BITS Faculty Publications
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Item An optimal delay aware task assignment scheme for wireless SDN networked edge cloudlets(Elsevier, 2020-01) Chalapathi, G.S.S.; Chamola, VinayOver the past decade, there has been an increasing demand for mobile devices to perform computationally intensive tasks. However, the computational capability of these devices is limited due to memory, power and portability constraints. One of the feasible and attractive ways to enhance the performance of the resource-limited mobile devices is to offload their computationally intensive tasks on to the cloud servers when internet connectivity is available. However, when cloud servers are involved in processing, the latency and cost of computation increases. To mitigate these problems, devices with high computational resources, called cloudlets, can be deployed in the locations close to the mobile users/devices. The mobile devices can then offload their computationally intensive tasks on to them. Due to easier access and nearness of the cloudlets, the cost and latency in processing the tasks decreases. In this work, we focus on task assignment problem in a multi-cloudlet network connected via a wireless SDN network, which services the task offload requests from mobile devices in a given locality. The aim of the proposed solution is to minimize latency and thus enhance the quality of service for mobile devices. We prove the optimality of the proposed solution mathematically and employ an admission control policy to maintain this optimality even in heavily loaded networks. We also perform numerical simulations for two scenarios of small and large networks and evaluate the performance for varying traffic and network parameters. The results demonstrate that the proposed task assignment method offers reduced latency compared to state-of-the-art task assignment approaches and hence improves the quality of service offered to mobile devices.Item Energy and latency aware mobile task assignment for green cloudlets(Elsevier, 2022-07) Chalapathi, G.S.S.; Chamola, VinayEdge computing places cloudlets with high computational capabilities near mobile devices to reduce the latency and network congestion encountered in cloud server-based task offloading. However, many cloudlets are required in such an edge computing network, leading to a tremendous increase in carbon emissions of computing networks globally. This increase in carbon emission envisages the need to employ green energy resources to power these cloudlets. This need has led to the concept of Green Cloudlet Networks (GCNs). But GCNs must deal with the problem of the unpredictability of green energy available to them while optimizing the performance (in terms of latency) delivered to the mobile user. This paper proposes a novel task-assignment called Green Energy and Latency Aware Task Assignment (Ge-LATA) for GCNs to address this issue. The primary aim of Ge-LATA is to optimize the latency and the green energy consumed in processing the offloaded tasks from the mobile devices. In this GCN, the cloudlets are connected in a network to process the incoming tasks cooperatively to ensure load-balancing at the cloudlets. Ge-LATA considers various factors like the current load, available green energy, service rate offered by cloudlets, and the distance from the mobile user, leading to optimal decisions in terms of latency and green energy consumed. Simulations are performed using the actual solar insolation data taken from the NREL database. Ge-LATA is tested with other offloading schemes for latency in processing the offloaded tasks and green-energy consumed under different solar insolation scenarios in these simulations. Simulation results show that Ge-LATA achieves up to 31.87% of reduction in the latency while ensuring up to 50.15% of reduction in the energy consumption than other comparable task-assignment schemes.Thus, Ge-LATA suggests that it leads to an optimal task assignment by considering the various factors mentioned above during the task assignment process. Thus, Ge-LATA considers the above-mentioned extensive set of parameters during the task allotment process. It also proposes an efficient green energy allotment scheme that adapts itself to actual weather and network conditions, leading to optimal task assignment decisions in GCNs.Item Latency aware mobile task assignment and load balancing for edge cloudlets(IEEE, 2017) Chamola, Vinay; Chalapathi, G.S.S.With the various technological advances, mobile devices are not just being used as a means to make voice calls; but are being used to accomplish a variety of tasks. Mobile devices are being envisioned to practically accomplish any task which could be done on a computer. This is hurdled by the limited computational resources available with the mobile devices due to their portable size. With the mobile devices being connected to the Internet, leveraging cloud services is being seen as a promising solution to overcome this hurdle. Computationally intensive tasks can be offloaded to the Cloud servers. However, owing to the latency and cost associated with using cloud services, edge devices (termed cloudlets) stationed near the mobile devices are being seen as a prospective alternative to replace/assist the Cloud services. The mobile devices have an easier access to the cloudlets being situated in their vicinity and can offload their task requests to them to be served at a lower cost. This paper considers a network of such connected cloudlets which provide service to the mobile devices in a given area. We address the issue of task assignment in such a scenario (i.e. which cloudlet serves which mobile device) aimed towards improving the quality of service experienced by the mobile devices in terms of minimizing the latency. Through numerical simulations we demonstrate the performance gains of the proposed task assignment scheme showing lower latency as compared to the traditional scheme for task assignment.