid data management and storage monitoring across heterogeneous storages (including cloud storages) using open data framework. We will illustrate the overall solution using two specific use cases a) Data Protection across on prem-cloud and b) Heterogeneous storage monitoring (where CSI has gaps, esp for storage monitoring). We will conclude with some thoughts on the future direction and pointers on container storage data management.

Containerize storage software kernel modules in Kubernetes  

Apurv Barve, Senior Principal Software Engineer, Veritas

Reena Kabra, Principal Software Engineer, Veritas

Prashant Bhople, Senior Software Engineer, Veritas

Abstract

This presentation aims to describe how to manage containerized software-define-storage software having kernel module through combination of Special Resource Operator and custom Operator. A container is a standard unit of software that packages the code and all its dependencies, so that the software can run on any computing environment. Containers are very lightweight compared to Virtual Machines as they share the same OS kernel. Many container orchestration platforms like RedHat Openshift mandate that every software be run as a container. There are no software installation tools available and file systems on root disks are ReadOnly, and so that no software can be installed on the host. Expectation is that software's lifecycle like deployment, upgrades etc. would be managed through Operator. An Operator is a method of packaging, deploying, and managing container applications – basic install, upgrades, failure recovery etc. Many software’s which consist of kernel modules are not shipped by default with the standard Operating Systems. These kernel modules are required for proper and performant functioning of special hardware like disk devices, HBA cards, software defined storage etc. This proposal discusses the use of two Operators – Special Resource Operator (SRO) and Custom Operator (CO) to seamlessly manage deployment, carry out software rolling upgrade and kernel/OS rolling upgrade when dealing with storage software having kernel modules.

Offloaded iSCSI vs Host iSCSI target infrastructure  

Tej Parkash, Advisory Software Engineer & Subhojit Roy, STSM, IBM

Abstract

Summary: This paper describes various design and implementation changes in iSCSI interconnect layer while using native software stack in comparison to iSCSI offload stack from NIC hardware vendors like Chelsio or Mellanox. Also, this study provides more details about how performance characteristics varies in both the iSCSI models. 

Abstract: Existing Ethernet Data centers uses iSCSI as a storage interconnect, mainly due to low cost infrastructure and its suitability to deploy with minimum network administration. Recently though, with advent of smartNIC technologies by NIC vendors, iSCSI and TCP/IP functionality could be offloaded to high performance CPUs on adapter. This allows application to utilize host CPUs better for more important mainline functionalities e.g access backend database. Host stack, however, provides better control for storage application with new features and APIs usages compare to offload stack where fixed set of transport APIs are used to access external network. Also Host stack functionality works on standard NIC adapters from low speed network of 1G to 100G and does not need any special functionality on NIC adapters to support storage protocol like iSCSI.

This paper also talks about challenges while building such storage applications and other practical considerations, given that storage applications are quite different from standard applications running on server environments.

Wading through the Ethernet Interoperability nightmare  

Bharti Soni, Senior System Staff Engineer, & Subhojit Roy, STSM, IBM

Abstract

With new emerging high-speed storage protocols like NVME-TCP, NVME-RDMA and low-cost infrastructure deployments, Ethernet as storage interconnect is becoming more and more eminent. Since Ethernet is widely used standard it provides various options for deployment like different standards, protocols, and specifications. With breakneck speed of new technology development and new standards coming up every few years, interoperability issues come to the forefront while designing/implementing an Ethernet Storage solution specially for cloud solutions. For example different ways of implementing FEC on different NIC vendors can cause serious issues like link becoming inactive. 
This talk shares some of our first-hand experiences in our journey in Ethernet storage world with Ethernet interoperability that arise due to the prevalence of numerous standards for various aspects of implementing and deploying Ethernet storage. e.g. Host- interop, hardware-interop etc. In this talk we will discuss these issues and the list of watchpoints and pitfalls for designing, implementing, and deploying an Ethernet storage solution. This paper will give insights about the practical considerations one has to keep in mind while deploying Ethernet storage solutions to avoid the challenges that we have faced. This paper will also talk about considerations one has to take for moving their solution to cloud.

Changing role of Object Storage in the High-performance Tier-1 workloads

Amit Deshmukh, Solutions Lead, Content and Data Intelligence, Hitachi Vantara

Abstract

The concept of Object Storage was introduced in 1990’s and since then it has evolved into a mainstream technology of choice for unstructured data. Today it has become the cornerstone for storing, protecting, and managing unstructured as well as semi-structured data. Unstructured and semi-structured data are predominantly generated by Humans and Machines. These are as high as 90% of the total enterprise data most of which remain dark data. This data is growing so fast that, as per Gartner Critical Capabilities report by 2024, large enterprises are going to triple their unstructured data stored as file or object storage on premises, at the edge or in the public cloud, compared with 2020.
Object storage through the years: 1st Generation object storage solutions were focused on data protection, archival and compliance. 2nd generation object storage solutions were focused on hybrid cloud, s3, metadata enrichment etc. But today the object storage has gone way beyond, is catering to almost all the tier-1 workloads. 
Digital transformation has accelerated adoption of Cloud, social media, Mobility, IoT, and Big data; as a result many new data sources and modern application workloads are getting discovered and are driving the object storage consumption. The applications such as ultra-high-performance AI/ML workloads, IoT and unified data warehouses are leading this race. 
According to another market projection (IDC), 80% of the data generated worldwide will be unstructured.
As analytics usage increases, organizations want faster performance to speed data-focused insights that can inform business decisions. A high-performance object storage solution can enable additional modern and cloud-native applications to gain the benefits of its scalability, fast data retrieval, and cost effectiveness.
Modern Data Platform: Navigating these challenges requires support for multiple use cases with visibility and control across all data. A modern digital platform enables easy management of volumes of data, seamless response to application demands and wide data accessibility, while satisfying compliance requirements. And hence, a modern data platform must have following characteristics/pillars:
1. Simple, Secure and Smart
2. Software Defined
3. Metadata-based
4. Limitless and Global
5. Multi-protocol and multi-cloud
6. APIs Driven
Traditionally, organizations have turned to object storage for highly scalable, cost-efficient, long-term storage with easy data retrieval—but not for performance. But today, organizations need to use all their data, not have some data stored in slow silos, where it is available in an emergency but not for production applications. Too much insight is available from data lakes, content repositories, and email archives for organizations to waste money storing it without usable performance.

An Efficient Approach to Achieve Greater Concurrency Using Object-based Transactional Memory for Multi-core Systems

Archit Somani, Research Scientist & Sweta Kumari, Research Scientist, Huawei

Abstract

Composing together the individual atomic methods of concurrent data structures (CDS) poses multiple design and consistency challenges. In this context composition provided by transactions in software transaction memory (STM) can be handy. However, most of the STMs offer read/write primitives to access shared CDS. These read/write primitives result in unnecessary aborts. Instead, semantically rich higher-level methods of the underlying CDS like lookup, insert, or delete (in case of hash-table or lists) aid in ignoring unimportant lower-level read/write conflicts and allow greater concurrency. 

We adapt the transaction tree model in databases to propose Object-based STM (OSTM) which enables efficient composition in CDS. We implement OSTM with concurrent closed addressed hash-table (HT-OSTM) and list (list-OSTM ) which exports the higher-level operations as transaction interface. Experimental evaluation shows that HT-OSTM and list-OSTM outperform state-of-the-art STMs and incur negligible aborts.

Computational Storage - Status

Scott Shadley, BoD Member, CS TWG Co-Chair, SNIA

Abstract

As we are reaching the back half of 2021, it is time to see what is going on with the CS TWG and CS SIG around bringing Computational Storage into the mainstream storage architectures. While SNIA is continuing to drive the Architectural Document work, the presentation will provide some insights into the first implementation of CS to real products, through our partner organization, NVM Express. They have a working group on the topic as well, and the efforts of the two standards are maintained in lockstep. We will leave the viewers with a preview of the latest version of the Architectural document and links and materials available to show the direction and growth of Computational Storage in the market. 

Day Two Plenary Abstracts

Digital Storage and Memory for AI at the edge and the Data Center  

Tom Coughlin, President, Coughlin Associates

Abstract

Applying AI at the edge and endpoints often requires working under non-data center environments and in power constrained conditions.  AI inference also requires significant memory to hold weighting values from training.  New non-volatile memories can help provide more memory in a given device die and use less power than NOR flash, SRAM or DRAM.  This presentation will talk about changes in the memory/storage hierarchy and how it will change memory and storage in data centers and embedded devices to support energy efficient and low latency AI applications.

Modernize data protection for the Hybrid Cloud

Cheryl George, Technical Marketing Engineer, NetApp

Abstract

Your business data represents your intellectual capital, competitive differentiation, and lifeblood of your organization. And not all data is equal. With clearly defined service level agreements for data protection, you can enable your Digital Transformation. Learn how to protect your enterprise application data depending on workload requirements such as backup, disaster recovery and compliance, and even archive to the Cloud for long-term retention. Empower your storage administrators today!

Learning Objectives

• Devise effective data protection strategy based on service level agreements defined by your business 
• Identify key Data Protection technologies required for your data on-prem or in the Cloud 
• Consistent and simplified data protection workflows across the Data Fabric

Amazon Aurora storage - Purpose built storage for databases

Murali Brahmadesam, Director of Engineering, Aurora Databases, Amazon

Abstract

In this talk you will learn about the reasons for building a specialized storage system for databases, and how Aurora databases are built using such a storage system in cloud.

Where is my data - Challenges of the next gen Storage and Apps

Ankit Mathur, Chief Architect, NAS Storage, Huawei

Abstract

The compute, network and storage are geared towards getting higher throughput and IOPS, creating immense amount of data. Organizations are opting for hybrid, multi-cloud and data center solutions for hosting data. And increasingly complex principles and rules now govern handling various types of data. Are the data management solutions around it keeping up?

Hitachi Polyphased Erasure Coding - A New Network Efficient Erasure Coding Invention

Kiat Wee Ang, Technical Expert, Data Management and Protection, Hitachi Vantara

Abstract

As Software Defined Storage (SDS) and Hyperconverged Infrastructure (HCI) become popular, users are hitting some limits in scalability & performance for these solutions. Typically, Mirroring & Erasure Coding are existing data protection techniques deployed for SDS & HCI. However, each of these techniques have their pros & cons. In particular, network has become the performance/scalability bottleneck. Hitachi has developed a new Polyphased Erasure Coding technique that can solve this problem and remove the barrier to higher scalability. Come to the presentation and find out more!

Day Two Track 1 Abstracts

Learning Objectives

• Use of open source High performance computing cluster for Big data Applications 
• Usage of parallel Processing Declarative language for Big Data Processing
• Design and Development of Generator and Discriminator models on HPCC platfom 
• Implementation of weight Transfer Methodology on MNIST database
• Evaluating the performance of the proposed algorithm

Ranking based Dynamic Hot Sparing  

Hemant Gaikwad, Senior Principal Engineer & Rahul Vishwakarma, Software Quality Senior Engineer, Dell Technologies

Abstract

Sparing mechanism is one of the factors to be considered for system reliability. However, configuring more spares than required, or shortage of spares, have their limitations. A shortage can increase the restoration time if there is a failure – thus, reducing availability and an excess of spare parts has its own financial implications. 
Usually, the traditional sparing mechanism fails to achieve the dilemma of excess and shortage of spares because their first level of “sparing method” (in any storage system) is to identify the “failed/fail-ing” disk in the RAID group and then replacing it with the spare drive (they can use any novel sparing mechanism). This is where the problem manifests – the so-called “bad drive” is labelled (Good or Bad) with failed/failing disk by underlying statistical or threshold-based machine learning methods. This approach still has its drawbacks; most prominently, it is a static (0 or 1) way of denoting a disk as either “Good” or “Bad”. 
To overcome this issue, an intelligent sparing mechanism is presented. The method is based on the “degree-of-badness” and helps in identifying which RAID group needs more attention and how to assign the spares. Moreover, it provides an automated configuration change for the spare drives – a healthier (low degree of bad disks) RAID group has low spare disks assigned, and whenever the degree-of-badness increases, the number of spare drives is increased.

Smart Data Archival in a distributed environment  

Ashit Kumar, Lead Architect, Huawei/SODA Foundation & Sainath Mathur, Storage and Peripherals Practice Head, Wipro

Abstract

Today, data plays a more crucial role for any business and with passing time role of data is ever increasing. For the whole software industry, primarily Data is the pivot. Storing these data in a cost-effective way is the need a necessity. But to determine when and where to store the data effectively and efficiently is the key. Consider a data used rarely but lying on your primary storage. This will have a high cost with less ROI. But considering various aspects, deleting these data may not be possible. So to move those data into secondary storage or archival storage or Cold storage can save cost as well as maintain compliance.
Data being generated and accessed can have a trend and pattern. Understanding those trends can help to be smart and decide when and which data to be Archived. These trends can be simple heuristic-based or based upon accessibility patterns or a catalog defined by the admins.
SODA Foundation is an Open Data Framework for data On-prem, on Cloud, and at the Edge. SODA has core projects which support Data Archival onto on-prem Cold Storage like SONY ODA and to Archival/Cold storage of different cloud providers.
This new solution intends to use this underlying platform and based upon the AI/ML/Cognitive methods or a policy-based catalog, the data can be smartly moved to and from Archival storage. This data movement intelligence reads the metadata and based upon defined algorithms moves data. 
The intent is to automate the data migration to and from the archival storage across the hybrid environment and save cost as well as maintain compliance

Performance engineering considerations for Unstructured Data Management solutions  

Anand Reddy, Senior Performance Engineer, Komprise

Abstract

Data Management is key Value ingredient in Data center Analytics. This involves File systems and Object store analysis, software scans, maps which enables data and analytics leaders to make better data management decisions for unstructured data. This in turn reduces risk and lowers costs associated with data storage and ensures higher performance per TB invested. 

Data classification for business value is a topic that more relevant than ever and more organizations are curious to understand their investments and rationalize the same based on file and object store analytics. Various vendors in this space differ in algorithmic choices made for File scanning,indexing and represeting the historical and current trends of Data layout. There can be associated performance implications and learnings by understanding the choices in design and architecture decisions. Depending on the storage methods of the metadata scans, in many cases there comes the questions on how much scale and performane can be expected for given solutions. Also implications on level of scanning can have implication on Latency and retreival of analytics for end user. Once the analytics part is hardened advanced steps can involve scanning content and making decisions to either move the content to a new destination or delete it to optimize efficiency and lower cost of ownership. 

In this talk resources lets walk through some of the Performance implications and learnings for managing unstructured data and providing operational efficiency for users in engaging with the data.

Metadata I/O Performance Improvement strategies for HPC workload  

Abhijit Halder, Lead Architect, Huawei

Abstract

Supercomputers running HPC workload generate vast amounts of data, typically organized into large directory hierarchies on parallel filesystems. Size of dataset can go from millions to few billions of files spread across thousands of directories with varying depth and span. Hence, performance of metadata operations become a crucial factor for overall system performance. File create and delete operations inside directories are usually serial. This is because the parent directory lock needs to be taken to protect directory against concurrent write access. Persisting metadata is found to be expensive that contribute the most to the overall latency.

We propose a batch commit technique to improve the metadata write I/O performance. Experiment shows that we improve metadata write I/O performance by around 50 percent. The supercomputing applications often require to perform query on directories. Typically these operations require complete directory traversal. In UNIX and UNIX like operating systems ‘find’ is a command that helps applications to locate files based on certain user-specified criteria and then apply some requested actions on each of these matched items. The ‘find’ command uses readdir, a POSIX API to read a directory and after getting the complete list of directory entries, it applies some filter to select a few of them that satisfy the user-specified criteria. It typically takes a regex (regular expression) pattern and does a pattern match with all the entries that are obtained from reading the target directory. User sometime may specify some attributes like size, last modification time etc. and also some selection criteria to match with those attributes. In that case the ‘find’ command needs to get the file attributes of those whose name matched with user-provided regex pattern by using getattr POSIX API. Finally, the ‘find’ command may perform some action on all the selected items like deletion or change file permission and so on. For local file-system this may be acceptable, but for remote file-system this requires the client to reach the server multiple times. Firstly to get all the directory entries, then to get file attributes of those that matched some user-provided pattern and finally to perform some action on matched items. If number of directory entries is too large, a single request may not be able to fetch all the entries, hence that needs some additional round-trip. Several round-trip and especially fetching all the entries and performing an elimination based on some user-specified criteria at client side is a complete wastage of valuable network bandwidth and both server and client CPU. Some protocol like NFS provides an extension over standard readdir API, called readdirplus that along with the directory entries also returns the attributes to optimize the number of network round-trips. But this requires extra disk I/O to get file attributes of directory entries don not satisfy user criteria. Furthermore, the data transfer over the network in this scheme becomes even higher. We propose a near data processing technique that solves all the aforementioned problems and improves the performance of ‘find’ command by more than 100 times.

Native filesystems for Zoned Block Device and recent enhancements

Anil Dhaneshwar, Principal Software Engineer, Veritas

Zoned storage devices are a class of storage devices with an address space that is divided into zones which have write constraints different from regular storage devices. Each zone written sequentially and reset before rewriting. The main type of ZBD currently available is SMR HDDs. The NVMe Zoned NameSpace (ZNS) is also recently standardized and defines a zone abstraction for NVMe namespaces similar to SMR HDD zones. The benefits of ZBD over traditional storage are better capacity, higher performance and lower cost. The initial support for Zoned Block Device in native filesystem was added in Linux kernel 4.10. Since then continuous changes are happening in kernel for this device support.

Among the existing Linux filesystems, F2FS already supports zoned devices, and allows normal operations on such devices. In addition, zonefs, a special filesystem for zoned devices, was included in the 5.6 kernel. Using zonefs requires applications designed for this purpose, as the filesystem does not support the creation of normal files. For Btrfs, the basic support is present now. Also the performance improvement and some other enhancement in Btrfs are added in kernel 5.12. Zoned block devices behave differently than the traditional ones, so filesystem requires layout changes to support those devices.

This talk is inspired by work of Damien Le Moal and Naohiro Aota and their patches submitted in Linux kernel. It will cover core principles of ZBD and the work done so far in native filesystems to support this device.

AI/ML Based Simulation of Customer Environment

Praveen Sathyanarayan Murthy, Expert Engineer & Shifali Sood, System Software Engineer, HPE

An innovative AI/ML based approach that simulates disk storage system customer environment for product qualification. Solution includes an automated framework that fetches customer system telemetry data and performs analysis to create ML models. These ML models recommend desired configuration and feature-sets which are most closure to maximum number of customer disk storage systems. This solution also analyses customer reported issues and tunes system configurations and features which are prone to error. This end to end automated solution not only aligns test environment with most commonly found customer telemetry data, but also ensures that product is qualified with real customer environments.

Backup Anomaly Detection to detect Ransomware/malware attack

Swapnil Dombale, Senior Principal Software Engineer, Veritas

Anomaly detection is the identification of rare events, abnormal data or trend comparing it with its past trend data.

In the event of Ransomware / Malware attack on a system, it can change some data on the system. For example, a Ransomware attack can encrypt all data on system or delete files on the system. So in this case if we have a past trend of activities or attributes such as files count or size, Anomaly detection can be used to identify if there is any Ransomware attack on the system.

A Backup and Recovery product collects various backup meta data during backup process at certain specified frequency. A Ransomware / Malware attack can possibly alter those backup meta data and using Anomaly detection after the backup is completed, we can identify the changes in meta data if they are anomalous or not to correlate it with attack. This presentation explains how Anomaly detection using machine learning capabilities can be used in Backup flow to identify the Ransomware / Malware attack on the system.  Also, this presentation will outline certain actions that users can take to enrich learning of Machine learning model for better detection capabilities.

Everything is Predictable - Who will click, who will buy, who will lie, and even who will die

Anand Kayande, Senior Principal Engineer, Veritas

Prediction is power.

Although, in general, predictions are not new. Since the beginning of human civilization, we all are predicting possible events that can happen in the future, using some or other approaches. The recent revolution in data science, machine learning, and artificial intelligence together is doing miracles in technology-based predictions. These predictions can be a game-changer for almost all businesses, as they can empower them to make the right business decisions. Prediction Analytics is a technique to look into the past, understand it thoroughly, and then based on it, predict the potential future events. Prediction is art and science as well.

There is this popular saying in data science– everything can be predicted – who will click, who will buy, who will lie, and even who will die.

There is no exaggeration in this statement. Statistically speaking, everything is predictable up to a very large extent. Insurance companies worldwide are doing this for so many years historically. We can predict which customer will buy which product. Also, we can predict which customer can buy something extra if promoted more or influenced more. The job site can predict the most possible match based on skills and requirements. An entertainment business can predict which movie you will like to watch next or a music application can predict which song you will like to listen to on Monday morning. Before elections, newspapers and media predict who will win the elections based on opinion polls and surveys. Social sites can predict possible 'friends you may know' or dating sites can predict potential "matches". Predictive analytics is not merely a corporate buzzword, but it is the philosophy by which compliance products are inspired and driven. Almost all digital compliance products deal with huge text data of customers in various formats like emails, lose files, images, audios, videos, and so on. They can understand it well and then predict. Just, for example, eDiscovery’s predictive coding (ref), advance supervision (ref), Information classifier (ref).

Most compliance products have abilities to provide visibility into customer data by enriching metadata around various business workflows and surfacing relevant information. They can find the most actionable content, that is more relevant, that is narrower. Lastly, in the coming future, data growth will be massive. Data will be the new oil. Businesses that develops intelligence to truly “understand” the data by reading between the lines, will thrive the market. There is huge potential to infer from data and predict actionable insights and this is going to be a continuous process, forever. 

AI/ML for Storage

Jayanta Basak, Consultant and Chief Architect, Huawei 

AI and Machine Learning is touching upon almost every walks of life. Systems and particularly storage systems and data services are no exception. In this talk, the various facets of AI/ML applications in storage systems will be discussed. First, we will discuss the type of AI for systems and then discuss some specific research directions of storage systems research that are being benefitted by using AI/ML.

A Novel and Efficient Way to Achieve Starvation-Freedom in Multi-Version Software Transactional Memory Systems

Sweta Kumari, Research Scientist & Archit Somani, Research Scientist, Huawei

Software Transactional Memory systems (STMs) have garnered significant interest as an elegant alternative for addressing synchronization and concurrency issues with multi-threaded programming in multi-core systems. Client programs use STMs by issuing transactions. STM ensures that transaction either commits or aborts. A transaction aborted due to conflicts is typically re-issued with the expectation that it will complete successfully in a subsequent incarnation. However, many existing STMs fail to provide starvation freedom, i.e., in these systems, it is possible that concurrency conflicts may prevent an incarnated transaction from committing. 

To overcome this limitation, we systematically derive a novel starvation-free algorithm for multi-version STM. Our algorithm can be used either with the case where the number of versions is unbounded and garbage collection is used or where only the latest K versions are maintained, KSFTM. We have demonstrated that our proposed algorithm performs better than existing state-of-the-art STMs on the applications of STAMP benchmark.