Mohammad Sajid

Fungus-mediated biological synthesis of gold nanoparticles: potential in detection of liver cancer

Background Nanomaterials are considered to be the pre-eminent component of the rapidly advancing field of nanotechnology. However, developments in the biologically inspired synthesis of nanoparticles are still in their infancy and consequently attracting the attention of material scientists throughout the world. Keeping in mind the fact that microorganism-assisted synthesis of nanoparticles is a safe and economically viable prospect, in the current study we report Candida albicans-mediated biological synthesis of gold nanoparticles. Methods and results Transmission electron microscopy, atomic force microscopy, and various spectrophotometric analyses were performed to characterize the gold nanoparticles. The morphology of the synthesized gold particles depended on the abundance of C. albicans cytosolic extract. Transmission electron microscopy, nanophox particle analysis, and atomic force microscopy revealed the size of spherical gold nanoparticles to be in the range of 20–40 nm and nonspherical gold particles were found to be 60–80 nm. We also evaluated the potential of biogenic gold nanoparticles to probe liver cancer cells by conjugating them with liver cancer cell surface-specific antibodies. The antibody-conjugated gold particles were found to bind specifically to the surface antigens of the cancer cells. Conclusion The antibody-conjugated gold particles synthesized in this study could successfully differentiate normal cell populations from cancerous cells.

Turnaround Time Minimization-Based Static Scheduling Model Using Task Duplication for Fine-Grained Parallel Applications onto Hybrid Cloud Environment

ABSTRACT Cloud computing is a multifarious computing paradigm incorporating the benefits of service-oriented architecture and utility computing through virtualization. Hybrid cloud, an amalgamation of two or more public and/or private clouds, is gaining high popularity between users due to various reasons involving improved performance, flexible business operations, capacity expansion, optimized costs, and enhanced security. The efficient execution of fine-grained parallel applications onto hybrid cloud system becomes limited due to a number of factors. From the application point of view, it ranges from the dynamicity of the applications to their precedence and communication constraints while for the computational resources, it includes heterogeneity of processors and participating clouds with their interconnection topology. This work proposes a compile time hybrid cloud-based task duplication strategy to execute the fine-grained applications represented as directed acyclic graph (DAG) onto the hybrid cloud environment. The proposed strategy schedules the tasks based on a degree relative to the critical path in the DAG and tries to achieve lower bound of the DAG. Furthermore, it makes an effort to avoid redundant duplication by duplicating only the required parent tasks considering the available idle slots to minimize the execution time of the application. The experimental study reveals that the proposed strategy performs better than its peers in terms of achieving the lower bound more efficiently with lesser degree of duplication for fine-grained jobs. The strategy is highly useful for cloud environment as it results in lower cost of usage of resources with enhanced system utilization.

Level based batch scheduling strategy with idle slot reduction under DAG constraints for computational grid

Abstract Scheduling in a grid environment optimizing the given objective parameters has been proven to be NP-complete. This work proposes a Level based Batch scheduling Strategy with Idle slot Reduction (LBSIR) while considering the inter module communication within the modules of the jobs represented using Direct Acyclic Graph (DAG) with the objective of optimizing the turnaround time and response time for a computational grid. The model works in two phases, allocation phase and idle slot reduction phase. Allocation phase begins by dividing the batch into a number of partitions as per the precedence level/depth level followed by the assignment of sub-jobs/modules from the partition to the best fit node in terms of the execution time offered for all the partitions. The idle slots generated during the allocation phase in each depth level are then reduced by inserting the best fit modules into these slots in the idle slot reduction phase after allocation of modules from higher depth level. Levelized allocation ensures minimizing the average response time being very useful for user interactive applications. An experimental study of the proposed strategy has been performed by comparing it with other similar methods having common objectives for evaluating its place in the middleware.

Energy-efficient scheduling algorithms for batch-of-tasks BoT applications on heterogeneous computing systems

One of the major design constraints of a heterogeneous computing system is optimal scheduling, that is, mapping of tasks on the processing nodes in order to optimize the QoS parameters. Because of the huge energy consumption by computing resources, negative environmental effects and reduced system reliability, energy has unavoidably been added as a new parameter to the list of QoS parameters. Energy optimization in scheduling strategies along with makespan makes it an even more challenging combinatorial optimization problem. This work proposes two energy‐aware scheduling algorithms G1 and G2 to schedule a batch‐of‐tasks, made of a collection of independent tasks, on heterogeneous processors in order to minimize the makespan and the energy consumption. The proposed algorithms schedule tasks based on weighted aggregation cost function to the appropriate processors followed by task migration phase designed to further minimize the makespan and the energy consumption. The study evaluates the performance of the proposed algorithms with some of the peers, that is, MinMin, MINSuff on account of makespan, energy consumption, flowtime, and utilization. An experimental study reveals that the proposed algorithm (G2) consistently performs better under various test conditions. Copyright

An Analytical Model for Resource Characterization and Parameter Estimation for DAG-Based Jobs for Homogeneous Systems

High Performance Computing (HPC) systems demand and consume a significant amount of resources (e.g. server, storage, electrical energy) resulting in high operational costs, reduced reliability, and sometimes leading to waste of scarce natural resources. On one hand, the most important issue for these systems is achieving high performance, while on the other hand, the rapidly increasing resource costs appeal to effectively predict the resource requirements to ensure efficient services in the most optimized manner. The resource requirement prediction for a job thus becomes important for both the service providers as well as the consumers for ensuring resource management and to negotiate Service Level Agreements (SLAs), respectively, in order to help make better job allocation decisions. Moreover, the resource requirement prediction can even lead to improved scheduling performance while reducing the resource waste. This work presents an analytical model estimating the required resources for the modular job execution. The analysis identifies the number of processors required and the maximum and minimum bounds on the turnaround time and energy consumed. Simulation study reveals that the scheduling algorithms integrated with the proposed analytical model helps in improving the average throughput and the average energy consumption of the system. As the work predicts the resource requirements, it can even play an important role in Service-Oriented Architectures (SOA) like Cloud computing or Grid computing.

Hybrid bio-inspired scheduling algorithms for batch of tasks on heterogeneous computing system

Due to high operational cost, the problem of scheduling a batch of tasks (BoT) applications on heterogeneous computing system (HCS) remains a challenging problem. Accordingly, a plethora of evolutionary algorithms (EAs) and non-EAs have been proposed as solutions. Due to the ability of exploration of major solution space, EAs have been proven to be very effective in addressing the job scheduling problem. This work proposes two hybrid bio-inspired scheduling algorithms VPG and VDG featuring the combined best properties of VNS, PSO, DE and GA. The expected-time-to-compute (ETC) benchmark have been used to first present the performance of eight non-EAs viz. MCT, MinMin, MaxMin, Sufferage, HLTF, relative cost, MINMin and MINSuff in terms of makespan and energy consumption. The study is then extended to evaluate the performance of VPG, VDG and their seeded variants with GA, PSO and DE. Simulation study establishes the superior performance of VDG over peers.

An Improved Fuzzy Voting Scheme for Fault Tolerant Systems

Voting is a widely used fault masking techniques for safety-critical systems to enhance the overall reliability of the system. Researchers over the period have proposed numerous advanced techniques in order to improve on the drawback of the existing methods. In this paper a fuzzy voting scheme has been survey and a generalized improved fuzzy voting scheme has been proposed. A comparative study of these schemes has also been carried out. It is found that proposed model is better than existing models. Single objective, multi-objective objective and many objective will be applied in future.

Level based task duplication strategy to minimize the job turnaround time

One of the major addressable issues in Parallel and Distributed Systems is scheduling i.e. mapping of tasks on processing nodes in order to optimize the QoS parameters. Task Duplication is an effective approach to minimize turnaround time and communication overheads and to improve system robustness. Task duplication effectively ensures redundantly execution of some task on which some other task critically depends. The task scheduling with duplication and without duplication is known to be an NP-complete problem. This work proposes a static scheduling based on module dependence degree and task duplication in order to minimize the turnaround time of the job.