Optimization of Resource Usage for Computer-Based Education through Mobile, Speech and Sky Computing Technology

Cloud computing encompasses any subscription-based or pay-per-use service over the Internet. Using a cloud that is owned by a single service provider has its demerit to the customer such as the risk of downtime or breakdown of equipment arising from disaster that can jeopardize the subscribers’ business. Data security and reliability due to over centralization of company’s data poses a high risk for subscribers, hence a call for distributed cloud also known as Sky Computing. When application is distributed across several clouds with varied interest, infrastructure, policy, etc, the issue therefore will be how to determine the most cost effective cloud during access time. The amount of money a student pays in accessing learning content is determined by how much an institution pay as subscription to cloud providers. The objective of this study is to utilize optimization theory to determine the most cost effective cloud for mobile virtual education in Sky Computing environment. This will be achieved by optimizing resource usage for Computer-based Education through Mobile, Speech and Sky Computing Technology. As a proof of concept, we will design and implement a cloud middle ware (CMW) to interface with an eEducation system. Access to the eEducation shall be Mobile, Speech and Web. Through the communication platform, the students can communicate with their teacher at any convenient time, and vice versa at the most reduced cost

Optimization of Resource Usage for Computer-Based Education through Mobile, Speech and Sky Computing Technology

Cloud computing encompasses any subscription-based or pay-per-use service over the Internet. Using a cloud that is owned by a single service provider has its demerit to the customer such as the risk of downtime or breakdown of equipment arising from disaster that can jeopardize the subscribers’ business. Data security and reliability due to over centralization of company’s data poses a high risk for subscribers, hence a call for distributed cloud also known as Sky Computing. When application is distributed across several clouds with varied interest, infrastructure, policy, etc, the issue therefore will be how to determine the most cost effective cloud during access time. The amount of money a student pays in accessing learning content is determined by how much an institution pay as subscription to cloud providers. The objective of this study is to utilize optimization theory to determine the most cost effective cloud for mobile virtual education in Sky Computing environment. This will be achieved by optimizing resource usage for Computer-based Education through Mobile, Speech and Sky Computing Technology. As a proof of concept, we will design and implement a cloud middle ware (CMW) to interface with an eEducation system. Access to the eEducation shall be Mobile, Speech and Web. Through the communication platform, the students can communicate with their teacher at any convenient time, and vice versa at the most reduced cost

Joint Computation Offloading and Prioritized Scheduling in Mobile Edge Computing

With the rapid development of smart phones, enormous amounts of data are generated and usually require intensive and real-time computation. Nevertheless, quality of service (QoS) is hardly to be met due to the tension between resourcelimited (battery, CPU power) devices and computation-intensive applications. Mobileedge computing (MEC) emerging as a promising technique can be used to copy with stringent requirements from mobile applications. By offloading computationally intensive workloads to edge server and applying efficient task scheduling, energy cost of mobiles could be significantly reduced and therefore greatly improve QoS, e.g., latency. This paper proposes a joint computation offloading and prioritized task scheduling scheme in a multi-user mobile-edge computing system. We investigate an energy minimizing task offloading strategy in mobile devices and develop an effective priority-based task scheduling algorithm with edge server. The execution time, energy consumption, execution cost, and bonus score against both the task data sizes and latency requirement is adopted as the performance metric. Performance evaluation results show that, the proposed algorithm significantly reduce task completion time, edge server VM usage cost, and improve QoS in terms of bonus score. Moreover, dynamic prioritized task scheduling is also discussed herein, results show dynamic thresholds setting realizes the optimal task scheduling. We believe that this work is significant to the emerging mobile-edge computing paradigm, and can be applied to other Internet of Things (IoT)-Edge applications

User subscription-based resource management for Desktop-as-a-Service platforms

The Desktop-as-a-Service (DaaS) idiom consists of utilizing a cloud or other server infrastructure to host the user's desktop environment as a virtual desktop. Typical for cloud and DaaS services is the pay-as-you-go pricing model in combination with the availability of multiple subscription types to accommodate the needs of the users. However, optimal cost-efficient allocation of the virtual desktops to the infrastructure proves to be a combinatorial NP-hard problem, for which a heuristic is presented in the current article. We present a cost model for the DaaS service, from which a revenue of different configurations of virtual desktops to the servers can be derived. In this cost model, both subscription fee and penalties for degraded service are recorded, that are described in service-level agreements (SLAs) between the service provider and the users, and make realistic assumptions that different subscription types result in particular SLA contracts. The heuristic proposed states that for a given user base for which the virtual desktops (VDs) must be hosted, the VDs should be spread evenly over the infrastructure. Experiments through discrete event simulation show that this heuristic yields an approximation within 1 % of the theoretically achievable revenue

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