PRIORITIZED TASK SCHEDULING IN FOG COMPUTING

Cloud computing is an environment where virtual resources are shared among the many users over network. A user of Cloud services is billed according to pay-per-use model associated with this environment. To keep this bill to a minimum, efficient resource allocation is of great importance. To handle the many requests sent to Cloud by the clients, the tasks need to be processed according to the SLAs defined by the client. The increase in the usage of Cloud services on a daily basis has introduced delays in the transmission of requests. These delays can cause clients to wait for the response of the tasks beyond the deadline assigned. To overcome these concerns, Fog Computing is helpful as it is physically placed closer to the clients. This layer is placed between the client and the Cloud layer, and it reduces the delay in the transmission of the requests, processing and the response sent back to the client greatly. This paper discusses an algorithm which schedules tasks by calculating the priority of a task in the Fog layer. The tasks with higher priority are processed first so that the deadline is met, which makes the algorithm practical and efficient

Cloudbus Toolkit for Market-Oriented Cloud Computing

This keynote paper: (1) presents the 21st century vision of computing and identifies various IT paradigms promising to deliver computing as a utility; (2) defines the architecture for creating market-oriented Clouds and computing atmosphere by leveraging technologies such as virtual machines; (3) provides thoughts on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain SLA-oriented resource allocation; (4) presents the work carried out as part of our new Cloud Computing initiative, called Cloudbus: (i) Aneka, a Platform as a Service software system containing SDK (Software Development Kit) for construction of Cloud applications and deployment on private or public Clouds, in addition to supporting market-oriented resource management; (ii) internetworking of Clouds for dynamic creation of federated computing environments for scaling of elastic applications; (iii) creation of 3rd party Cloud brokering services for building content delivery networks and e-Science applications and their deployment on capabilities of IaaS providers such as Amazon along with Grid mashups; (iv) CloudSim supporting modelling and simulation of Clouds for performance studies; (v) Energy Efficient Resource Allocation Mechanisms and Techniques for creation and management of Green Clouds; and (vi) pathways for future research.Comment: 21 pages, 6 figures, 2 tables, Conference pape

Decision Support for Selection of Cloud Service Providers

Clear and consistent assessment of the variouscapabilities of cloud service providers (CSPs) will become anessential factor in deciding on which CSPs to use in the future,particularly as cloud service provision expands futher into moresensitive and regulated areas. This paper describes an approachthat is useful in this regard. Specifically, we describe a mechanismin which context is gathered relating to CSPs; this is inputted to arule-based system and decisions are output about the suitabilityof each CSP, including an analysis of privacy and security riskand recommended stipulations to be taken into account whennegotiating contracts and SLAs

Hybrid ant colony system and genetic algorithm approach for scheduling of jobs in computational grid

Metaheuristic algorithms have been used to solve scheduling problems in grid computing.However, stand-alone metaheuristic algorithms do not always show good performance in every problem instance. This study proposes a high level hybrid approach between ant colony system and genetic algorithm for job scheduling in grid computing.The proposed approach is based on a high level hybridization.The proposed hybrid approach is evaluated using the static benchmark problems known as ETC matrix.Experimental results show that the proposed hybridization between the two algorithms outperforms the stand-alone algorithms in terms of best and average makespan values

A dual-band priority assignment algorithm for dynamic QoS resource management

Future high-quality consumer electronics will contain a number of applications running in a highly dynamic environment, and their execution will need to be efficiently arbitrated by the underlying platform software. The multimedia applications that currently execute in such similar contexts face frequent run-time variations in their resource demands, originated by the greedy nature of the multimedia processing itself. Changes in resource demands are triggered by numerous reasons (e.g. a switch in the input media compression format). Such situations require real-time adaptation mechanisms to adjust the system operation to the new requirements, and this must be done seamlessly to satisfy the user experience. One solution for efficiently managing application execution is to apply quality of service resource management techniques, based on assigning and enforcing resource contracts to applications. Most resource management solutions provide temporal isolation by enforcing resource assignments and avoiding any resource overruns. However, this has a clear limitation over the cost-effective resource usage. This paper presents a simple priority assignment scheme based on uniform priority bands to allow that greedy multimedia tasks incur in safe overruns that increase resource usage and do not threaten the timely execution of non-overrunning tasks. Experimental results show that the proposed priority assignment scheme in combination with a resource accounting mechanism preserves timely multimedia execution and delivery, achieves a higher cost-effective processor usage, and guarantees the execution isolation of non-overrunning tasks