Efficient Task Scheduling Algorithms for Cloud Computing Environment

Sindhu, Savita; Mukherjee, Saswati

doi:10.1007/978-3-642-22577-2_11

Cited by 64 publications

(31 citation statements)

References 7 publications

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“…In addition, Fog nodes can compress and reorganize the web objects for optimal speed. In addition, various compelling research studies [30][31][32] have been presented for improving the performance of SDN and virtual machines by making use of cloudlets, which are able to perform dynamic VM synthesis, single-hop low-latency wireless access and creates the VM overlays to only load the difference of desired custom VM and its base VM. These features have been implemented by Carnegie Mellon University in a project called Elijah and is available on Github repository [33].…”

Section: Software Defined and Virtualized Radio Access Networkmentioning

confidence: 99%

Fog computing security: a review of current applications and security solutions

2017

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Fog computing is a new paradigm that extends the Cloud platform model by providing computing resources on the edges of a network. It can be described as a cloud-like platform having similar data, computation, storage and application services, but is fundamentally different in that it is decentralized. In addition, Fog systems are capable of processing large amounts of data locally, operate on-premise, are fully portable, and can be installed on heterogeneous hardware. These features make the Fog platform highly suitable for time and location-sensitive applications. For example, Internet of Things (IoT) devices are required to quickly process a large amount of data. This wide range of functionality driven applications intensifies many security issues regarding data, virtualization, segregation, network, malware and monitoring. This paper surveys existing literature on Fog computing applications to identify common security gaps. Similar technologies like Edge computing, Cloudlets and Micro-data centres have also been included to provide a holistic review process. The majority of Fog applications are motivated by the desire for functionality and end-user requirements, while the security aspects are often ignored or considered as an afterthought. This paper also determines the impact of those security issues and possible solutions, providing future security-relevant directions to those responsible for designing, developing, and maintaining Fog systems.

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Section: Software Defined and Virtualized Radio Access Networkmentioning

confidence: 99%

Fog computing security: a review of current applications and security solutions

2017

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“…In [12] Budget and Deadline Constrained Heuristic is presented which is based upon Heterogeneous Earliest Finish Time and results in reduction of the cost and execution time of the system. S. Sindhu and S. Mukherjee [13] proposed two algorithms LCFP and SCFP which schedule the cloudlets on the VMs according to their length. In LCFP, longest cloudlet is scheduled first in the system which results n reduction in the reduction in makespan of the system.…”

Section: Related Workmentioning

confidence: 99%

Optimization of Task Scheduling Algorithm through QoS Parameters for Cloud Computing

Monika¹,

Jindal²

2016

MATEC Web of Conferences

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Abstract. Cloud computing is an incipient innovation which broadly spreads among analysts. It furnishes clients with foundation, stage and programming as enhancement which is easily available by means of web. A cloud is a sort of parallel and conveyed framework comprising of a gathering of virtualized PCs that are utilized to execute various tasks to accomplish good execution time, accomplish due date and usage of its assets. The scheduling issue can be seen as the finding an ideal task of assignments over the accessible arrangement of assets with the goal that we can accomplish the wanted objectives for tasks. This paper presents an optimal algorithm for scheduling tasks to get their waiting time as a QoS parameter. The algorithm is simulated using Cloudsim simulator and experiments are carried out to help clients to make sense of the bottleneck of utilizing no. of virtual machine parallely.

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“…It is challenging to develop efficient solutions for task scheduling in a cloud environment, as can be seen in various examples (Sindhu and Mukherjee, 2011;Ma et al, 2013;Tsaia et al, 2013).…”

Section: Sce Schedulermentioning

confidence: 99%

Distributed Algorithms for Spatial Retrieval Queries in Geospatial Analysis

Bellini

Bruno

Cenni

et al. 2016

STCC

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Cloud infrastructures needs to become more smarter and dynamic in managing resources, requiring new solutions and tools to cope with processes of configuration and reconfiguration, automatic scaling, elastic computing and healthiness control. This paper presents a Smart Cloud solution for automatically managing cloud infrastructures, and programming the monitoring aspects directly from the configuration and deploy phases. This means that the proposed solution can enforce smart cloud intelligence to almost all Cloud Configuration management systems and orchestrators by using REST calls and XML files. The solution proposed includes a Smart Cloud Engine, an advanced Supervisor and Monitor, and a Knowledge Base. The Knowledge Base is grounded on a cloud ontology modeling cloud resources and relationships, Service Level Agreements and their evolution, constraints, metrics for assessment, etc. The Knowledge Base enables the reasoning on cloud structures and permits to implement strategies of efficient smart cloud management and intelligence: (i) verify and validate the configurations and related changes according to the cloud model and available resources; (ii) easily generate the processing control for verifying cloud resource healthiness and service level agreement verification; (iii) take decision about dynamic reconfiguration on cloud, for example for scaling, balancing, migration, etc. The proposed solution has been validated in the context of ICARO project on complex cloud configurations with good performance and low operating workload. The validation has also assessed the reliability and the scalability.

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Efficient Task Scheduling Algorithms for Cloud Computing Environment

Cited by 64 publications

References 7 publications

Fog computing security: a review of current applications and security solutions

Fog computing security: a review of current applications and security solutions

Optimization of Task Scheduling Algorithm through QoS Parameters for Cloud Computing

Distributed Algorithms for Spatial Retrieval Queries in Geospatial Analysis

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