Demand-Based Computation Offloading Framework for Mobile Devices

Kumar, Jitender; Malik, Amita; Dhurandher, Sanjay Kumar; Nicopolitidis, Petros

doi:10.1109/jsyst.2017.2706178

Cited by 17 publications

(9 citation statements)

References 16 publications

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“…For instance, many frameworks support dynamic application partitioning and consider the C mig as one of the key metric in the offloading decisions 31,36,44 . Similarly, some CSP side resource management proposals are also there to handle the application partitioning preferences (e.g., SLA aware systems) 23,41 . In the same way, importance of context aware infrastructure selection mechanisms has also been considered by many of the research solutions 7,28,51,53 …”

Section: Discussionmentioning

confidence: 99%

“…Complicating the situation further is the fact that offloading all components may not be beneficial in terms of intended objectives and application partitioning itself exerts the extra overheads on the resource constraint SMDs. On the basis of these tradeoffs, the current computation offloading solutions employ the following types of algorithms for partitioning the application logic: (a) static application partitioning algorithms 32,33 or (b) dynamic application partitioning algorithms 31,34–41 …”

Section: Taxonomy Of MCCmentioning

confidence: 99%

“…In addition to the minimization of energy consumption within delay constraints, Kumar and Malik 41 also consider user‐specified demands because cloud services are available on pay‐per‐use basis. The values of the demand parameter lie between 0 and 100.…”

Section: Review Of Current MCC Solutionsmentioning

confidence: 99%

“…Soyata et al 53 and Hoang et al 77 exploited the on‐demand VM provisioning concept for maximizing profits from the limited resource of cloudlets. Unlike reserved VM provisioning, it also imparts flexibility to the CSPs in terms of increasing/decreasing cloud resources according to the arriving workloads and alternately paves way for improving the cloud resource utilization 23,33,41,78,79 . Besides this, users also need to pay for what they use.…”

Section: Review Of Current MCC Solutionsmentioning

confidence: 99%

“…The working mechanism of these systems is similar to the example discussed in Section 2.2. One such approach is also delivered in Kumar and Malik 41 . The service facility is modeled as a G/G/s queuing station.…”

Section: Review Of Current MCC Solutionsmentioning

confidence: 99%

See 4 more Smart Citations

Convergence of user and service provider perspectives in mobile cloud computing environment: Taxonomy and challenges

Kumar

Rani

Dhurandher

2020

Int J Communication

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SummaryMobile cloud computing (MCC) has been introduced with the intuition to ascertain the computing requirements of resource constraint smart mobile devices (SMDs). Meanwhile, the research community has put forward a wide variety of solutions by independently considering the operational issues of cloud service user (CSU) side (e.g., when to offload) and cloud service provider (CSP) side (e.g., where to deploy the cloud data centers). As the MCC will evolve, the decision regarding the adoption of a solution at one end would likely rely on the features supported or required from the other side; for example, a CSU might decide to offload the execution of delay‐sensitive applications only if the CSP deploys the cloud data centers in his/her proximity. The identification and convergence of such dependencies are vital for flourishing the MCC. However, a cognitive representation of CSU and CSP side research proposals can only aid in the identification of such dependencies. In this paper, we develop a thematic taxonomy of the current CSU side as well as CSP side frameworks. Further, we review the current CSU side and CSP side frameworks by using the thematic taxonomy and analyze the implications as well as critical aspects of proposed solutions. We also outline the potential dependencies between the solutions of both sides. These dependencies are derived on the basis of elicited taxonomy. Finally, we put forward open research challenges that demand future efforts for the seamless integration of CSU side perspectives and CSP side perspectives.

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