In a sensor-cloud infrastructure scheduling and allocation of resources are challenging tasks. Unlike computational cloud infrastructure, sensor may have varying capabilities and contexts of services. Moreover, some sensors may be available for a fixed time duration periodically. Depending on some dynamic factors, performance of a sensor may also vary. Such non-uniform performance may be predicted from previous observations. This article proposes novel scheduling algorithms for heterogeneous resources having varying capabilities, contexts of usage and non-uniform performance over time. Simulation results show that the algorithms perform efficiently.

In this paper we focus on solving the problem of scheduling heterogenous resources in Sensor Cloud Infrastructure. We consider that the sensing resources are periodically available and they vary, not only by their types, but also by their capabilities. It is also assumed that the performance of the resources are time varying and non-uniform. The requests specify the type and the capabilities it requires, and a time span it is interested in. There may not be a single resource that is available through the requested time span. Our objective is to schedule one or more such periodically available resources, that match the requested type and capabilities while minimizing resource migrations and maximizing the overall performance of the service.

Abstract The use of cloud computing and mobile devices is increasing in healthcare service delivery primarily because of the huge storage capacity of cloud, the heterogeneous structure of health data and the user-friendly interfaces on mobile devices. We propose a healthcare delivery scheme where a large knowledge base is stored in the cloud and user responses from mobile devices are input to this knowledge base to reach a preliminary diagnosis of diseases based on patients’ symptoms. However, instead of sending every response to the cloud and getting data from cloud server, it may often be desirable to prune a portion of the knowledge base that is stored in a graph form and download in to the mobile devices. Downloading data from cloud depends on the storage, battery power, processor of a mobile device, wireless network bandwidth and cloud processor capacity. In this paper, we focus on developing mathematical expressions involving the above mentioned criteria and show how these parameters are dependent on each other. The expressions built in this paper can be used in real-life scenarios to take decisions regarding the amount of data to be pruned in order to save energy as well as time.

This paper studies some challenges in Internet of Things-based remote healthcare and smart environment sensing domains. A cloudlet architecture with virtual sensors is proposed and its advantages are discussed in the context of resolving certain issues. The issues are handled by applying sensor virtualization techniques in cloudlet and developing a PaaS platform to support relatively expensive computation at the edge. The utility of our design has been summarized with a number of use case implementations, test results, and performance metrics, based on the challenges we initially faced.

In this paper, we propose a sensor-cloud environment which incorporates remotely located physical sensor devices in the generic resource family along with other resources like CPU, memory etc. The environment enables ubiquitous and on-demand access to virtual sensors which are abstractions of physical devices with enhanced capabilities. An architecture of the sensor-cloud environment is discussed in this paper that spans multiple infrastructures hosting local and remote resources. Responsibilities of each of the components in the sensor-cloud architecture are defined and algorithms for provisioning these components are discussed along with some proposed enhancements with respect to our earlier work. As forwarding data traffic is an important task, a high performance I/O redirection algorithm is also proposed to deliver sensed traffic to multiple instances of virtual sensors that is accumulated from one or multiple physical sensors. Performance of the I/O redirection algorithm is observed and the experimental results are presented in this paper.

Cloud service providers (CSPs) adapt different pricing models for their offered services. Some of the models are suitable for short term requirement while others may be suitable for the cloud service user's (CSU) long term requirement. For example, reservation-based pricing model is appropriate for a CSU's long term demand for resources. Finding the optimal amount of resources to be reserved in advance, to minimize the total cost, needs sufficient research effort. Various algorithms were discussed in the last couple of years to solve the resource reservation problem but most of them are based on integer programming problem (IPP) which is NP in nature. In this paper, we derive some heuristic-based polynomial time algorithms to find some near optimal solution to this problem. We show that the cost for CSU using our approach is comparable to the solution obtained using optimal IPP.

Dynamic or demand-driven service deployment in a Grid or Cloud environment is an important issue considering the varying nature of demand. Most distributed frameworks either offer static service deployment which results in resource allocation problems, or, are job-based where for each invocation, the job along with the data has to be transferred for remote execution resulting in increased communication cost. An alternative approach is dynamic demand-driven provisioning of services as proposed in earlier literature, but the proposed methods fail to account for the volatility of resources in a Grid environment. In this paper, we propose a unique peer-to-peer based approach for dynamic service provisioning which incorporates a Bit-Torrent like protocol for provisioning the service on a remote node. Being built around a P2P model, the proposed framework caters to resource volatility and also incurs lower provisioning cost.

In some applications of wireless sensor networks (WSN), sensor nodes are mobile while the sinks are static. In such dynamic environment, situations may arise where many sensor nodes are forwarding data through the same sink node resulting in sink overloading. One of the obvious effects of sink overloading is packet loss. It also indirectly affects the network lifetime in loss-sensitive WSN applications. Therefore, proper placement of sinks in such dynamic environment has a great impact on the performance of WSN applications. Multiple sink placement may not also work in some situations as node density may not be uniform. This paper introduces a sink placement scheme that aims at gathering experiences about sensor node density in region at different times and based on these observations, the scheme proposes candidate sink locations in order to reduce sink overloading. Next, based upon current sensor node density pattern, sinks at these locations are scheduled to active mode, while sinks at remaining candidate locations are scheduled to sleep mode. The second phase is repeated periodically. The scheme is implemented in a simulation environment and compared with another well-known strategy, namely Geographic Sink Placement (GSP). It has been observed that the proposed scheme exhibits better performance with respect to sink overloading and packet loss in comparison with GSP.

In this paper, we studied different issues related to storing and retrieval of health data. A data model is discussed for storing health records keeping in mind popular national and international standards of Electronic Health Records. The data model is then mapped to a big data solution, namely Cassandra. The mapping is done on the basis of a predetermined set of queries. Next, each of the queries is executed with varying datasizes and performance of the queries is observed.

Complexity of cloud services acts as a barrier towards adopting cloud to some of the Cloud Service Users. Cloud Service Middleware plays an important role to get rid of such problems. The middleware manages and optimizes the cloud resources to execute various jobs submitted by the users. A middleware can be enhanced to utilize the idle time of the reserved resources in cloud environment by scheduling these resources free of cost to jobs submitted by the same Cloud Service User (CSU) or a different CSU. This enhancement not only makes it possible to utilize the resources to their fullest extent, but also reduces the usage cost of the CSU who reserved the resources (or a different CSU in certain cases). However, finding the mapping between the jobs and available pool of resources is a key challenge to the design of a middleware. This paper proposes some scheduling algorithms to find such mappings that minimizes the job execution cost within public cloud.

Wireless Sensor Network(WSN) consists of sensor nodes which are deployed densely in an area of interest. The area is intended to be sensed or monitored. Each sensor node is a tiny and power constrained device which is assigned the task of monitoring. One of the most important requirements for wireless sensor networks is systematic collection of data, where sensed data are collected at sensor nodes and forwarded to a base station through number of hops for further processing. This paper presents an event-driven data gathering scheme in a Sensor-Cloud infrastructure. The scheme uses Fuzzy logic to ensure efficient data collection and report a select set of data which is required. Aim of this work is to minimal use of communication resource and reduce overhead while collecting sensor data. Also, an attempt is made to implement the scheme on Tiny OS using TelosB motes for real-time testing.

With the advent of Smart City, quality of life is bound to be better. But huge need arises to provide proper health-care services as the population is increasingly becoming urban-centric worldwide. The need-gap may be augmented with the help of modern technologies. Providing remote health-care services is a step forward. Successful diagnosis of health problems requires continuous monitoring of several health parameters. Health monitoring devices are power constrained and with limited communication capability. The devices are equipped with powerful microprocessors which are capable enough to take intelligent decisive actions by processing the received data. In order to prevent faster energy dissipation and constrained communication, selective data collection is an option. In this paper, a fuzzy assisted data gathering and alert scheme is proposed for healthcare services. Thus unnecessary waste of energy by transmission of unnecessary information is avoided. We have implemented it using Arduino and eHealth sensor kit.

Amazon's spot instances allow customers to bid on unused Amazon EC2 capacity and run those instances for as long as their bid exceeds the current spot price. Customers may expect their services at lower cost with spot instances compared to on-demand or reserved. However, the reliability is compromised since the instances providing the service may become unavailable at any time. In this paper, we study various check pointing schemes that can be used with spot instances. Also we devise some algorithms for check pointing scheme on top of application-centric resource provisioning framework that increase the reliability while reducing the cost significantly.

In recent years most of the cloud computing community adopt an infrastructure centric approach to optimize the cost of cloud resources considering both Cloud Computing Service Users(CCSU) and Cloud Computing Service Providers(CCSP). In this paper we have brought up the concept of Application-centric Cloud. With Application-centric Cloud we propose an architecture to provide a generalized framework for auto deploying, sharing, providing scalability, robustness and availability of any cloud based application. Along with application independence, the framework also considers CCSP independence so that an application can use resources across providers.

Resource management is one of the focus areas of Grid which identifies Job Modeling to be a very important part of it. A proper Job Modeling can be helpful in allocating jobs to their most suitable resource providers in Grid. This paper presents a feedback-guided Automatic Job Modeling technique that describes the process required to identify the most suitable resource provider for a particular job.

Performance analysis on a conventional distributed system consisting of a static (usually homogeneous) pool of computational resources comprises various techniques related to instrumentation, data collection, measurement, analysis and visualization. However, in a heterogeneous, dynamic environment, like grid, post-mortem analysis is of no use and data needs to be collected and analyzed in real time. Novel techniques are also required for dynamically tuning the application's performance and resource brokering in order to maintain the desired QoS. The objective of this paper is to present an agent framework for performance monitoring and analysis of applications running in grid environment and implementation of local tuning techniques for improving their performances. Results of a preliminary implementation are also presented in order to demonstrate the effectiveness of the agent framework.

In a dynamic environment like Grid, it is difficult to manage resources at application or user level. In order to support application execution in the context of Grid, a Resource Broker is essential and the task of resource brokering in such a heterogeneous, fast changing, distributed environment is non-trivial. In this paper we present the design and implementation of resource brokering strategies within a multi-agent framework. These strategies help in finding out an optimal allocation of resources for executing multiple concurrent jobs in a Grid environment. We discuss the different stages in resource brokering and their implementation within the framework. The paper also presents results of a preliminary implementation and demonstrates the effectiveness of our strategies.

In this paper we present the design and implementation of a multiagent framework for executing multiple concurrent jobs in a grid environment. Resource broker plays an imperative role in our framework. The aim of the resource broker is to find, select, reserve and allocate suitable resources to each job submitted by the client. Resource broker always tries to optimize resource utilization and ensure fairness. The multiagent framework assists in executing multiple concurrent jobs in an adaptive execution environment. Services offered by the framework include resource brokering, performance analysis, local tuning and more importantly rescheduling depending on the changing resource requirements. Part of the framework makes use of mobile agents for scheduling and rescheduling purposes. The paper discusses the implementation of the resource brokering and also describes the implementation of a part of the framework which schedules and reschedules the jobs and manages the resources on the basis of some performance monitoring data

An experimental study of four different schemes for parallelisation of FORTRAN codes is presented.One scheme is manual (performed by the programmer), the other three are automatic (performed entirely by software).The performance of code generated for two parallel computers from seven different test cases is compared, and reasons for differences in achieved performance between the four parallelisation schemes are analysed.It is concluded that, even using sophisticated techniques, high performance parallelised code cannot be generated by automatic tools unless they take into account feedback about execution-time behaviour.Both post-execution performance analysis and interaction with the programmer are necessary for success.This observation argues for user-centred, feedback-driven parallelisation tools that aid the manual process.