Prasanta K. Jana

SLA-based task scheduling algorithms for heterogeneous multi-cloud environment

Service-level agreement (SLA) is a major issue in cloud computing because it defines important parameters such as quality of service, uptime, downtime, period of service, pricing, and security. However, the service may vary from one cloud service provider (CSP) to another. The collaboration of the CSPs in the heterogeneous multi-cloud environment is very challenging, and it is not well covered in the recent literatures. In this paper, we present two SLA-based task scheduling algorithms, namely SLA-MCT and SLA-Min-Min for heterogeneous multi-cloud environment. The former algorithm is a single-phase scheduling, whereas the latter one is a two-phase scheduling. The proposed algorithms support three levels of SLA determined by the customers. Furthermore, the algorithms incorporate the SLA gain cost for the successful completion of the service and SLA violation cost for the unsuccessful end of the service. We simulate the proposed algorithms using benchmark and synthetic datasets. The experimental results of the proposed SLA-MCT are compared with three single-phase task scheduling algorithms, namely CLS, Execution-MCT, and Profit-MCT, and the results of the proposed SLA-Min-Min are compared with two-phase scheduling algorithms, namely Execution-Min-Min and Profit-Min-Min in terms of four performance metrics, namely makespan, average cloud utilization, gain, and penalty cost of the services. The results clearly show that the proposed algorithms properly balance between makespan and gain cost of the services in comparison with other algorithms.

A novel cost-efficient approach for deadline-constrained workflow scheduling by dynamic provisioning of resources

Abstract Workflow scheduling is a crucial aspect of cloud computing that should be performed in an efficient manner for optimal utilization of resources. The development of a cost-efficient algorithm has always been an important topic of research in this regard. In this paper, we propose a novel workflow scheduling algorithm, which is cost-efficient and deadline-constrained. The proposed algorithm is consolidated by dynamic provisioning of the resources, using k -means clustering technique and a variant of the Subset-Sum problem. In the algorithm, we consider level based scheduling using the concept of Bag of Tasks ( b o t s ) and develop a new technique for associating deadlines with each b o t . Through extensive simulation runs, we show that the proposed algorithm outperforms the existing algorithms like Dynamic Provisioning Dynamic Scheduling (DPDS) and Infrastructure as a Service (IaaS) Cloud-Partial Critical Path (IC-PCP). The effectiveness of our algorithm over these two algorithms is also illustrated through the popular statistical test ANOVA and its subsequent post-hoc analysis.

Application of wireless sensor network for environmental monitoring in underground coal mines: A systematic review

Abstract The production, productivity and safety of underground coal mines are greatly affected by the environmental conditions of the mines. Hence, continuous monitoring of the complex and hazardous mine environment is essential for ensuring safe coal production. Nowadays, wireless sensor network (WSN) technique is widely used for monitoring of workplace environment and other aspects in underground coal mines. This paper presents a systematic literature review on the state-of-the-art researches on application of WSN in underground coal mines and to identify the gray areas needing more attention for wide application of WSN technique. Advanced search is conducted on various digital libraries for extracting relevant studies for the review. The search strategy identified 762 studies, among which 52 relevant studies are selected for thorough review. Application of WSN for monitoring of environmental parameters and other aspects in underground coal mines, such as mine gases, temperature and humidity, dust, fire, roof fall, etc. are discussed. Moreover, the need for further research for effective utilization of WSN technique and application of new advanced techniques for efficient monitoring of underground coal mines are explored in this paper.

A Gravitational Search Algorithm for Energy Efficient Multi-sink Placement in Wireless Sensor Networks

Optimal placement of multi-sink has been accepted an energy efficient approaches of extending the life of wireless sensor networks (WSNs). In this paper, a Gravitational Search Algorithm (GSA) based approach called GSA-MSP (Gravitational Search Algorithm based Multi-Sink Placement) for multi-sink placement for sensor network has been proposed. The algorithm has been designed with proper encoding scheme and a new fitness function. We consider the energy, Euclidian distance from the gateways to the sinks, and data rate of gateways are as parameters for the efficient design of GSA-MSP. The GSA-MSP has been tested vigorously over a varying number of sensors, gateways and sinks on various scenarios of WSNs. To show the efficacy of the GSA-MSP has been compared with some existing algorithms.

A multi-objective and PSO based energy efficient path design for mobile sink in wireless sensor networks

Abstract Data collection through mobile sink (MS) in wireless sensor networks (WSNs) is an effective solution to the hot-spot or sink-hole problem caused by multi-hop routing using the static sink. Rendezvous point (RP) based MS path design is a common and popular technique used in this regard. However, design of the optimal path is a well-known NP-hard problem. Therefore, an evolutionary approach like multi-objective particle swarm optimization (MOPSO) can prove to be a very promising and reasonable approach to solve the same. In this paper, we first present a Linear Programming formulation for the stated problem and then, propose an MOPSO-based algorithm to design an energy efficient trajectory for the MS. The algorithm is presented with an efficient particle encoding scheme and derivation of a proficient multi-objective fitness function. We use Pareto dominance in MOPSO for obtaining both local and global best guides for each particle. We carry out rigorous simulation experiments on the proposed algorithm and compare the results with two existing algorithms namely, tree cluster based data gathering algorithm (TCBDGA) and energy aware sink relocation (EASR). The results demonstrate that the proposed algorithm performs better than both of them in terms of various performance metrics. The results are also validated through the statistical test, analysis of variance (ANOVA) and its least significant difference (LSD) post hoc analysis.

An efficient scheduling scheme for mobile charger in on-demand wireless rechargeable sensor networks

Abstract Existing studies on wireless sensor networks (WSNs) have revealed that the limited battery capacity of sensor nodes (SNs) hinders their perpetual operation. Recent findings in the domain of wireless energy transfer (WET) have attracted a lot of attention of academia and industry to cater the lack of energy in the WSNs. The main idea of WET is to restore the energy of SNs using one or more wireless mobile chargers (MCs), which leads to a new paradigm of wireless rechargeable sensor networks (WRSNs). The determination of an optimal order of charging the SNs (i.e., charging schedule) in an on-demand WRSN is a well-known NP-hard problem. Moreover, care must be taken while designing the charging schedule of an MC as requesting SNs introduce both spatial and temporal constraints. In this paper, we first present a Linear Programming (LP) formulation for the problem of scheduling an MC and then propose an efficient solution based on gravitational search algorithm (GSA). Our method is presented with a novel agent representation scheme and an efficient fitness function. We perform extensive simulations on the proposed scheme to demonstrate its effectiveness over two state-of-the-art algorithms, namely first come first serve (FCFS) and nearest job next with preemption (NJNP). The simulation results reveal that the proposed scheme outperforms both the existing algorithms in terms of charging latency. The virtue of our scheme is also proved by the well-known statistical test, analysis of variance (ANOVA), followed by post hoc analysis.

A GSA based hybrid algorithm for bi-objective workflow scheduling in cloud computing

Abstract Workflow Scheduling in cloud computing has drawn enormous attention due to its wide application in both scientific and business areas. This is particularly an NP-complete problem. Therefore, many researchers have proposed a number of heuristics as well as meta-heuristic techniques by considering several issues, such as energy conservation, cost and makespan. However, it is still an open area of research as most of the heuristics or meta-heuristics may not fulfill certain optimum criterion and produce near optimal solution. In this paper, we propose a meta-heuristic based algorithm for workflow scheduling that considers minimization of makespan and cost. The proposed algorithm is a hybridization of the popular meta-heuristic, Gravitational Search Algorithm (GSA) and equally popular heuristic, Heterogeneous Earliest Finish Time (HEFT) to schedule workflow applications. We introduce a new factor called cost time equivalence to make the bi-objective optimization more realistic. We consider monetary cost ratio (MCR) and schedule length ratio (SLR) as the performance metrics to compare the performance of the proposed algorithm with existing algorithms. With rigorous experiments over different scientific workflows, we show the effectiveness of the proposed algorithm over standard GSA, Hybrid Genetic Algorithm (HGA) and the HEFT. We validate the results by well-known statistical test, Analysis of Variance (ANOVA). In all the cases, simulation results show that the proposed approach outperforms these algorithms.

An Efficient Request-Based Virtual Machine Placement Algorithm for Cloud Computing

The energy efficiency of cloud computing has drawn gigantic attention due to the explosive growth of cloud services. Moreover, this growth extends the capacity of various resources of the datacenters. As a circumstance, the amount of carbon footprints generated from the datacenters is sharply increased. Therefore, the objective is to use the datacenter’s resources proficiently without compromising the user requirements such that energy consumption is minimized. The recent studies have shown that the user requirements are provided in the form of virtual machines (VMs) which are deployed in the physical machines (PMs) of the datacenters based on the resource utilization or decreasing order of the VM capacity. However, these studies have not considered the capacity of the user requests. In this paper, we propose a request-based VM placement (RVMP) algorithm by considering the capacity of the requests. The proposed algorithm assigns the user requests to the VMs and further assigns the used VMs to the PMs based on the capacity of the requests and VMs respectively. Our simulation results on five different datasets, which are generated using Monte Carlo method, show that RVMP improves performance in terms of the number of used VMs and PMs, average PM utilization and energy consumption of PMs compared to state-of-the-art algorithms.

Minimum spanning tree-based delay-aware mobile sink traversal in wireless sensor networks.

Summary Energy efficient data collection in a delay-bound application is a challenging issue for mobile sink–based wireless sensor networks. Many researchers have proposed the concept of rendezvous points (RPs) to design the path for the mobile sink. Rendezvous points are the locations in the network where the mobile sink halts and collects data from the nearby sensor nodes. However, the selection of RPs for the design of path has a significant impact on timely data collection from the network. In this paper, we propose an efficient algorithm for selection of the RPs for efficient design of mobile sink trajectory in delay-bound applications of wireless sensor networks. The algorithm is based on a virtual path and minimum spanning tree and shown to maximize network lifetime. We perform extensive simulations on the proposed algorithm and compare results with the existing algorithms to demonstrate the efficiency of the proposed algorithm of various performance metrics.

Energy Efficient Clustering for Wireless Sensor Networks: A Gravitational Search Algorithm

Clustering is an efficient technique for saving energy of wireless sensor networks (WSNs). In this paper, a Gravitational Search Algorithm (GSA) based approach has been presented called GSA-EEC (GSA based Energy Efficient Clustering). The algorithm is designed with an efficient encoding scheme of an and a new fitness function. For the efficient design of WSNs. we consider the Euclidian distance from the sensors to gateways and gateways to sink and residual energy of gateways. The GSA-EEC is simulated extensively with varying number of sensor and gateways and various scenarios of WSNs. To show the efficacy of the GSA-EEC, we compared with some of the benchmark clustering algorithms.