Tamal Adhikary

An intelligent approach for virtual machine and QoS provisioning in cloud computing

Cloud Computing has become the most popular distributed computing environment because it does not require any user level management and controlling on the low-level implementation of the system. However, efficient resource provisioning is a key challenge for cloud computing and resolving such kind of problem can reduce under or over utilization of resources, increase user satisfaction by serving more users during peak hours, reduce implementation cost for providers and service cost for users. Existing works on cloud computing focuses to accurate estimation of the capacity needs, static or dynamic VM (Virtual Machine) creation and scheduling. But significant amount of time is required to create and destroy VMs which could be used to serve more user requests. In this paper, an adaptive QoS (Quality of Service) aware VM provisioning mechanism is developed that ensures efficient utilization of the system resources. The VM for similar type of requests has been recycled so that the VM creation time can be minimized and used to serve more user requests. In the proposed model, QoS is ensured by serving all the tasks within the requirements described in SLA. Tasks are separated using multilevel queue and the most urgent task is given high priority. The simulation-based experimental results shows that a great number of tasks can be served compared to others which will help to satisfy customers during the peak hour.

A QoS and profit aware cloud confederation model for IaaS service providers

Cloud confederation is the union of cloud data centers that allows outsourcing user requests by a cloud provider during peak hours to liberate it from the constraint of limited physical resources. Federation extends some of the features of cloud like low cost, scalability, robustness and availability while increases the price and revenue of the providers along with maintaining a high QoS by effectively utilizing the resources of the data centers. Existing works on cloud federation do not consider all three categories of instances and hence cannot maximize their profits. Moreover, the process of establishing cloud federation and taking cost efficient decisions based on QoS parameters has not yet been provided in recent works. In this paper, we have designed a data communication model for the members of a cloud confederation, which exploits clear understanding of the usage pattern, types of requests in addition to infrastructure expenses. We develop an algorithm and a model for cost calculation, which enhances the decision making process over all the VM types (on-demand, reserved, spot) to increase resources utilization and profit. The simulation results, conducted on CloudSim, indicate that, our proposed model and algorithm enhances the profit, utilization, and QoS in a cloud confederation environment compared to a number of state-of-the-art approaches.

Energy-Efficient Scheduling Algorithms for Data Center Resources in Cloud Computing

A significant amount of energy is consumed to render high-level computation tasks in large scale cloud computing applications. The state-of-the-art energy saving techniques based on centralized job placement approaches reduce the reliability of operation due to a single point of failure. Moreover, the existing works do not consider energy consumption cost for communication devices and network appliances which contribute a lot. In this paper, we have proposed a mechanism for cluster formation based on network vicinity among the data servers. We have developed two distributed and localized intra-cluster and inter-cluster VM scheduling algorithms based on energy calculation, resource requirement and availability. Our proposed scheduling algorithms manage VMs to reduce the energy consumption of both the servers and networking devices. Simulation results show that our proposed distributed VM scheduling algorithms can conserve significant amount of energy compared to state-of-the-art works.

Quality of service aware cloud resource provisioning for social multimedia services and applications

The increasing number of next-generation multimedia services and social media applications in cloud computing put additional challenges in efficient resource provisioning that targets to minimize under or over utilization of resources as well as to increase user satisfaction. Most of the works in the literature focused either on resource estimation and scheduling approaches or energy consumption for executing social media data processing applications. However, they do not consider energy consumption cost for communication devices and network appliances and schedule Virtual Machines (VMs) based on centralized job placement approach. In this paper, we develop a Quality of Service (QoS) aware cloud resource management system that decreases energy consumption and increases resource utilization by diverse multimedia social applications. In order to minimize the VM creation time we allow recycling of VM resources for user request with similar resource requirements. We have developed two distributed and localized resource management algorithms based on energy conservation, and requirements and availability of resources. The results of simulation experiments depict that the resource scheduling system greatly reduces the amount of energy consumption while maintaining the QoS of social multimedia applications.

Quality of Service Aware Reliable Task Scheduling in Vehicular Cloud Computing

Vehicular Cloud Computing (VCC) facilitates real-time execution of many emerging user and intelligent transportation system (ITS) applications by exploiting under-utilized on-board computing resources available in nearby vehicles. These applications have heterogeneous time criticality, i.e., they demand different Quality-of-Service levels. In addition to that, mobility of the vehicles makes the problem of scheduling different application tasks on the vehicular computing resources a challenging one. In this article, we have formulated the task scheduling problem as a mixed integer linear program (MILP) optimization that increases the computation reliability even as reducing the job execution delay. Vehicular on-board units (OBUs), manufactured by different vendors, have different architecture and computing capabilities. We have exploited MapReduce computation model to address the problem of resource heterogeneity and to support computation parallelization. Performance of the proposed solution is evaluated in network simulator version 3 (ns-3) by running MapReduce applications in urban road environment and the results are compared with the state-of-the-art works. The results show that significant performance improvements in terms of reliability and job execution time can be achieved by the proposed task scheduling model.

Big media healthcare data processing in cloud: a collaborative resource management perspective

Nowadays, big media healthcare data processing in cloud has become an effective solution for satisfying QoS demands of medical users. It can support various healthcare services such as pre-processing, storing, sharing, and analysis of monitored data as well as acquiring context-awareness. However, to support energy and cost savings, the union of cloud data centers termed as cloud confederation can be an promising approach, which helps a cloud provider to overcome the limitation of physical resources. However, the key challenge in it is to achieve multiple contradictory objectives, e.g., meeting the required level of services defined in service level agreement, maintaining medial users’application QoS, etc. while maximizing profit of a cloud provider. In this paper, for executing heterogeneous big healthcare data processing requests from users, we develop a local and global cloud confederation model, namely FnF, that makes an optimal selection decision for target cloud data center(s) by exploiting Fuzzy logic. The FnF trades off in between profit of cloud provider and user application QoS in selecting federated data center(s). In addition, FnF enhances its decision accuracy by precisely estimating the resource requirements for the big data processing tasks using multiple linear regression. The proposed FnF model is validated through numerical as well as experimental evaluations. Simulation results depict the effectiveness and efficiency of the FnF model compared to state-of-the-art approaches.

Test implementation of a sensor device for measuring soil macronutrients

Continuous cropping without adequate measurement and provisioning of soil nutrient may endanger the sustainability of agriculture. Soil nutrient measurement is greatly required for proper plant growth and effective fertilization. Existing methods of soil testing are costly and time consuming. In this paper, we have developed a sensing system using high precision, wide spectral range Photo Diode (PD), low spectral-width Light Emitting Diode (LED), microcontroller, analog-to-digital converter (ADC) for measuring soil macronutrients. We have integrated a GPRS modem with our sensing unit for remote data collection to a server. A cell of 5.5 mm path length is used to contain soil solutions. The resolution of 0.1-20 mg/100g has been used as standard solution. The test samples are taken from different farmlands and the results are compared with those obtained by a color chart judgement after laboratory analysis. The results found from our proposed system has good level of agreement with the laboratory results.

A distributed wake-up scheduling algorithm for base stations in green cellular networks

Recently, designing energy-efficient techniques in cellular communication has got immense research interest around the world. In this paper, we focus on energy-efficient operation of base transceiver stations (BTSs) in cellular network. We present a distributed wake-up scheduling algorithm (DWS) for the BTSs that use three different modes of operation: off, sleep and active. In DWS, a BTS dynamically takes decision on its operation mode according to the measured traffic load of itself and its neighborhood BTSs in a distributed manner. Our simulation results show that the DWS can save as much as 28% more energy compared to a state-of-the-art Eco-Inspired [6] algorithm.

Tradeoff between execution speedup and reliability for compute-intensive code offloading in mobile device cloud

With the advent of different mobile computing technologies, mobile devices have opened up a plethora of computational infrastructure to provide improved performance for compute-intensive applications to the end users. Mobile Device Cloud (MDC) technology brings the code offloading mechanism from distant cloud to neighbor mobile devices. However, the major challenges of code offloading in MDC systems include maximization of computation speedup and reliability; unfortunately, these two performance parameters often oppose each other. In this paper, an optimization framework, namely TESAR, has been devised to tradeoff between application execution speedup and reliability while maintaining device energy within a predefined range. We also provide an algorithm for developing a dependency tree among the modules of an application so as to allow higher number of parallel executions, wherever and whenever it is possible. The emulation results of the proposed algorithm outperform the relevant state-of-the-art works in terms of application completion time, communication latency and rescheduling overhead.

Parsimonious renewable energy management policies for smart IoT devices

Energy scarcity at homes is becoming a critical issue due to exponential growth of energy consumption by numerous smart home appliances. Renewable energy sources help to reduce the pressure of grid operation, enabling Smart Home Energy Management System (HEMS) using Internet of Things (IoT) devices. In this paper, we have developed policies for parsimonious renewable energy management using smart IoT devices. With the help of Exponential Weighted Moving Average (EWMA), an HEMS predicts energy production and consumption. The results of the prediction decides whether to sell or purchase energy following it is in energy surplus or deficit, respectively. Our energy budget management follows max-min fairness algorithm. The performance evaluation of our proposed system shows that significant performance improvement in terms of monetary expense and user utility can be achieved compared to state-of-the-art works.