Long Term Evolution (LTE) is the new standard specified by 3GPP on the way towards the fourth generation mobile network. The LTE introduces enhanced data link mechanisms to support successful implementation of new dat...
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Long Term Evolution (LTE) is the new standard specified by 3GPP on the way towards the fourth generation mobile network. The LTE introduces enhanced data link mechanisms to support successful implementation of new data services across the network. The incorporated scheduling mechanisms can significantly contribute to this goal. In this paper novel scheduling algorithms are presented and compared to the Max C/I Downlink scheduler for LTE, which is characterized by high data rates at cell level, but poor fairness. The newly developed algorithms allow fair distribution of available resources. Simulation results presented in the paper show that the implementation of these newly developed algorithms enables improvement of the overall system capacity and user level performances.
Server less computing solutions within the cloud presentsan exceptional opportunity for corporations to gain progressed scalability, availability, and fee efficiency. To reap such blessings, establishments need to uti...
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ISBN:
(数字)9798350394399
ISBN:
(纸本)9798350394405
Server less computing solutions within the cloud presentsan exceptional opportunity for corporations to gain progressed scalability, availability, and fee efficiency. To reap such blessings, establishments need to utilize the handiest dynamic scheduling algorithms that are tailor-made to their unique needs. These algorithms can include predictive scheduling, call for-driven scheduling, and application-conscious scheduling, among others. Predictive scheduling algorithms are looking for to expect capacity call for to prevent erratic performance. This study focuses on dynamic scheduling algorithms for serverless computing solutions in the cloud. The researchers explore the characteristics of serverless computing models and the challenges of dynamic scheduling. A comprehensive evaluation is conducted on various scheduling algorithms, taking into consideration performance metrics such as throughput, response time, and resource utilization. The results show that dynamic scheduling algorithms are effective in optimizing resource allocation and improving overall system performance. Specific values derived from the results include a significant reduction in resource wastage, improved scalability, and increased cost-effectiveness. These findings suggest that dynamic scheduling algorithms are crucial for efficient and scalable serverless computing solutions in the cloud. With the aid of applying the maximum suitable dynamic scheduling algorithms tailor-made to precise desires, corporations could be higher prepared to fulfill their formidable cloud-computing dreams.
Internet is indispensable in all of the activities of today's society. Cloud computing emerges as a demanded recent technology to make the use of internet in a simplified, cost-affordable and timely available form...
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ISBN:
(纸本)9781665416658
Internet is indispensable in all of the activities of today's society. Cloud computing emerges as a demanded recent technology to make the use of internet in a simplified, cost-affordable and timely available form to the user. The primary purpose of cloud computing is to utilize the resource efficiently, allocate the tasks to the resources, balance their load and satisfy the agreements of user without fail. This research paper includes a brief analysis and comparison of the traditional task scheduling algorithms like FCFS, RR, and MCT with recent algorithms such as IWD, TSCSA, LBMPSO. All of the recent time algorithms are satisfying multi-objective qualities and ensures economic feasibility.
Cloud computing is a model for delivering information technology services, wherein resources are retrieved from the Internet through web-based tools and applications instead of a direct connection to a server. The cap...
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ISBN:
(纸本)9781467384919
Cloud computing is a model for delivering information technology services, wherein resources are retrieved from the Internet through web-based tools and applications instead of a direct connection to a server. The capability to provision and release cloud computing resources with minimal management effort or service provider interaction led to the rapid increase of the use of cloud computing. Therefore, balancing cloud computing resources to provide better performance and services to end users is important. Load balancing in cloud computing means balancing three important stages through which a request is processed. The three stages are data center selection, virtual machine scheduling, and task scheduling at a selected data center. User task scheduling plays a significant role in improving the performance of cloud services. This paper presents a review of various energy-efficient task scheduling methods in a cloud environment. A brief analysis of various scheduling parameters considered in these methods is also presented. The results show that the best power-saving percentage level can be achieved by using both DVFS and DNS.
Coflow is a network abstraction used to represent communication patterns in data centers. The coflow scheduling problem encountered in large data centers is a challenging NP-hard problem. This paper tackles the schedu...
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This paper describes the development status and key technologies of IPTV, and discusses the advantages of video multicast and the basic theory of multicast. Based on the analysis of the basic idea of stream scheduling...
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ISBN:
(数字)9781728147437
ISBN:
(纸本)9781728147444
This paper describes the development status and key technologies of IPTV, and discusses the advantages of video multicast and the basic theory of multicast. Based on the analysis of the basic idea of stream scheduling strategy and the behavior characteristics of user on demand, the mathematical model of stream scheduling strategy is established, and the performance evaluation parameters of stream scheduling algorithm are proposed. The stream scheduling algorithm of patch stream is elaborated in detail. Two improved stream scheduling algorithms of patching stream are proposed, and their advantages are analyzed through experiments.
Virtual CPU (VCPU) scheduling algorithms that efficiently manage processing-resource at the machine virtualization layer are key to facilitate resource sharing and workload consolidation in Clouds. Such algorithms are...
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Virtual CPU (VCPU) scheduling algorithms that efficiently manage processing-resource at the machine virtualization layer are key to facilitate resource sharing and workload consolidation in Clouds. Such algorithms are mostly inherited from pre-virtualization designs, thus need to be revamped and re-evaluated. This paper presents a simulation framework based on SAN model to rapidly evaluate Virtual CPU scheduling algorithms. The paper also demonstrates an evaluation of three VCPU scheduling algorithms using this framework.
The data warehouse is a centralized repository for analyzing and storing huge amounts of data. In distributed data warehouse, data can be shared across multiple data repositories which belong to one or more organizati...
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ISBN:
(纸本)9781467351416
The data warehouse is a centralized repository for analyzing and storing huge amounts of data. In distributed data warehouse, data can be shared across multiple data repositories which belong to one or more organizations. In general, clients expect short response times for their queries. The scheduling algorithms are used to resolve the issues and to accomplish the task with quick manner. In this paper, we are dealing grid based scheduling algorithms for query scheduling and resource management in distributed data warehouses. The algorithms are Optimal Resource Constraints (ORC), Grouping based Fine-grained Job scheduling (GFJS), Research on Novel Dynamic Resource Management (RNDRM) and New Resource Mechanism with Negotiate Solution (NRMNS). When we are combining both job and resource scheduling algorithms, the query processing time and memory size performances are lower than individual scheduling responses.
On-demand wireless data broadcast is an efficient way to disseminate data to a large number of mobile users. In many applications, such as stock quotes and flight schedules, users may have to download multiple data it...
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ISBN:
(纸本)9781467399548
On-demand wireless data broadcast is an efficient way to disseminate data to a large number of mobile users. In many applications, such as stock quotes and flight schedules, users may have to download multiple data items per request. However the multi-item request scheduling has not yet been thoroughly investigated for on-demand wireless data broadcasts. In this paper, we step-up on investigating this problem from viewpoint of theory and simulation. We develop a two-stage scheduling scheme to arrange the requested data items with the objective of minimizing the average access latency. The first stage is to select the data items to be broadcast in the next time period and the second stage is to schedule the broadcasting order for the data items selected in the first stage. We develop algorithms for the two stages respectively and analyze them both theoretically and practically. We also compare the proposed algorithms with other well known scheduling methods through simulation. The theoretical findings and simulation results reveal that significantly better access latency can be obtained by using our scheduling scheme rather than its competitors.
In the context of heterogeneous distributed systems like modern High-Performnace Computing (HPC) that must respond to unpredictable requests of variable complexity with variable resource requirements (processing power...
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In the context of heterogeneous distributed systems like modern High-Performnace Computing (HPC) that must respond to unpredictable requests of variable complexity with variable resource requirements (processing power as well as storage capacity), a classical scheduling algorithm would not be suitable. Therefore, hybrid dynamic scheduling approaches have been adopted. These later have the ability to adapt over time based on the knowledge gained from the results of previous work. Several techniques are thus used to optimize these algorithms such as resources clustering. In this paper, we propose a comparative study of some of most popular algorithms in order to highlight the situations in which each algorithm is more suitable. We evaluate their performance and evolution in a realistic setting of CloudSim tool without neglecting load-balancing, and measure these performance metrics at runtime.
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