Evaluation and Enhancement of the EdgeCloudSim using Poisson Interarrival time and Load capacity

Zaitoun, Toqa' Abu; Issa, Manal Bani; Banat, Sajeda; Mardini, Wail

doi:10.1109/csit.2018.8486288

Cited by 4 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…EdgeCloudSim simulator tool has been proposed as a means of supporting experimentation and finding optimal solutions for tasks when both sets of resources are available [9], [10]. Using EdgeCloudSim, an edge-cloud orchestration decision maker could handle up to 250 individual edge devices such as mobile phones with only short queuing delays; however, no clear advantage of using cloud resources for task completion times was demonstrated [9].…”

Section: Related Work and Contributionsmentioning

confidence: 99%

“…Using EdgeCloudSim, an edge-cloud orchestration decision maker could handle up to 250 individual edge devices such as mobile phones with only short queuing delays; however, no clear advantage of using cloud resources for task completion times was demonstrated [9]. Handling up to 750 devices was considered in [10] but the focus was on task failures and network delays rather than on task completion times.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

“…With Big Data applications, however, the advantages of using edge nodes for computation are less clear. The relatively small computing resources of edge nodes compare negatively with the much greater cloud resources [9], [10]; on the other hand, data transmission times to may be prohibitively lengthy to those superior cloud computing resources while job queuing and other delays may also adversely affect total task completion times.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

To Offload or Not? An Analysis of Big Data Offloading Strategies from Edge to Cloud

Singh

Kovács

Kiss

2022

2022 IEEE World AI IoT Congress (AIIoT)

View full text Add to dashboard Cite

Major research efforts have been recently made to develop resource orchestration solutions to flexibly link edge nodes with centralised cloud resources so as to maximise the efficiency with which such a continuum of resources can be accessed by users. In this context, we consider the case of Big Data analytics in which total task completion time reductions can be achieved by routing tasks initially to edge servers and subsequently to cloud resources. We demonstrate that the task complexity of the computational jobs, the Wide Area Network (WAN) speed and the potential overload of edge servers (as reflected by CPU workloads) are crucial for achieving total task completion time reductions by offloading from edge to cloud resources. The edge-cloud orchestrators are situated in the edge nodes and, therefore, require continuous access to the parameters of WAN speeds (and their fluctuations), edge server CPU workload and the task complexities in Big Data analytics requirements to make accurate edge-to-cloud offloading decisions. With favourable values for these three parameters, large reductions in completion times can result from transfer of large-scale data from edge nodes to cloud resources, which can reduce the completion times by up to 97% and meet client deadlines for computational tasks with responsive and agile solutions.

show abstract

Section: Related Work and Contributionsmentioning

confidence: 99%

Section: Related Work and Contributionsmentioning

confidence: 99%