BITS Faculty Publications
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Item A Simulated Annealing based Energy Efficient Task Scheduling Algorithm for Multi-core Processors(IJCCI, 2021) Mishra, AbhishekIn this paper we propose a Simulated Annealing (SA) based energy-efficient task scheduling algorithm for multi-core processors, the Simulated Annealing Energy Efficient Task Scheduling Algorithm (SAEETSA), and compare it with another algorithm, the Energy Efficient Task Scheduling Algorithm (EETSA). Our results show that for dual-core processors the SAEETSA algorithm is taking up to 16.78% less energy as compared to the EETSA algorithm, and for tri-core processors, the SAEETSA algorithm is taking up to 26.97% less energy as compared to the EETSA algorithm. 1 IItem The Orienteering Problem: A Review of Variants and Solution Approaches(WMSCI, 2022) Mishra, AbhishekOrienteering Problem (OP) fetched great attention in recent years because apart from the NP-hard routing problems, it is applicable in various applications like mobile crowd-sensing, manufacturing, etc. OP intends to maximize the overall price collected from the places covered in the itinerary within a timebound. In this paper, the latest improvements in NP-hard routing problems are discussed. Some variations of the traveling salesman problem (TSP), OP, and their recent solutions based on nature-inspired algorithms are explored. Finally, we present the future scope of the OP and its variants.Item Performance Evaluation of Simulated Annealing-Based Task Scheduling Algorithms(Springer, 2020-09) Mishra, AbhishekThe performance of simulated annealing (SA)-based task scheduling algorithms is evaluated. First, various parameters of SA are varied, and it is seen how it affects the schedule length (SL). The parameters that are varied are initial temperature, number of iterations, initial clustering, and cooling schedule. Then, one SA-based task scheduling algorithm is selected and compared with other task scheduling algorithms. The algorithms selected for comparison are cluster pair priority scheduling (CPPS), dominant sequence clustering (DSC), edge zeroing (EZ), and linear clustering (LC). Random task graphs are used for comparisonItem On Spectra of Corona Graphs(Springer, 2015) Mishra, AbhishekProduct graphs have been gainfully used in literature to generate mathematical models of complex networks which inherit properties of real networks. Realizing the duplication phenomena imbibed in the definition of corona product of two graphs, we define corona graphs. Given a small simple connected graph which we call basic graph, corona graphs are defined by taking corona product of the basic graph iteratively. We calculate the possibility of having a node of degree k in any corona graph which lead to obtain degree distribution of corona graphs. We determine explicit formulae of eigenvalues, Laplacian eigenvalues and signless Laplacian eigenvalues of corona graphs when the basic graph is regular. Computable expressions of eigenvalues and signless Laplacian eigenvalues are also obtained when the basic graph is a star graph.Item Benchmarking the contention aware nature inspired metaheuristic task scheduling algorithms(Springer, 2019-05) Mishra, AbhishekIn this paper, we consider the contention aware task scheduling problem on a grid topology of processors. By contention awareness, we mean that simultaneous communication on a link has to be serialized. To solve this problem, we propose several nature inspired metaheuristic algorithms: Simulated Annealing (SA), Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Bat Algorithm (BA), Cuckoo Search (CS), and Firefly Algorithm (FA). We perform benchmark evaluation of these algorithms for the Normalized Schedule Length (NSL) parameter. The benchmark task graphs that we consider are: random task graphs, peer set task graphs, systolic array task graphs, Gaussian elimination task graphs, divide and conquer task graphs, and fast Fourier transform task graphs.Item A Randomized Scheduling Algorithm For Multiprocessor Environments(World Scientific, 2012) Mishra, AbhishekIn this paper, we propose a randomized scheduling algorithm on a fully connected homogeneous multiprocessor environment. This is a randomized version of our earlier algorithm in which we used priority of modules that was dependent on the computation and the communication times associated with the modules. First we propose a generalization of our earlier scheduling algorithm with restricted number of clusters to reduce the time complexity. Then we apply randomization to the generalized algorithm and demonstrate its superiority over our previous work. We show the complexity of our proposed algorithm as O(ab |V| (|V|+|E|) log (|V|+|E|)). Here a is the number of randomization steps, and b is a limit on the number of clusters formed. If we use a and b as constants, then this gives a better complexity in comparison with the complexity of our previous algorithm that was O(|V|2(|V|+|E|) log (|V|+|E|)). In comparison with our previous work we get a performance improvement of up to 6.63% and a performance improvement of up to 12.56% when compared with Sarkar's Edge Zeroing algorithm.Item Complexity of a problem of energy efficient real-time task scheduling on a multicore processor(Wiley, 2014-06) Mishra, AbhishekThe problem of scheduling independent tasks with a common deadline for a multicore processor is investigated. The speed of cores can be varied (from a finite set of core speeds) using software controlled Dynamic Voltage Scaling. The energy consumption is to be minimized. This problem was called the Energy Efficient Task Scheduling Problem (EETSP) in a previous work in which a Monte Carlo algorithm was proposed for solving it. This work investigates the complexity of the EETSP problem. The EETSP problem is proved to be NP-Complete. Under the assumption of urn:x-wiley:10762787:media:cplx21561:cplx21561-math-0001, the EETSP problem is also proved to be inapproximableItem A Randomized Scheduling Algorithm for Multiprocessor Environments Using Local Search(World Scientific, 2016) Mishra, AbhishekThe LOCAL(A, B) randomized task scheduling algorithm is proposed for fully connected multiprocessors. It combines two given task scheduling algorithms (A, and B) using local neighborhood search to give a hybrid of the two given algorithms. Objective is to show that such type of hybridization can give much better performance results in terms of parallel execution times. Two task scheduling algorithms are selected: DSC (Dominant Sequence Clustering as algorithm A), and CPPS (Cluster Pair Priority Scheduling as algorithm B) and a hybrid is created (the LOCAL(DSC, CPPS) or simply the LOCAL task scheduling algorithm). The LOCAL task scheduling algorithm has time complexity O(|V||E|(|V |+|E|)), where V is the set of vertices, and E is the set of edges in the task graph. The LOCAL task scheduling algorithm is compared with six other algorithms: CPPS, DCCL (Dynamic Computation Communication Load), DSC, EZ (Edge Zeroing), LC (Linear Clustering), and RDCC (Randomized Dynamic Computation Communication). Performance evaluation of the LOCAL task scheduling algorithm shows that it gives up to 80.47 % improvement of NSL (Normalized Schedule Length) over other algorithms.Item An edge priority-based clustering algorithm for multiprocessor environments(Wiley, 2018-12) Mishra, AbhishekIn multiprocessor environments, the scheduling algorithms play a significant role in maximizing system performance. In this paper, we propose a clustering-based task scheduling algorithm called Edge Priority Scheduling (EPS) for multiprocessor environments. The proposed algorithm extends the idea of edge zeroing heuristic and uses the concept of edge priority to minimize the makespan of the task graph. The complexity of the EPS algorithm is O(|V||E|(|V| + |E|)), where |E| represents the number of edges and |V| denotes the number of nodes in the task graph. The experiments are performed for random task graphs and the task graphs generated from some representative real-world applications such as Gaussian Elimination and Fast Fourier Transform. The performance of the EPS algorithm is compared with six well-known algorithms such as EZ (Edge Zeroing), LC (Linear Clustering), CPPS (Cluster Pair Priority Scheduling), DCCL (Dynamic Computation Communication Load), RDCC (Randomized Dynamic Computation Communication), and LOCAL. The results show that the EPS algorithm outperforms the compared algorithms in terms of the normalized schedule length and speedup.