Minhaj Ahmad Khan

Bio: Minhaj Ahmad Khan is an academic researcher from Bahauddin Zakariya University. The author has contributed to research in topics: Code generation & Cloud computing. The author has an hindex of 6, co-authored 27 publications receiving 1419 citations. Previous affiliations of Minhaj Ahmad Khan include Versailles Saint-Quentin-en-Yvelines University.

Scheduling for heterogeneous Systems using constrained critical paths

Abstract: A complex computing problem may be efficiently solved on a system with multiple processing elements by dividing its implementation code into several tasks or modules that execute in parallel. The modules may then be assigned to and scheduled on the processing elements so that the total execution time is minimum. Finding an optimal schedule for parallel programs is a non-trivial task and is considered to be NP-complete. For heterogeneous systems having processors with different characteristics, most of the scheduling algorithms use greedy approach to assign processors to the modules. This paper suggests a novel approach called constrained earliest finish time (CEFT) to provide better schedules for heterogeneous systems using the concept of the constrained critical paths (CCPs). In contrast to other approaches used for heterogeneous systems, the CEFT strategy takes into account a broader view of the input task graph. Furthermore, the statically generated CCPs may be efficiently scheduled in comparison with other approaches. The experimentation results show that the CEFT scheduling strategy outperforms the well-known HEFT, DLS and LMT strategies by producing shorter schedules for a diverse collection of task graphs.

An efficient energy-aware approach for dynamic VM consolidation on cloud platforms

Abstract: The cloud computing environments rely heavily on virtualization that enables the physical hardware resources to be shared among cloud users by creating virtual machines (VMs). With an overloaded physical machine, the resource requests by virtual machines may not be fulfilled, which results in Service Level Agreement (SLA) violations. Moreover, the high performance servers in cloud data centers consume large amount of energy. The dynamic VM consolidation techniques use live migration of virtual machines to optimize resource utilization and minimize energy consumption. An excessive migration of virtual machines may however deteriorate application performance due to the overhead incurring at runtime. In this paper, we propose a normalization-based VM consolidation (NVMC) strategy that aims at placing virtual machines in an online manner while minimizing energy consumption, SLA violations, and the number of VM migrations. The proposed strategy uses resource parameters for determining over-utilized hosts in a virtualized cloud environment. The comparative capacity of virtual machines and hosts is incorporated for determining over-utilized hosts, while the cumulative available-to-total ratio (CATR) is used to find under-utilized hosts. For migrating virtual machines to appropriate hosts, the VM placement uses a criteria based on normalized resource parameters of hosts and virtual machines. For evaluating the performance of VM consolidation, we have performed experimentation with a large number of virtual machines using traces from the PlanetLab workloads. The results show that the NVMC approach outperforms other well-known approaches by achieving a significant improvement in energy consumption, SLA violations, and number of VM migrations.

Blockchain for AI: Review and Open Research Challenges

Abstract: Recently, artificial intelligence (AI) and blockchain have become two of the most trending and disruptive technologies. Blockchain technology has the ability to automate payment in cryptocurrency and to provide access to a shared ledger of data, transactions, and logs in a decentralized, secure, and trusted manner. Also with smart contracts, blockchain has the ability to govern interactions among participants with no intermediary or a trusted third party. AI, on the other hand, offers intelligence and decision-making capabilities for machines similar to humans. In this paper, we present a detailed survey on blockchain applications for AI. We review the literature, tabulate, and summarize the emerging blockchain applications, platforms, and protocols specifically targeting AI area. We also identify and discuss open research challenges of utilizing blockchain technologies for AI.

Integrated Blockchain and Edge Computing Systems: A Survey, Some Research Issues and Challenges

Abstract: Blockchain, as the underlying technology of crypto-currencies, has attracted significant attention. It has been adopted in numerous applications, such as smart grid and Internet-of-Things. However, there is a significant scalability barrier for blockchain, which limits its ability to support services with frequent transactions. On the other side, edge computing is introduced to extend the cloud resources and services to be distributed at the edge of the network, but currently faces challenges in its decentralized management and security. The integration of blockchain and edge computing into one system can enable reliable access and control of the network, storage, and computation distributed at the edges, hence providing a large scale of network servers, data storage, and validity computation near the end in a secure manner. Despite the prospect of integrated blockchain and edge computing systems, its scalability enhancement, self organization, functions integration, resource management, and new security issues remain to be addressed before widespread deployment. In this survey, we investigate some of the work that has been done to enable the integrated blockchain and edge computing system and discuss the research challenges. We identify several vital aspects of the integration of blockchain and edge computing: motivations, frameworks, enabling functionalities, and challenges. Finally, some broader perspectives are explored.